Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapy
This work addresses the need for validating COVID-19 treatments using real-world evidence, but it is incremental as it applies existing ML methods to a new dataset.
The study applied machine learning to real-world data from COVID-19 hospital admissions in Spain to analyze the efficacy of various pharmacotherapies, finding that remdesivir and tocilizumab significantly increased survival time with hazard ratios of 0.41 and 0.21, while other treatments showed no benefits.
Introduction: Real-world data generated from clinical practice can be used to analyze the real-world evidence (RWE) of COVID-19 pharmacotherapy and validate the results of randomized clinical trials (RCTs). Machine learning (ML) methods are being used in RWE and are promising tools for precision-medicine. In this study, ML methods are applied to study the efficacy of therapies on COVID-19 hospital admissions in the Valencian Region in Spain. Methods: 5244 and 1312 COVID-19 hospital admissions - dated between January 2020 and January 2021 from 10 health departments, were used respectively for training and validation of separate treatment-effect models (TE-ML) for remdesivir, corticosteroids, tocilizumab, lopinavir-ritonavir, azithromycin and chloroquine/hydroxychloroquine. 2390 admissions from 2 additional health departments were reserved as an independent test to analyze retrospectively the survival benefits of therapies in the population selected by the TE-ML models using cox-proportional hazard models. TE-ML models were adjusted using treatment propensity scores to control for pre-treatment confounding variables associated to outcome and further evaluated for futility. ML architecture was based on boosted decision-trees. Results: In the populations identified by the TE-ML models, only Remdesivir and Tocilizumab were significantly associated with an increase in survival time, with hazard ratios of 0.41 (P = 0.04) and 0.21 (P = 0.001), respectively. No survival benefits from chloroquine derivatives, lopinavir-ritonavir and azithromycin were demonstrated. Tools to explain the predictions of TE-ML models are explored at patient-level as potential tools for personalized decision making and precision medicine. Conclusion: ML methods are suitable tools toward RWE analysis of COVID-19 pharmacotherapies. Results obtained reproduce published results on RWE and validate the results from RCTs.