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Updated: Feb 3, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Development of a Malawi Intensive care Mortality risk Evaluation (MIME) model, a prospective cohort study
Meghan Prin1, Stephanie Pan2, Clement Kadyaudzu3
1Department of Anesthesiology and Department of Obstetrics & Gynecology, Baylor College of Medicine, Houston, TX 77030, USA.
Introduction:
Intensive care medicine can contribute to population health in low-income countries by reducing premature mortality related to surgery, trauma, obstetrical and other medical emergencies. Quality improvement is guided by risk stratification models, which are developed primarily within high-income settings. Models validated for use in low-income countries are needed.
Methods:
This prospective cohort study consisted of 261 patients admitted to the intensive care unit (ICU) of Kamuzu Central Hospital in Malawi, from September 2016 to March 2018. The primary outcome was in-hospital mortality. We performed univariable analyses on putative predictors and included those with a significance of 0.15 in the Malawi Intensive care Mortality risk Evaluation model (MIME). Model discrimination was evaluated using the area under the curve.
Results:
Males made up 37.9% of the study sample and the mean age was 34.4 years. A majority (73.9%) were admitted to the ICU after a recent surgical procedure, and 59% came directly from the operating theater. In-hospital mortality was 60.5%. The MIME based on age, sex, admitting service, systolic pressure, altered mental status, and fever during the ICU course had a fairly good discrimination, with an AUC of 0.70 (95% CI 0.63-0.76).
Conclusions:
The MIME has modest ability to predict in-hospital mortality in a Malawian ICU. Multicenter research is needed to validate the MIME and assess its clinical utility.
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