Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.
Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.
复制标题
退伍军人健康管理局门诊手术不良事件监测模型的开发。
DOI：
10.1111/1475-6773.13037
复制
发表时间：
2018
影响因子：
3.4
通讯作者：
Rosen,AmyK
中科院分区：
文献类型：
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作者：
Mull,HillaryJ;Itani,KamalMF;Pizer,StevenD;Charns,MartinP;Rivard,PeterE;McIntosh,Nathalie;Hawn,MaryT;Rosen,AmyK
根据先前开发的电子触发器，开发并验证一个监测模型，以识别门诊手术不良事件（AE）。数据来源退伍军人健康管理局的企业数据仓库。研究设计六个手术AE触发器，包括术后急诊室就诊和住院，应用于FY 2012 -2014门诊手术（n= 744，355）。我们随机抽取触发标记和未标记病例进行护士病历审查，以记录AE并测量触发因素的阳性预测值（PPV）。接下来，我们使用病历审查数据迭代估计多水平逻辑回归模型，以预测AE的概率，从六个触发因素开始，并添加患者、手术和设施特征，以提高模型拟合度。我们通过将系数应用于2015财年门诊手术数据（n= 256，690）并审查高和中等AE概率病例的图表来验证最终模型。主要发现在2012 -2014财年审查的1，730例手术中，350例发生AE（20%）。最终监测模型c统计量为0.81。在2015财年，不良事件预测概率>0.8的手术中（n= 405，0.15%），PPV为85%;在不良事件预测概率为0.4-0.5的手术中，PPV为38%.ConclusionsThe监测模型表现良好，准确识别出不良事件概率高的门诊手术。
ObjectiveDevelop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.Data SourcesVeterans Health Administration's Corporate Data Warehouse.Study DesignSix surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012–2014 outpatient surgeries (n= 744,355). We randomly sampled trigger‐flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n= 256,690) and reviewing charts for cases at high and moderate probability of an AE.Principal FindingsOf 1,730 FY2012–2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c‐statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n= 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4–0.5 predicted probability of an AE, PPV was 38 percent.ConclusionsThe surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE.