Predicting 30-day mortality in hospitalized patients with community-acquired pneumonia using statistical and machine learning approaches.

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP. Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine learning methods were used to develop patientlevel prediction models for hospitalized patients with CAP. For each model, nine different statistics were calculated to provide measures of the overall performance of the models. Results: A total of 3249 unique hospitalized patients with CAP were enrolled in the study, 2743 were included in the model building (training) dataset, while the remaining 686 were included in the testing dataset. From the full population, death at 30-days post discharge was documented in 458 (13.4%) patients. All models resulted in high variation in the ability to predict survivors and non-survivors at 30 days. Conclusions: In conclusion, this study suggests that accurate patient-level prediction of 30-day mortality in hospitalized patients with CAP is difficult with statistical and machine learning approaches. It will be important to evaluate novel variables and other modeling approaches to better predict poor clinical outcomes in these patients to ensure early and appropriate interventions are instituted.