What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study

BACKGROUND

Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

OBJECTIVE

We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Adult patients discharged from a CCHS hospital April 2017-September 2020.

MAIN MEASURES

Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

RESULTS

The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

CONCLUSIONS

The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Accuracy of the Simplified HOSPITAL Score in Predicting COVID-19 Readmissions-Exploring Outcomes from a Hospital-at-Home Program

SUMMARY

External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Introduction

Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC’s Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC’s Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.

Methods

A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1^st of January 2018 and the 31^st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X² goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.

Results

At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676–0.708), 0.703 (95% CI 0.687–0.719) and 0.705 (95% CI 0.690–0.720). The Hosmer-Lemeshow X² tests had values of p<0.001.

Conclusion

In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability—model updating is warranted.

Quality of life after hospitalization predicts one-year readmission risk in a large Swiss cohort of medical in-patients

PURPOSE

Estimating the probability of readmission following hospitalization using prediction scores can be complex. Quality of life (QoL) may provide an easy and effective alternative.

METHODS

Secondary analysis of the prospective "TRIAGE" cohort. All medical in-patients admitted to a Swiss tertiary care institution (2016-2019) ≥18 years with a length of stay of ≥2 days (23,309 patients) were included. EQ-5D VAS, EQ-5D index, and Barthel index were assessed at a single telephone interview 30-day after admission. Patients lost to follow-up were excluded. Readmission was defined as a non-elective hospital stay at our institution >24 h within 1 year after discharge and assessed using area under the curve (AUC) analysis with adjustment for confounders.

RESULTS

12,842 patients (43% females, median age 68, IQR 55-78) were included. Unadjusted discrimination was modest at 0.59 (95% CI 0.56-0.62) for EQ-5D VAS. Partially adjusted discrimination (for gender) was identical. Additional adjustment for insurance, Charlson comorbidity index, length of stay, and native language increased the AUC to 0.66 (95% CI 0.63-0.69). Results were robust irrespective of time to event (12, 6 or 3 months). A cut-off in the unadjusted model of EQ-5D VAS of 55 could separate cases with a specificity of 80% and a sensitivity of 30%.

CONCLUSION

QoL at day 30 after admission can predict one-year readmission risk with similar precision as more intricate tools. It might help for identification of high-risk patients and the design of tailored prevention strategies.