Predicting Mortality in Low-Income Country ICUs: The Rwanda Mortality Probability Model (R-MPM)

Abstract

IntroductionIntensive Care Unit (ICU) risk prediction models are used to compare outcomes for quality improvement initiatives, benchmarking, and research. While such models provide robust tools in high-income countries, an ICU risk prediction model has not been validated in a low-income country where ICU population characteristics are different from those in high-income countries, and where laboratory-based patient data are often unavailable. We sought to validate the Mortality Probability Admission Model, version III (MPM0-III) in two public ICUs in Rwanda and to develop a new Rwanda Mortality Probability Model (R-MPM) for use in low-income countries.MethodsWe prospectively collected data on all adult patients admitted to Rwanda’s two public ICUs between August 19, 2013 and October 6, 2014. We described demographic and presenting characteristics and outcomes. We assessed the discrimination and calibration of the MPM0-III model. Using stepwise selection, we developed a new logistic model for risk prediction, the R-MPM, and used bootstrapping techniques to test for optimism in the model.ResultsAmong 427 consecutive adults, the median age was 34 (IQR 25–47) years and mortality was 48.7%. Mechanical ventilation was initiated for 85.3%, and 41.9% received vasopressors. The MPM0-III predicted mortality with area under the receiver operating characteristic curve of 0.72 and Hosmer-Lemeshow chi-square statistic p = 0.024. We developed a new model using five variables: age, suspected or confirmed infection within 24 hours of ICU admission, hypotension or shock as a reason for ICU admission, Glasgow Coma Scale score at ICU admission, and heart rate at ICU admission. Using these five variables, the R-MPM predicted outcomes with area under the ROC curve of 0.81 with 95% confidence interval of (0.77, 0.86), and Hosmer-Lemeshow chi-square statistic p = 0.154.ConclusionsThe MPM0-III has modest ability to predict mortality in a population of Rwandan ICU patients. The R-MPM is an alternative risk prediction model with fewer variables and better predictive power. If validated in other critically ill patients in a broad range of settings, the model has the potential to improve the reliability of comparisons used for critical care research and quality improvement initiatives in low-income countries.

Current Challenges in the Management of Sepsis in ICUs in Resource-Poor Settings and Suggestions for the Future

“…The mean age of adult patients admitted to ICUs in HICs is consistently higher than those reported in LMICs. For example, in European ICUs, the mean age of adult admissions is typically 55-66 years, much higher than the median age of 34 years in a Rwandan ICU [68], the mean age of 32 years in one of the few ICUs in Uganda [1], and the median age of 37, 51, and 49 years in ICUs in Bangladesh, Nepal, and India [69]. Although not specified for admission diagnoses, it likely also reflects the younger age of sepsis patients.…”

Global Critical Care: Moving Forward in Resource-Limited Settings

“…Mortality prediction scores to risk-adjust in research and quality improvement efforts must also be developed and validated in relevant populations [54,55]. Region-specific equations or adaptations for resource-limited settings can improve performance and facilitate implementation [55][56][57][58][59].…”

“…Mortality prediction scores to risk-adjust in research and quality improvement efforts must also be developed and validated in relevant populations [54,55]. Region-specific equations or adaptations for resource-limited settings can improve performance and facilitate implementation [55][56][57][58][59]. Risk assessment tools, such as the Modified Early Warning Score, which might help triage critically patients and allocate resources [54] have shown conflicting results when applied in resource-limited settings and need additional modifications and validation [60,61].…”

Development and internal validation of the Simplified Mortality Score for the Intensive Care Unit (SMS‐ICU)

“…1,2 Work undertaken by our groups in resourcelimited settings has demonstrated challenges in the development and application of relatively simple prognostic models such as APACHE II. [3][4][5][6] Missing predictor variables, lack of post-ICU outcome measures, poor clinical uptake of scores due to perceived complexity and poor adherence to guidelines such as TRIPOD transcend healthcare settings. [3][4][5]7 As data missingness is widespread in resource-limited settings, we support the a priori decision to exclude variables with missingness > 25% and to utilise multiple imputation to address lesser proportions of missingness.…”

“…We also suggest cross-validation of simplified prognostic models developed in both High Income Countries 1,2 and LMICs. [4][5][6][7] This will also provide insights into generalisability of the models across diverse settings, with regard to differences in casemix, available healthcare facilities and variations in presentation.…”