African Covid-19 Critical Care Outcomes Study: An African, Multi-centre Evaluation of Patient Care and Clinical Outcomes for Patients With COVID-19 Admitted to High-care or Intensive Care Units
Conditions: Severe Acute Respiratory Syndrome Coronavirus 2
Sponsor: University of Cape Town

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(SAP added 8 Sept 2020)       African Covid-19 Critical Care Outcomes Study (ACCCOS) 
Protocol v2.0 
 
 
 
 
The African Covid-19 Critical Care Outcomes Study (ACCCOS) 
 
 
An African, multi-centre evaluation of patient care and clinical outcomes for 
patients with COVID-19 admitted to high-care or intensive care units 
 
 
Study protocol version 2.0 
23 April 2020 
 
(Statistical analysis plan version 1: Interim analysis) 
(Added 8 September 2020) 
 
 
 
 
 
 
 
Signature 
 
 
 
 
 
Signature 
Bruce Biccard  
 
 
 
 
Dean Gopalan 
 
 
 
Co-principal investigators 
 
 

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Steering committee 
Bruce Biccard MBChB FCA(SA) FFARC(I) MMedSci PhD. Department of Anaesthesia and 
Perioperative Medicine, University of Cape Town, South Africa 
P. Dean Gopalan MBChB (Ntl); FCA (SA); Crit Care; PhD. Discipline of Anaesthesiology and 
Critical Care, School of Clinical Medicine, University of KwaZulu Natal, South Africa.  
Greg Calligaro BSc. Hons. (Phys.), MBBCh (Wits), Dip PEC(SA), MMed (UCT), FCP(SA), Cert. 
Pulm(SA). Associate Professor, Specialist Physician and Pulmonologist, Division of Pulmonology, 
Department of Medicine, Groote Schuur Hospital, University of Cape Town Lung Institute, South 
Africa 
David Fredericks MBChB (UCT), FCEM (SA), Cert Critical Care (SA). Department of Critical Care, 
Groote Schuur Hospital, South Africa 
Ivan Joubert MBBCh, FCA(SA), Cert Crit Care. Professor and Head, Division of Critical Care, 
Department of Anaesthesia and Perioperative Medicine, University of Cape Town and Groote 
Schuur Hospital, South Africa 
Hyla-Louise Kluyts, MBChB, MMed, DMed. Executive Director, Safe Surgery SA NPC 
Mervyn Mer, MBBCh, Dip PEC (SA), FCP (SA), Pulmonology subspecialty Cert Critical Care (SA), 
MMed (Int Med), FRCP (London), FCCP (USA), PhD. Professor, Divisions of Critical Care and 
Pulmonology, Department of Medicine, Charlotte Maxeke Johannesburg Academic Hospital and 
Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa 
Malcolm Miller MBChB, DA(SA), FCA, Cert Crit Care(SA). Intensivist, Department of Anaesthesia 
and Perioperative Medicine, University of Cape Town, South Africa  
William L Michell MBChB, DA, FFA(Crit Care). Emeritus Associate Professor, Division of Critical 
Care and Department of Surgery, University of Cape Town, South Africa 
Fathima Paruk MBChB (Natal), FCOG(SA), Cert Crit Care (SA), MD (Natal). Associate Professor, 
Clinical and Academic HOD, Critical Care, University of Pretoria  and Steve Biko Academic 
Hospital  
Jenna L Piercy BSc(Hons), MBBS (Lond), FCA(SA), Cert Crit Care. Head, Clinical Unit, Division of 
Critical Care, Department of Anaesthesia and Perioperative Medicine, University of Cape Town 
and Groote Schuur Hospital, South Africa 
David Thomson MBChB, FCS, MMed(Surg), Cert Crit Care. Division of Critical Care, Department 
of Surgery, University of Cape Town and Groote Schuur Hospital, South Africa 
Juan Scribante Dipl Gen Nursing and Midwifery, Dipl ICU, Dipl Community Nursing, B Cur Ed ad 
Admin, M Cur, PhD.  School of Clinical Medicine, University of the Witwatersrand, South Africa 
DEA van Straaten, Bio-informatician, Safe Surgery SA NPC 
 
 

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Co-Principal investigator contact details 
Bruce Biccard 
Department of Anaesthesia and Perioperative Medicine 
University of Cape Town  
E-mail: bruce.biccard@uct.ac.za 
Telephone: +27 (0) 76 160 6387 
 
Dean Gopalan 
Head: Discipline of Anaesthesiology & Critical Care,  
School of Clinical Medicine, College of Health Sciences 
University of KwaZulu-Natal 
E-mail: Gopalan@ukzn.ac.za  
Telephone: +27 (0) 312604328 
 
Funders 
 
 
 

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Protocol v2.0 
Contents 
Introduction ........................................................................................................................................................................ 5 
Research questions .......................................................................................................................................................... 6 
Primary outcome ......................................................................................................................................................... 6 
Secondary outcomes .................................................................................................................................................. 6 
Methods ................................................................................................................................................................................ 6 
Inclusion criteria .......................................................................................................................................................... 6 
Exclusion criteria ......................................................................................................................................................... 6 
Centres ............................................................................................................................................................................. 7 
Ethics approval ............................................................................................................................................................. 7 
Data collection and collation ........................................................................................................................................ 8 
Dataset.............................................................................................................................................................................. 8 
Sample size calculation ............................................................................................................................................. 9 
Statistical analysis ....................................................................................................................................................... 9 
Primary outcome measure ............................................................................................................................... 10 
Secondary outcome measures ........................................................................................................................ 10 
Organisation .................................................................................................................................................................... 10 
Country co-ordinators ............................................................................................................................................ 10 
Local co-ordinators .................................................................................................................................................. 11 
Data management and ownership .......................................................................................................................... 11 
Publication plan ............................................................................................................................................................. 11 
Deliverables ..................................................................................................................................................................... 12 
References ........................................................................................................................................................................ 13 
Appendix 1 ....................................................................................................................................................................... 14 
The African Covid-19 Critical Care Outcomes Study (ACCCOS) case record form ........................ 14 
 
 
 
 
 

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Introduction 
The infectious disease COVID-19, caused by coronavirus SARS-CoV-2 (severe 
acute respiratory syndrome coronavirus 2), has been declared a pandemic and an 
international healthcare emergency by the World Health Organization (WHO). It has 
spread across the globe, overwhelming healthcare systems by causing high rates of 
critical illness. Mortality from COVID-19 exceeds 4%, with older people with 
comorbidities being extremely vulnerable.1 It is expected that between 50-80% of the 
world’s population may contract SARS-CoV-2 over the next two years.  
We expect the outcomes to be potentially worse in Africa, because firstly, there is 
a limited workforce,2 and secondly there are limited intensive care facilities and critical 
care resources across Africa to provide sufficient care.  It is estimated that there are 0.8 
(95% confidence interval 0.3 to 1.45) critical care beds per 100,000 population in 
Africa.3 It is likely that the volume of unplanned admissions associated with COVID-19 
will further adversely affect critical care outcomes in Africa,4 especially as the ability of 
healthcare systems in Africa to adapt and expand during the pandemic to meet the 
clinical workload is unknown. Furthermore, the patient outcomes in critical care is 
poorly documented in this under-resourced environment. 
Most countries in Africa are still in the early phase of the epidemic which 
provides the opportunity to study the utilization of critical care resources and their 
impact on patient outcomes. It is important therefore to establish what resources, 
comorbidities and interventions are potentially associated with either mortality or 
survival in Africa. Rapid dissemination of these findings may help inform appropriate 
resource prioritisation and utilisation during the COVID-19 pandemic in Africa. These 
points provide the rationale for the African Covid-19 Critical Care Outcomes Study 
(ACCCOS).  
 
 

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Research questions 
 
Primary outcome 
The primary outcome is in-hospital mortality in adult patients referred to 
intensive care or high-care units following suspected or known COVID-19 infection in 
Africa. 
Secondary outcomes 
To determine the risk factors (resources, comorbidities and interventions) 
associated with mortality in adult patients with suspected or known COVID-19 infection 
in Africa.  
 
The research questions to be addressed are the following. In patients with suspected 
or known COVID-19 infection in Africa;  
1. What are the critical care resources associated with survival? 
2. What patient comorbidities and other risk factors are associated with in-hospital 
mortality? 
3. What hospital interventions are associated with in-hospital survival? 
 
Methods  
An African national multi-centre prospective observational cohort study of adult 
(≥18 years) patients referred to intensive care or high-care units in Africa with 
suspected or known COVID-19 infection in Africa. Patient follow up will be for a 
maximum of 30 days in-hospital.  
This study will run between April to December 2020, with an interim analysis 
after 250 to 300 deaths registered in the study, or in June or July 2020 if this number of 
deaths has not been reported in the study. The reason for the interim analysis, is to 
potentially provide data which may be associated with improved outcomes in Africa, in 
timeous manner for possible implementation during this COVID-19 pandemic.  
 This study will be registered on ClinicalTrials.gov. 
 
Inclusion criteria 
All consecutive patients at participating centres referred for high-care unit or 
intensive care unit admission who have suspected or known COVID-19.  
Exclusion criteria 
None 

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Centres 
Our plan is to recruit as many centres as possible in Africa and ask them to 
include all eligible patients in the study.  
 
Ethics approval 
Ethics approval will be obtained from each university centre. Steering committee 
members will ensure ethics approval is obtained from their respective centres. Centres 
will not be permitted to record data unless ethics approval or an equivalent waiver is in 
place.  
The consent process and data inclusion in the study will be the following. We 
expect that for most patients, consenting at the time of admission to critical care will be 
inappropriate due to the patient’s condition and the critical care management needed. 
Therefore, in most cases we will only be able to explain the study to the patient 
following clinical improvement from their critical condition. We will also be able to 
explain the study to a legal representative or proxy, once the patient’s management has 
been stabilised in the critical care. For these reasons, we are applying for: 
i) ‘Delayed consent’ for patients admitted to critical care, which can be given by 
the patient (following stabilisation or recovery in critical care) or a legal representative 
or proxy (should the patient be unable to provide consent).  
ii) If there is no opportunity to acquire ‘delayed consent’ (i.e. the patient does not 
recover sufficiently to provide consent, and there is no legal representative or proxy to 
provide consent for participation) before the study outcome is reached (i.e. 30 days in 
hospital, or the patient dies in hospital), then we request that the ethics committee 
considers permission to include the patient’s data in the study. 
The justifications for this consent process and data inclusion are the following. 
We expect that almost all critical care admissions will be urgent or emergent. Attempts 
to obtain traditional consent in predominantly urgent and emergent critical care 
admissions, which may include patients with a decreased level of consciousness may 
lead to non-consecutive patient enrolment in the study. It is likely that this would lead 
to a biased sample, with artificially low estimates of adverse outcomes in African Covid-
19 critical care patients, and data which is not generalisable to the majority of African 
Covid-19 critical care patients. Finally, generating biased and poorly generalizable data 
would not address the research question, and thus would dishonour the contributions 
of the other included patients, and would be wasteful research in a resource-limited 
environment.  

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Data collection and collation 
Data will be collected in individual centres on paper case record forms (CRFs) for 
every patient recruited. Paper CRFs will be stored within a locked office in each centre 
as they will include identifiable patient data in order to allow follow-up of clinical 
outcomes. Data will then be pseudo-anonymised by generation of a unique numeric 
code and transcribed by local investigators onto an internet based electronic CRF. Each 
patient will only be identified on the electronic CRF by their numeric code; thus the co-
ordinating study team cannot trace data back to an individual patient without contact 
with the local team.  A participant (patient) list will be used in each centre to match 
identifier codes in the database to individual patients in order to record clinical 
outcomes and supply any missing data points. Access to the data entry system will be 
protected by username and password delivered during the registration process for 
individual local investigators. All electronic data transfer between participating centres 
and the co-ordinating centre will be encrypted using a secure protocol (HTTPS/SSL 3.0 
or better). Data will be anonymised during the transcription process using the Research 
Electronic Data Capture (REDCap) tools hosted by Safe Surgery South Africa (SSSA). 
REDCap is a secure, we-based application designed to support data capture for research 
studies.5 Soft limits will be set for data entry, prompting investigators when data were 
entered outside these limits. In countries with poor internet access, paper case record 
forms may be forwarded to SSSA, for entry by SSSA. 
Pseudo-anonymised (coded) data may also be sent by e-mail to the coordinating 
centre if necessary. 
Each centre will maintain a secure trial file including a protocol, local 
investigator delegation log, ethics approval documentation, the participant list, etc.  
A final summary printout of included patients with major variables should be 
produced for each centre together with final data submission to double check for 
completeness and accuracy. 
Dataset 
A realistic data set will be fundamental to the success of the investigation, and 
this was confirmed in previous studies e.g. EuSOS study where nearly complete data 
was available on 46 000 patients,6 and ASOS in a resource limited African environment 
had complete data in over 95% of participants.2  We have adopted a leaner dataset than 
used in EuSOS or ASOS, and therefore believe that these key data points will not 
discourage centres from participating because of an excessive burden of data collection.  
Centre co-ordinators may request the addition of a limited number of data points 
to support the ACCCOS data collection and for subsequent regional analyses. All 
additional data points must be discussed with the co-principal investigators and if 
necessary the steering committee.  

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Centre specific data will be collected once for each hospital including: 
secondary/tertiary centre, number of hospital beds, number and level of critical care 
beds, details about the reimbursement status of the hospital and public holidays or 
other local factors affecting patient care during study period e.g. nurse to patient ratio. 
The case record form (CRF) will be completed for every eligible patient admitted 
to the high-care unit or the intensive care unit during the study period (appendix 1). 
Patients will be followed up until hospital discharge. This will be censored at thirty days 
i.e. patients will be followed up until discharge or for thirty days whichever is the 
shorter period. 
 
Sample size calculation 
The sample size will depend upon the number of centres recruited and their 
respective caseloads. Each centre will also complete a screening tool of all eligible 
patients during study participation. The duration of enrolment at a site will be 
determined by the local lead investigator, and the circumstances allowing participation 
during the COVID-19 pandemic. 
There are approximately 25 to 30 variables in the dataset which may be 
associated with mortality in COVID-19 patients requiring critical care admission. We 
plan to do an interim analysis once 250 to 300 deaths are registered in the trial, to allow 
assessment of these risk factors in a logistic regression analysis, allowing for 
approximately 10 events per variable.7 Should this milestone not be reached by June or 
July 2020, a limited logistic regression will be conducted to provide some data to inform 
further management in Africa during COVID-19.  
 
Statistical analysis 
Data will be presented at a national level and at a continental African level. All 
institutional level data will be anonymised prior to publication. Categorical variables 
will be described as proportions and will be compared using chi-square tests. 
Continuous variables will be described as mean and standard deviation if normally 
distributed or median and inter-quartile range if not normally distributed. Comparisons 
of continuous variables between groups will be performed using t-tests, one-way 
ANOVA or equivalent non parametric tests as appropriate. Univariate analysis will be 
performed to test factors associated with postoperative complications, critical care 
admission and in-hospital death. 
Single-level and hierarchical multi-level logistic regression models will be 
constructed to identify factors independently associated with these outcomes and to 
adjust for differences in confounding factors. Factors will be entered into the models 

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based on their univariate relation to outcome (p<0.05), biological plausibility and low 
rate of missing data.  
Results of logistic regression will be reported as adjusted odds ratios (OR) with 
95% confidence intervals. The models will be assessed through the use of sensitivity 
analyses to explore possible interacting factors and examine any effect on the results. A 
single final analysis is planned at the end of the study.  
 
Primary outcome measure  
Incidence of in-hospital mortality in adult patients referred to intensive care or 
high-care units following suspected or known COVID-19 infection in Africa. 
Secondary outcome measures 
To determine the risk factors (resources, comorbidities and interventions) 
associated with mortality in adult patients with suspected or known COVID-19 infection 
in Africa.  
Organisation 
ACCCOS is performed under the auspices of the Critical Care Society of South 
Africa (CCSSA). The Steering Committee will be chaired by BB and PDG. The Critical 
Care Society of South Africa is represented by PDG. The study management team will be 
appointed by the Steering Committee and led by BB and PDG. The duties of this team 
will include administration of all project tasks, communication between project 
partners (including funders, steering committee members, national and local co-
ordinators, etc.), data collation and management and preparation of reports for 
individual study sites. The Steering Committee is responsible for the scientific conduct 
and consistency of the project. The Steering Committee will ensure communication 
between the funder(s), study management team and co-ordinators as necessary. 
 
Country co-ordinators 
Country co-ordinators will be appointed by the steering committee to lead the project 
within individual countries and:  
• Identify local co-ordinators in participating hospitals  
• Assist with translation of study paperwork as required  
• Ensure distribution of research manuals, eCRF and other materials  
• Ensure necessary regulatory approvals are in place prior to the start date  
• Ensure good communication with the participating sites in his/her country 
 

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Local co-ordinators 
Local co-ordinators in individual institutions will have the following responsibilities:  
• Provide leadership for the study in their institution  
• Ensure all relevant regulatory approvals are in place for their institution  
• Ensure adequate training of all relevant staff prior to data collection  
• Supervise daily data collection and assist with problem solving  
• Act as guarantor for the integrity and quality of data collected  
• Ensure timely completion of eCRFs  
• Communicate with the relevant national coordinator 
Data management and ownership 
On behalf of the Steering Committee, the Safe Surgery South Africa (SSSA) will 
act as custodian of the data. In line with the principles of data preservation and sharing, 
the steering committee will, after publication of the overall dataset, consider all 
reasonable requests to conduct secondary analyses. The primary consideration for such 
decisions will be the quality and validity of any proposed analysis. Only summary data 
will be presented publicly and all institutional and patient level data will be strictly 
anonymised. Individual patient data provided by participating hospitals remain the 
property of the respective institution. Once each local co-ordinator has confirmed the 
data provided from their hospital are both complete and accurate, they will be provided 
with a spreadsheet of the raw (un-cleaned) data for their hospital. 
 
The complete ACCCOS dataset, anonymised with respect to participating patients 
and hospitals, will be made freely and publicly available two years following publication 
of the main scientific report. Prior to this, the steering committee is not under any 
obligation to release data to any collaborator or third party if they believe this is not in 
keeping with the wider aims of the ACCCOS project. 
Publication plan 
The steering committee will appoint a writing committee to draft the scientific 
report(s) of this investigation, which will be disseminated in a timely manner. The 
group will be known as ‘The ACCCOS Investigators’. It is anticipated that a number of 
secondary analyses will be performed. ACCCOS investigators will be given priority to 
lead such analyses and are encouraged to do so. Participation and authorship 
opportunities will be based on contribution to the primary study. The steering 
committee will consider the scientific validity and the possible effect on the anonymity 
of participating centres prior to granting any such requests. Where necessary, a prior 
written agreement will set out the terms of such collaborations. The steering committee 
must approve the final version of all manuscripts including ACCCOS data prior to 
submission. In the event of disagreement within the steering committee, the co-

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principal investigators will make a ruling. Any analysis incorporating ACCCOS data from 
two or more study sites will be considered a secondary analysis and subject to these 
rules. The Steering Committee must approve the final version of all manuscripts prior to 
submission, whether they relate to part or all of the ACCCOS dataset. 
 
Deliverables 
The main deliverables will be scientific reports of preliminary findings for 
general and specialty journals, abstracts for presentation to national and international 
meetings including those of the supporting societies and a final report summarising the 
overall findings. 

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References 
 
1. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with 
COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet 
2020;395(10229):1054-62. doi: 10.1016/S0140-6736(20)30566-3 
2. Biccard BM, Madiba TE, Kluyts HL, et al. Perioperative patient outcomes in the African 
Surgical Outcomes Study: a 7-day prospective observational cohort study. Lancet 
2018;391(10130):1589-98. doi: 10.1016/S0140-6736(18)30001-1 [published Online 
First: 2018/01/08] 
3. Ayebale AET, Kassebaum NJ, Roche AM, et al. Africa’s Critical Care Capacity Before COVID-19. 
Lancet 2020;Submission under review 
4. Skinner DL, De Vasconcellos K, Wise R, et al. Critical care admission of South African (SA) 
surgical patients: Results of the SA Surgical Outcomes Study. S Afr Med J 
2017;107(5):411-19. doi: 10.7196/SAMJ.2017.v107i5.11455 [published Online First: 
2017/05/12] 
5. Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)--a metadata-
driven methodology and workflow process for providing translational research 
informatics support. J Biomed Inform 2009;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010 
6. Pearse RM, Moreno RP, Bauer P, et al. Mortality after surgery in Europe: a 7 day cohort study. 
Lancet 2012;380(9847):1059-65. doi: 10.1016/S0140-6736(12)61148-9 [published 
Online First: 2012/09/25] 
7. Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable 
in logistic regression analysis. J Clin Epidemiol 1996;49(12):1373-9. doi: S0895-
4356(96)00236-3 [pii] [published Online First: 1996/12/01] 
  
 
 

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Appendix 1 
 
The African Covid-19 Critical Care Outcomes Study (ACCCOS) case record form 
 
 
 

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African Covid-19 Critical Care Outcomes Study (ACCCOS) 
 
 
An African, multi-centre evaluation of patient care and clinical outcomes for 
patients with COVID-19 admitted to high-care or intensive care units 
 
 
Statistical analysis plan version 1 
(Interim analysis) 
8 September 2020 
 
 
 
 
 
 
 
 
 
Bruce Biccard  
 
 
 
 
 
Co-principal investigator 
 
 

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Steering committee 
Bruce Biccard MBChB FCA(SA) FFARC(I) MMedSci PhD. Department of Anaesthesia and 
Perioperative Medicine, University of Cape Town, South Africa 
P. Dean Gopalan MBChB (Ntl); FCA (SA); Crit Care; PhD. Discipline of Anaesthesiology and 
Critical Care, School of Clinical Medicine, University of KwaZulu Natal, South Africa.  
Greg Calligaro BSc. Hons. (Phys.), MBBCh (Wits), Dip PEC(SA), MMed (UCT), FCP(SA), Cert. 
Pulm(SA). Associate Professor, Specialist Physician and Pulmonologist, Division of Pulmonology, 
Department of Medicine, Groote Schuur Hospital, University of Cape Town Lung Institute, South 
Africa 
David Fredericks MBChB (UCT), FCEM (SA), Cert Critical Care (SA). Department of Critical Care, 
Groote Schuur Hospital, South Africa 
Ivan Joubert MBBCh, FCA(SA), Cert Crit Care. Professor and Head, Division of Critical Care, 
Department of Anaesthesia and Perioperative Medicine, University of Cape Town and Groote 
Schuur Hospital, South Africa 
Hyla-Louise Kluyts, MBChB, MMed, DMed. Executive Director, Safe Surgery SA NPC 
Mervyn Mer, MBBCh, Dip PEC (SA), FCP (SA), Pulmonology subspecialty Cert Critical Care (SA), 
MMed (Int Med), FRCP (London), FCCP (USA), PhD. Professor, Divisions of Critical Care and 
Pulmonology, Department of Medicine, Charlotte Maxeke Johannesburg Academic Hospital and 
Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa 
Malcolm Miller MBChB, DA(SA), FCA, Cert Crit Care(SA). Intensivist, Department of Anaesthesia 
and Perioperative Medicine, University of Cape Town, South Africa  
William L Michell MBChB, DA, FFA(Crit Care). Emeritus Associate Professor, Division of Critical 
Care and Department of Surgery, University of Cape Town, South Africa 
Fathima Paruk MBChB (Natal), FCOG(SA), Cert Crit Care (SA), MD (Natal). Associate Professor, 
Clinical and Academic HOD, Critical Care, University of Pretoria  and Steve Biko Academic 
Hospital  
Jenna L Piercy BSc(Hons), MBBS (Lond), FCA(SA), Cert Crit Care. Head, Clinical Unit, Division of 
Critical Care, Department of Anaesthesia and Perioperative Medicine, University of Cape Town 
and Groote Schuur Hospital, South Africa 
David Thomson MBChB, FCS, MMed(Surg), Cert Crit Care. Division of Critical Care, Department 
of Surgery, University of Cape Town and Groote Schuur Hospital, South Africa 
Juan Scribante Dipl Gen Nursing and Midwifery, Dipl ICU, Dipl Community Nursing, B Cur Ed ad 
Admin, M Cur, PhD.  School of Clinical Medicine, University of the Witwatersrand, South Africa 
DEA van Straaten, Bio-informatician, Safe Surgery SA NPC 
 
 
 

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Co-Principal investigator contact details 
Bruce Biccard 
Department of Anaesthesia and Perioperative Medicine 
University of Cape Town  
E-mail: bruce.biccard@uct.ac.za 
Telephone: +27 (0) 76 160 6387 
 
Dean Gopalan 
Head: Discipline of Anaesthesiology & Critical Care,  
School of Clinical Medicine, College of Health Sciences 
University of KwaZulu-Natal 
E-mail: Gopalan@ukzn.ac.za  
Telephone: +27 (0) 312604328 
 
 
Funders 
Critical Care Society of South Africa 
 
 

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Contents 
Introduction .......................................................................................................................................... 19 
Statistical analysis plans ........................................................................................................................ 20 
Primary outcome .............................................................................................................................. 20 
Statistical analysis plan for primary objective .............................................................................. 20 
Secondary outcomes ......................................................................................................................... 20 
Statistical analysis plan ................................................................................................................. 20 
Sensitivity analyses for the secondary outcome........................................................................... 22 
Additional analyses ....................................................................................................................... 22 
References ............................................................................................................................................ 23 
 
 
 
 
 

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Introduction 
The infectious disease COVID-19, caused by coronavirus SARS-CoV-2 (severe 
acute respiratory syndrome coronavirus 2), has been declared a pandemic and an 
international healthcare emergency by the World Health Organization (WHO). It has 
spread across the globe, overwhelming healthcare systems by causing high rates of 
critical illness. Mortality from COVID-19 exceeds 4%, with older people with 
comorbidities being extremely vulnerable.1 It is expected that between 50-80% of the 
world’s population may contract SARS-CoV-2 over the next two years.  
We expect the outcomes to be potentially worse in Africa, because firstly, there is 
a limited workforce,2 and secondly there are limited intensive care facilities and critical 
care resources across Africa to provide sufficient care.  It is estimated that there are 0.8 
(95% confidence interval 0.3 to 1.45) critical care beds per 100,000 population in 
Africa.3 It is likely that the volume of unplanned admissions associated with COVID-19 
will further adversely affect critical care outcomes in Africa,4 especially as the ability of 
healthcare systems in Africa to adapt and expand during the pandemic to meet the 
clinical workload is unknown. Furthermore, the patient outcomes in critical care is 
poorly documented in this under-resourced environment. 
Most countries in Africa are still in the early phase of the epidemic which 
provides the opportunity to study the utilization of critical care resources and their 
impact on patient outcomes. It is important therefore to establish what resources, 
comorbidities and interventions are potentially associated with either mortality or 
survival in Africa. Rapid dissemination of these findings may help inform appropriate 
resource prioritisation and utilisation during the COVID-19 pandemic in Africa. These 
points provide the rationale for the African Covid-19 Critical Care Outcomes Study 
(ACCCOS).   
  
 
 

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Statistical analysis plans 
 
Primary outcome 
The primary outcome is in-hospital mortality in adult patients referred to 
intensive care or high-care units following suspected or known COVID-19 infection in 
Africa. 
Statistical analysis plan for primary objective 
We will present the incidence and 95% CI for these data. 
 
Secondary outcomes 
To determine the risk factors (resources, comorbidities and interventions) 
associated with mortality in adult patients with suspected or known COVID-19 infection 
in Africa.  
The research questions to be addressed are the following. In patients with suspected 
or known COVID-19 infection in Africa;  
4. What are the critical care resources associated with survival? 
5. What patient comorbidities and other risk factors are associated with in-hospital 
mortality? 
6. What hospital interventions are associated with in-hospital survival? 
Statistical analysis plan 
Categorical variables will be described as proportions and compared using chi-
square tests, Fisher’s exact tests, Pearson’s chi-square tests or chi-square tests with 
Yates correction, as appropriate. Continuous variables will be described as mean and 
standard deviation if normally distributed or otherwise median and interquartile range 
(IQR). Comparisons of continuous variables between groups will be performed using 
unpaired t-tests or one-way ANOVA as appropriate. Univariate analysis will be 
performed to test for risk factors associated with in-hospital death. 
The main model will only include patients with complete outcome data (i.e. 
patients who are still in hospital receiving therapy, and have not reached the outcome 
definition of death, discharge, or in-hospital at 30 days will be excluded). Generalized 
linear mixed models using a logit link will be used to identify independent risk factors 
for the binary outcome of mortality. We will use a three-level generalized mixed model, 
with patients being at the first level, hospital at the second and country at the third 
level, to account for the expected correlation in outcomes within hospitals and 
countries. We will exclude patients with missing values for potential risk predictors, and 
only use a complete case analysis if there are <5% of the dataset with incomplete 
potential clinical risk predictors.8 All risk factors will be entered into the model, unless 
the number of reported deaths is insufficient to provide 10 events (deaths) per 
variable.7 Should the events per variable be <10, then variables with a univariate 
association of p<0.05, and variables with biological plausibility and a low rate of missing 
data will be used. 

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(SAP added 8 Sept 2020)       African Covid-19 Critical Care Outcomes Study (ACCCOS) 
Protocol v2.0 
Variables to be considered for inclusion in the model;  
i. 
Age 
ii. 
Sex 
iii. 
BMI 
iv. 
COVID-19 positive or suspected (sensitivity only known positive) 
v. 
Coronary artery disease 
vi. 
Congestive heart failure 
vii. 
Hypertension 
viii. 
Stroke or Transient ischaemic attack 
ix. 
Diabetes mellitus 
x. 
Cancer 
xi. 
Current smoker 
xii. 
Chronic Lung Disease 
xiii. 
Active TB 
xiv. 
Chronic Liver Disease 
xv. 
HIV/AIDS (no, yes, yes on anti-retrovirals) 
xvi. 
Chronic/previous malaria 
xvii. 
Chronic kidney disease 
xviii. 
Cardiorespiratory arrest in 24 hours prior to critical care referral 
xix. 
Quick SOFA score on referral 
xx. 
SOFA score on referral/ admission 
xxi. 
Was admission delayed due to lack of resources (e.g. bed, staffing etc) 
xxii. 
Nurse to patient ratio 
xxiii. 
Ability to provide invasive ventilation for patient if required  
xxiv. 
Physician available on site 24/7 for patient 
xxv. 
Indication for admission (number of organ system support i.e. none, one, 
two, three or more) 
xxvi. 
Respiratory support (None, Oxygen mask, HFNO, CPAP) 
xxvii. 
Proned (None, not ventilated, on mech ventilation) 
xxviii. 
Ventilation (None, Non-invasive ventilation, Invasive ventilation) 
xxix. 
Intubation (no, yes elective, yes emergency) 
xxx. 
Inotropes/ vasoconstrictors 
xxxi. 
Dialysis 
xxxii. 
Therapeutic anticoagulation 
xxxiii. 
Steroid therapy 
xxxiv. 
Repurposed/ experimental Covid-19 drug therapy 
xxxv. 
ECMO 
Collinearity will be assessed using the variance inflation factor. If collinearity is 
detected, then variables will either be excluded or combined. The model fit will be 
evaluated.  

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Protocol v2.0 
Results of the logistic regression will be reported as adjusted odds ratios (OR) 
with 95% confidence intervals (CI). The models will be assessed through the use of 
sensitivity analyses to explore possible interacting factors and examine any effect on the 
results.  
A single final analysis is also planned at the end of the study.  
Univariate and multivariate statistical analyses will be performed using the 
Statistical Package for the Social Sciences (SPSS) version 24 (SPSS Inc., Chicago, IL, USA).  
 
Sensitivity analyses for the secondary outcome 
1. Analysis of confirmed Covid-19 positive patients only 
2. Analysis which excludes all patients who had withdrawal of life support or 
decision to limit therapy 
 
Additional analyses 
Description of participating sites will include the following;  
1. Number of hospital beds 
2. Number of critical care beds allowing invasive ventilation 
3. Number of critical care and high-care beds not allowing invasive ventilation 
4. Nurse to patient ratio for critical care patients during the day 
5. Nurse to patient ratio for critical care patients during the night 
6. Number of specialist intensivists managing patients in critical care unit 
7. Number of other specialist doctors managing patients in critical care unit 
8. Number of non-specialist doctors managing patients in critical care unit 
9. Doctor to patient ratio for critical care patients 
10. Doctor on site in the critical care unit after hours 
11. How many extra ventilators could be used for a critical care surge? 
12. ICU type ventilators 
13. Operating room type ventilators 
14. ECMO 
15. Proning during mechanical ventilation 
16. Pulse oximetry for all, some or no patients 
17. Ability to do arterial blood gases 
 
 
 

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Protocol v2.0 
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