Propensity-Score, Causal-Forest and Explainable Machine-Learning Analyses of a Historical Registry for Pentoxifylline and In-Hospital Mortality in Acute Myocardial Infarction

Background: Pentoxifylline, a hemorheological drug with anti-inflammatory properties, has been proposed as adjunctive therapy in acute myocardial infarction, yet its effect on mortality along with any differences in patient phenotypes remain unclear. Methods: We carried out a registry-based study on 1700 patients with acute myocardial infarction, of which 1684 had a known exposure status to pentoxifylline. The admission-time confounders were analyzed using 1:1 propensity-score matching (PSM) to balance baseline characteristics between pentoxifylline users and non-users. We employed an extreme gradient boosting (XGBoost) model on a hold-out set along with explanations from SHapley Additive exPlanations and Accumulated Local Effects analyses. Results: In the PSM-matched pair analysis, mortality was observed to be 16.4% with and 15.4% without pentoxifylline (risk difference: 1.1% [95% CI: −5.2, 7.4]; odds ratio: 1.09% [95% CI: 0.68, 1.75]. Sensitivity analyses were concordant with the overall findings. Neither any subgroup interaction nor heterogeneity was observed with causal forest. The XGBoost model demonstrated good discriminatory performance (test-set AUC: 0.834, 95% CI: 0.785–0.884). Conclusions: In the present analysis, pentoxifylline use in the intensive care unit was not associated with a reduction in the in-hospital mortality both in overall or in any of the inflammatory, metabolic or infarct-defined subgroup.