Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study

Zhihong Chen; Jiajia Wang; Hanchao Wang; Yu Yao; Huojin Deng; Junnan Peng; Xinglong Li; Zhongruo Wang; Xingru Chen; Wei Xiong; Qin Wang; Tao Zhu

doi:10.3389/fmed.2023.1105854

Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study

Front Med (Lausanne). 2023 Mar 28:10:1105854. doi: 10.3389/fmed.2023.1105854. eCollection 2023.

Authors

Zhihong Chen¹, Jiajia Wang², Hanchao Wang³, Yu Yao³, Huojin Deng⁴, Junnan Peng⁵, Xinglong Li⁵, Zhongruo Wang⁶, Xingru Chen⁵, Wei Xiong⁵, Qin Wang⁵, Tao Zhu³

Affiliations

¹ Respiratory Medicine and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, China.
² Rheumatology Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
³ Respiratory Medicine and Critical Care Medicine, and Preclinical Research Center, Suining Central Hospital, Suining, China.
⁴ Respiratory Medicine and Critical Care Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, China.
⁵ Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁶ Department of Mathematics, University of California, Davis, CA, United States.

Abstract

Introduction: Intrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (AECOPD) were not well explored. Machine learning showed a promising role in medical practice, including diagnosis prediction and classification. Then, sex differences in clinical manifestations of AECOPD were explored by machine learning approaches in this study.

Methods: In this cross-sectional study, 278 male patients and 81 female patients hospitalized with AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. The K-prototype algorithm was used to explore the degree of sex differences. Binary logistic regression, random forest, and XGBoost models were performed to identify sex-associated clinical manifestations in AECOPD. Nomogram and its associated curves were established to visualize and validate binary logistic regression.

Results: The predictive accuracy of sex was 83.930% using the k-prototype algorithm. Binary logistic regression revealed that eight variables were independently associated with sex in AECOPD, which was visualized by using a nomogram. The AUC of the ROC curve was 0.945. The DCA curve showed that the nomogram had more clinical benefits, with thresholds from 0.02 to 0.99. The top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, seven clinical features, including smoking, biomass fuel exposure, GOLD stages, PaO₂, serum potassium, serum calcium, and blood urea nitrogen (BUN), were concurrently identified by three models. However, CAD was not identified by machine learning models.

Conclusions: Overall, our results support that the clinical features differ markedly by sex in AECOPD. Male patients presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction, and hyperkalemia than female patients with AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decision-making.

Keywords: K-prototypes algorithm; XGBoost model; acute exacerbation of chronic obstructive pulmonary disease; binary logistic regression; machine learning; nomogram; random forest model; sex.