Pneumomlpred: Innovating Pneumonia Management with Guideline Adherence Review and Explainable Machine Learning-Based Mortality Prediction - A Single-Centre Retrospective Study

Authors

• Kamel A. Samara, Jefferson Abington HospitalFollow
• Hiba Jawdat Barqawi
• Saryia Adra
• Batool Aldaher
• Drishti D. Kampani
• Enad Haddad, Jefferson Abington HospitalFollow
• Hebah Soudan
• Islam Ibrahim Anwar
• Mohamad Besher Adi
• Omar Hamadi
• Yumna Almour
• Mohamed Nassef
• Zaher Al Aghbari
• Eman Abu-Gharbieh

Document Type

Article

Publication Date

5-16-2026

Comments

This article is the author's final published version in Respiratory Medicine, Volume 259, August 2026, Article number 108896.

The published version is available at https://doi.org/10.1016/j.rmed.2026.108896. Copyright © 2026 The Authors.

Abstract

Introduction: Community-acquired pneumonia (CAP) is one of the leading causes of death globally. Changing antibiotic resistance profiles, paucity of reliable CAP mortality prediction models, and ageing populations will increase its burden. This study aims to evaluate adherence to CAP management guidelines as well as develop an ML-based mortality prediction model.

Methods: This retrospective, chart review included all CAP cases between 2016 and 2021. Variables collected included patients' demographics, comorbidities, vitals, laboratory, imaging, and culture findings, as well as initial and targeted antibiotic regimens. Data was pre-processed and analysed in python-3. Adherence to the American thoracic society/infectious diseases society of America 2019 guidelines (ATS/IDSA) was evaluated. The minimum required antibiotic dosages used to determine guideline adherence were defined according to the 2019 ATS/IDSA guidelines for an average adult with normal renal function. Random forest was used for feature selection, while several base classifiers and ensembles were evaluated. SHAP was used to generate interpretable factors for the best-performing model.

Results: 783 cases were included, all of which were Pneumonia Severity Index (PSI) class IV/V; overall mortality rate was 15.75%. Etiological diagnosis was rarely established and was not used to guide antibiotic usage. Prescribed doses were frequently below the recommended levels. Less than one-third of cases were adhering to the 2019 ATS/IDSA management guidelines. PneumoMLPred, an explainable support vector classifier-based pneumonia prognostication model, had accuracy = 88.5%, specificity = 96.3%, and AUROC = 0.87. SHAP revealed that PSI, CRP, and kidney diseases were the top three drivers behind in-hospital mortality.

Conclusions: Adherence to the 2019 ATS/IDSA guidelines was very limited. PneumoMLPred has great potential for in-hospital CAP mortality prediction.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Recommended Citation

Samara, Kamel A.; Barqawi, Hiba Jawdat; Adra, Saryia; Aldaher, Batool; Kampani, Drishti D.; Haddad, Enad; Soudan, Hebah; Anwar, Islam Ibrahim; Adi, Mohamad Besher; Hamadi, Omar; Almour, Yumna; Nassef, Mohamed; Al Aghbari, Zaher; and Abu-Gharbieh, Eman, "Pneumomlpred: Innovating Pneumonia Management with Guideline Adherence Review and Explainable Machine Learning-Based Mortality Prediction - A Single-Centre Retrospective Study" (2026). Abington Jefferson Health Papers. Paper 144.
https://jdc.jefferson.edu/abingtonfp/144

PubMed ID

42162923