Dataset Information

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

ABSTRACT:

Introduction

Intensive 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.

Methods

We 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.

Results

Among 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.

Conclusions

The 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.

SUBMITTER: Riviello ED

PROVIDER: S-EPMC4873171 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Riviello Elisabeth D ED Kiviri Willy W Fowler Robert A RA Mueller Ariel A Novack Victor V Banner-Goodspeed Valerie M VM Weinkauf Julia L JL Talmor Daniel S DS Twagirumugabe Theogene T

PloS one 20160519 5

<h4>Introduction</h4>Intensive 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 Probabil ...[more]

PMID: 27196252

Similar Datasets

Project description:BackgroundBabies with low birthweight (<2500 g) are at increased risk of early mortality. However, low birthweight includes babies born preterm and with fetal growth restriction, and not all these infants have a birthweight less than 2500 g. We estimated the neonatal and infant mortality associated with these two characteristics in low-income and middle-income countries.MethodsFor this pooled analysis, we searched all available studies and identified 20 cohorts (providing data for 2,015,019 livebirths) from Asia, Africa, and Latin America that recorded data for birthweight, gestational age, and vital statistics through 28 days of life. Study dates ranged from 1982 through to 2010. We calculated relative risks (RR) and risk differences (RD) for mortality associated with preterm birth (<32 weeks, 32 weeks to <34 weeks, 34 weeks to <37 weeks), small-for-gestational-age (SGA; babies with birthweight in the lowest third percentile and between the third and tenth percentile of a US reference population), and preterm and SGA combinations.FindingsPooled overall RRs for preterm were 6·82 (95% CI 3·56-13·07) for neonatal mortality and 2·50 (1·48-4·22) for post-neonatal mortality. Pooled RRs for babies who were SGA (with birthweight in the lowest tenth percentile of the reference population) were 1·83 (95% CI 1·34-2·50) for neonatal mortality and 1·90 (1·32-2·73) for post-neonatal mortality. The neonatal mortality risk of babies who were both preterm and SGA was higher than that of babies with either characteristic alone (15·42; 9·11-26·12).InterpretationMany babies in low-income and middle-income countries are SGA. Preterm birth affects a smaller number of neonates than does SGA, but is associated with a higher mortality risk. The mortality risks associated with both characteristics extend beyond the neonatal period. Differentiation of the burden and risk of babies born preterm and SGA rather than with low birthweight could guide prevention and management strategies to speed progress towards Millennium Development Goal 4--the reduction of child mortality.FundingBill & Melinda Gates Foundation.

Project description:BackgroundWell-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation's African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.MethodsEight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.ResultsProject successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.ConclusionBased on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external "audit."

Project description:BackgroundPrior research has demonstrated that low- and low-middle-income countries (LLMICs) bear a higher burden of critical illness and have a higher rate of mortality from critical illness than high-income countries (HICs). There is a pressing need for improved critical care delivery in LLMICs to reduce this inequity. This systematic review aimed to characterise the range of critical care interventions and services delivered within LLMIC health care systems as reported in the literature.MethodsA search strategy using terms related to critical care in LLMICs was implemented in multiple databases. We included English language articles with human subjects describing at least one critical care intervention or service in an LLMIC setting published between 1 January 2008 and 1 January 2020.ResultsA total of 1620 studies met the inclusion criteria. Among the included studies, 45% of studies reported on pediatric patients, 43% on adults, 23% on infants, 8.9% on geriatric patients and 4.2% on maternal patients. Most of the care described (94%) was delivered in-hospital, with the remainder (6.2%) taking place in out-of-hospital care settings. Overall, 49% of critical care described was delivered outside of a designated intensive care unit. Specialist physicians delivered critical care in 60% of the included studies. Additional critical care was delivered by general physicians (40%), as well as specialist physician trainees (22%), pharmacists (16%), advanced nursing or midlevel practitioners (8.9%), ambulance providers (3.3%) and respiratory therapists (3.1%).ConclusionsThis review represents a comprehensive synthesis of critical care delivery in LLMIC settings. Approximately 50% of critical care interventions and services were delivered outside of a designated intensive care unit. Specialist physicians were the most common health care professionals involved in care delivery in the included studies, however generalist physicians were commonly reported to provide critical care interventions and services. This study additionally characterised the quality of the published evidence guiding critical care practice in LLMICs, demonstrating a paucity of interventional and cost-effectiveness studies. Future research is needed to understand better how to optimise critical care interventions, services, care delivery and costs in these settings.RegistrationPROSPERO CRD42019146802.

Dataset Information

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

Introduction

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets