In-ICU Outcomes of Critically Ill Patients in a Reference Cameroonian Intensive Care Unit: A Retrospective Cohort Study.

Introduction: Mortality rate amongst critically ill patients admitted to the intensive care unit (ICU) is disproportionately high in sub-Saharan African countries such as Cameroon. Identifying factors associated with higher in-ICU mortality guides more aggressive resuscitative measures to curb mortality, but the dearth of data on predictors of in-ICU mortality precludes this action. We aimed to determine predictors of in-ICU mortality in a major referral ICU in Cameroon. Methodology. This was a retrospective cohort study of all patients admitted to the ICU of Douala Laquintinie Hospital from 1st of March 2021 to 28th February 2022. We performed a multivariable analysis of sociodemographic, vital signs on admission, and other clinical and laboratory variables of patients discharged alive and dead from the ICU to control for confounding factors. Significance level was set at p < 0.05. Results: Overall, the in-ICU mortality rate was 59.4% out of 662 ICU admissions. Factors independently associated with in-ICU mortality were deep coma (aOR = 0.48 (0.23-0.96), 95% CI, p = 0.043), and hypernatremia (>145 meq/L) (aOR = 0.39 (0.17-0.84) 95% CI, p = 0.022). Conclusion: The in-ICU mortality rate in this major referral Cameroonian ICU is high. Six in 10 patients admitted to the ICU die. Patients were more likely to die if admitted with deep coma and high sodium levels in the blood.

Back to the basics: Clinical assessment yields robust mortality prediction and increased feasibility in low resource settings.

INTRODUCTION: Mortality prediction aids clinical decision-making and is necessary for trauma quality improvement initiatives. Conventional injury severity scores are often not feasible in low-resource settings. We hypothesize that clinician assessment will be more feasible and have comparable discrimination of mortality compared to conventional scores in low and middle-income countries (LMICs). METHODS: Between 2017 and 2019, injury data were collected from all injured patients as part of a prospective, four-hospital trauma registry in Cameroon. Clinicians used physical exam at presentation to assign a highest estimated abbreviated injury scale (HEAIS) for each patient. Discrimination of hospital mortality was evaluated using receiver operating characteristic curves. Discrimination of HEAIS was compared with conventional scores. Data missingness for each score was reported. RESULTS: Of 9,635 presenting with injuries, there were 206 in-hospital deaths (2.2%). Compared to 97.5% of patients with HEAIS scores, only 33.2% had sufficient data to calculate a Revised Trauma Score (RTS) and 24.8% had data to calculate a Kampala Trauma Score (KTS). Data from 2,328 patients with all scores was used to compare models. Although statistically inferior to the prediction generated by RTS (AUC 0.92-0.98) and KTS (AUC 0.93-0.99), HEAIS provided excellent overall discrimination of mortality (AUC 0.84-0.92). Among 9,269 patients with HEAIS scores was strongly predictive of mortality (AUC 0.93-0.96). CONCLUSION: Clinical assessment of injury severity using HEAIS strongly predicts hospital mortality and far exceeds conventional scores in feasibility. In contexts where traditional scoring systems are not feasible, utilization of HEAIS could facilitate improved data quality and expand access to quality improvement programming.

Factors predicting in-hospital all-cause mortality in COVID 19 patients at the Laquintinie Hospital Douala, Cameroon.

BACKGROUND: Despite being a global pandemic, little is known about the factors influencing in-hospital mortality of COVID-19 patients in sub-Saharan Africa. This study aimed to provide data on in-hospital mortality among COVID-19 patients hospitalized in a single large center in Cameroon. METHODS: A hospital-based prospective follow-up was conducted from March 18 to June 30, 2020, including patients >18 years with positive PCR for SARS-COV-2 on nasopharyngeal swab admitted to the Laquintinie Douala hospital COVID unit. Predictors of in-hospital mortality were assessed using Kaplan Meir survival curves and Weibull regression for the accelerated time failure model. Statistical significance was considered as p < 0.05. RESULTS: Overall 712 patients (65,7% men) were included, mean age 52,80 ± 14,09 years. There were 580 (67,8% men) in-hospital patients. The median duration of hospital stay was eight days. The in-hospital mortality was 22.2%. Deceased patients compared to survivors were significantly older, had a higher temperature, respiratory rate, and heart rate, and lowest peripheral oxygen saturation at admission. After adjusting for age, sex, and other clinical patient characteristics, increased heart rate, increased temperature, decreased peripheral oxygen saturation. The critical clinical status was significantly associated with increased in-hospital mortality. In contrast, hospitalization duration greater than eight days and the use of hydroxychloroquine (HCQ) + azithromycin (AZM) therapy was associated with decreased risk of in-hospital mortality. CONCLUSION: One in five hospitalized COVID-19 patients die in a low-middle income setting. Critical clinical status, dyspnea, and increased heart rate were predictors of in-hospital mortality. This study will serve as a prerequisite for more robust subsequent follow-up studies. Also, these results will aid in revising national guidelines for the management of COVID-19 in Cameroon.

Clinical and epidemiological characteristics and outcomes of patients hospitalized for COVID-19 in Douala, Cameroon.

INTRODUCTION: the coronavirus disease (COVID-19) is a disease that originated from Wuhan in December 2019. It rapidly spread across the globe causing high mortality especially among the elderly. Africa though not spared has limited studies regarding its effects on its population. We therefore sought to describe the epidemiological and clinical characteristics of COVID-19 in Douala, Cameroon. METHODS: we conducted a single-centre, retrospective, and observational study by reviewing records of patients managed for COVID-19 between the 8th March 2020 and 31st, May 2020. Cases were confirmed by real-time reverse transcriptase - polymerase chain reaction and were analysed for epidemiological, demographic, clinical, and radiological features. Outcomes were either clinical improvement by Day-28 or in-hospital mortality. RESULTS: we analyzed 282 case files, 192 were males (M: F=2: 1). The mean age was 52 (+/- 15) years. Hypertension and diabetes accounted for 75% of the chronic medical conditions identified. Main presenting complaints were dyspnea, cough, asthenia, and fever (55-60%). Radiographic analysis showed a ground-glass appearance in 85% of cases. Chloroquine/Hydroxychloroquine was the most (91.8%) frequently used drug in management protocols, 35% needed oxygen supplementation while 6 patients were intubated. Severe pneumonia (11.3%) was the commonest complication. They were 91 admissions in the intensive care unit. The average length of hospital stay was 10 (+/- 5) days. The mortality rate was 32%. CONCLUSION: our findings are concordant with universally reported data of COVID-19 hospitalised patients. These parameters are essential in designing effective prevention and control programs aimed at reducing the impact of the COVID-19 pandemic particularly in countries with limited resources.

Financial Risk Protection and Hospital Admission for Trauma in Cameroon: An Analysis of the Cameroon National Trauma Registry.

BACKGROUND: Half of the global population is at risk for catastrophic health expenditure (CHE) in the event that they require surgery. Universal health coverage fundamentally requires protection from CHE, particularly in low- and middle-income countries (LMICs). Financial risk protection reports in LMICs covering surgical care are limited. We explored the relationship between financial risk protection and hospital admission among injured patients in Cameroon to understand the role of health insurance in addressing unmet need for surgery in LMICs. METHODS: The Cameroon National Trauma Registry, a database of all injured patients presenting to the emergency departments (ED) of three Cameroonian hospitals, was retrospectively reviewed between 2015 and 2017. Multivariate regression analysis identified predictors of hospital admission after injury and of patient report of cost inhibiting their care. RESULTS: Of the 7603 injured patients, 95.7% paid out-of-pocket to finance ED care. Less than two percent (1.42%) utilized private insurance, and more than half (54.7%) reported that cost inhibited their care. In multivariate analysis, private insurance coverage was a predictor of hospital admission (OR 2.17, 95% CI: 1.26, 3.74) and decreased likelihood of cost inhibiting care (OR 0.34, 95% CI: 0.20, 0.60) when compared to individuals paying out-of-pocket. CONCLUSION: The prevalence of out-of-pocket spending among injured patients in Cameroon highlights the need for financial risk protection that encompasses surgical care. Patients with private insurance were more likely to be admitted to the hospital, and less likely to report that cost inhibited care, supporting private health insurance as a potential financing strategy.

Baseline Patient Safety Culture in Cameroon: Setting a Foundation for Trauma Quality Improvement.

BACKGROUND: Trauma quality improvement (QI) has resulted in decreased trauma mortality and morbidity in high-income countries and has the potential to do the same in low- and middle-income countries. Effective implementation of QI programs relies on a foundational culture of patient safety; however, studies on trauma-related patient safety culture in Sub-Saharan Africa remain scarce. This study assesses baseline patient safety culture in Cameroon to best identify opportunities for improvement. MATERIALS AND METHODS: Over a 3-week period, the Hospital Survey on Patient Safety Culture was administered in three hospitals in the Littoral region of Cameroon. Percentages of positive responses (PPRs) were calculated across 42 items in 12 survey dimensions. A mixed-effects logistic regression model was used to summarize dimension-level percentages and confidence intervals. RESULTS: A total of 179 trauma-related hospital personnel were surveyed with an overall response rate of 76.8%. High PPRs indicate favorable patient safety culture. Of the 12 dimensions evaluated by the Hospital Survey on Patient Safety Culture, nine had a PPR below 50%. Dimensions particularly pertinent in the context of QI include Nonpunitive Response to Errors with a PPR of 25.8% and Organization Learning-Continuous Improvement with a PPR of 64.7%. CONCLUSIONS: The present study elucidates an opportunity for the development of trauma patient safety culture in Cameroon. Low PPR for Nonpunitive Response to Errors indicates a need to shift cultural paradigms from ascribing individual blame to addressing systemic shortcomings of patient care. Moving forward, data from this study will inform interventions to cultivate patient safety culture in partnering Cameroonian hospitals.

Machine learning without borders? An adaptable tool to optimize mortality prediction in diverse clinical settings.

BACKGROUND: Mortality prediction aids clinical decision making and is necessary for quality improvement initiatives. Validated metrics rely on prespecified variables and often require advanced diagnostics, which are unfeasible in resource-constrained contexts. We hypothesize that machine learning will generate superior mortality prediction in both high-income and low- and middle-income country cohorts. METHODS: SuperLearner, an ensemble machine-learning algorithm, was applied to data from three prospective trauma cohorts: a highest-activation cohort in the United States, a high-volume center cohort in South Africa (SA), and a multicenter registry in Cameroon. Cross-validation was used to assess model discrimination of discharge mortality by site using receiver operating characteristic curves. SuperLearner discrimination was compared with standard scoring methods. Clinical variables driving SuperLearner prediction at each site were evaluated. RESULTS: Data from 28,212 injured patients were used to generate prediction. Discharge mortality was 17%, 1.3%, and 1.7% among US, SA, and Cameroonian cohorts. SuperLearner delivered superior prediction of discharge mortality in the United States (area under the curve [AUC], 94-97%) and vastly superior prediction in Cameroon (AUC, 90-94%) compared with conventional scoring algorithms. It provided similar prediction to standard scores in the SA cohort (AUC, 90-95%). Context-specific variables (partial thromboplastin time in the United States and hospital distance in Cameroon) were prime drivers of predicted mortality in their respective cohorts, whereas severe brain injury predicted mortality across sites. CONCLUSIONS: Machine learning provides excellent discrimination of injury mortality in diverse settings. Unlike traditional scores, data-adaptive methods are well suited to optimizing precise site-specific prediction regardless of diagnostic capabilities or data set inclusion allowing for individualized decision making and expanded access to quality improvement programming. LEVEL OF EVIDENCE: Prognostic and therapeutic, level II and III.