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Real-world data generated from clinical practice can be used toanalyze the real-world evidence (RWE) of COVID-19 pharmacotherapy and validatethe results of randomized clinical trials (RCTs). Machine learning (ML) methodsare being used in RWE and are promising tools for precision-medicine. In thisstudy, ML methods are applied to study the efficacy of therapies on COVID-19hospital admissions in the Valencian Region in Spain. Methods: 5244 and 1312COVID-19 hospital admissions - dated between January 2020 and January 2021 from10 health departments, were used respectively for training and validation ofseparate treatment-effect models (TE-ML) for remdesivir, corticosteroids,tocilizumab, lopinavir-ritonavir, azithromycin andchloroquine/hydroxychloroquine. 2390 admissions from 2 additional healthdepartments were reserved as an independent test to analyze retrospectively thesurvival benefits of therapies in the population selected by the TE-ML modelsusing cox-proportional hazard models. TE-ML models were adjusted usingtreatment propensity scores to control for pre-treatment confounding variablesassociated to outcome and further evaluated for futility. ML architecture wasbased on boosted decision-trees. Results: In the populations identified by theTE-ML models, only Remdesivir and Tocilizumab were significantly associatedwith an increase in survival time, with hazard ratios of 0.41 (P = 0.04) and0.21 (P = 0.001), respectively. No survival benefits from chloroquinederivatives, lopinavir-ritonavir and azithromycin were demonstrated. Tools toexplain the predictions of TE-ML models are explored at patient-level aspotential tools for personalized decision making and precision medicine.Conclusion: ML methods are suitable tools toward RWE analysis of COVID-19pharmacotherapies. Results obtained reproduce published results on RWE andvalidate the results from RCTs.

Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapy