Interpretable prediction model for severe community-acquired pneumonia in adults: machine learning development and validation using routine laboratory parameters
- 1Department of Medical Laboratory, Hospital of Xinjiang Production and Construction Corps, Second Affiliated Hospital of Shihezi University School of Medicine, Urumqi, China
- 2Bingtuan Clinical Medical Research Center for Medical Laboratory, Urumqi, China
Abstract
Background: Community-acquired pneumonia (CAP) can lead to severe community-acquired pneumonia (SCAP), which is associated with an increased risk of complications and mortality. However, the clinical utility of existing SCAP assessment tools is limited by their insufficient timeliness and objectivity. Therefore, the early identification of patients at high risk of progression to SCAP is crucial for improving their outcomes. Here, we aimed to develop and validate a model using readily available laboratory parameters to predict SCAP in individuals diagnosed with CAP.
Methods: Patients with CAP were enrolled from the Xinjiang Production and Construction Corps Hospital. Two cohorts were established: training (310 SCAP and 741 non-SCAP patients) and temporal validation (72 SCAP and 337 non-SCAP patients). Following feature selection via Least Absolute Shrinkage and Selection Operator regression, we developed and evaluated six machine learning models and identified the optimal model. Model interpretability was achieved through the application of the SHapley Additive exPlanations (SHAP) framework. To evaluate its robustness, the developed model was tested on a temporal validation cohort.
Results: In the training cohort, the Light Gradient Boosting Machine (LightGBM) achieved an area under the receiver operating characteristic curve of 0.893 (95% confidence interval [CI]: 0.870–0.915) and calibration curve Brier score of 0.135 (95%CI: 0.127–0.143), indicating excellent predictive performance. The SHAP analysis identified the following top predictors of SCAP onset, in descending order of importance: albumin, age, D-dimer, lactate dehydrogenase, C-reactive protein, oxygen saturation, neutrophil count, and blood urea nitrogen. The model’s robustness and generalizability were successfully validated in the temporal validation cohort, achieving an area under the curve of 0.879 (95%CI: 0.836–0.922) and Brier score of 0.131 (95%CI: 0.109–0.155).
Conclusions: A robust early prediction model for SCAP, leveraging routine laboratory parameters and employing the LightGBM algorithm, was developed. The model exhibited strong performance and holds promise as an effective clinical decision-support tool to assist in early intervention strategies for SCAP.
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BibTeX
@article{zha2026interpretable,
title = {{Interpretable prediction model for severe community-acquired pneumonia in adults: machine learning development and validation using routine laboratory parameters}},
author = {Hefei Zha and Qian Shi and Chunyan Liu and Mengjie Liang and Yongxin Li and Zhaohui Deng and Xin Zhang},
journal = {BMC Medical Informatics and Decision Making},
year = {2026},
month = jul,
doi = {10.1186/s12911-026-03737-1},
url = {https://doi.org/10.1186/s12911-026-03737-1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Interpretable prediction model for severe community-acquired pneumonia in adults: machine learning development and validation using routine laboratory parameters
AU - Hefei Zha
AU - Qian Shi
AU - Chunyan Liu
AU - Mengjie Liang
AU - Yongxin Li
AU - Zhaohui Deng
AU - Xin Zhang
PY - 2026
DA - 2026/07/29
JO - BMC Medical Informatics and Decision Making
DO - 10.1186/s12911-026-03737-1
UR - https://doi.org/10.1186/s12911-026-03737-1
AB - Background: Community-acquired pneumonia (CAP) can lead to severe community-acquired pneumonia (SCAP), which is associated with an increased risk of complications and mortality. However, the clinical utility of existing SCAP assessment tools is limited by their insufficient timeliness and objectivity. Therefore, the early identification of patients at high risk of progression to SCAP is crucial for improving their outcomes. Here, we aimed to develop and validate a model using readily available laboratory parameters to predict SCAP in individuals diagnosed with CAP. Methods: Patients with CAP were enrolled from the Xinjiang Production and Construction Corps Hospital. Two cohorts were established: training (310 SCAP and 741 non-SCAP patients) and temporal validation (72 SCAP and 337 non-SCAP patients). Following feature selection via Least Absolute Shrinkage and Selection Operator regression, we developed and evaluated six machine learning models and identified the optimal model. Model interpretability was achieved through the application of the SHapley Additive exPlanations (SHAP) framework. To evaluate its robustness, the developed model was tested on a temporal validation cohort. Results: In the training cohort, the Light Gradient Boosting Machine (LightGBM) achieved an area under the receiver operating characteristic curve of 0.893 (95% confidence interval [CI]: 0.870–0.915) and calibration curve Brier score of 0.135 (95%CI: 0.127–0.143), indicating excellent predictive performance. The SHAP analysis identified the following top predictors of SCAP onset, in descending order of importance: albumin, age, D-dimer, lactate dehydrogenase, C-reactive protein, oxygen saturation, neutrophil count, and blood urea nitrogen. The model’s robustness and generalizability were successfully validated in the temporal validation cohort, achieving an area under the curve of 0.879 (95%CI: 0.836–0.922) and Brier score of 0.131 (95%CI: 0.109–0.155). Conclusions: A robust early prediction model for SCAP, leveraging routine laboratory parameters and employing the LightGBM algorithm, was developed. The model exhibited strong performance and holds promise as an effective clinical decision-support tool to assist in early intervention strategies for SCAP.
SN - 1472-6947
ER -