Clinical Characteristics of Overweight Patients With Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD).

INTRODUCTION: Low body weight in patients with COPD is associated with a poor prognosis and more comorbidities. However, the impact of increased body weight in patients with COPD remains controversial. The aim of this study was to explore the clinical features of overweight patients with AECOPD. METHODS: In this multicenter cross-sectional study, a total of 647 AECOPD patients were recruited. Finally, 269 normal weight and 162 overweight patients were included. Baseline characteristics and clinical and laboratory data were collected. The least absolute shrinkage and selection operator (LASSO) regression was performed to determine potential features, which were substituted into binary logistic regression to reveal overweight-associated clinical features. The nomogram and its associated curves were established to visualize and verify the logistic regression model. RESULTS: Six potential overweight-associated variables were selected by LASSO regression. Subsequently, a binary logistic regression model identified that the rates of type 2 diabetes (T2DM) and hypertension and levels of lymphocytes (LYM)%, and alanine aminotransferase (ALT) were independent variables of overweight in AECOPD patients. The C-index and AUC of the ROC curve of the nomogram were 0.671 and 0.666, respectively. The DCA curve revealed that the nomogram had more clinical benefits if the threshold was at a range of 0.22~0.78. CONCLUSIONS: Collectively, we revealed that T2DM and hypertension were more common, and LYM% and ALT were higher in AECOPD patients with overweight than those with normal weight. The result suggests that AECOPD patients with overweight are at risk for additional comorbidities, potentially leading to worse outcomes.

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

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.