The application of machine learning models in a resource-constrained environment.

Background: Machine learning models (MLMs) used to influence surgical decision making often require large and complex datasets upon which to train. However, there is a paucity of literature pertaining to the ability to apply standard MLMs to small ICU datasets within resource-constrained institutions.

Methods: ML models were applied to a prospective cohort of critically ill mechanically ventilated patients from a teaching hospital in rural Kenya. Characteristics included an ICU scoring system specifically for resource-constrained environments (Tropical Intensive Care Score (TropICS)). Outputs included AUC of the ROC and the feature importance table. Python-based MLMs included XGBoost and KNN. AUC of the ROC was calculated to predict mortality as the primary endpoint.

Results: There were 294 patients, with an average age of 40.2 years, 64.3% male, 23.8% trauma, and an overall mortality of 60.2%. With respect to mortality patients who died were older (43.5 versus 35 years; p < 0.001), but with no difference in male gender (64.8% versus 63.8%; p = 0.9), or having been transferred from outside facilities (34% versus 21.5%; p = 0.5). Whilst there was no difference in the rate of tachycardia or acidosis, patients who died were more likely to present with hemodynamic instability (31% versus 6%; p < 0.001) and higher clinical severity scores. In predicting mortality, the ML models performed very well (XGBoost AUC = 0.82). Within MLM feature importance, the Tropical Intensive Care Score (TropICS) performed as well as APACHE-II and the SAPS.

Conclusion: ML models can be effectively applied to a small ICU dataset within resource-constrained environments. ML models must demonstrate functionality prior to incorporating within prospective clinical predictive models. Contextualized ICU scoring systems (TropICS) performed well within MLMs.