Bonet-Papell Maria Glòria, Company-Se Georgina, Delgado-Capel María, Díez-Sánchez Beatriz, Mateu-Pruñosa Lourdes, Paredes-Deirós Roger, Ara Del Rey Jordi, Nescolarde Lexa
Department of Hospital at Home, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain.
Department of Medicine, Faculty of Medicine, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
Front Med (Lausanne). 2025 Mar 26;12:1469245. doi: 10.3389/fmed.2025.1469245. eCollection 2025.
During the coronavirus disease 2019 (COVID-19) pandemic, the Hospital-at-Home (HaH) program played a key role in expanding healthcare capacity and managing COVID-19 pneumonia. This study aims to evaluate the factors contributing to readmission from HaH to conventional hospitalization and to apply classification algorithms that support discharge decisions from conventional hospitalization to HaH.
Blood biomarkers (IL-6, Hs-TnT, CRP, ferritin, and D-dimer) were collected from 871 patients transferred to HaH after conventional hospitalization for COVID-19 at the . Of these, 840 patients completed their recovery without any complications, while 31 of them required readmission. Statistical tests were conducted to assess differences in blood biomarkers between the first day of conventional hospitalization and the first day of HaH, as well as between patients who successfully completed HaH and those who were readmitted. Various classification algorithms (bagged trees, KNN, LDA, logistic regression, Naïve Bayes, and the support vector machine [SVM]) were implemented to predict readmission, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and the Matthews Correlation Coefficient (MCC).
Significant differences were observed in IL-6, Hs-TnT, CRP ( < 0.001), and ferritin ( < 0.01) between the first day of conventional hospitalization and the first day of HaH for patients who were not readmitted. However, no significant differences were found in patients who were readmitted. At HaH, readmitted patients exhibited higher CRP and Hs-TnT values. Among the classification algorithms, the SVM showed the best performance, achieving 85% sensitivity, 87% specificity, 86% accuracy, 84% F1 score, and 71% MCC.
Hs-TnT was a key predictor of readmission for COVID-19 patients discharged to HaH. Classification algorithms can aid clinicians in making informed decisions regarding patient transfers from conventional hospitalization to HaH.
在2019冠状病毒病（COVID-19）大流行期间，居家医院（HaH）项目在扩大医疗能力和管理COVID-19肺炎方面发挥了关键作用。本研究旨在评估导致从HaH再次入院至传统住院治疗的因素，并应用支持从传统住院治疗出院至HaH的分类算法。
从在[医院名称]因COVID-19接受传统住院治疗后转至HaH的871例患者中收集血液生物标志物（白细胞介素-6、高敏肌钙蛋白T、C反应蛋白、铁蛋白和D-二聚体）。其中，840例患者无任何并发症地完成康复，而其中31例需要再次入院。进行统计检验以评估传统住院治疗第一天与HaH第一天之间以及成功完成HaH治疗的患者与再次入院患者之间血液生物标志物的差异。实施了各种分类算法（袋装树、K近邻、线性判别分析、逻辑回归、朴素贝叶斯和支持向量机[SVM]）来预测再次入院情况，并使用准确率、灵敏度、特异性、F1分数和马修斯相关系数（MCC）评估性能。
未再次入院的患者在传统住院治疗第一天与HaH第一天之间，白细胞介素-6、高敏肌钙蛋白T、C反应蛋白（<0.001）和铁蛋白（<0.01）存在显著差异。然而，再次入院的患者未发现显著差异。在HaH时，再次入院的患者C反应蛋白和高敏肌钙蛋白T值较高。在分类算法中，支持向量机表现最佳，灵敏度达85%，特异性达87%，准确率达86%，F1分数达84%，MCC达71%。
高敏肌钙蛋白T是出院至HaH的COVID-19患者再次入院的关键预测指标。分类算法可帮助临床医生就患者从传统住院治疗转至HaH做出明智决策。
