Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis

Abstract

BackgroundAccurate prediction of prognostic outcomes in patients with COVID-19 could facilitate clinical decision-making and medical resource allocation. However, little is known about the ability of machine learning (ML) to predict prognosis in COVID-19 patients. ObjectiveThis study aimed to systematically examine the prognostic value of ML in patients with COVID-19. MethodsA systematic search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and IEEE Xplore up to December 15, 2021. Studies predicting the prognostic outcomes of COVID-19 patients using ML were eligible for inclusion. Risk of bias was evaluated by a tailored checklist based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pooled sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated to evaluate model performance. ResultsA total of 33 studies that described 35 models were eligible for inclusion, with 27 models presenting mortality, four intensive care unit (ICU) admission, and four use of ventilation. For predicting mortality, ML gave a pooled sensitivity of 0.86 (95% CI, 0.79–0.90), a specificity of 0.87 (95% CI, 0.80–0.92), and an AUC of 0.93 (95% CI, 0.90–0.95). For the prediction of ICU admission, ML had a sensitivity of 0.86 (95% CI, 0.78–0.92), a specificity of 0.81 (95% CI, 0.66–0.91), and an AUC of 0.91 (95% CI, 0.88–0.93). For the prediction of ventilation, ML had a sensitivity of 0.81 (95% CI, 0.68–0.90), a specificity of 0.78 (95% CI, 0.66–0.87), and an AUC of 0.87 (95% CI, 0.83–0.89). Meta-regression analyses indicated that algorithm, population, study design, and source of dataset influenced the pooled estimate. ConclusionThis meta-analysis demonstrated the satisfactory performance of ML in predicting prognostic outcomes in patients with COVID-19, suggesting the potential value of ML to support clinical decision-making. However, improvements to methodology and validation are still necessary before its application in routine clinical practice.