Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model

Abstract

Objectives: Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF.

Design: Retrospective cohort study.

Setting: Ten independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system.

Participants: We evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization.

Measurements: The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation.

Results: Among 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months.

Conclusions and implications: We developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.

Keywords: Post-acute; readmission risk; skilled nursing facility.

Figure 1.

Main Effect Plots for 9 Predictor Variables. For each set of rectangles, the widths correspond to consecutive deciles of the original data and heights correspond to the empirical readmission rate for observations falling within that range. The red curve in each plot is a smoothing spline fit to gradient boosting machine predictions for each observation across the respective variable. ED indicates emergency department.

Figure 2.

Performance results for the GBM model, Charlson Comorbidity Index, and Length of Stay were compared by using corresponding ROC curves on out-of-sample observations via 10-fold cross-validation, along with 95% pointwise confidence bands (obtained via 1,000 bootstrap samples). AUC indicates area under the receiver-operator curve; CCI, Charlson Comorbidity Index; GBM, gradient boosting machine; LOS, length of stay; ROC, receiver operating curve.

Figure 3.

Variable Group Importance. The red axis is a variable group’s individual AUC, ie, the performance of GBM when fit using only that group of variables. The blue axis is the change in AUC when GBM is fit to all variables vs when GBM is fit to all variables except those in the given variable group. The change quantifies how much model performance is diminished if a given variable group is excluded. Both axes have been scaled by a factor of 100 to improve readability, and the bars represent 95% CIs, calculated via 2,000 bootstrap samples. AUC indicates area under the receiver-operator curve; ICU, intensive care unit; LOS, length of stay.