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首页> 外文期刊>Anesthesia and Analgesia: Journal of the International Anesthesia Research Society >Incidence, Prediction, and Causes of Unplanned 30-Day Hospital Admission After Ambulatory Procedures
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Incidence, Prediction, and Causes of Unplanned 30-Day Hospital Admission After Ambulatory Procedures

机译:门诊手术后无计划30天医院入学的发病率,预测和原因

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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.
机译:背景:意想不到的医院入院被视为不良围手术期患者护理的衡量标准。然而,与住院后手术后的检查再次入学的人相比,在动态程序后,以前公布的风险预测研究。我们旨在评估车身手术后意外入学的发病率和原因,并开发术前风险评估的预测工具。方法:这项回顾性队列研究包括在2007年至2017年间马萨诸塞州的2张高等护理中心进行了在麻萨诸塞州的2个高级护理中心进行的成人患者,在美国,在美国,在美国,美国2014年。发初级结果是在出院后30天内预计的医院入院。我们创建了一种预测工具(门诊过程中的预测入门程序[Preop]得分)使用逐步向后回归分析来预测未计划的医院入学,基于Medicare&Medicate服务的中心使用的标准,在Massachusetts医院的手术后30天内网络注册表。模型预测因子包括患者人口统计,合并症和程序因素。我们在纽约州注册处验证了外部的得分。评估了无计划预备的原因。结果:Massachusetts医院网络登记处共包含170,983名患者,纽约州注册处1,232,788名。其中,观察到的无计划率分别为2.0%(3504)和1.7%(20,622)。预测模型在训练中显示出良好的识别,C统计为0.77(置信区间,0.77-0.78)的C型统计数据,验证组符合验证组,C统计为0.71(95%CI,0.70-0.71 )。无计划进入的风险从0.4%(95%CI,0.3-0.4)中的患者中的患者中的0.4%(95%CI,0.3-0.4)变化,其分数在第99位的患者中的第一个百分位数至21.3%(95%CI,20.0-22.5)中百分位数。预测在训练中的观察到预期事件的总比率良好校准,在培训中,在外部验证集中,92.6%(95%CI,88.8-96.4)。无计划的入学均往往与恶性肿瘤,非非直接遗址感染和手术并发症有关。结论:我们提出了一种用于预测无误的30天入院的仪器,在包括学术和非遗传学医院的全州群体和外科手术中心验证的麻醉护理下的气象学手术后。该仪器可用于识别高风险的患者30天未计划的医院入学,可用于基准测试医院,外国手术中心和从业者。

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