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Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home
Maria Glòria Bonet-Papell1,2, Georgina Company-Se3, María Delgado-Capel4
1Department of Hospital at Home, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain.
Insights
Hospital-at-Home (HaH) programs effectively managed COVID-19 pneumonia. High Hs-TnT levels predict readmission risk for patients transitioning from hospital to HaH care.
Area of Science:
- Healthcare Management
- Infectious Diseases
- Biomarker Research
Background:
- The COVID-19 pandemic highlighted the need for flexible healthcare delivery models.
- Hospital-at-Home (HaH) programs proved crucial for managing patient surges and COVID-19 pneumonia.
- Understanding readmission factors is vital for optimizing HaH transitions.
Purpose of the Study:
- To identify factors contributing to readmission from HaH to conventional hospitalization.
- To apply classification algorithms for predicting readmission risk.
- To support informed discharge decisions from hospital to HaH settings.
Main Methods:
- Analysis of blood biomarkers (IL-6, Hs-TnT, CRP, ferritin, D-dimer) in 871 COVID-19 patients transferred to HaH.
- Comparison of biomarker levels between patients who completed HaH successfully and those readmitted.
- Implementation and evaluation of classification algorithms (including SVM) for readmission prediction.
Main Results:
- Significant biomarker differences (IL-6, Hs-TnT, CRP, ferritin) were noted in non-readmitted patients between hospital and HaH admission.
- Readmitted patients showed higher CRP and Hs-TnT levels during HaH care.
- Support Vector Machine (SVM) achieved 86% accuracy in predicting readmissions.
Conclusions:
- Hs-TnT is a key predictor for COVID-19 patient readmission from HaH.
- Classification algorithms can assist clinicians in discharge decisions for HaH transfers.
- Optimizing HaH transitions improves patient outcomes and healthcare capacity.
Objectives:
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.
Methods:
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 Hospital Universitari Germans Trias i Pujol. 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).
Results:
Significant differences were observed in IL-6, Hs-TnT, CRP (p < 0.001), and ferritin (p < 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.
Conclusion:
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.
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