INVESTIGATING THE CORRELATION BETWEEN 

DEMOGRAPHIC AND COMORBIDITY PROFILES WITH 

CHEMOTHERAPEUTIC TOXICITY EXPERIENCES IN 

EARLY-STAGE BREAST CANCER PATIENTS IN A PRIVATE 

MEDICAL ONCOLOGY PRACTICE IN SOUTH AFRICA. 

 

Chantelle Pienaar Minns 

2623792 

 

 

 

 

 

 

 

 

Supervisors: 

Mrs Zelna Booth (Zelna.booth@wits.ac.za 

Mrs Rubina Shaikh (Rubina.shaikh@wits.ac.za) 

Assoc. Prof. Neelaveni Padayachee (Neelaveni.padayachee@wits.ac.za) 

 

A Research Report submitted to the Faculty of Health Sciences, University of the 

Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree 

of Master of Science in Medicine 

 

 

Johannesburg, 2024 

 



ii 
 

Declaration 

 

I, Chantelle Pienaar Minns, declare that this research report is my own, unaided work and has 

not been submitted before for any degree or examination at any other University. I have 

appropriately indicated and acknowledged all sources I used or quoted through complete 

references. This research report is being submitted for the Master of Science in Medicine 

Degree at the University of the Witwatersrand, Johannesburg. 

 

________________________ 

     Chantelle Pienaar Minns  

 

__  _day of___    _______20 ____in____________________                   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

04 June 24 Johannesburg



iii 
 

Dedication 

 

 

I dedicate this study to my parents, James and Mary-Ann Minns. I appreciate all your 

sacrifices and the privileges I have access to because of you. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



iv 
 

Abstract 

 

Background: An estimated 24 million people will be diagnosed with cancer globally by 2050, 

with approximately 16.8 million expected to be residing in low- and middle-income countries.  

Breast cancer is one of the most prevalent types of cancer diagnosed in women worldwide 

and 23% of all diagnosed malignancies are attributed to breast cancer. The prevalence of 

chemotherapy-induced adverse drug reactions ranges globally between a vast 60 - 80% 

amongst patients, negatively impacting overall treatment outcomes.  

 

Aim of study: This study aimed to determine a potential correlation between demographic 

profiles and the presence of pre-existing comorbidities on the chemotherapy-related adverse 

effects experienced by patients with stage 0-III breast cancer at a private oncology centre in 

Gauteng. Furthermore, interventions applied by the oncologists to mitigate the adverse effects 

were investigated and reported adverse events were compared to the WHO VigiAccess Adverse 

Drug Reaction database. 

 

Methods: A quantitative, retrospective cohort analysis of patient charts from January 2018 to 

December 2019 at the private Sandton Oncology Centre was undertaken.  The study sample 

size was 54 participants. Patient files were randomly selected. Demographic and comorbidity 

profiles, as well as the staging (0 – III) data were retrieved from patient medical charts, in 

accordance with the study inclusion criteria. Furthermore, the chemotherapeutic toxicities, 

experienced by patients, treated with a particular chemotherapeutic agent were reviewed. 

Interventions employed to alleviate toxicity were further recorded for data analysis (dose 

modifications, dose reductions, and premature discontinuation of oncology treatment). 

Descriptive statistics was analysed using pivot tables in Microsoft Excel. Inferential statistics 

was analysed with Stata software version 18. Ethical clearance was obtained before patient 

files were accessed and confidentiality of patient information was maintained throughout the 

study. 

 

Results: Most patients included in the study were white (57.4%), aged 50 – 59 years (29.6%), 

and diagnosed with stage II breast cancer (48.2%). Most of the patients had tumours which 

were oestrogen (66.7%) and progesterone positive (57.4%) and Human Epidermal Growth 

Factor Receptor 2 (HER2) negative (48.2%). The majority of patients, irrespective of ethnicity, 

received a combination of an anthracycline and cyclophosphamide followed by a taxane 

(51.8%). The most documented comorbidities were hypertension, obesity, dyslipidaemia, and 

diabetes. Of those patients reporting adverse effects, 77.8% reported adverse effects after the 



v 
 

first cycle of chemotherapy. The chemotherapy-related adverse effects show similarity to the 

adverse effects reported on the World Health Organisation’s VigiAccess Adverse Drug 

Reaction database, particularly adverse effects of the digestive, integumentary, 

haematological and lymphatic systems. 

Conclusions: The number of comorbidities present increases with age. White patients with 

more comorbidities experienced more chemotherapy-related adverse effects. The majority of 

the patients for which dose reductions were implemented, experienced five or more adverse 

effects during their treatment. More than half of the termination of treatment cases were 

preceded by a dose reduction. No statistically significant correlation was found between any 

of the ethnic groups or age categories and the total number of adverse effects experienced. A 

statistically significant correlation was found between other comorbidities and the number of 

psychiatric adverse effects (p=0.014). Reported infections were significantly higher in patients 

with hypertension (p=0.043) and lymphatic system adverse effects were higher in patients with 

dyslipidaemia (p=0.017). 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



vi 
 

Acknowledgements 

 

I want to thank my supervisors, Mrs. Zelna Booth, Prof. Neelaveni Padayachee and Mrs 

Rubina Shaikh, for their guidance throughout the research process. Your expertise and 

insights have been invaluable, and I am genuinely grateful for the mentorship you provided. 

 

I am profoundly grateful to my family, James, Mary-Ann, Anneke, and Maryka who have been 

my constant cheerleaders. Your encouragement has been instrumental in staying focused on 

the goal. 

 

My sincere thanks also to Dr Sze-Wai Chan at the Sandton Oncology Centre, for providing 

insight into the refining of my research topic and allowing access to the study site and patient 

charts to conduct my research. 

 

And then lastly, I would like to express my deepest gratitude to my husband, Adriaan, whose 

unwavering support and understanding have been pillars throughout the last two years. Your 

encouragement, patience, and belief in my abilities have been the driving force behind the 

completion of my studies. Your support has allowed me the time and space to complete my 

studies.  

 

Thank you. 

 

 

 

 

 

 

 

 

 

 

 

 



vii 
 

Table of contents 

 

Declaration .............................................................................................................................ii 

Dedication ............................................................................................................................. iii 

Abstract................................................................................................................................. iv 

Acknowledgements ............................................................................................................... vi 

Table of contents .................................................................................................................. vii 

List of figures ....................................................................................................................... xii 

List of tables ........................................................................................................................ xiii 

List of equations .................................................................................................................. xiv 

List of abbreviations ............................................................................................................. xv 

Chapter 1: Introduction .......................................................................................................... 1 

1.     Chapter overview .......................................................................................................... 1 

1.1    Background................................................................................................................... 1 

1.2   Breast cancer staging and risk factors ........................................................................... 2 

1.3   Treatment of breast cancer ........................................................................................... 2 

1.4   Chemotherapeutic toxicity ............................................................................................. 3 

1.4.1 Side effects versus adverse effects .............................................................................. 3 

1.4.2  Adverse drug reactions ................................................................................................ 4 

1.5     Reporting of chemotherapeutic toxicity ........................................................................ 4 

1.5.1  Pharmacovigilance ...................................................................................................... 4 

1.5.2  VigiAccess adverse drug reaction database ................................................................ 5 

1.6     Short-term adverse drug reactions (ADRs) .................................................................. 5 

1.6.1  Nausea and vomiting................................................................................................... 5 

1.6.2  Venous thromboembolism ........................................................................................... 6 

1.6.3   Alopecia ..................................................................................................................... 6 

1.6.4   Fatigue, sleep disturbances and mood disorders ....................................................... 7 

1.7 Long-term adverse drug reactions .............................................................................. 7 

1.7.1   Cardiovascular toxicity ............................................................................................... 7 



viii 
 

1.7.2   Neurotoxicity .............................................................................................................. 8 

1.7.3   Cognitive function ....................................................................................................... 8 

1.7.4   Marrow neoplasm after adjuvant oncology chemotherapy .......................................... 9 

1.7.5   Cessation of menses, menopause, and fertility ........................................................ 10 

1.7.6   Weight gain .............................................................................................................. 11 

1.8 Comorbidities amongst cancer patients .................................................................... 11 

1.8.1   Demographic profiles of cancer patients and associated comorbidities .................... 12 

Age and gender factors ....................................................................................................... 12 

Ethnicity profiles .................................................................................................................. 14 

1.8.2    Common comorbidities amongst cancer patients .................................................... 14 

Human Immunodeficiency Virus (HIV)................................................................................. 14 

Diabetes.............................................................................................................................. 16 

Hypertension ....................................................................................................................... 20 

Dyslipidaemia ..................................................................................................................... 21 

1.9 Problem statement ................................................................................................... 22 

1.10 Aim and objectives of the study ................................................................................ 22 

Chapter 2: Research methodology ...................................................................................... 24 

2. Chapter overview ..................................................................................................... 24 

2.1 Study design ............................................................................................................ 25 

2.2 Study site ................................................................................................................. 25 

2.3 Study population and sampling ................................................................................. 25 

Inclusion criteria .................................................................................................................. 26 

2.4 Data collection tool ................................................................................................... 26 

2.5 Study procedure and data collection ......................................................................... 26 

2.6 Data analysis ............................................................................................................ 27 

2.7 Ethical considerations ............................................................................................... 28 

Chapter 3: Results and Discussion ..................................................................................... 29 

3. Chapter overview ..................................................................................................... 29 

3.1      Demographic profiles represented in patient charts .................................................. 29 



ix 
 

3.2      Disease profiles represented in patient medical charts ............................................. 31 

3.2.1   Breast cancer staging and hormone receptor status of participants .......................... 31 

3.2.2   Specific comorbidities frequently presented ............................................................. 32 

3.3 Treatment profiles for patients represented in the study sample ............................... 37 

3.4 Practices employed to mitigate the experienced adverse drug reactions .................. 38 

3.4.1   Change of chemotherapeutic agent .......................................................................... 38 

3.4.2   Dose reduction ......................................................................................................... 39 

3.4.3   Termination of treatment ........................................................................................... 39 

3.5 Adverse drug reactions at Sandton Oncology ........................................................... 40 

3.6 The pattern of chemotherapy-related adverse effects in relation to age and ethnicity 44 

3.6.1   Skeletal adverse drug reactions reported in patient charts ....................................... 48 

3.6.3   Nervous system adverse drug reactions reported in patient charts........................... 49 

3.6.4   Cardiovascular system adverse drug reactions reported in patient charts ................ 49 

3.6.5   Lymphatic system adverse drug reactions reported in patient charts ........................ 50 

3.6.6   Respiratory system adverse drug reactions reported in patient charts ...................... 50 

3.6.7   Digestive system adverse drug reactions reported in patient charts ......................... 51 

3.6.8   Urinary system adverse drug reactions reported in patient charts ............................ 51 

3.6.9   Reproductive system adverse drug reactions reported in patient charts ................... 52 

3.6.10 Integumentary system adverse drug reactions reported in patient charts ................. 52 

3.6.11 Blood-related adverse drug reactions reported in patient charts ............................... 53 

3.6.12 Psychiatric adverse drug reactions reported in patient charts ................................... 53 

3.6.13 Vascular adverse drug reactions reported in patient charts ...................................... 54 

3.6.14 Infections reported in patient charts .......................................................................... 55 

3.6.15 Nutrition-related adverse drug reactions reported in patient charts ........................... 55 

3.6.16 Eye-related adverse drug reactions reported in patient charts .................................. 56 

3.7      Data from the WHO VigiAccess Adverse Drug Reaction database ........................... 57 

3.7.1   Paclitaxel .................................................................................................................. 57 

3.7.2   Docetaxel ................................................................................................................. 57 

3.7.3   Doxorubicin .............................................................................................................. 58 



x 
 

3.7.4    Epirubicin ................................................................................................................ 58 

3.7.5    Cyclophosphamide .................................................................................................. 59 

3.7.6    Trastuzumab ........................................................................................................... 59 

3.7.7    Carboplatin .............................................................................................................. 60 

3.7.8    Pertuzumab ............................................................................................................. 61 

3.7.9    Cyclophosphamide, Methotrexate, and 5-Fluorouracil (CMF) .................................. 61 

3.8       A comparative analysis between study findings and the WHO VigiAccess Adverse   

Drug Reaction database ..................................................................................................... 62 

3.8.1    Taxane-based treatment .......................................................................................... 62 

3.8.2    Combination of a taxane and Trastuzumab treatment ............................................. 62 

3.8.3     Combination of an anthracycline followed by a taxane treatment ........................... 62 

3.8.4     Combination of an anthracycline and Cyclophosphamide followed by a taxane ..... 63 

and Trastuzumab treatment ................................................................................................ 63 

3.8.5     Combination of a taxane, Trastuzumab and Pertuzumab treatment ....................... 63 

3.8.6     Combination of a taxane, Trastuzumab and Pertuzumab treatment ....................... 63 

3.8.7     Taxotere (Docetaxel), Carboplatin and Herceptin (Trastuzumab) treatment ............ 64 

3.8.8     Cyclophosphamide, Methotrexate, and 5-Fluorouracil treatment ............................ 64 

Chapter 4: Conclusion ......................................................................................................... 65 

4.  Chapter overview .................................................................................................... 65 

4.1       Conclusion .............................................................................................................. 65 

4.2  Study strengths ....................................................................................................... 66 

4.3   Limitations .............................................................................................................. 67 

4.4   Recommendations.................................................................................................. 68 

References ......................................................................................................................... 69 

Appendices ......................................................................................................................... 81 

Appendix 1: Data collection tool (REDCap®) ...................................................................... 81 

Appendix 2: Study protocol ................................................................................................. 86 

1. Introduction ..................................................................................................................... 87 

1.1 Problem statement ........................................................................................................ 90 



xi 
 

1.2 Purpose of study ........................................................................................................... 90 

2. Aim and objectives .......................................................................................................... 91 

3. Methods .......................................................................................................................... 92 

3.1 Study design ................................................................................................................. 92 

3.2 Study site ...................................................................................................................... 92 

3.3 Study participants.......................................................................................................... 92 

3.3.1 Inclusion criteria ......................................................................................................... 92 

3.3.2 Exclusion criteria ........................................................................................................ 92 

3.4 Sampling method .......................................................................................................... 92 

3.5 Data collection tool ........................................................................................................ 93 

3.6 Study procedure ............................................................................................................ 93 

3.7 Data analysis ................................................................................................................ 94 

3.8 Ethics ............................................................................................................................ 95 

4. Timeline .......................................................................................................................... 95 

5. Funding ........................................................................................................................... 96 

6. Budget ............................................................................................................................ 96 

7. Dissemination and translation .......................................................................................... 96 

References ......................................................................................................................... 97 

Appendix 3: Ethics clearance for the study ........................................................................ 102 

Appendix 4: Approval from the study site for data collection .............................................. 105 

Appendix 5: Turnitin Plagiarism Report ............................................................................. 106 

 

 

 

 

 

 

 



xii 
 

List of figures 

 

Chapter 2 

 

Figure 2.1: Flow diagram outlining the phases undertaken in the study. .............................. 24 

 

Chapter 3 

 

Figure 3.1: Representation of the ethnic distribution versus the hormone receptor status of 

the study sample. ................................................................................................................ 32 

Figure 3.2: Stage of breast cancer and number of comorbidities shown per ethnic group. .. 33 

Figure 3.3: Other comorbidities listed in the patient charts of the study sample. .................. 35 

Figure 3.4: Average age versus number of comorbidities. ................................................... 37 

Figure 3.5: ADRs that were most frequently documented in relation to physiological systems.

 ........................................................................................................................................... 41 

Figure 3.6: Types of adverse effects experienced specific to each treatment arm ............... 42 

Figure 3.7: Ethnic distribution in each treatment arm........................................................... 43 

Figure 3.8: Age distribution in each treatment arm .............................................................. 44 

Figure 3.9: Age, ethnic and gender distribution of skeletal ADRs. ....................................... 48 

Figure 3.10: Age, ethnic and gender distribution of muscular ADRs. ................................... 48 

Figure 3.11: Age, ethnic and gender distribution of nervous system ADRs. ......................... 49 

Figure 3.12: Age, ethnic and gender distribution of cardiovascular ADRs. ........................... 49 

Figure 3.13: Age, ethnic and gender distribution of lymphatic ADRs. ................................... 50 

Figure 3.14: Age, ethnic and gender distribution of respiratory ADRs. ................................. 50 

Figure 3.15: Age, ethnic and gender distribution of digestive system ADRs. ....................... 51 

Figure 3.16: Age, ethnic and gender distribution of urinary system ADRs. ........................... 51 

Figure 3.17: Age, ethnic and gender distribution of reproductive system ADRs. .................. 52 

Figure 3.18: Age, ethnic and gender distribution of integumentary system ADRs. ............... 52 

Figure 3.19: Age. ethnic and gender distribution of blood related ADRs. ............................. 53 

Figure 3.20: Age, ethnic and gender distribution of psychiatric ADRs. ................................. 54 

Figure 3.21: Age, ethnic and gender distribution of vascular ADRs. .................................... 54 

Figure 3.22: Age, ethnic and gender distribution of infection. .............................................. 55 

Figure 3.23: Age, ethnic and gender distribution of nutrition related ADRs. ......................... 56 

Figure 3.24: Age, ethnic and gender distribution of eye related ADRs. ................................ 56 

 

 



xiii 
 

List of tables 

 

Chapter 3 

Table 3.1: Demographic profile of the study participants (n=54) .......................................... 29 

Table 3.2: Profile of the cancer status of patients included in the study sample (n=54) ....... 31 

Table 3.3: Number of comorbidities experienced by the same patients (n=54). ................... 34 

Table 3.4: Details of comorbidities seen in the patients. ...................................................... 36 

Table 3.5: Distribution of regimen prescribed and adverse effects experienced. .................. 38 

Table 3.6: Most prevalent adverse effects that led to dose reductions (n=19). ..................... 39 

Table 3.7: Mean, standard deviation (SD), interquartile range (IQR) and p-values for age, 

ethnicity and the number of comorbidities. .......................................................................... 45 

Table 3.8: Correlations for comorbidities against the number of adverse effects per 

physiological system. .......................................................................................................... 46 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



xiv 
 

List of equations 

 

Chapter 2 

Equation 2.1: Sample size formula ...................................................................................... 25 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



xv 
 

List of abbreviations 

 

5-FU –  5-Fluorouracil  

ART –  Antiretroviral Therapy 

ABW –  Actual body weight 

AC –   Adriamycin (Doxorubicin) and Cyclophosphamide 

ADR –   Adverse Drug Reaction 

ADRs – Adverse Drug Reactions  

BMI –   Body Mass Index 

BSA –  Body Surface Area 

BRCA – Breast Cancer Gene 

CINV –  Chemotherapy-induced nausea and vomiting  

CIPN –   Chemotherapy-induced peripheral neuropathy 

CMF –  Cyclophosphamide, Methotrexate and 5-Fluorouracil 

CTCAE – Common Terminology Criteria for Adverse Events 

CVD –  Cardiovascular disease 

CYP –  Cytochrome 

EC –   Epirubicin and Cyclophosphamide 

G-CSF – Granulocyte colony-stimulating factor  

HDL –  High-density lipoprotein 

HER2 – Human Epidermal Growth Factor Receptor 2 

HIV –   Human Immunodeficiency Virus 

HREC –   Human Research Ethics Committee 

LDL –  Low-density lipoprotein 

LVF –   Left Ventricular Function 

MDS –    Myelodysplastic Syndrome  



xvi 
 

NSABP – National Surgical Adjuvant Breast and Bowel Project  

PIDM – Programme for International Drug Monitoring 

POPIA – Protection of Personal Information Act 

SA –  South Africa 

SABCHO – South African Breach Cancer and HIV Outcomes  

SEER – Surveillance, Epidemiology and End Results 

TCH –   Taxotere (Docetaxel), Carboplatin and Herceptin (Trastuzumab) 

TNBC – Triple Negative Breast Cancer 

VTE –  Venous thromboembolism 

WHO –  World Health Organisation 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



1 
 

Chapter 1: Introduction 

1. Chapter overview 

 

This chapter provides a background on cancer with specific reference to breast cancer. It 

provides insight into the short-and long-term adverse drug reactions that can be expected 

following treatment with a chemotherapeutic agent. This chapter also discusses the effect of 

age, ethnicity, HIV infection, diabetes, obesity, hypertension, and dyslipidaemia on the severity 

of adverse drug reactions experienced from oncology therapy use. 

 

1.1 Background 

 

Cancer is the foremost contributor to mortality in numerous wealthy nations (Whiteman and 

Wilson, 2016). In 2020, 19.3 million people were diagnosed with cancer, resulting in around 

10 million deaths from various types of the disease. Of these, 683,100 women succumbed 

specifically to breast cancer (Sung et al., 2021). Approximately 24 million people are estimated 

to be diagnosed with cancer by 2050, with 16.8 million of those residing in economically 

developing countries (Kingham et al., 2013).  

 

Cancer care is fast becoming essential in sub-Saharan African countries. Lifestyle changes 

and advancements in modern medicine are attributed to the rise in cancer occurrences among 

certain population groups (Kingham et al., 2013). Unlike other regions, Africa has a higher 

proportion of cancer deaths (7.2%) compared to its share of cancer cases (5.7%) (Sung et al., 

2021). Poor infrastructure, scarcity of healthcare workers, advanced stage of cancer at 

diagnosis, use of traditional therapies, limited treatment options, and poor treatment 

compliance all contribute to the high mortality rate among people living with cancer in 

emerging economic regions (Kingham et al., 2013).  

 

In 2020, South Africa saw 108,168 new cancer cases (The Global Cancer Observatory, 2021), 

with a projected 112,921 new cases anticipated for 2023 (Singh et al., 2015). According to 

statistics from the Global Cancer Observatory (2021), the prevalent cancer types diagnosed 

in women in South Africa in 2020 included breast, cervical, colorectal, lung, and endometrial 

cancers. In men, cancer of the prostate, lung and colorectum were most prevalent, along with 

Kaposi sarcoma and non-Hodgkin lymphoma (The Global Cancer Observatory, 2021). The 

most frequently diagnosed malignancy in women globally, requiring chemotherapeutic 

treatments, is breast cancer. Furthermore, the Cancer Association of South Africa (CANSA) 



2 
 

reported that South Africa might possess the highest incidence of male breast cancer cases 

worldwide (Herbst, 2021). 

 

Approximately 1-3% of individuals diagnosed with breast cancer in South Africa are male 

(Herbst, 2021). With 23% of all malignancies presenting as breast cancer and 14% of overall 

cancer deaths attributable to breast cancer, a significant public health threat is posed, 

warranting research efforts to reduce breast cancer fatalities (Singh et al., 2014).  

 

1.2 Breast cancer staging and risk factors 

 

A tumour in the breast develops when normal cells mutate and spread rapidly. In non-

metastatic breast cancer (stage 0), the tumour is only present in the milk lobules (Ghai et al., 

2021). Stage I breast cancer is a small primary invasive tumour without the involvement of 

lymph nodes. Regional lymph nodes are involved with stage II breast cancer. A large tumour 

fixed to the chest wall and extensive lymph node involvement characterises Stage III breast 

cancer (Wells et al., 2015). These tumour cells can infiltrate the surrounding organs or other 

body parts, known as metastatic cancer (stage IV). The surrounding tissue is infiltrated in 

approximately 80% of breast cancer cases (Ghai et al., 2021). 

 

Risk factors predominantly linked to breast cancer are advancing age and gender, with 

additional variables including early onset of menstruation, absence of childbirth, delayed age 

at first birth, hormone replacement therapy, personal and family medical history, mutations in 

Breast Cancer Gene (BRCA) 1 and BRCA2 genes, as well as environmental and lifestyle 

factors, such as radiation exposure (Wells et al., 2015). Afifi et al. (2020) conducted a study in 

the United States and found that cardiovascular and cerebrovascular disease were among the 

most common non-breast cancer causes of death. Other significant non-cancer causes of 

death included chronic liver disease, septicaemia, various infectious and parasitic diseases, 

and suicide. 

 

1.3 Treatment of breast cancer 

 

Orthodox therapies for breast cancer are surgery, radiation, and chemotherapy (On et al., 

2022). Patients diagnosed with early breast cancer usually undergo surgery and receive 

adjuvant systemic therapies to decrease the risk of local and metastatic disease recurrence 

(Hopwood et al., 2006). Chemotherapy, targeted therapy or hormone therapy are the systemic 

therapy options for managing breast cancer. Physicians should base the selection of systemic 



3 
 

therapy on the biological characteristics of the tumour, such as hormone and HER2 (human 

epidermal growth factor receptor 2) receptor status, proliferation, and grade, the tumour's 

stage, and the patient's comorbidities and preferences (Suter and Pagani, 2018). 

 

Treatment of non-metastatic breast cancer regularly involves anthracycline-based treatment 

plans. The combination of chemotherapy and a dose-dense regimen proves more efficacious 

in breast cancer treatment than traditional treatment plans but results in higher levels of 

toxicity. According to the National Cancer Institute (2023), dose-dense chemotherapy 

treatment plans involve less time between cycles than conventional treatment plans (Han et 

al., 2011). 

 

Some new advancements in breast cancer treatment include immunotherapy, hormone-based 

therapy, and stem cell therapy (Ghai et al., 2021). Ongoing efforts to address the high rates of 

relapse and disease progression in breast cancer have led to the development of new targeted 

therapies. These targeted therapies include HER2 inhibitors, phosphoinositide-3-kinase, v-akt 

murine thymoma viral oncogene homolog, mammalian target of rapamycin (mTOR) inhibitors, 

poly (ADP-ribose) polymerase inhibitors, cyclin-dependent kinase 4/6 inhibitors, vascular 

endothelial growth factor inhibitors, and immune checkpoint inhibitors (Ju et al., 2018).  

 

1.4 Chemotherapeutic toxicity 

 

1.4.1 Side effects versus adverse effects 

 

The Centre for Disease Control and Prevention (CDC) and Food and Drug Administration 

(FDA) define a side effect as an adverse drug reaction. Their definition does not include 

positive or neutral side effects. The WHO defines a side effect as any unintended effect which 

occurs at a normal dose related to pharmacological properties (Due, 2023). The WHO's 

definition implies that side effects, although not the primary therapeutic aim, may be 

advantageous rather than harmful (Edwards & Aronson, 2000). 

 

Adverse effects are any unwanted outcomes resulting from the use of a medication (Marsh, 

2023). An adverse drug reaction is characterized as a response to a medication that is harmful 

and unintended, occurring at typical therapeutic doses. There is an underlying relationship 

between a medicine and an adverse drug reaction in contrast to an adverse event which does 

not necessarily have an underlying relationship with the medicine (Nebeker et al., 2004). 



4 
 

The terms’ adverse reaction and adverse effect are essentially synonyms, with a slight 

distinction: an adverse effect is perceived from the drug's perspective, whereas an adverse 

reaction is viewed from the patient's standpoint (Edwards & Aronson, 2000). 

The term adverse drug reaction will be used in this research report to refer to 

chemotherapeutic toxicity experienced by patients who receive chemotherapy to treat early-

stage breast cancer.  

 

1.4.2 Adverse drug reactions 

 

Drug-induced mortality is a significant public health concern, as some countries spend 20% of 

their healthcare budget to manage adverse drug reactions (Ghai et al., 2021). An adverse drug 

reaction (ADR) is harmful and unplanned and happens at doses typically used for prevention, 

assessment, or treatment. The incidence of chemotherapy-induced adverse drug reactions 

(ADRs) ranges from 60-80% (On et al., 2022), and recent studies estimate that ADRs are the 

fourth to sixth leading contributors to mortality (Ghai et al., 2021). Adverse drug reactions lead 

to reduced quality of life and alterations in treatment plans like delays between cycles or 

reduction of cycles and dose (Han et al., 2011). Adverse drug reactions include nausea and 

vomiting, blood and lymphatic system disorders, hand-foot syndrome, fatigue, hypersensitivity 

reactions, gastrointestinal adverse effects, adverse neurological effects, and 

myelosuppression (Han et al., 2011; On et al., 2022). The type of ADR depends on the 

chemotherapeutic agent and the dose thereof, number of cycles administered, history of 

chemotherapy induced ADRs, comorbid disease, health status, age, and genetics (On et al., 

2022).  

 

1.5 Reporting of chemotherapeutic toxicity 

 

1.5.1 Pharmacovigilance 

 

Pharmacovigilance is a dynamic procedure for evaluating the safety of a pharmaceutical 

product throughout its entire lifespan (WHO, 2024). It commences in the preclinical 

development phase, persists throughout premarketing clinical trials, and extends into the post-

market environment. Post-marketing safety monitoring is a mandatory obligation for marketing 

authorization holders. This involves implementing a transparent pharmacovigilance system, 

strict reporting duties to regulatory authorities in cases of significant safety issues, and 

submitting regular safety update reports (Cooper et al., 2024). 



5 
 

Past studies have demonstrated that ADRs are linked to significant financial implications, 

particularly concerning expenses associated with hospitalisation (De Rosa et al., 2020). The 

goal of pharmacovigilance is to prevent adverse events related to treatments (Schlam et al., 

2023). 

 

1.5.2 VigiAccess adverse drug reaction database 

 

The World Health Organisation (WHO) introduced VigiAccess in 2015 to grant public access 

to information stored in VigiBase, the global database managed by WHO for reported potential 

adverse effects of medicinal products. VigiAccess is an online tool designed to facilitate 

searches within VigiBase, enabling users to retrieve condensed statistical summaries of the 

data related to potential adverse effects reported to the WHO Programme for International 

Drug Monitoring (PIDM). VigiBase, the exclusive worldwide database for reported potential 

adverse effects of medicinal products, has accumulated information since 1968 from members 

of the WHO PIDM. ADRs are reported to the WHO PIDM by national pharmacovigilance 

centres or regulatory authorities in member countries. The WHO PIDM, established in 1968 

to ensure the safe and efficient utilization of medicinal products, saw South Africa become a 

member in 1992 (WHO, 2024). 

 

1.6 Short-term adverse drug reactions (ADRs) 

 

1.6.1 Nausea and vomiting 

 

Nausea ranks among the predominant adverse effects of chemotherapy in breast cancer 

patients, and it is considered a more distressing symptom compared to vomiting. Breast 

cancer patients frequently undergo chemotherapy-induced nausea and vomiting (CINV) due 

to the presence of highly emetogenic agents in breast cancer chemotherapy. Notably, almost 

half of patients undergoing chemotherapy have reported experiencing CINV, even when using 

antiemetic medications (Wicaksono et al., 2023). Breast cancer chemotherapies are potent 

triggers of nausea and vomiting, which are perceived as the most upsetting and severe ADR. 

Despite significant advancements in managing acute chemotherapy-induced vomiting, as well 

as anticipatory and delayed nausea and vomiting, these adverse effects remain notable 

challenges for individuals with breast cancer. CINV persist as a common adverse effect that 

can significantly impact the lives of breast cancer patients. Adhering closely to antiemetic 

guidelines can help reduce the incidence of CINV. Despite strict adherence, breakthrough or 

refractory CINV may occur, necessitating further medical assessment. Despite substantial 



6 
 

therapeutic progress in the last forty years, 60% to 80% of patients still experience CINV 

during chemotherapy (Gautam et al., 2023).  

 

1.6.2 Venous thromboembolism 

 

Breast cancer is linked to a heightened risk of venous thromboembolism (VTE), including 

conditions such as pulmonary embolism and deep vein thrombosis. In contrast to individuals 

without cancer, those diagnosed with cancer face an 8-9 times higher risk of VTE within the 

first year of cancer diagnosis. The presence of cancer along with VTE is linked to a threefold 

increase in the risk of mortality. While the risk of VTE in breast cancer is comparatively lower 

than in many other solid tumours, the substantial prevalence of breast cancer makes breast 

cancer-associated thrombosis a significant health concern. Following cancer progression, 

VTE stands as the second most prevalent cause of death among patients undergoing 

chemotherapy for cancer, contributing to around 10% of deaths associated with chemotherapy 

(Kirwan and Blower, 2022). The National Surgical Adjuvant Breast and Bowel Project (NSABP) 

protocol 20 trial documented a thrombosis occurrence of 1.9% among patients in the 

tamoxifen treatment group, compared with 7.5% in those receiving tamoxifen plus a 

combination of cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) treatment group. A 

Canadian trial found similar results with a thrombosis incidence of 2.6% in the tamoxifen 

treatment group compared to 13.6% on the tamoxifen plus CMF treatment group (Partridge et 

al., 2001). 

 

1.6.3 Alopecia 

 

Alopecia is a frequent, distressing (Vasconcelos et al., 2018) adverse drug reaction of 

chemotherapy. The estimated occurrence is 65% in individuals undergoing treatment. 

Alopecia typically begins 1 to 3 weeks after the start of chemotherapy, reaches full effect by 

eight weeks, and hair regrowth is anticipated within 3 to 6 months after the completion of 

treatment (Fonia et al., 2017). Alopecia is frequently seen with anthracycline-based regimens 

and is more pronounced compared to non-anthracycline-based regimens (Partridge et al., 

2001). 

 

In a study by Partridge et al. (2001), the most frequently reported adverse effects reported for 

a combination of CMF included nausea, alopecia (partial hair loss), thrombocytopenia, 

vomiting, diarrhoea, and mucositis (Partridge et al., 2001). Adverse effects most frequently 



7 
 

reported for a combination of Doxorubicin and Cyclophosphamide (AC) included nausea, 

moderate to severe neutropenia, vomiting and mucositis (Partridge et al., 2001). 

 

1.6.4 Fatigue, sleep disturbances and mood disorders 

 

Breast cancer patients undergoing chemotherapy also experience cancer-related fatigue, 

sleep disturbances, and a depressed mood. These symptoms have been documented to occur 

in 68%–90%, 54%–78%, and 58%–79% of patients with breast cancer, respectively (Wong et 

al., 2023). 

 

1.7 Long-term adverse drug reactions 

 

1.7.1 Cardiovascular toxicity 

 

The use of chemotherapy has been associated with persistent adverse effects, particularly 

cardiovascular conditions. Conditions such as hypertension, heart failure, and myocardial 

ischemia are common complications following the administration of chemotherapy (Alimperti 

et al., 2023). Cardiotoxicity impacts around 30% of patients (Langeh et al., 2023). 

 

Doxorubicin is classified as an anthracycline and is a critical component in the chemotherapy 

regimen for breast cancer. Nevertheless, its application is restricted due to the cumulative and 

dose-dependent risk of irreversible cardiotoxicity (Zhao et al., 2023). The cardiotoxic effects 

associated with anthracycline begin with damage to myocardial cells, leading to subsequent 

left ventricular dysfunction. Anthracyclines trigger an elevated generation of free radicals and 

iron ions within cardiomyocytes, resulting in processes such as apoptosis induction and lipid 

bilayer damage (Pestana et al., 2024). 

 

Data from Surveillance, Epidemiology and End Results (SEER)-Medicare indicate that older 

patients treated with anthracycline-based adjuvant chemotherapy face a heightened risk of 

cardiovascular events. Notably, many early breast cancer patients, particularly those with an 

elevated Body Mass Index (BMI), are more prone to future cardiovascular events than cancer 

recurrence (Tao et al., 2015). Reports indicate that up to 20% of patients may encounter 

cardiotoxic events such as QT interval prolongation, bradycardia, and atrial fibrillation following 

taxane administration (Alimperti et al., 2023). 

 



8 
 

Trastuzumab monotherapy can cause extensive reversible cardiotoxicity (Zhao et al., 2023) 

and also when combined with anthracyclines due to the HER2 pathway being a crucial part of 

cardiac homeostasis (Tao et al., 2015). Currently, older age, a low baseline left ventricular 

function (LVF), and a history of hypertension are recognised as contributing factors associated 

with an increased probability of cardiotoxicity from trastuzumab after anthracycline use. 

Consequently, many healthcare practitioners avoid anthracycline use altogether (Tao et al., 

2015). Research conducted by Yu et al. (2015) revealed that around 10.90% of breast cancer 

patients discontinued anti-HER-2 therapy because of cardiac toxicity, potentially resulting in 

tumour recurrence. 

 

1.7.2 Neurotoxicity 

 

Adjuvant taxane regimens frequently cause adverse neurological reactions. The occurrence 

of grade 2-4 adverse effects ranges from 13% to 22% in sequential anthracycline-taxane 

treatments. Current findings suggest no correlation between toxicity and the probability of 

clinical advantages, allowing clinicians to decrease doses without compromising drug efficacy. 

Regrettably, there are few strategies to prevent or address the painful neuropathy induced by 

taxanes (Tao et al., 2015). 

 

1.7.3 Cognitive function 

 

Suter and Pagani (2018) reported that long-term adverse events of chemotherapy include 

cognitive impairment. Cancer-related cognitive impairment has been described as "chemo-

brain," marked by impairment in memory function, decision-making abilities, and attention 

(Langeh et al., 2023). 

 

Schagen et al. (1999) studied 39 women for about two years after receiving six cycles of CMF 

(with or without subsequent tamoxifen), comparing them with 34 women who underwent 

localized therapy only. The CMF group exhibited a higher incidence (28%) of cognitive 

dysfunction, primarily manifesting as difficulties in concentration, memory, word-finding, and 

motor skills testing, compared to the control group (12%). Interestingly, endocrine therapy did 

not impact self-reported symptoms or cognition.  

 

A study by Van Dam et al. (1998) observed a dose-dependent relationship between 

chemotherapy and cognitive impairment. Two years after completing chemotherapy, 32% of 

patients treated with high-dose chemotherapy, 17% with standard-dose chemotherapy, and 

9% of women with stage I breast cancer who did not undergo chemotherapy exhibited 



9 
 

impaired cognitive function. Brezden et al. (2000) surveyed 31 women undergoing 

chemotherapy, 40 previously receiving chemotherapy, and a healthy control arm. Women on 

active treatment exhibited more frequent impaired cognition compared to control subjects, 

which did not seem to be associated with anxiety or depression. 

 

It's essential to highlight that, in the studies conducted by Van Dam et al. (1998) and Schagen 

et al. (1999), there was no correlation between self-reported cognitive dysfunction and formal 

testing scores. The women reporting poor cognition were not necessarily performing poorly 

on testing.  

 

Decreases in cognitive performance on tests of memory, executive function, processing 

speed, and attention have been reported in several prospective longitudinal studies evaluating 

cognitive function before and after chemotherapy; 75% of patients experience these cognitive 

problems during treatment, and up to 35% of patients continue to be afflicted several years 

post-treatment (Janelsins et al., 2022). Janelsins et al. (2022) conducted a study to determine 

a relationship between inflammation and cognitive function pre- and post-chemotherapy in 

patients with breast cancer. This preliminary study sampled from a nationwide, longitudinal 

cohort of female breast cancer patients and age-matched non-cancer controls. Janelsins and 

colleagues (2022) discovered that Interleukin (IL)-6, MCP-1, sTNFRI, and sTNFRII, but not IL-

1β or IL-8, increased during chemotherapy and that changes in these cytokines were 

associated with changes in cognitive function, particularly worse executive function, verbal 

memory, and verbal fluency.  

 

1.7.4 Marrow neoplasm after adjuvant oncology chemotherapy 

 

The occurrence of leukaemia after breast cancer was initially documented in the 1980s (Tao 

et al., 2015). The most severe long-term consequence of chemotherapy and radiation 

therapy is arguably the damage to hematopoietic stem cells, leading to secondary leukemia 

and myelodysplasia (Pullarkat et al., 2009). The increased rates of early tumour diagnosis 

and the effectiveness of adjuvant chemotherapy have significantly elevated the number of 

long-term survivors among cancer patients, enabling the detection of more long-term 

complications such as leukemias (Campone et al., 2005). 

 

In the case of early-stage breast cancer, particularly when tumours exceed 1 cm in size, a 

majority of patients undergo adjuvant chemotherapy, typically involving cyclophosphamide 

and an anthracycline with or without a taxane. The development of secondary leukemia and 

myelodysplasia is a result of the damage inflicted on hematopoietic stem cells by 



10 
 

chemotherapy or radiation therapy and is considered a significant and potentially severe 

long-term consequence of these treatments (Pullarkat et al., 2009). 

 

Curtis et al. (1992) conducted a case–control study involving nearly 82,700 women treated for 

breast cancer in the 1970s and 1980s. Their findings suggest that the total dose of 

Cyclophosphamide is a significant contributing factor for the development of leukaemia and 

myelodysplastic syndrome (MDS). Higher doses or dose-dense regimens of 

cyclophosphamide and doxorubicin on the NSABP protocol B-22 and B-25, showed a higher 

incidence of leukaemia and MDS, ranging from 0.1% to 1.2% (Campone et al., 2005). 

Cyclophosphamide is associated with leukaemia development 4 to 7 years after exposure. On 

the other hand, topoisomerase inhibitors such as doxorubicin and epirubicin, lead to leukaemia 

within 1-3 years (Campone et al., 2005) without a preleukemic phase, and the prognosis is 

relatively better than alkylating agent-associated leukaemia (Mirili et al., 2018). 

Some evidence suggests that anthracycline-based treatment schedules may pose a bigger 

risk than traditional CMF regimens. The general occurrence of leukaemia among women with 

breast cancer following standard-dose adjuvant therapy using anthracyclines typically ranges 

from 0.1% to 1.5% within a 5 to 10-year follow-up period (Partridge et al., 2001).  

 

1.7.5 Cessation of menses, menopause, and fertility 

 

Adjuvant chemotherapy in premenopausal women often leads to early menopause. The risk 

of experiencing early menopause is associated with factors such as the patient's age, the 

chemotherapeutic agents used, and the total administered dose (Tao et al., 2015).  

 

Typically, approximately 35% of women who undergo treatment with AC experience 

amenorrhea within 12 months after completing chemotherapy. This percentage rises to 45% 

for those who receive additional taxane treatment and rises to 60% for individuals treated with 

CMF. Generally, women under the age of 35 are more likely to regain their menstrual cycles 

fully, while those over 40 are less likely to do so (Tao et al., 2015). Among individuals under 

40, menstruation typically resumes within one year after completing therapy in 90% of cases, 

while the percentage is slightly lower at nearly 70% for those over 40 (Langeh et al., 2023).  

 

Although premature ovarian failure may have a positive impact on the breast cancer 

prognosis, especially for individuals with hormone receptor-positive tumours, premature 

menopause brings about symptoms like hot flashes and genitourinary issues. It can have 

significant physiological, psychosocial and psychosexual consequences (Partridge et al., 



11 
 

2001). Women in early menopause experience quicker loss of bone mineral density. 

Nevertheless, women over the age of 35 face a higher risk of infertility when undergoing 

various adjuvant chemotherapy regimens, with rates ranging from 60% to 100% (Langeh et 

al., 2023). 

 

1.7.6 Weight gain 

 

Weight gain has been observed in most women undergoing adjuvant chemotherapy, with 33% 

of women gaining more than 5.0 kg (Lankester et al., 2001). Some researchers have reported 

more substantial weight gain, reaching 10–20 kg, in up to 20% of patients. This phenomenon 

seems more prevalent in women pre-menopause. Those women who are in menopause 

concurrently with chemotherapy seem to face an elevated risk of weight gain (Partridge et al., 

2001). Gandhi et al. (2019) identified a lower initial BMI as the most robust predictor of 

substantial weight gain. Additionally, more significant weight increases were linked to CMF 

treatment compared to anthracycline therapy (Gandhi et al., 2019). Weight gain, especially 

when significant, can significantly impact a woman's physical and mental well-being (Partridge 

et al., 2001). 

 

The exact reason for the increase in weight during chemotherapy remains unknown. Evidence 

proposes that reduced physical activity during therapy, potential fluctuations in basal metabolic 

rate, and a decline in muscle mass following chemotherapy may contribute to weight gain 

(Lankester et al., 2001). 

 

1.8 Comorbidities amongst cancer patients 

 

Comorbidity is defined as a disease present simultaneously and independently from the 

disease being studied (Newschaffer et al., 1997). According to Sreenivas on WebMD (2021), 

the most common comorbidities in the general population are high blood pressure, diabetes, 

cardiac disease, and respiratory disease. Curative treatments like surgery, radiotherapy, and 

chemotherapy may not be possible to perform or administer when the comorbidity involves a 

decline in organ function e.g., respiratory, cardiac, or renal function (Land et al., 2012).  

 

The frequency of cancer rises with age and chronic diseases (Parés-Badell et al., 2017). In 

addition, most cancer patients have more than one comorbid condition which influence the 

timing of diagnosis by concealing the symptoms, leading to delayed consultation with a 

healthcare worker, which delays referral for further examination (Boakye et al., 2021). In 



12 
 

addition, comorbidities affect the choice and effectiveness of treatment options (Michalopoulou 

et al., 2021).  

 

Cancer patients with comorbidities have a worse prognosis than those without comorbid 

conditions (Boakye et al., 2021). Increased mortality has been linked with comorbid diseases, 

but the presence of these comorbid diseases may have a different effect on mortality 

depending on the location of the cancer (Parés-Badell et al., 2017 and Michalopoulou et al., 

2021). Patients with comorbid diseases use cancer treatment less often which affect cancer 

survival. Comorbidities also directly cause non-cancer death (Boakye et al., 2021).  

 

On the other hand, patients with comorbid diseases visit healthcare workers frequently, which 

may result in increased chances of screening for cancer, leading to early detection and 

diagnosis (Boakye et al., 2021).  

 

1.8.1 Demographic profiles of cancer patients and associated comorbidities   

 

Age and gender factors 

 

The National Cancer Institute in the United States released statistics that show that 54% of all 

cancer patients are 65 years and older. Sixty-one years is the mean age for breast cancer 

diagnosis (Stavrou et al., 2012). An estimated two-thirds of patients 65 years and older suffer 

from one or more comorbid diseases and take an average of four prescribed medications to 

manage these diseases (Stavrou et al., 2012). 

 

More than 90% of patients aged 70 years and older have some comorbidity. Furthermore, 

40% of these comorbidities are classified as severe (Kim et al., 2019). Elderly patients often 

have a lower physiologic reserve, are functionally dependent on a caregiver, and have 

inadequate social support (Kim et al., 2019). Elderly patients also experience higher 

hospitalisation rates and adverse drug reactions from cancer treatment (McCleary et al., 

2022). Older patients have a higher risk of experiencing treatment-related ADRs due to 

comorbidities and reduced organ function (Battisti et al., 2021).  

 

Age should not impact the choice of systemic therapies (Suter and Pagani, 2018). Regrettably, 

older patients often face considerable difficulties with chemotherapy. Physicians are less 

inclined to recommend chemotherapy to older individuals, likely due to perceived lower 

tolerance, higher risks related to myelosuppression, and a perceived decrease in effectiveness 



13 
 

compared to younger patients. Furthermore, when provided with the choice, older women are 

less inclined to opt for chemotherapy, primarily due to concerns about potential subjective 

adverse effects like hair loss, nausea, and vomiting (Vogel et al., 1999).  

 

Larger, more recent research provide evidence against the increased toxicity in older adults, 

despite a few smaller studies suggesting the opposite (Partridge et al., 2001). Crivellari et al. 

(2000) investigated the utilization of tamoxifen with adjuvant CMF in older women. While there 

was a larger hematologic and mucosal toxicity in women 65 years of age or older compared 

to younger women, quality of life evaluations indicated that the subjective treatment burden 

was similar for both age groups. In more recent prospective research, 44 women with early-

stage breast cancer, ages 35 to 79, had adjuvant AC chemotherapy for four cycles. 

Myelosuppression was more severe in older women, although age did not substantially affect 

consequences such neutropenia, changes in cardiac function, or changes in quality-of-life 

scores (Dees et al., 2000).  

 

When diagnosed with breast cancer, women under 40 years old have unique challenges, such 

as family planning, and fertility preservation. Young women should be offered genetic 

counselling, especially those with a family history of breast cancer (Suter & Pagani, 2018). 

Women under 40 make up less than 7% of all breast cancer patients in industrialized nations. 

The most common malignancy in women between the ages of 15 and 39 is breast cancer. In 

high-income nations, breast cancer ranks as the third leading cause of mortality for young 

women. Higher HER2 positive rates, greater triple-negative histology, and higher-grade 

hormone-responsive tumours might all account for this. Young women typically exhibit 

symptoms at the time of diagnosis because they are more likely to experience a delayed 

diagnosis due to diagnostic delays. One distinctive problem with screening in this population 

is that breast imaging has to be scheduled around the menstrual cycle. To lower the risk of 

false positives from functional contrast enhancement, breast MRIs and mammograms should 

be performed between days 5 and 12 of the cycle (Suter and Pagani, 2018).  

 

Family planning is frequently incomplete in women under 40 years old, making fertility 

preservation a sensitive subject that should be addressed at the beginning of treatment. 

Pregnancy should be avoided while receiving active therapy for breast cancer due to the 

teratogenic risk of chemotherapeutic therapies. Proactive counselling is essential and should 

revolve around contraception discussions and addressing sexuality concerns, such as 

dyspareunia, vaginal dryness, and loss of libido. Medications like vaginal moisturisers or 

lubricants may be prescribed (Suter and Pagani, 2018). Additional challenges encompass 



14 
 

changes in body image, fatigue, and difficulties with shoulder and arm movements (Hopwood 

et al., 2006). 

 

Ethnicity profiles 

 

The age distribution of breast cancer in South Africa exhibited an opposite trend compared to 

the rest of Africa. It mirrored the Caucasian age distribution in Europe. This could be partly 

due to the proportion of Caucasian residents in South Africa. However, in four of the seven 

South African studies where race was documented, 90% of the patients were black. It was not 

specified whether these patients were of mixed-race black descent (Olayide et al., 2021). 

The overall lifetime risk of developing breast cancer (from birth to age 74) is one in 36, but this 

varies significantly across different racial groups. For black women, the risk is one in 81, while 

for white women, it is one in 13. Mixed-race women have a lifetime risk of one in 63, and Asian 

women (mainly of Indian origin) have a risk of one in 21. Breast cancer is less prevalent among 

black women compared to other population groups (Vorobiof et al., 2001).  

 

Unique factors in the epidemiology of breast cancer for the black population include a slightly 

later age of menarche (14.7 years in rural black women and 13.9 in urban black women, 

compared to 12.6 in white women), an earlier age at first childbirth, high parity, and prolonged 

lactation. A demographic survey in South Africa found that 17.8% of black females aged 15 to 

19 had ever been pregnant, compared to 2.2% of white females. Only 22% of black female 

patients presented with early-stage breast cancer (stages I and II), while nearly 69% of 

nonblack patients were diagnosed at these early stages. In contrast, stages III and IV were 

most prevalent among black women (77.7%) compared to nonblack women (30.7%) (Vorobiof 

et al., 2001). 

 

Additionally, a study from Cape Town identified that a higher educational level, belonging to a 

medical aid fund or insurance, urban residence, and a family history of breast cancer were 

determinants of early-stage presentation (Hoffman et al., 2000). 

 

1.8.2 Common comorbidities amongst cancer patients  

 

Human Immunodeficiency Virus (HIV) 

 

More than 40% of HIV-infected patients are anticipated to develop cancer during their illness 

(Hurley et al., 2001). On an international scale, studies exploring the association between HIV 



15 
 

infection and breast cancer have been scarce. Most data generated in developed countries 

have been derived from relatively small study populations, often comprising of only 20 

patients, and have failed to establish any correlation between HIV infection and breast cancer 

(Ngidi et al., 2017). In South Africa (SA), where a few studies have been conducted with 

substantially larger populations, no increased incidence of breast cancer among HIV-positive 

individuals was observed (Ngidi et al., 2017). However, these studies did reveal that HIV-

infected women tended to present at a younger age. Additionally, existing research suggests 

that cancer patients with HIV often have a worse prognosis than non-infected patients with 

comparable staging for the same disease. Additionally, they are more likely to be diagnosed 

with more serious illness (Ngidi et al., 2017).  

 

Concerns arise among medical doctors and oncologists when combining combination 

antiretroviral therapy (ART) with chemotherapy. Given the possible overlap in toxicities, the 

effect of chemotherapy on immunological function, and the possibility of drug-drug 

interactions, these concerns are reasonable. Several combination antiretroviral therapies 

(ART) show toxicities such myelosuppression, neuropathies, nausea, and diarrhoea that are 

also frequent with chemotherapeutic agents (Deeken et al., 2012). 

 

The metabolism of paclitaxel primarily involves Cytochrome (CYP) 2C8, CYP3A4, and 

CYP3A5. As such, higher plasma concentrations of paclitaxel may occur from co-

administration of it with CYP3A4 inhibitors such as ritonavir. Adverse events such as 

myelosuppression, elevated liver function tests, and peripheral neuropathy can result from this 

elevation. CYP2B6 and CYP3A4 metabolise cyclophosphamide. Modulating CYP3A4 activity 

may impact neurotoxicity, with induction potentially increasing adverse events and inhibition 

minimising them. Fortunately, the CYP3A4 pathway is only used by 10% of the administered 

drug dose. Anthracyclines have minimal potential for drug-drug interactions during concurrent 

ART administration as doxorubicin is not metabolised by the CYP system. Protease inhibitors 

and chemotherapeutic medications that are metabolized by the hepatic cytochrome P450 3A 

enzyme family may interact to alter the toxicity and effectiveness of both drug groups. Drugs 

that interact with ART might be employed in chemotherapy for prophylaxis against 

opportunistic infections. Therefore, their use should be carefully considered and potentially 

adjusted (Bressan et al., 2021). 

 

Ngidi et al. (2017) carried out a retrospective patient chart review of all breast cancer patients 

who underwent chemotherapy from January 2012 to December 2015 at the Inkosi Albert 

Luthuli Central Hospital and Addington Hospital in Kwa-Zulu Natal, SA. Among the most 

severe haematological toxicities linked to chemotherapy, neutropenia was the most notable. 



16 
 

The neutropenic episodes occurred at a ratio of 3:1 in HIV-infected individuals compared to 

uninfected patients. HIV-infected patients consistently exhibited a lower baseline absolute 

neutrophil count and mean white cell count than uninfected patients, which persisted 

throughout the chemotherapy cycles. The lowest absolute neutrophil count in the HIV-infected 

group was observed after the first chemotherapy infusion. In contrast, the lowest absolute 

neutrophil count in the non-infected group was noted after the second cycle of chemotherapy. 

Neutropenia and its complications were more prevalent among HIV-infected patients between 

cycles.  

 

Notably, neutropenia brought on by chemotherapy was found to be independently predicted 

by HIV infection. This result implied that HIV-positive individuals were more likely to experience 

neutropenia because of comorbidities, myelosuppressive medications, and lowered immunity. 

As an alternative to dose reduction, growth factor support—specifically, granulocyte colony-

stimulating factor (G-CSF), such as filgrastim—was recommended. This underlines that 

treating existing neutropenia with G-CSF is less advantageous than preventing it (Ngidi et al., 

2017).  

 

Gomez et al. (2015) conducted a retrospective patient file review of HIV-positive patients 

diagnosed with breast cancer at the University of Miami/Jackson Memorial Hospital in Florida, 

United States of America. Patients diagnosed between January 1, 1989, and December 31, 

2013, were included. A comparison of the 48 consecutive HIV-infected breast cancer patients 

revealed that these patients were more likely to be younger and African American. Between 

2006 and 2010, the median age of a diagnosis of breast cancer was 46, whereas the median 

age of the general population was 61. These HIV-infected breast cancer patients were younger 

than the median age for African American women without breast cancer (57 years). Among 

the 30 patients who received chemotherapy, 14 patients (46%) experienced serious adverse 

events. In ten patients, the primary complication was myelosuppression or neutropenic fever, 

leading to one fatality attributed to neutropenic fever. The main cause of death was breast 

cancer, and the mortality had a statistical relationship with the stage of the disease upon 

presentation. At five years, the overall survival rate for HIV-positive women with breast cancer 

was 57%, while the general population's rate was 89.2%, as reported by the SEER database 

for the years 2004–2010 (Gomez et al., 2015). 

 

Diabetes 

 

Diabetes is a common comorbidity in patients with solid tumours, and outcomes are generally 

worse for these patients (Sempere-Bigorra et al., 2021). For individuals with type 2 diabetes 



17 
 

mellitus, controlling daily lifestyle choices and maintaining blood pressure and glucose levels 

acceptable are essential to delaying the onset and progression of complications from the 

disease as well as atherosclerotic diseases (Terao and Suzuki, 2021).  

 

Four to eighteen percent of diabetic cancer patients have hyperglycaemic events while 

receiving chemotherapy. Several factors contribute to hyperglycaemia: nutrition, less physical 

activity, stress, infection, old age, obesity, glucocorticoids, and chemotherapeutic agents. 

Hyperglycaemia increases the probability of suffering from chemotherapy-induced toxicities 

like neutropenia and neuropathy (Ahn et al., 2020).  

 

Corticosteroids used for antiemetic and allergy prophylaxis during chemotherapy can cause 

hyperglycaemia in diabetic people. Terao and Suzuki (2021) compiled the findings from 13 

trials that examined adverse effects in patients with diabetes who were receiving 

chemotherapy. During treatment, blood glucose levels were measured as high as 16.67 

mmol/l. Within a few hours of starting chemotherapy, blood glucose levels began to rise, 

peaked at around 10 hours, and then started to decline after 24 hours. The day following 

therapy, the mean fasting blood glucose level rose dramatically from 7.3 ± 2 mmol/l before to 

9.1 ± 2.9 mmol/l before breakfast. In the first round of chemotherapy, 80% of the 40 patients 

had hyperglycaemia. The cumulative dosages of dexamethasone were found to be 

significantly positively correlated with elevated HbA1c and blood glucose levels. Notably, blood 

glucose and HbA1c levels significantly increased with cumulative dosages of dexamethasone 

greater than 150 mg. 

 

One common adverse effect of chemotherapy is chemotherapy-induced peripheral 

neuropathy (CIPN), which can have a dose limit for a variety of chemotherapeutic drugs. 

Hyperglycaemia causes peripheral nerve damage, and for that reason, diabetic patients may 

have a greater risk of developing CIPN. The type of chemotherapeutic agent, the number of 

cycles, and the dose received play the most prominent roles in the occurrence of CIPN. 

Diabetic patients are more likely to experience neuropathy due to the loss of axonal integrity 

because of decreased regeneration (Sempere-Bigorra et al., 2021).  

 

Sempere-Bigorra et al. (2021) found that peripheral neuropathy developed in 74.4% of diabetic 

patients during treatment with paclitaxel compared to 58.4% in patients without diabetes. The 

adverse effects seen by diabetes patients were substantially more severe; 51.2% of them had 

grade 2-3 toxicities, compared to 27.7% of non-diabetic patients. Additionally, individuals who 

have diabetes are more likely than non-diabetic patients to experience a delay in the resolution 

of CIPN. Female patients suffering from diabetes saw higher rates of chemotherapy delays 



18 
 

(20.9%) and dose reductions (32.6% vs. 11.9%) than females without diabetes (7.1%). 

(Sempere-Bigorra et al., 2021). 

 

Bhatnagar et al. (2014) reviewed 123 medical records of breast cancer patients treated with 

sequential taxane between January 1, 2008, and December 31, 2011. Patients with diabetes 

had a two times greater likelihood of the taxane dose getting reduced. The study further 

demonstrated that dose reductions due to CIPN were more likely in patients receiving 

paclitaxel than docetaxel (Bhatnagar et al., 2014). Paclitaxel-induced peripheral neuropathy 

typically starts with numbness and paraesthesia (Sempere-Bigorra et al., 2021).  

 

Peripheral nerve dysfunction was identified in 52% of individuals with diabetes for less than 

five years and 75% of patients with diabetes for more than five years, in trials with over fifty 

patients (Terao and Suzuki, 2021). The risk of new infections rose by 68% in patients with 

diabetes compared to those without the disease in a study involving patients who received 

intravenous chemotherapy with steroids. Furthermore, patients with diabetes had a 37.0% 

hospitalization risk for infection within the first year following initial treatment, compared to a 

29.2% incidence for patients without diabetes. Chemotherapy with alkylating agents was 

found to be the main contributing factor to the development of infectious illnesses in these 

patients. Hospitalisations were primarily attributed to infection, neutropenia, and anaemia 

(Terao and Suzuki, 2021).  

 

In a study by Terao and Suzuki (2021), oral mucositis caused by chemotherapy was noted in 

5.9% of patients without diabetes and 6.9% of individuals with diabetes. Diabetes patients 

frequently have severe anorexia, nausea, and physical exhaustion. When individuals 66 years 

of age or older were receiving adjuvant chemotherapy for breast cancer, more individuals with 

diabetes (32.7%) than without diabetes (25.1%) were admitted to the hospital during the 

treatment. Diabetes was also found to be a predictor of heart failure among patients receiving 

chemotherapy for breast cancer (Terao and Suzuki, 2021). 

 

Obesity 

 

Obesity is a BMI of ≥30kg/m2. The South African Demographic and Health Survey Report 

(2016) indicated that 31% of men and 68% of South African females are obese. Furthermore, 

one in five females have a BMI equal to or above 35kg/m2 (National Cancer Strategic 

Framework, 2022). Obesity was previously perceived as an issue primarily affecting high-

income countries. Prevalence however has increased notably in the urban areas of low- and 

middle-income countries (Furlanetto et al., 2016). 



19 
 

Diabetes, obesity, and cancer regularly co-occur. A study conducted in Sweden showed that 

obesity raised the probability of 15 different types of cancer, with Hodgkin lymphoma 5 in men 

having the highest standardised incidence ratio of 3.3. After a meta-analysis of 221 datasets, 

it was ascertained that the risk for common and rare malignancies increases with every 

5kg/m2 increase in BMI (Mao et al., 2021). 

 

Several factors may lead to a poorer prognosis due to obesity (Lomma et al., 2023), including 

delayed self-detection, delayed diagnosis, increased surgical and radiation treatment 

complications, decreased efficacy of hormonal treatments, underdosing systematic 

chemotherapy, and a decline in health-related quality of life (James et al., 2015).  

 

Obesity is recognised as a significant public health issue and is linked to an elevated risk of 

chronic diseases and cancer (Furlanetto et al., 2016). Obesity has been correlated with 

various health issues, such as coronary heart disease, hypertension, cerebrovascular events, 

type II diabetes mellitus, osteoarthritis, and depression (Horowitz and Wright, 2015). In the 

case of females with breast cancer, obesity independently serves as a prognostic factor for 

the development of distant metastases and cancer-related deaths (Furlanetto et al., 2016). 

Obesity poses a high risk for vascular complications and early mortality in patients with 

diabetes. Oxidative stress, inflammation, and endothelial dysfunction cause organ damage in 

obese and hyperglycaemic patients (Mao et al., 2021). Patients with obesity tend to be 

diagnosed with more advanced diseases compared to non-obese counterparts (Furlanetto et 

al., 2016). 

 

International guidelines advise chemotherapy doses based on actual body weight without 

capping or relying on ideal body weight, irrespective of a patient's body mass. Evidence 

suggests that obese patients receive lower chemotherapy doses despite these 

recommendations. A British investigation, discovered that 20% of obese individuals with early-

stage breast cancer were given a dose cap. On the other hand, 70% of oncologists use either 

a capped dose or doses based on ideal body weight when administering chemotherapy to 

obese patients, according to Australian research covering all cancer types, which found that 

only 6% of physicians utilize actual body weight. Determining if individuals with obesity are at 

a higher risk of toxicity from medication is important because it can affect the efficacy of 

treatment when toxicity results in dose decrease or termination (Lomma et al., 2023). 

 

To provide safe and effective cancer care for a growing percentage of the population, it is 

imperative to establish suitable dose strategies for chemotherapy in obese patients. Body 

Surface Area (BSA)-based dose calculations can result in very high doses. Concerned about 



20 
 

potential excess toxic effects, many practitioners reduce the chemotherapy doses given to 

obese patients. Some practitioners opt for calculating the dose based on ideal body weight 

(IBW) instead of actual body weight (ABW) when calculating BSA for obese patients, 

potentially resulting in a 25% reduction in chemotherapy compared to patients dosed with 

ABW. Finally, some healthcare professionals limit the total dosage of a medicine given to 

obese individuals or use a maximum BSA of 2.0m2 when administering chemotherapy 

(Hourdequin et al., 2013). The reduced efficacy noted in obese patients is often attributed to 

this reduction in chemotherapy dosage (Furlanetto et al., 2016).  

 

Obese patients treated at adjusted doses experienced significantly lower rates of febrile 

neutropenia, thrombocytopenia, and thromboembolic events than those dosed on actual body 

weight in a retrospective analysis evaluating the association between obesity and toxicity from 

dose-dense or dose-intense chemotherapy in 555 obese patients. Nevertheless, regardless 

of dosage administration, there was no change in progression-free survival or overall survival 

when compared to individuals who were not obese. Furthermore, unrestricted dose in obese 

patients receiving neoadjuvant chemotherapy was linked to higher rates of pathological full 

response and better disease-free survival (Lomma et al., 2023). 

 

In the Geriatric Assessment-Driven Intervention (GAIN) study conducted in Europe between 

August 2004 and July 2008, most obese patients received chemotherapy adjusted according 

to BSA. Notably, when obese patients received full dose-dense or intense dose-dense 

chemotherapy, a higher rate of toxicities was observed, particularly high-grade haematological 

toxicities. In the intense dose-dense ETC (epirubicin, paclitaxel, and cyclophosphamide) arm, 

obese patients were three times more likely to experience febrile neutropenia than non-obese 

patients despite G-CSF prophylaxis. Additionally, obese individuals receiving full dose-dense 

chemotherapy experienced twice as many high-grade thromboembolic events in the dose-

dense EC-TX (epirubicin, cyclophosphamide, paclitaxel, and capecitabine) group as those 

getting modified dosages (Furlanetto et al., 2016). 

 

Hypertension 

 

African American breast cancer survivors suffer from a more significant number of comorbid 

conditions than non-Hispanic White survivors which causes a 42% greater likelihood of dying 

from breast cancer. Williams et al. (2020) studied the association between hypertension, a 

significant cardiovascular disease (CVD) risk factor and ethnicity among breast cancer 

survivors. The study was based on data from the National Health and Nutrition Examination 



21 
 

Survey (NHANES) from 1999 to 2014. Williams et al. (2020) reported that hypertension 

occurred 30% more in African American women than in white women after adjusting for 

demographic and health-related factors. Compared to white women, African American women 

were less likely to smoke and more physically active, yet a larger proportion of African 

American breast cancer survivors had diabetes or obesity. A 25% occurrence of hypertension 

was reported in obese African American women and a 37% occurrence in African American 

women who suffered from diabetes and obesity (Williams et al., 2020). 

 

African American breast cancer survivors have a significantly higher risk of dying from 

cardiovascular disease (CVD) than their white counterparts, according to data from the 

National Cancer Institute's SEER database. The American Heart Association (AHA) reiterated 

this increased risk for CVD morbidity and mortality among African American breast cancer 

survivors. Hypertension and other preventable CVD risk factors may increase the cardiotoxic 

effects of chemotherapy (Williams et al., 2020). 

 

Dyslipidaemia 

 

A well-established risk factor for cardiovascular diseases is an unbalanced lipid profile, which 

is characterized by increased total cholesterol, decreased levels of triglycerides and low-

density lipoprotein (LDL) cholesterol, and decreased levels of high-density lipoprotein (HDL) 

cholesterol, apolipoprotein A, and apolipoprotein B (Li et al., 2018). 

 

From May 2016 to February 2017, Storph et al. (2019) conducted research in the female 

surgical ward and laboratory unit at the Cape Coast Teaching Hospital. Fifty-one patients with 

invasive ductal carcinoma and advanced breast cancer undergoing chemotherapy were 

included in their study. All lipid parameters increased, but only HDL values increased 

significantly throughout each treatment cycle in a steady manner (p=0.054). This observation 

correlated with earlier studies (Storph et al., 2019). 

 

Alimperti et al. (2023) carried out research at a tertiary hospital in Athens involving women 

admitted to the oncology clinic. The study included individuals aged 18 years and above 

diagnosed with primary breast cancer who were undergoing chemotherapy following either a 

total or partial mastectomy. Their findings revealed that throughout chemotherapy for breast 

cancer, there is a notable rise in total cholesterol, LDL, and triglyceride levels, while HDL levels 

remained unchanged by the last cycle of chemotherapy (Alimperti et al., 2023). 

 



22 
 

Various chemotherapy medications can induce significant dyslipidaemia in breast cancer 

patients’ post-chemotherapy. Anthracyclines like doxorubicin have been found to diminish the 

expression of the ABCA1 gene and apolipoprotein A1 in HepG2 cells, both of which play vital 

roles in HDL levels (Alimperti et al., 2023). The effectiveness of treatment regimens involving 

taxanes is limited by notable toxicities. Taxanes, particularly paclitaxel, significantly decrease 

HDL levels compared to breast cancer patients not undergoing chemotherapy (Alimperti et al., 

2023). 

 

1.9 Problem statement 

 

Clinical findings have indicated that chemotherapeutic toxicity amongst breast cancer patients 

varies with patient-specific demographics. Furthermore, many patients requiring 

chemotherapy for breast cancer treatment, often present with comorbidities, such as diabetes, 

hypertension, and obesity due to lifestyle habits. There is limited literature available in the South 

African context, reporting on evidence of correlations between chemotherapeutic toxicities with 

varied demographic and comorbidity profiles of treated patients with stage 0-III breast cancer. 

 

1.10 Aim and objectives of the study 

 

This study aimed to determine a potential correlation between demographic profiles and the 

presence of pre-existing comorbidities on the chemotherapy-related adverse effects 

experienced by patients with stage 0-III breast cancer at a private oncology centre in Gauteng. 

Furthermore, the study aimed to compare the study findings in a South African context, with 

adverse effects currently recorded on the WHO VigiAccess Adverse Drug Reaction database 

for chemotherapeutic toxicities. 

 

The aim of the study was achieved through undertaking the objectives listed: 

 

a. To investigate the occurrence of chemotherapy-related adverse effects experienced 

by patients with stage 0-III breast cancer in relation to demographic profiles (age, 

gender, ethnicity) of sampled patient charts. 

b. To determine specific pre-existing comorbidities frequently recorded in sampled 

patient charts for those receiving chemotherapy for stage 0-III breast cancer, and to 

investigate the correlation between chemotherapeutic toxicity experiences and patient 

comorbidities. 



23 
 

c. To investigate interventions that were applied to mitigate the experienced adverse 

effects of chemotherapy amongst patients with stage 0-III breast cancer 

(chemotherapeutic agent modifications, dose reductions, or premature discontinuation 

of chemotherapy). 

d. To compare the adverse events reported in patient charts to the WHO VigiAccess 

Adverse Drug Reaction database for chemotherapeutic toxicities. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



24 
 

Chapter 2: Research methodology 

2. Chapter overview 

 

This chapter presents the research methodology used to conduct the study. It describes the 

study site, sampling, data collection and data analysis methods. The chapter also details the 

ethical considerations for the study.  

 

Figure 2.1: Flow diagram outlining the phases undertaken in the study. 

•Step 1: Undertook literature review

•Step 2: Study protocol submission (inclusive of data 
collection tool development) and approval through 
research committee at the university. 

•Step 2: Submission and receipt of ethics clearance 
from HREC to undertake study. 

•Step 3: Communication with study site to obtain 
permission to review patient charts. 

Study planning and design

•Step 1: Uploaded data collection to mobile device on 
RedCAP®.

•Step 2: Confirmed accuracy of captured data 
collection tool before sending it to production phase in 
REDCap®.

•Step 2: Scheduled data collection appointments with 
the study site.

•Step 3: Reviewed patient charts in accordance with 
inclusion and exclusion criteria.

•Step 4: Completed capturing of data from patient 
charts on REDCap® and ensured sample size met.

Data collection

•Step 1: Transcribed data from REDCap® into MS 
Excel spreadsheet (downloaded) and cleaned data 
excel sheet.

•Step 2: Analysed data according to frequencies and 
percentages. 

•Step 3: Consulted with biostatistician to assist with 
statistical tests required to meet objectives of the 
study. 

•Step 4: Completed statistical analysis of data.

Data analysis

•Step 1: Undertook literature review to complete 
Chapter 1.

•Step 2: Documented methodology employed.

•Step 3: Presented results in an appropriate format 
and discussed results in relation to existing literature.

•Step 4: Completed all further sections for a research 
report and identified study limitations and future 
recommendations were made. 

•Step 5: Submitted final research report for 
examination. 

Final write-up preparation and 
submission

•Step 1: Findings were presented to the staff at the 
study site to allow for practice-based translations of 
findings and recommendations.

Sharing of results



25 
 

2.1 Study design 

 

This study was a quantitative, retrospective (January 2018 to December 2019) descriptive 

cohort analysis of patient medical charts from a large, private, oncology centre in Gauteng, 

South Africa.  

 

2.2 Study site 

 

The study site was the Sandton Medical Oncology Centre. It is a large, well-established, 

private medical oncology practice located in Sandton, Johannesburg. Sandton is an affluent 

suburb. Most of the patients treated at the practice, reside in Gauteng. The minority of patients 

travel from nearby provinces e.g. Mpumalanga and North West to receive treatment at this 

practice. The majority of the patients seen at the practice is a member of a medical aid. The 

practice has four practising medical oncologists. The Sandton Medical Oncology Centre offers 

various services to its patients, including medical oncology, radiation therapy, psychosocial 

care, rehabilitation facilities and sub-acute wards. Types of cancer mostly treated at the centre 

include breast, ovarian, non-small cell lung cancer, colorectal and urothelial cancers. 

 

2.3 Study population and sampling 

 

Approximately 432 patients receive treatment at the Sandton Medical Oncology Centre each 

year for a number of cancer types. The Global Cancer Observatory (2021), estimated that 

14.3% of South Africans of a diverse range in gender, age and ethnicity, were diagnosed with 

breast cancer in 2020. Based on these statistics, approximately 62 patients are diagnosed 

with breast cancer at the Sandton Medical Oncology Centre annually. The Raosoft online 

sample size calculator was used to determine the sample size of 54 patient charts (Raosoft, 

2004).  

Sample size = 

𝑧2 𝑥 𝑝 (1−𝑝)

𝑒2

1+( 
𝑧2 𝑥 𝑝(1−𝑝)

𝑁 𝑥 𝑒2 )
 

Equation 2.1: Sample size formula 
Where N = sample population size; z = statistical level of confidence (95%); p = expected 
proportion of the sample with particular characteristics (p = 0.5); d = margin of error (d = 
0.05).  
 

 



26 
 

Inclusion criteria 

 

Patient charts that were included in the study sample have been detailed below: 

• diagnosis of stage 0, I, II, and III breast cancer from January 2018 to December 

2019; 

• undergoing chemotherapy; 

• Age ≥ 18 years; 

• All genders (male; female; other). 

 
Patient charts retrieved and reviewed that were incomplete in terms of the study data collection 

tool were excluded from the study. 

 

2.4 Data collection tool  

 

The data collection tool (Appendix 1) was developed by the researcher, in relation to the 

correlations outlined in the objectives of the study. The objectives were to investigate whether 

specific patient demographics and their presenting comorbidities correlate with particular 

chemotherapeutic toxicity experiences and the interventions by oncologists to toxicities. The 

data collection tool was transcribed into REDCap® for easier and more timely data collection. 

The data collection tool consisted of five sections, namely patient demographic, disease and 

treatment information, chemotherapeutic toxicity experiences, and corrective actions taken in 

response. The data collection tool was designed with check-box options within each of these 

sections, except for situations where “other” was a selected option, whereby further 

information was to be recorded manually. Demographics necessary to draw correlations 

outlined in the study objectives were only listed in the tool. Disease information referred to 

both the current cancer status (HER2, oestrogen, progesterone), along with comorbidities. 

The treatment section focused on the chemotherapeutic regimen prescribed for breast cancer, 

with the chemotherapeutic toxicities section based on the Common Terminology Criteria for 

Adverse Events (CTCAE) version 5.0. Resultant recourse applied by the oncologist in 

response to toxicity experiences included “yes” and “no” checkboxes, with an additional “other” 

section to specify fewer common interventions employed.  

  

2.5 Study procedure and data collection 

 

Upon approval of the study protocol (Appendix 2), receipt of ethical clearance (Appendix 3) 

and study site permission granted (Appendix 4), patient charts were reviewed in accordance 

with the sampling method and inclusion criteria parameters. Patient charts were accessed 



27 
 

from the archive room located in the Sandton Medical Oncology Centre and manually 

retrieved, with charts reviewed on-site. Patient charts were randomly selected and reviewed 

to select the patient profiles that meet the inclusion criteria. This process of random selection 

was followed until the sample size was met. Patient charts included in the study were allocated 

a file number for researcher identification purposes only. No direct patient identifiers (name, 

identity number, contact details) were recorded at any point in time. Required data was 

extracted from the patient charts and transcribed on the REDCap® data collection tool 

(Appendix 1). Data collected from patient charts at the study site was done so following ethical 

guidelines and Protection of Personal Information Act (POPIA) adherence. Once the sample 

size was reached, the data was transferred to Microsoft Excel for review, cleaning, and 

analysis. Data storage occurred on the researcher's laptop, along with an additional back-up, 

and was shared with supervisors. All devices used were password-protected, and the 

researcher kept the REDCap® registration and sign-in details confidential. No paper-based 

records or data were taken off the study site.  

 

2.6 Data analysis  

 

Data analysis consisting of percentage frequencies was calculated using Microsoft Excel. 

Descriptive analyses of the patient demographic characteristics with adverse effect profiles 

experienced were determined through pivot tables and recorded as percentages and 

frequencies. Furthermore, comorbidity prevalence was analysed in patients experiencing 

chemotherapeutic adverse effects. Chemotherapeutic toxicity reported in patient charts from 

the study site was compared with adverse drug reactions already documented in the WHO 

VigiAccess Adverse Drug Reaction database for each chemotherapy regimen. Data collected 

at Sandton Medical Oncology Centre was further compared with the type of reported potential 

adverse effects in the WHO VigiAccess Adverse Drug Reaction database. The ten most 

prevalent adverse effects in the WHO VigiAccess Adverse Drug Reaction were listed for each 

treatment regimen. In the cases where the adverse effects were not reported for the regimen, 

the adverse effects were listed for each chemotherapeutic agent that makes up the regimen. 

 

Inferential statistical analysis was conducted using Stata software version 18 (Stata Corp., 

College Station, TX, USA). Age groups were categorized as follow: 30-39 years, 40-49 years, 

50-59 years, 60-69 years, and >70 years. The two patients aged >80 years were included in 

the >70 years category to prevent skewing the data. Ethnic groups were categorized as 

follows: black, other, and white. Ethnic data was originally split up to specify Asian, Black, 

Coloured, Indian, Other, and White. During the data analysis the researcher took the decision 



28 
 

to use the “Other” category to additionally include Asian, Coloured, and Indian collectively. 

This is due to the Asian, Coloured, and Indian category sizes being too small to produce values 

that can be extrapolated to the population. The p-values for the age categories against the 

number of adverse effects were determined using the Kruskal-Wallis test. The p-values for the 

different ethnic groups against the number of adverse effects were determined using the one-

way ANOVA test, as well as for the number of comorbidities against the number of adverse 

effects. The p-values were determined for the different age groups against the number of side 

effects experienced as well as the age groups against the number of comorbidities present at 

the time of breast cancer diagnosis. The p-values ≤0.05 were considered statistically 

significant.  

 

2.7 Ethical considerations 

 

An ethics application was submitted to the University of the Witwatersrand Human Research 

Ethics Committee (HREC) - Medical, before any data collection was undertaken, making use 

of the study protocol approved by the Faculty of Health Sciences Research Office at the 

University of the Witwatersrand (Appendix 2). Data collection only commenced following 

receipt of the ethics clearance certificate (Appendix 3) and study site permissions to access 

patient charts (Appendix 4). 

 

Current research ethics guidelines established by the University of the Witwatersrand, Human 

Research Ethics Committee (HREC) – Medical, were read and applied throughout the 

research study. An accredited human research ethics short course was attended and 

completed, by both principal investigator and supervisors, as a requirement during the ethics 

clearance process (Appendix 3).   

 

This study entailed the retrospective analysis of patient charts; therefore, no patient consent 

was needed, following ethics guidelines from HREC - Medical. Confidentiality of patient 

information collected was maintained throughout the study, through coding during data 

collection, to ensure patient information collected and documented remained anonymous. 

Patient charts also remained on-site. 

 

All sources of information considered in this study were correctly referenced, with a plagiarism 

report run on all written work through Turnitin (Appendix 5). 

 

 



29 
 

Chapter 3: Results and Discussion 

3. Chapter overview 

 

This chapter presents the findings of the retrospective review of patient charts from the study 

site. It provides a detailed descriptive analysis of the results and discusses the implications of 

the findings for the practice, as well as compares findings with other literature and VigiBase. 

 

3.1       Demographic profiles represented in patient charts 

 

The demographic information of the patients reviewed during the retrospective analysis of 

patient charts has been summarised in Tab