Related Experiment Video
Updated: Jan 6, 2026
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Lace Index: Predict the High-Risk of 30-Days Readmission of Patients With Acute Myocardial Infarction: National
Vasuki Rajaguru1, Whiejong Han2, Suk-Yong Jang1
1Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul, Korea.
Insights
The LACE index effectively predicts 30-day readmissions in Korean acute myocardial infarction (AMI) patients. Higher LACE scores indicate increased readmission risk, supporting its use in clinical risk stratification.
Area of Science:
- Cardiology
- Health Services Research
- Predictive Analytics
Background:
- Readmission after acute myocardial infarction (AMI) presents significant healthcare challenges.
- The LACE index (Length of stay, Acuity of admission, Comorbidities, Emergency department visits) is a common readmission prediction tool.
- Its utility in large-scale Korean populations requires further investigation.
Purpose of the Study:
- To validate the predictive performance of the LACE index for 30-day readmissions in AMI patients.
- To assess the LACE index in a nationally representative Korean cohort.
Main Methods:
- Retrospective cohort study using the Korean National Health Insurance Service Sample (NHISS) database (2011-2020).
- Included 609,640 adult patients hospitalized for AMI.
- Calculated LACE index, identified 30-day readmissions, and used logistic regression and ROC analysis for prediction and discrimination assessment.
Main Results:
- A LACE score of ≥10 significantly increased readmission odds (OR=2.65).
- Middle-aged adults (35-64) showed elevated readmission risk (OR=3.42).
- The LACE index demonstrated moderate predictive performance (C-statistic=0.71).
Conclusions:
- The LACE index is a valuable tool for predicting 30-day readmissions in Korean AMI patients.
- Its simplicity supports its use in clinical and policy-level risk stratification.
- Future studies should incorporate additional clinical variables to refine prediction models.
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.
More Related Videos
06:16Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
01:28Acute Coronary Syndrome IV: Interprofessional Care
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:26Acute Coronary Syndrome V: Nursing Management