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使用行政数据识别护理并发症。

Identifying complications of care using administrative data.

作者信息

Iezzoni L I, Daley J, Heeren T, Foley S M, Fisher E S, Duncan C, Hughes J S, Coffman G A

机构信息

Department of Medicine, Harvard Medical School, Beth Israel Hospital, Boston, MA 02215.

出版信息

Med Care. 1994 Jul;32(7):700-15. doi: 10.1097/00005650-199407000-00004.

Abstract

The Complications Screening Program (CSP) is a method using standard hospital discharge abstract data to identify 27 potentially preventable in-hospital complications, such as post-operative pneumonia, hemorrhage, medication incidents, and wound infection. The CSP was applied to over 1.9 million adult medical/surgical cases using 1988 California discharge abstract data. Cases with complications were significantly older and more likely to die, and they had much higher average total charges and lengths of stay than other cases (P < 0.0001). For most case types, 13 chronic conditions, defined using diagnosis codes, increased the relative risks of having a complication after adjusting for patient age. Cases at larger hospitals and teaching facilities generally had higher complication rates. Logistic regression models to predict complications using demographic, administrative, clinical, and hospital characteristics variables, had modest power (C statistics = 0.64 to 0.70). The CSP requires further evaluation before using it for purposes other than research.

摘要

并发症筛查项目(CSP)是一种利用标准医院出院摘要数据来识别27种潜在可预防的院内并发症的方法,如术后肺炎、出血、用药事故和伤口感染。CSP使用1988年加利福尼亚州出院摘要数据应用于超过190万例成人内科/外科病例。有并发症的病例年龄显著更大且更有可能死亡,并且与其他病例相比,他们的平均总费用和住院时间要高得多(P < 0.0001)。对于大多数病例类型,使用诊断代码定义的13种慢性病在调整患者年龄后增加了发生并发症的相对风险。大型医院和教学机构的病例通常并发症发生率更高。使用人口统计学、管理、临床和医院特征变量预测并发症的逻辑回归模型的预测能力一般(C统计量 = 0.64至0.70)。在将CSP用于研究以外的目的之前,需要进一步评估。

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