Incidence, Prediction, and Causes of Unplanned 30-Day Hospital Admission After Ambulatory Procedures. (August 2020)

• Record Type: Journal Article
• Authors: Teja, Bijan; Raub, Dana; Friedrich, Sabine; Rostin, Paul; Patrocínio, Maria D.; Schneider, Jeffrey C.; Shen, Changyu; Brat, Gabriel A.; Houle, Timothy T.; Yeh, Robert W.; Eikermann, Matthias
• Is Part Of: Anesthesia & analgesia. Volume 131:Number 2(2020)
• Publication Date: 2020-08
• Subjects: Anesthesiology -- Periodicals; Anesthesia; Anesthesiology; Analgesia; Analgesics; Anesthesiology -- Periodicals; 617.9605
• DOI: 10.1213/ANE.0000000000004852
• Languages: English
• ISSNs: 0003-2999

Abstract

Abstract : BACKGROUND: Unanticipated hospital admission is regarded as a measure of adverse perioperative patient care. However, previously published studies for risk prediction after ambulatory procedures are sparse compared to those examining readmission after inpatient surgery. We aimed to evaluate the incidence and reasons for unplanned admission after ambulatory surgery and develop a prediction tool for preoperative risk assessment. METHODS: This retrospective cohort study included adult patients undergoing ambulatory, noncardiac procedures under anesthesia care at 2 tertiary care centers in Massachusetts, United States, between 2007 and 2017 as well as all hospitals and ambulatory surgery centers in New York State, United States, in 2014. The primary outcome was unplanned hospital admission within 30 days after discharge. We created a prediction tool (the PREdicting admission after Outpatient Procedures [PREOP] score) using stepwise backward regression analysis to predict unplanned hospital admission, based on criteria used by the Centers for Medicare & Medicaid Services, within 30 days after surgery in the Massachusetts hospital network registry. Model predictors included patient demographics, comorbidities, and procedural factors. We validated the score externally in the New York state registry. Reasons for unplanned admission were assessed. RESULTS: A total of 170, 983 patients were included in the Massachusetts hospital network registry and 1, 232, 788 in the New York state registry. Among those, the observed rate of unplanned admission was 2.0% (3504) and 1.7% (20, 622), respectively. The prediction model showed good discrimination in the training set with C-statistic of 0.77 (95% confidence interval [CI], 0.77–0.78) and satisfactory discrimination in the validation set with C-statistic of 0.71 (95% CI, 0.70–0.71). The risk of unplanned admission varied widely from 0.4% (95% CI, 0.3–0.4) among patients whose calculated PREOP scores were in the first percentile to 21.3% (95% CI, 20.0–22.5) among patients whose scores were in the 99th percentile. Predictions were well calibrated with an overall ratio of observed-to-expected events of 99.97% (95% CI, 96.3–103.6) in the training and 92.6% (95% CI, 88.8–96.4) in the external validation set. Unplanned admissions were most often related to malignancy, nonsurgical site infections, and surgical complications. CONCLUSIONS: We present an instrument for prediction of unplanned 30-day admission after ambulatory procedures under anesthesia care validated in a statewide cohort comprising academic and nonacademic hospitals as well as ambulatory surgery centers. The instrument may be useful in identifying patients at high risk for 30-day unplanned hospital admission and may be used for benchmarking hospitals, ambulatory surgery centers, and practitioners.