There have been insufficient data for African patients with COVID-19 who are critically ill. The African COVID-19 Critical Care Outcomes Study (ACCCOS) aimed to determine which resources, comorbidities, and critical care interventions are associated with mortality in this patient population.
From May to December, 2020, 6779 patients were referred to critical care. Of these, 3752 (55·3%) patients were admitted and 3140 (83·7%) patients from 64 hospitals in ten countries participated (mean age 55·6 years; 1890 [60·6%] of 3118 participants were male). The hospitals had a median of two intensivists (IQR 1–4) and pulse oximetry was available to all patients in 49 (86%) of 57 sites. In-hospital mortality within 30 days of admission was 48·2% (95% CI 46·4–50·0; 1483 of 3077 patients). Factors that were independently associated with mortality were increasing age per year (odds ratio 1·03; 1·02–1·04); HIV/AIDS (1·91; 1·31–2·79); diabetes (1·25; 1·01–1·56); chronic liver disease (3·48; 1·48–8·18); chronic kidney disease (1·89; 1·28–2·78); delay in admission due to a shortage of resources (2·14; 1·42–3·22); quick sequential organ failure assessment score at admission (for one factor [1·44; 1·01–2·04], for two factors [2·0; 1·33–2·99], and for three factors [3·66, 2·12–6·33]); respiratory support (high flow oxygenation [2·72; 1·46–5·08]; continuous positive airway pressure [3·93; 2·13–7·26]; invasive mechanical ventilation [15·27; 8·51–27·37]); cardiorespiratory arrest within 24 h of admission (4·43; 2·25–8·73); and vasopressor requirements (3·67; 2·77–4·86). Steroid therapy was associated with survival (0·55; 0·37–0·81). There was no difference in outcome associated with female sex (0·86; 0·69–1·06).
Mortality in critically ill patients with COVID-19 is higher in African countries than reported from studies done in Asia, Europe, North America, and South America. Increased mortality was associated with insufficient critical care resources, as well as the comorbidities of HIV/AIDS, diabetes, chronic liver disease, and kidney disease, and severity of organ dysfunction at admission.
The ACCCOS was partially supported by a grant from the Critical Care Society of Southern Africa.
with older people (eg, people older than 62 years) who have comorbidities known to be more susceptible than younger people.
Moreover, there is a concern of further mortality with subsequent waves across regions globally.
having a low number of intensive care facilities, and the scarcity of critical care resources.
We also hypothesised that unplanned admissions would further adversely affect critical care outcomes in Africa
as the ability of health-care systems to respond to meet the clinical workload is limited. Finally, patient outcomes following critical care for COVID-19 were not sufficiently documented in this under-resourced environment,
despite a call for prevention and response measures in low-income and middle-income countries.
we designed the African COVID-19 Critical Care Outcomes Study (ACCCOS) to determine which resources, patient comorbidities, and critical care interventions were associated with mortality or survival in these patients. Wide dissemination of these findings could help to inform resource prioritisation necessary to manage patients who are critically ill with COVID-19 in Africa. This objective remains relevant as a recent meta-analysis
reported no critical care outcomes data from Africa, or patient management data in resource-limited settings.
Evidence before this study
We searched MEDLINE, Embase, the Cochrane Library, Africa-Wide Information, and SciELO Citation Index between Jan 1 and Sept 23, 2020 (as well as an updated search from Sept 23 to Dec 6, 2020) using the search terms “(Betacoronavirus OR Betacoronaviruses)”, “(Corona Virus OR Corona Viruses OR Coronavirus OR Coronaviruses)”, “(COVID OR COVID19 OR COVID-19)”, “(CoV OR CoV2 OR HCoV-19 OR nCoV OR 2019nCoV)”, “(Severe Acute Respiratory Syndrome CoV OR severe acute respiratory syndrome coronavirus 2 OR SARS CoV 2 OR SARS-CoV-2 OR SARSCoV OR SARS-CoV OR SARS2)”, “(Intensive care OR intensive care unit* OR ICU*)”, “(ITU* or intensive treatment* OR intensive treatment unit*)”, “critical care”, “critical* ill*”, “1 or 2 or 3 or 4 or 5”, “6 or 7 or 8 or 9”, “10 and 11”, “Limit 12 to yr=“2020”. Studies that included patients with COVID-19 who were not in critical care, and critical care cohorts restricted to a specific patient subgroup were excluded. Studies published in all languages were considered in our search. There is little data to guide the management of critically ill patients with COVID-19 in under-resourced environments. Previously published systematic reviews confirmed that there were no published outcomes data from Africa, and little data for factors associated with mortality or survival in under-resourced environments.
Added value of this study
Mortality following critical care admission for patients with suspected COVID-19 infection and confirmed COVID-19 infection in this African cohort was 48·2% (95% CI 46·4–50·0). The meta-analysis reports a global mortality of 31·5% (27·5–35·5), with the African data reporting an excess mortality of 11 (in the best case scenario) to 23 (in the worst case scenario) deaths per 100 patients compared with the global average. The excess mortality could be explained by the shortage of critical care resources. In our study, only one in two patients referred for critical care were admitted. Patients were admitted to units with limited access to dialysis, proning, extracorporeal membrane oxygenation (ECMO), arterial blood gases, and pulse oximetry. Furthermore, at the patient level, access to interventions (eg, dialysis, proning, and ECMO) were estimated to be between seven-times and 14-times lower than what is needed to manage critically ill patients with COVID-19. Adjusted analyses suggest that critical care mortality is associated with increasing age, the patient comorbidities of HIV/AIDS, diabetes, chronic liver disease, and kidney disease, the severity of organ dysfunction on presentation to critical care, and the initial need for increasing respiratory and cardiovascular support. The quick sequential organ failure assessment score at admission was associated with patient mortality and could be a simple and feasible risk stratification tool to use in under-resourced environments.
Implications of all the available evidence
In our study, mortality was strongly associated with organ dysfunction and the level of organ support needed at critical admission. The use of the quick SOFA score could provide guidance for appropriate triage decision making at the time of referral to critical care in an under-resourced setting when managing critically ill patients with COVID-19. Strategies are needed to mitigate risk in patients with COVID-19 in Africa with coexisting HIV/AIDS, diabetes, chronic liver disease, and kidney disease. It is likely that patient outcomes will continue to be severely compromised until the problems surrounding critical care resource scarcity are addressed.
We aimed to determine which critical care resources, patient comorbidities, and hospital interventions were associated with in-hospital mortality in patients with suspected or confirmed COVID-19 who were referred to critical care in ten African countries.
Study design and participants
The ACCCOS study was a multicentre, prospective, observational cohort study in adults (aged 18 years or older) who were referred to intensive care or high-care units with suspected or confirmed COVID-19 in ten African countries (ie, Egypt, Ethiopia, Ghana, Kenya, Libya, Malawi, Mozambique, Niger, Nigeria, and South Africa). The study was open to all African countries, and these ten countries fulfilled the ethics and regulatory requirements to participate. A high-care unit was defined as a patient area that provides a level of care between that given in an intensive care unit and a general ward but that does not usually provide invasive ventilation. Eligible patients included all patients admitted to a high-care or intensive care unit with suspected or confirmed COVID-19. Patient follow-up was until hospital discharge, censored at 30 days if the patient was in hospital. The study recruited from May 7 to Dec 18, 2020. The primary ethics approval was from the Human Research Ethics Committee of the University of Cape Town (Cape Town, South Africa).
All patients received standard of care for patients with suspected or confirmed COVID-19 and who required critical care admission. We planned to recruit as many sites as possible in Africa. Sites were requested to include all eligible patients and to recruit for as long as possible with the understanding that they could stop recruiting at any point if they were overwhelmed by clinical commitments. Each site had to complete an eligible patients’ screening log.
To ensure a representative sample, we planned to include as many sites as possible with the requirement for inclusion of all consecutive patients using the delayed consent procedure.
The primary outcome of our study was in-hospital mortality within 30 days of admission. The secondary outcome was to determine the factors (ie, human and facility resources, patient comorbidities, and critical care interventions) that were associated with mortality in adult patients with suspected or confirmed COVID-19.
An interim analysis preprint
was published in October, 2020, once that sample size was reached. The database was locked on Dec 18, 2020, with 1483 deaths in the cohort.
and full SOFA
score on referral or admission. Resource variables included admission delayed due to the shortage of resources (eg, bed and staffing), nurse-to-patient ratio in critical care, ability to provide invasive ventilation, and physician availability on site 24 h per day, 7 days a week. Therapy variables included organ support at admission, respiratory support, proning, ventilatory support, intubation, inotropes or vasoconstrictors, dialysis, therapeutic anticoagulation, steroid therapy, repurposed or experimental COVID-19 drug therapy, and extracorporeal membrane oxygenation (ECMO). Collinearity was assessed using the variance inflation factor. Collinearity was associated with intubation, respiratory and ventilatory interventions, number of organs requiring support, and anticoagulation. Therefore, we created a single categorical variable for respiratory support (ie, none, oxygen, high flow nasal oxygen, continuous positive airway pressure, and invasive mechanical ventilation) and removed the dialysis and ECMO variables, which had collinearity with anticoagulation. The subsequent variance inflation factor showed collinearity between the subject variable “chronic malaria or malaria within 3 months” and the therapy variable “repurposed or experimental COVID-19 drug therapy”. We removed “repurposed or experimental COVID-19 drug therapy” as this therapy variable was a heterogeneous variable compared with “chronic malaria or malaria within 3 months”. No further collinearity was identified. A three-level random-intercept mixed effects logistic regression was done on each of the five imputed datasets using the glmer function in the lme4 package
in R. Estimates were combined from the five repeated complete data analyses using the pool function from the mice package.
The pool function implements the rules for combining the separate estimates and SEs from each of the imputed datasets to provide an overall estimate with SEs, CIs, and p values.
A p value of less than 0·05 was considered significant. To allow for comparison with the imputed datasets, the complete case analysis was also presented.
The results of the GLMM are reported as adjusted odds ratios with 95% CIs. Sensitivity analyses defined a priori were: (1) confirmed SARS-CoV-2-positive patients only, (2) confirmed SARS-CoV-2 patients excluding patients who had life support withdrawn or therapy limited (ie, the decision not to provide additional therapy, such as ventilation, adrenaline, and dialysis, to the patient’s current therapy because of the expected poor prognosis), and (3) only patients who died or who were discharged alive (excluding in-hospital patients). A post-hoc sensitivity analysis that was requested by the reviewers excluded “cardiorespiratory arrest within 24 h of admission” as a potential variable. All analyses were done by AH and BMB.
done by EHT, KDMM, MElh, and JS. We have presented the case fatality rate for COVID-19 infections by region and did a meta-analysis of the mean age and SOFA score per region. The regional case fatality rate, critical care outcomes meta-analysis, and the meta-analysis of means for ages and SOFA scores provide context for rates of mortality in critical care in Africa.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Table 1The characteristics of the hospitals included in the study
Data are median (IQR) or n/N (%).
Most (2136 [68·0%] of 3140 patients) did not have a full SOFA score done at admission.
Table 2Description of African COVID-19 critical care cohort
Data are mean (SD), n (%), n (proportion), or median (IQR). Odds ratios were constructed for in-hospital mortality with univariate binary logistic regression analysis. CPAP=continuous positive airway pressure. SBP=systolic blood pressure. SOFA=sequential organ failure assessment.
The length of critical care stay was 7 days (IQR 4–12). The decision to limit therapy was made in 284 (9·2%) of 3086 patients, and therapy was withdrawn in 81 (2·6%) patients. 72 (88·9%) of the 81 patients in whom therapy was withdrawn had already had therapy limited.
In-hospital mortality in the 30 days after admission to a high-care or critical care unit occurred in 1483 (48·2%; 95% CI 46·4–50·0) of 3077 patients, with 261 (16·4%) of 1594 patients alive and in hospital at 30 days (35 of these 244 [data from 17 patients is missing with respect to whether they were in an intensive care unit or not] patients were still in intensive care), and 1333 (83·6%) patients had been discharged. The primary outcome was unknown for 63 patients.
Table 3Generalised linear mixed model (pooled results of the imputed datasets) for patients referred to critical care with suspected or confirmed COVID-19 infection with full dataset for mortality
CPAP=continuous positive airway pressure. SOFA=sequential organ failure assessment. Controls are patients alive in hospital and alive and discharged at 30 days (n=3140).
A post-hoc analysis exploring the frequency of interventions received by patients who required invasive mechanical ventilation showed that 148 (12·9%) of 1146 of these patients also received dialysis.
The principal finding of our study was that in-hospital mortality following critical care admission for COVID-19 infection in Africa occurred in 48·2% (95% CI 46·4–50·0) of 3077 patients in the 30 days after high-care or intensive care unit admission, with an excess mortality of 11–23 deaths per 100 patients compared with the global average. Mortality was associated with increasing age, HIV/AIDS, diabetes, chronic liver disease, kidney disease, a high severity of organ dysfunction on presentation, and increasing respiratory and cardiovascular support. A shortage of critical care resources could have contributed to increased mortality. The SOFA score could represent a simple, quick tool for risk stratification of patients with COVID-19 at the point of critical care admission.
It is possible that women have an increased mortality risk generally because of the barriers to accessing care and limitations or biases in care when critically ill,
which could have moderated the differences between men and women here. Previously, the clinical course of patients with HIV and COVID-19 infection was unknown.
Our data suggests that HIV/AIDS is an important risk factor for COVID-19 mortality. Our study also supports the use of steroid therapy to decrease mortality from COVID-19 in this patient population.
with only one in two patients referred to critical care being admitted. Yet, the full SOFA scores suggest that the patients who were admitted could be relatively healthier than patients admitted in countries with more critical care resources (appendix p 31). Third, when considering the proportion of sites that could provide dialysis, proning, ECMO, arterial blood gases, and pulse oximetry, data from our study suggests that the African countries included in our study had very under-resourced critical care facilities. These data suggest that these countries had a low-volume critical care capacity, which could have adversely affected outcomes.
Lastly, merely counting the available critical care resources necessary for intervention does not accurately reflect the proportion of patients who actually receive the interventions. We estimate that patient access to interventions was between seven-times lower (for dialysis and proning) and 14-times lower (for ECMO) than what is required. Dialysis was available in 39 (68%) of 57 sites and was offered to only 330 (10%) of 3073 patients. Yet, acute kidney injury could occur in over 90% of patients with COVID-19 admitted to intensive care units, with one in four patients who have been ventilated requiring renal replacement therapy.
As 75% of patients with COVID-19 referred to critical care develop acute respiratory distress syndrome,
at least six times more patients should have received proning in our cohort. Similarly, ECMO was only available in nine (15·8%) of 57 sites (table 1), but ECMO was offered to less than 1% of patients; yet large registry data supports its use in patients with COVID-19 with refractory respiratory failure.
Lack of access to these interventions could partly explain the high mortality in Africa, and why one in eight patients had therapy withdrawn or limited.
The human resources available to these critical care units were somewhat good with respect to availability of a physician 24 h per day (7 days a week), and nurse-to-patient ratio. However, the inability to admit approximately half of the referred patients to the critical care unit could reflect intensive care units working at full capacity with the available resources, and therefore the effect of limited critical care human resources might have resulted in adverse outcomes outside the critical care unit, which we could not assess.
There are several limitations to our study. First, our study presents data from predominantly tertiary hospitals, yet pulse oximetry was not universally available. It is likely that lower level hospitals with less resourced critical care units might have had worse outcomes than those reported in this cohort. Furthermore, referral to higher level centres might have further increased mortality before patients were able to reach an appropriate critical care unit. We did not distinguish between cardiorespiratory arrests that occurred in hospital and those out of hospital, and it is likely that patients who had cardiorespiratory arrests outside of hospital might have had a higher mortality, and might be poorly represented in this cohort. It is therefore possible that the mortality for patients with COVID-19 who are critically ill might be higher than the mortality we report. The outcomes of the patients who were referred to critical care but not admitted are also unknown. It is unlikely that the findings of this study are generalisable to those patients, as their disease severity and resources available for therapy would differ from patients who are admitted to critical care.
and it is therefore likely that our estimate of excess mortality is an underestimate when matched for age and severity of comorbidities.
It is therefore difficult to determine the generalisability of these results although, to the best of our knowledge, these data provide the largest cohort of critically ill patients with COVID-19 who are from under-resourced environments (appendix p 28).
This is a large, prospective, multicentre study from a previously unreported African setting and, to the best of our knowledge, the only study in this setting that has also included a large number of patients with HIV. The statistical analysis plan was published before data inspection and was adequately powered to adjust for the association between human resources, patient comorbidities, and critical care interventions and mortality. All prespecified sensitivity analyses confirm the main findings.
most sites analysed in our study could not assess a full SOFA score because of the scarcity of resources. The quick SOFA score is a simple risk stratification or triage tool that is feasible in low resource environments.
Mortality is associated with organ dysfunction and organ support needed at critical admission; yet there are insufficient resources to provide adequate support in this setting. Early warning systems, risk stratification, and early intervention are needed to avoid delays in instituting necessary organ support. Strategies are needed to mitigate risk in patients who are infected with SARS-CoV-2 in Africa with coexisting HIV/AIDS, diabetes, chronic liver disease, and kidney disease.
BMB contributed to the overall conception and design of the study, acquisition of data, local and national study leadership, statistical analysis, writing the first draft of the paper, critically revising the work for submission, and the final approval of the version of the study to be submitted. MMi, WLM, DT, AA, EA, GC, HTD, MElfi, MG, AGB, IJ, FK, H-LK, ZM, AM, WM, AN, ZN, AO, JLP, JS, YSA, DEAvS, and PDG contributed to the overall conception and design of the study, acquisition of data, local and national study leadership, critical revision of the paper for submission, and were involved in final approval of the study to be submitted. MSC contributed to the overall conception and design of the study, acquisition of data, local and national study leadership, patient recruitment, and data collection. MElh applied for nationwide and local hospital ethical approvals and recruited collaborators for data collection from the hospital, was involved in local and national study leadership, critically revised the study for submission, and was involved in the final approval of the version of the study to be submitted. MF contributed to the overall conception and design of the study, acquisition of data, local and national study leadership, critical revision the work for submission, final approval of the version of the study to be submitted, was a national hospital team leader, and was involved in patient recruitment in the study. DF was involved in designing the intensive care unit triage form, acquisition of data, critical revision of the work for submission, and final approval of the version of the study to be submitted. AH was involved in the statistical analyses, critical revision of the work for submission, and the final approval of the version of the study to be submitted. VM and CO were involved in the acquisition of data, local and national study leadership, critical revision of the work for submission, and final approval of the version of the study to be submitted. EHT was involved in the conception and design of the meta-analysis, the acquisition of data for meta-analysis, data analysis and writing, critical revision of the work for submission, and final approval of the version to be submitted. Mme contributed to the overall conception and design of the study, local and national study leadership, and the final approval of the version of the study to be submitted. MEl was involved in local and national leadership, critical revision of the work for submission, and final approval of the version of the study to be submitted. KDMM was involved in the acquisition of data for meta-analysis, data analysis and writing, critical revision of the work for submission, and final approval of the version to be submitted. FP was involved in designing the intensive care unit triage form, acquisition of data, critical revision of the work for submission, and final approval of the version of the study to be submitted. BMB and AH accessed and verified the data.
Data will be disclosed only upon request and approval of the proposed use of the data by the steering committee. Data are available to the journal for evaluation of reported analyses. Data requests from non-ACCCOS investigators will not be considered until 2 years after the close of the trial. Data will be de-identified for participant, hospital, and country, and will be available with a signed data access agreement.
Declaration of interests
MMe has received honoraria for services related to speakers bureau and advisory boards. These have related to purely educational talks that have been given in an objective fashion for educational purposes and with no vested interest or agenda other than for educational purposes. Companies that MMe gave talks to were Pfizer, Merck, Astellas, Sanofi-Aventis, Aspen, and Sun. IJ is the former president of the Critical Care Society of Southern Africa and is a current councillor and board member of the Critical Care Society of Southern Africa. All other authors declare no competing interests.
We would like to acknowledge Rema Ramakrishnan for their assistance with the meta-analysis forest plot and Dilshaad Brey for their assistance with the database search for the meta-analysis. The ACCCOS was supported by a grant from the Critical Care Society of Southern Africa.
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