LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Abstract

BackgroundThe LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).MethodsThis was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.ResultsOf the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.ConclusionThe LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

“…Numerous studies have been conducted on the performance of the LACE index and HOSPITAL score for 30-day readmission risk prediction in some specific disease settings, including heart failure, plastic surgery, COPD, and pneumonia with variable predictive ability [67][68][69][70]. In this study, we further examined the discrimination ability of these two risk scores in older hospitalized patients with malnutrition and physical dysfunction.…”

Increasing illness severity of skilled nursing facility patients over time: Implications for readmission penalties

“…While LACE, CCI, and the simplified HOSPITAL score have been individually applied to patients to determine their risk of readmission and aid in their management clinically, to our knowledge, no study has examined all three indices for each patient admitted to an SNF at a particular point in time 22,23 . Using LACE, CCI, and simplified HOSPITAL score as standard proxies for patient acuity, our study demonstrates that, in this large urban SNF admitting patients predominantly for subacute rehabilitation, acuity levels of the patient population as a whole increased progressively over time.…”

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study

“…Various predictive models have been suggested for estimating readmission rates across different clinical settings [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Nevertheless, our findings are difficult to compare with the existing literature because, as far as we know, no previous research has been focused specifically on the prediction of hospital readmission in a selected cohort of patients hospitalized for the management of severe obesity.…”

“…The role of age and smoking as risk factors for hospitalizations is well established. Older adults are more likely to experience acute medical conditions and injuries that require hospital admission [ 36 , 40 , 43 ]. Smoking is strongly associated with increased morbidity due to its causal role in a range of medical conditions such as cardiovascular disease, respiratory illness, and cancer [ 51 , 52 , 53 ]; people who smoke are more likely to require hospitalization for these conditions, and quitting smoking can reduce the risk of hospitalization and improve overall health outcomes [ 54 ].…”

“…Several predictive models have been proposed to predict the likelihood of hospital readmission in various clinical settings, such as acute myocardial infarction [ 36 ], heart failure [ 37 , 38 , 39 ], diabetes mellitus [ 40 , 41 ], chronic obstructive pulmonary disease [ 42 ], sepsis [ 43 , 44 ], among others. These models have been developed by including demographic and clinical variables that might influence readmission risk.…”