Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Abstract

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488-0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582-0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068-0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician's model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

“…Since readmission reduction plan takes extra cost to the standard follow-up, ruling-out readmission (higher NPV and -LR, higher power to identify patients had a lower risk for readmission) can save the excessive cost of additional preventive measure to low-risk patients, and direct resources to high-risk ones [32,33]. This finding is compatible with our previous simulation [20], and this is likely why there is still room for improvement for LACE score, but a substantial reduction in readmission rate.…”

“…Comorbidities are measured by the Charlson comorbidity index (CCI) score [19]. The primary or secondary diagnostic International Classification of Diseases, Ninth Revision (ICD-9) in the previous year of the index admission was used in calculating the CCI score [20].…”

“…Due to its simplicity features, LACE score gained attention among many emerging readmission predictive models [19]. According to LACE model, the probability of a patient's readmission risk can be ideally calculated by four components, including the length of stay (L), acute admission (A), comorbidity (C), and emergency department visits in the previous six months (E) so that home care providers can stratify the patients and directed resource to the patients in need [20]. Although LACE score has been used in quite a few scenarios, it has been criticized for the poor performance in certain conditions [21][22][23].…”

“…As home care services are under government payment, the health system would be heavily impacted by home care patients' readmission [11,26]. As a result, a novel home care delivery method, LACE score-based readmission risk management tool had been proposed and simulated in our home care setting previously [20]. While there has been a great deal of research on predictive models and readmission reduction, very few studies focus on integrating predictive models into actual practice for LTHC patients.…”

Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study

“…This model was developed to identify the risk of potentially avoidable 30-day readmission in medical patients before discharge (Donzé et al, 2013). Both the LACE index and HOSPITAL score models have been used effectively to identify patients at a high risk of readmission (Su et al, 2020) and to predict mortality in multimorbid older patients (Aubert et al, 2022). However, the characteristics of these high-risk patients are not addressed in these models.…”