From the Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Anesth Analg. 2020 Aug;131(2):497-507. doi: 10.1213/ANE.0000000000004852.
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.
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.
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.
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.
非计划性住院被认为是围手术期患者护理不良的衡量标准。然而,与住院手术患者再入院相关的研究相比,用于预测门诊手术后风险的研究相对较少。我们旨在评估门诊手术后计划性入院的发生率和原因,并开发一种用于术前风险评估的预测工具。
本回顾性队列研究纳入了美国马萨诸塞州 2 家三级护理中心接受麻醉下门诊非心脏手术的成年患者,以及 2014 年美国纽约州所有医院和门诊手术中心。主要结局为出院后 30 天内非计划性住院。我们使用逐步向后回归分析创建了一个预测工具(预测门诊手术后入院[PREOP]评分),该评分基于医疗保险和医疗补助服务中心使用的标准,预测马萨诸塞州医院网络登记处手术后 30 天内的非计划性住院。模型预测因子包括患者人口统计学、合并症和手术因素。我们在纽约州登记处对该评分进行了外部验证。评估了非计划性入院的原因。
共纳入马萨诸塞州医院网络登记处 170983 例患者和纽约州登记处 1232788 例患者。其中,计划性入院率分别为 2.0%(3504 例)和 1.7%(20622 例)。预测模型在训练集中具有良好的区分度,C 统计量为 0.77(95%置信区间[CI],0.77-0.78),在验证集中具有令人满意的区分度,C 统计量为 0.71(95%置信区间[CI],0.70-0.71)。计划性入院的风险差异很大,从计算 PREOP 评分在第 1 百分位的患者中 0.4%(95%CI,0.3-0.4)到评分在第 99 百分位的患者中 21.3%(95%CI,20.0-22.5)。预测结果与观察到的事件与预期事件的比例总体上相符,训练集为 99.97%(95%CI,96.3-103.6),外部验证集为 92.6%(95%CI,88.8-96.4)。计划性入院最常见的原因是恶性肿瘤、非手术部位感染和手术并发症。
我们提出了一种用于预测麻醉下门诊手术后 30 天内计划性入院的工具,该工具在包括学术和非学术医院以及门诊手术中心的全州队列中得到验证。该工具可能有助于识别 30 天内计划性入院风险较高的患者,可用于基准比较医院、门诊手术中心和执业医师。