Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia. Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge. A total of 49 clinical features were evaluated, and machine learning classifiers including logistic regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and neural network were applied to predict PCC. Results: Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. KNN demonstrated the highest predictive performance, with an AUC of 0.80, sensitivity of 0.73, and specificity of 0.69. Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC. Conclusions: Machine learning models, particularly KNN, showed some promise in predicting PCC in cancer patients, offering the potential for early intervention and personalised care. These findings emphasise the importance of long-term monitoring for cancer patients recovering from COVID-19 to mitigate PCC impact.
背景：COVID-19大流行已导致广泛存在的长期并发症，即新冠后遗症（PCC），对癌症患者等易感人群影响尤为显著。本研究旨在利用俄罗斯莫斯科四所主要大学医院开展的纵向队列研究数据，预测住院癌症患者PCC的发生率。方法：在急性期及出院后6个月和12个月的随访期间收集临床数据。共评估49项临床特征，并应用逻辑回归、随机森林、支持向量机（SVM）、K近邻（KNN）及神经网络等机器学习分类器预测PCC。结果：采用受试者工作特征曲线下面积（AUC）、敏感性和特异性评估模型性能。KNN模型表现出最佳预测性能，其AUC为0.80，敏感性0.73，特异性0.69。重症COVID-19与既存合并症是PCC的重要预测因素。结论：机器学习模型（尤其是KNN）在预测癌症患者PCC方面展现出一定潜力，为早期干预和个体化诊疗提供了可能。这些发现强调了长期监测COVID-19康复期癌症患者对减轻PCC影响的重要性。
肿瘤(癌症)患者之家