Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches

Xiaolin Song; Hui Li; Qingsong Chen; Tao Zhang; Guangbin Huang; Lingyun Zou; Dingyuan Du

doi:10.3389/fsurg.2022.1060691

Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches

Front Surg. 2023 Jan 6:9:1060691. doi: 10.3389/fsurg.2022.1060691. eCollection 2022.

Authors

Xiaolin Song^{1

2}, Hui Li², Qingsong Chen², Tao Zhang^{1

2}, Guangbin Huang², Lingyun Zou³, Dingyuan Du²

Affiliations

¹ School of Medicine, Chongqing University, Chongqing, China.
² Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China.
³ Clinical Data Research Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China.

Abstract

Objective: Pneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients.

Methods: From January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3. Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC). The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method.

Results: Of 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50.80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0.895 (sensitivity: 84.3%; specificity: 80.0%).Pneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS.

Conclusion: Our study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model's output clinically meaningful.

Keywords: extreme gradient boosting; flail chest; machine learning; pneumonia; risk factors.