Predicting non-home discharge in epithelial ovarian cancer patients: External validation of a predictive model

Abstract

ObjectiveTo externally validate a model predicting non-home discharge in women undergoing primary cytoreductive surgery (CRS) for epithelial ovarian cancer (EOC). MethodsWomen undergoing primary CRS via laparotomy for EOC at three tertiary medical centers in an academic health system from January 2010 to December 2015 were included. Patients were excluded if they received neoadjuvant chemotherapy, had a non-epithelial malignancy, were not undergoing primary cytoreduction, or lacked documented model components. Non-home discharge included skilled nursing facility, acute rehabilitation facility, hospice, or inpatient death. The predicted probability of non-home discharge was calculated using age, pre-operative CA-125, American Society of Anesthesiologists (ASA) score and Eastern Cooperative Oncology Group (ECOG) performance status as described in the previously published predictive model. Model discrimination was calculated using a concordance index and calibration curves were plotted to characterize model performance across the cohort. ResultsA total of 204 admissions met inclusion criteria. The overall rate of non-home discharge was 12% (95% CI 8–18%). Mean age was 60.8 years (SD 11.0). Median length of stay (LOS) was significantly longer for patients with non-home discharge (8 vs. 5 days, P < 0.001). The predictive model had a concordance index of 0.86 (95% CI 0.76–0.93), which was similar to model performance in the original study (CI 0.88). The model provided accurate predictions across all probabilities (0 to 100%). ConclusionsNon-home discharge can be accurately predicted using preoperative clinical variables. Use of this validated non-home discharge predictive model may facilitate preoperative patient counseling, early discharge planning, and potentially decrease cost of care.