Data-driven methodology for discovery and response to pulmonary symptomology in hypertension through AI and machine learning: Application to COVID-19 related pharmacovigilance

Xu, X.; Kawakami, J.; Indika Millagaha Gedara, N.; Riviere, J.; Meyer, E.; Wyckoff, G. J.; Jaberi-Douraki, M.

Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. In this study, we implemented adaptive signal detection approaches to correct spurious association, hidden factors, and confounder misclassification when the covariates are unknown or unmeasured on medications affecting the renin-angiotensin system (RAS), potentially creating an increased risk of life-threatening outcomes in high-risk patients. We consider pulmonary ADE (pADE) profiles in a long-standing group of therapeutics, RAS-acting agents, in patients with hypertension associated with high-risk for COVID-19. Using these techniques, we confirmed our hypothesis that drugs from the same drug class could have very different pADE profiles affecting outcomes in acute respiratory illness. Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pADEs. Macitentan and bosentan were associates with 64% and 56% of pADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by our signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy.