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

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

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1105854

DOI=10.3389/fmed.2023.1105854

ISSN=2296-858X

ABSTRACT=Intrinsically, COPD is a highly heterogonous disease. Several sex differences in COPD, such as risk factor and prevalence, were identified. However, sex differences in clinical features of AECOPD weren’t 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. In this cross-sectional study, 278 males and 81 females hospitalized AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. K-prototypes 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. The predictive accuracy of sex was 83.930% using k-prototypes algorithm. Binary logistic regression revealed that 8 variables were independently associated with sex in AECOPD, which was visualized by nomogram. The AUC of ROC curve was 0.945. The DCA curve showed that the nomogram took more clinical benefits with the thresholds from 0.02 to 0.99. Top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, 7 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. Overall, our results support that the clinical features differ markedly by sex in AECOPD. Males presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction and hyperkalemia than females in AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decisions.