Risk Prediction Models for Hospital Readmission In Surgery: A Systematic Review

Mehta, Hemalkumar B.; Sieloff, Eric M.; Veeranki, Sreenivas P.; Sura, Sneha; Riall, Taylor S.; Senagore, Anthony J.; Goodwin, James S.

doi:10.1016/j.jamcollsurg.2017.07.832

Cited by 4 publications

(3 citation statements)

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“…Previous studies suggest that readmissions occurred frequently in 15-25% of patients and are associated with excess harm and numerous burdens to the health system [3,4]. The LACE index and the HOSPITAL score are two of most commonly used prediction models that proactively stratify and identify patients at a high risk of readmission [5][6][7][8][9]. The simplicity of the LACE index and HOSPITAL score distinguishes them from other models, and they have been widely adopted in current clinical information systems as a result [9].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

et al. 2020

IJERPH

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wang

Chen

et al. 2020

IJERPH

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“…of 13 Our study had some limitations. First, our study focused on LTHC patients, a particular group of disabled patients with a high percentage of nasogastric tubes, urinary catheters, or tracheostomy tubes; thus, our result may not apply to a general home care population.…”

Section: Discussionmentioning

confidence: 92%

“…LACE score is a readmission predictive model for various clinical scenarios including inpatients [12], surgical patients [13], chronic obstructive pulmonary disease (COPD) [14], and heart failure patients [15]. Preventive intervention can be prioritized according to patients' risk [2,10,13,[16][17][18]. Due to its simplicity features, LACE score gained attention among many emerging readmission predictive models [19].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Huang

et al. 2021

IJERPH

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Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.

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