A Preliminary Investigation into Use of Admission-Recorded Photoplethysmograms for Predicting Hospital Mortality in Children with Confirmed or Suspected Infection in Resource-Poor Settings

Although there have been notable advances in child survival, the rate of infection-related hospital fatalities remains high among pediatric patients in resource-limited settings. Hence, the critical need for enhancing child survival demands immediate attention. Present risk prediction tools for in-hospital complications have been developed through statistical modelling methods and exhibit specific constraints. This study sought to create and compare an array of machine learning-based models using admission-recorded information to predict all-cause in-patient death in two cohorts of children in Uganda: those under 6 months and those aged between 6 and 60 months. 190 (7.0%) out of 2,698 children under six months and 164 (4.3%) out of 3,835 children over six months of age died following admission. For each cohort, five supervised machine learning algorithms were trained and internally validated on 67% and 33% of the data, respectively. The models incorporated demographics, clinical variables, and photoplethysmography-extracted features as inputs. The balanced random forest classifier demonstrated superior performance on the test set in both cohorts. In the first cohort, the model achieved a sensitivity of 0.95, specificity of 0.71, positive predictive value (PPV) of 0.15, and negative predictive value (NPV) of 0.99. Comparing this to the model that exclusively utilized demographic and clinical parameters, an increase of 14.5% was observed in sensitivity, while specificity and PPV experienced reductions of 4% and 11.8%, respectively. NPV, however, remained unchanged. In the second cohort, the model's sensitivity was 0.75, with a specificity of 0.73, a PPV of 0.08, and an NPV of 0.99. Upon training the model with only demographic and clinical variables in this cohort, the corresponding metrics were 0.67, 0.76, 0.09, and 0.98, respectively. By integrating photoplethysmography data with machine learning algorithms, it may be possible to develop predictive models that can identify high-risk patients who are more likely to experience adverse outcomes like in-hospital death. Such models could empower healthcare providers in ill-equipped settings to allocate limited resources effectively and deliver targeted interventions to those most in need.

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