Lace Index: Predict the High‐Risk of 30‐Days Readmission of Patients With Acute Myocardial Infarction: National Health Insurance Claims Data 2011–2020

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Abstract
Background: Readmission following acute myocardial infarction (AMI) poses significant challenges to health systems and patient outcomes. The LACE index, a composite of Length of stay, Acuity of admission, Comorbidities, and Emergency department visits, is widely used for readmission prediction. However, its performance in large‐scale, real‐world Korean cohorts remains understudied. Objective: This study aimed to validate the predictive performance of the LACE index for 30‐day readmissions in AMI patients using a nationally representative Korean cohort. Methods: This retrospective cohort study analyzed data from the Korean National Health Insurance Service Sample (NHISS) database from 2011 to 2020. A total of 609,640 adult patients hospitalized for AMI were included. The LACE index was calculated for each patient, and 30‐day readmissions were identified. Logistic regression was used to estimate odds ratios (ORs) for readmission. Model discrimination was assessed using ROC curve analysis and C‐statistics. Subgroup and survival analyses were performed by age, LACE score, and comorbidity burden. Results: Among 609,640 AMI patients, 205 (0.034%) experienced 30‐day readmission. Patients with a LACE score of ≥ 10 had significantly higher odds of readmission (OR = 2.65; 95% CI: 1.68–4.19, p < 0.001) compared to those with scores 0–4. Middle‐aged adults (35–64 years) also showed elevated readmission risk (OR = 3.42; 95% CI: 1.74–6.73, p < 0.001), while older adults (≥ 65 years) did not have significantly different risk. The LACE index showed moderate discriminatory performance (C‐statistics = 0.71). Kaplan–Meier survival curves demonstrated significantly lower 30‐day survival among patients with LACE ≥ 10. Conclusions: Study findings suggest the LACE index is a useful tool for predicting 30‐day readmissions among AMI patients in Korea. Its simplicity and moderate accuracy support its application in clinical and policy‐level risk stratification strategies. Future prospective studies should refine prediction models by incorporating additional clinical variables.

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