Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study.
Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study.
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机器学习揭示了慢性阻塞性肺疾病急性加重的临床特征的性别差异：多中心横断面研究。
DOI：
10.3389/fmed.2023.1105854
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发表时间：
2023
影响因子：
3.9
通讯作者：
中科院分区：
文献类型：
--
作者：
慢性阻塞性肺疾病（COPD）是一种高度异质性的疾病。确定了COPD的几个性别差异，如风险因素和患病率。然而，慢性阻塞性肺疾病急性加重期（AECOPD）的临床特征的性别差异还没有得到很好的探讨。机器学习在医疗实践中显示出很有前途的作用，包括诊断预测和分类。然后，本研究通过机器学习方法探讨AECOPD临床表现的性别差异。在这项横断面研究中，纳入了278例男性和81例女性AECOPD住院患者。分析基线特征、临床症状和实验室参数。使用K-原型算法来探索性别差异的程度。进行二元逻辑回归、随机森林和XGBoost模型，以确定AECOPD患者的性别相关临床表现。建立诺模图及其相关曲线，以可视化和验证二元逻辑回归。使用k-原型算法对性别的预测准确率为83.930%。二元逻辑回归分析显示，八个变量与AECOPD的性别独立相关，这是可视化使用诺模图。ROC曲线的AUC为0.945。DCA曲线显示诺模图具有更多的临床益处，阈值从0.02到0.99。前15名的性别相关的重要变量分别确定随机森林和XGBoost。随后，7个临床特征，包括吸烟，生物质燃料暴露，GOLD分期，PaO 2，血清钾，血清钙，血尿素氮（BUN），同时确定了三个模型。然而，CAD并没有被机器学习模型识别出来。总的来说，我们的研究结果支持AECOPD的临床特征在性别上有显著差异。与AECOPD女性患者相比，男性患者表现出更差的肺功能和氧合，更少的生物质燃料暴露，更多的吸烟，肾功能不全和高钾血症。此外，我们的研究结果还表明，机器学习在临床决策中是一种有前途的强大工具。
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. 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. 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. 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.
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