Prediction of Patient Readmission by LACE Index components at Cardiac Care Unit of an Iranian Hospital: A Cohort Study

One approach to improve efficiency in health care is to identify patients with high risks of readmission so that resources should be distributed in a way they would benefit targeted care. A model named LACE (length of stay, acuity of admission, Charlson comorbidity index (CCI(, and number of emergency department visits in preceding 6 months) has been proposed to predict patient readmission which is widely used due to its simplicity to rank factors’ risks. The aim of this study is to determine if LACE Index could be used to predict Iranian hospital readmission.
This was a prospective cohort study in which the prediction of readmission for patients admitted to the cardiac intensive care of Shahid Beheshti Hospital of Qom during April to June 2012 within one month after the discharge was evaluated based on 4 items of LACE index. Following-up readmission states by making calls within a month after discharge. Purposive sampling was used to select the sample, patients having four most prevalent chronic heart diseases in the CCU of the hospital were selected and at last sample size was 109 patients. We used logistic regression, the phi and Spearman correlation coefficient to analyze data using SPSS18. the significance level was considered as 5% in all tests.
Among the items of LACE model, 48.6% of patients stayed at the hospital for 4 to 6 days. Only 11 patients (10.09%) referred to the hospital after a month. None of the components of the LACE index could enter the stepwise logistic regression model.
Considering that LACE model with its four items is a weak in predicting readmission, in order to improve the model in predicting the readmission of cardiac patients, it is recommended that individual variables and factors associated with the service providers be added to it.