ArticleAmerican journal of hypertension2025
A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.
Article in American journal of hypertension, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTransformer-based neural networks excel in modelling high-dimensional, time-series data with complex dependencies. This proof-of-concept study applies a transformer-X-learner framework to estimate treatment effects using real-world data, using antihypertensive drug exposure and COVID-19 risk as an exemplar.
methodsWe conducted a case-control study of 303,220 NHS Greater Glasgow and Clyde patients aged ≥ 40 years during the first two COVID-19 pandemic waves. Using a transformer-X-learner framework that incorporated temporal patterns in medication usage and comorbidities, we controlled for confounding effects and estimated individual and average treatment effects ACEIs, beta-blockers (BBs), calcium channel blockers (CCBs), thiazides (THZs), and statins on 180-day SARS-CoV-2 infection risk.
resultsThe transformer-X-learner framework outperformed traditional approaches, achieving an F1 score of 0.82 and area under the precision-recall curve (AUPRC) of 0.78. ACEIs showed a negligible overall impact on COVID-19 risk (ATE: 0.97%±5.5), while BBs (-8.3%±7.3%) and CCBs (-9.7%±8.1%) were protective. Statins (3.5%±6.1%) and THZs (4.3%±10.8%) showed slight increases in risk. Treatment effects were consistent across age, gender, and socioeconomic categories.
conclusionsACEIs do not substantially increase the risk of COVID-19 infection while the protective effects of BBs and CCBs warrant further investigation. This study highlights the potential of transformer-based causal inference models as a powerful tool for evaluating treatment safety and efficacy in complex healthcare scenarios.
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