ArticleAnnals of medicine2026
Predictive value of systemic inflammatory indices for new-onset atrial fibrillation after acute myocardial infarction: an interpretable machine learning study.
Article in Annals of medicine, 2026. 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
backgroundNew-onset atrial fibrillation (NOAF) after acute myocardial infarction (AMI) is associated with adverse outcomes. Inflammation contributes to atrial fibrillation (AF) onset and progression. We developed an interpretable machine learning (ML) framework integrating complete blood count (CBC)-derived inflammatory indices with routine clinical parameters for NOAF risk assessment.
methodsWe retrospectively analyzed 3,077 AMI patients without prior AF (2018-2023). Multivariable logistic regression, restricted cubic splines (RCS), and threshold analyses evaluated six inflammatory indices: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). Boruta and LASSO selections were intersected. Eight ML models were evaluated, with Shapley Additive Explanations (SHAP) for interpretation.
resultsNOAF incidence was 7.73%. Six indices were associated with NOAF in separate adjusted models (all
conclusionsCBC-derived inflammatory indices were associated with NOAF, but their incremental predictive value beyond routine clinical variables remains uncertain. Given moderate predictive performance and no external validation, multicenter validation is needed before clinical implementation.
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