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The Impact of Under Coding of Cardiac Severity and Comorbid Diseases on the Accuracy of Hospital Report Cards.

机译:心脏严重度和共病疾病编码不足对医院报告卡准确性的影响。

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CONTEXT:: Hospital report cards usually are based on administrative discharge abstracts. However, cardiac severity and comorbidities generally are under-reported in administrative data. OBJECTIVE:: We sought to determine how undercoding of cardiac severity and comorbidities affects the determination that some hospitals are high-mortality outliers. DESIGN:: Simulations using retrospective data on 18,795 patients admitted with an acute myocardial infarction (AMI) to 109 acute care hospitals in Ontario. MAIN OUTCOME MEASURE:: Change in the number of hospitals that remained high-mortality outliers after adjusting for potentially increased prevalence of as many as 9 separate measures of cardiac severity and comorbid conditions, individually or together. RESULTS:: For most measures of cardiac severity and comorbidities, increasing the prevalence of each factor to the highest observed hospital-specific prevalence seldom altered the status of high-mortality outlier hospitals. Increases in the prevalence of cardiogenic shock or acute renal failure to even the median level led to reclassification of up to 4 of the 12 high-mortality outlier hospitals to nonoutlier status. Most high-mortality outlier hospitals were reclassified if the maximum prevalence was imputed for these 2 factors. Simultaneously increasing the prevalence of all comorbidities to the median level typically converted the status of about half the outlier hospitals. Not until the prevalence of all measures of cardiac severity and comorbidities were simultaneously increased to the maximum observed hospital-specific prevalence, did all hospitals initially classified as high-mortality outliers revert to nonoutlier status. CONCLUSIONS:: Undercoding of severity and comorbidities in administrative data in itself is very unlikely to account for the outlier status of most hospitals. However, some potential for misclassification of individual institutions exists if influential factors are variably coded.
机译:语境:医院报告卡通常基于行政出院摘要。但是,在行政管理数据中,心脏严重程度和合并症通常报告不足。目的::我们试图确定心脏严重程度和合并症的编码不足如何影响某些医院是高死亡率异常值的确定。设计:使用回顾性数据对安大略省109所急诊医院的18795例急性心肌梗死(AMI)患者进行回顾性研究。主要观察指标:单独或联合校正多达9种单独的心脏病严重程度和合并症条件的潜在流行率后,仍保持高死亡率异常的医院数量发生变化。结果:对于大多数心脏严重程度和合并症的测量,将每种因素的患病率增加到观察到的医院特定患病率的最高水平,很少改变高死亡率异常医院的状况。心源性休克或急性肾功能衰竭的患病率上升至中位水平,导致12家高死亡率异常医院中的多达4家重新分类​​为非异常状态。如果根据这两个因素得出最大患病率,则将大多数高死亡率的异常医院重新分类。同时将所有合并症的患病率提高到中位水平通常可将约一半偏远医院的状况转变为医院。直到将所有对心脏严重程度和合并症的测量值的患病率同时提高到观察到的医院特定患病率的最大值,所有最初归类为高死亡率异常值的医院才恢复为非异常值状态。结论:在管理数据本身中严重性和合并症的编码不足以解释大多数医院的异常状况。但是,如果对影响因素进行可变编码,则存在个别机构分类错误的可能性。

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