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

• URL: http://arxiv.org/abs/2107.10239v1
• Date: Mon, 19 Jul 2021 16:28:54 GMT
• Title: Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapy
• Authors: Aurelia Bustos (1), Patricio Mas_Serrano (2 and 3), Mari L. Boquera (2), Jose M. Salinas (4) ((1) MedBravo, (2) Hospital General Universitario de Alicante Spain -HGUA, (3) Institute for Health and Biomedical Research of Alicante -ISABIAL, (4) Department of Health Informatics, Hospital Universitario San Juan de Alicante Spain)
• Abstract summary: Real-world data generated from clinical practice can be used to analyze the real-world evidence of COVID-19 pharmacotherapy. Machine learning (ML) methods are being used 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 of Spain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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