预测冠状动脉搭桥手术的院内死亡情况。不同的严重程度衡量指标会给出不同的预测结果吗？

Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions?

作者信息

Iezzoni L I, Ash A S, Shwartz M, Landon B E, Mackiernan Y D

机构信息

Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.

出版信息

Med Care. 1998 Jan;36(1):28-39. doi: 10.1097/00005650-199801000-00005.

DOI:10.1097/00005650-199801000-00005

PMID:9431329

Abstract

OBJECTIVES

Severity-adjusted death rates for coronary artery bypass graft (CABG) surgery by provider are published throughout the country. Whether five severity measures rated severity differently for identical patients was examined in this study.

METHODS

Two severity measures rate patients using clinical data taken from the first two hospital days (MedisGroups, physiology scores); three use diagnoses and other information coded on standard, computerized hospital discharge abstracts (Disease Staging, Patient Management Categories, all patient refined diagnosis related groups). The database contained 7,764 coronary artery bypass graft patients from 38 hospitals with 3.2% in-hospital deaths. Logistic regression was performed to predict deaths from age, age squared, sex, and severity scores, and c statistics from these regressions were used to indicate model discrimination. Odds ratios of death predicted by different severity measures were compared.

RESULTS

Code-based measures had better c statistics than clinical measures: all patient refined diagnosis related groups, c = 0.83 (95% C.I. 0.81, 0.86) versus MedisGroups, c = 0.73 (95% C.I. 0.70, 0.76). Code-based measures predicted very different odds of dying than clinical measures for more than 30% of patients. Diagnosis codes indicting postoperative, life-threatening conditions may contribute to the superior predictive power of code-based measures.

CONCLUSIONS

Clinical and code-based severity measures predicted different odds of dying for many coronary artery bypass graft patients. Although code-based measures had better statistical performance, this may reflect their reliance on diagnosis codes for life-threatening conditions occurring late in the hospitalization, possibly as complications of care. This compromises their utility for drawing inferences about quality of care based on severity-adjusted coronary artery bypass graft death rates.

摘要

目的

全国各地均公布了按医疗服务提供者划分的冠状动脉旁路移植术（CABG）手术的严重程度调整死亡率。本研究考察了五种严重程度衡量方法对相同患者的严重程度评级是否不同。

方法

两种严重程度衡量方法使用从前两天住院临床数据中获取的数据对患者进行评级（MedisGroups，生理学评分）；另外三种方法使用标准计算机化医院出院摘要中编码的诊断和其他信息（疾病分期、患者管理类别、所有患者细化诊断相关组）。该数据库包含来自38家医院的7764例冠状动脉旁路移植术患者，住院死亡率为3.2%。进行逻辑回归以根据年龄、年龄平方、性别和严重程度评分预测死亡情况，并使用这些回归的c统计量来表示模型辨别力。比较不同严重程度衡量方法预测的死亡比值比。

结果

基于编码的衡量方法的c统计量优于临床衡量方法：所有患者细化诊断相关组，c = 0.83（95%置信区间0.81, 0.86），而MedisGroups为c = 0.73（95%置信区间0.70, 0.76）。对于超过30%的患者，基于编码的衡量方法预测的死亡几率与临床衡量方法非常不同。表明术后危及生命状况的诊断编码可能有助于基于编码的衡量方法具有更高的预测能力。