Using electronic medical records to predict mortality in primary care patients with heart disease: prognostic power and pathophysiologic implications.
Using electronic medical records to predict mortality in primary care patients with heart disease: prognostic power and pathophysiologic implications.
复制标题
使用电子病历预测初级保健心脏病患者的死亡率：预后力和病理生理学影响。
DOI：
10.1007/bf02599583
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发表时间：
1996
影响因子：
5.7
通讯作者：
Zhou,XH
中科院分区：
文献类型：
--
作者：
Tierney,WM;Takesue,BY;Vargo,DL;Zhou,XH
目的：通过使用存储在电子病历系统中的数据来预测6年死亡率，以确定心脏病高危患者。设计：回顾性队列研究。设置：学术初级保健普通内科实践附属于城市教学医院，拥有最先进的电子病历系统。患者：在1986年访问城市初级保健实践的2，434例缺血性心脏病或心力衰竭或两者的证据中，一半用于推导比例风险模型，一半用于验证它。测量：在开始日期的6年内任何原因导致的死亡率。模型的歧视与C统计进行了评估，并与校准曲线和Hosmer-Lemeshow statistics.Main结果：这些患者中有82%的缺血性心脏病，53%心力衰竭，35%的证据，这两个条件的拟合优度进行了测量。653例（27%）死亡患者的平均生存期为2.8年;幸存者的平均随访期为5.0年。患有两种心脏病的患者死亡率最高（6年时为45%），其次是孤立性心力衰竭（39%）和缺血性心脏病（18%）。在300个潜在的预测特征中，100个通过了单变量筛选，并进行了多变量比例风险回归。12个变量提供了独立的预测信息：年龄，体重，一次以上的心力衰竭住院史，以及诊断测试和问题列表中显示的9种情况。没有药物治疗变量是独立的预测因素。模型C统计量在患者的衍生样本中为0.76，在随机选择的验证样本中为0.74，并且得到了很好的校准。在最低和最高的四分位数的风险不同的患者超过五倍，他们的平均risk.CONCLUSIONS：常规的临床数据存储在患者的电子病历能够预测心脏病患者的死亡率。这可以使越来越稀缺的卫生保健资源集中在那些死亡风险最高的人身上。
OBJECTIVE:To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality.DESIGN:Retrospective cohort study.SETTING:Academic primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system.PATIENTS:Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it.MEASUREMENTS:Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration curve and Hosmer-Lemeshow statistic.MAIN RESULTS:Of these patients 82% had evidence of ischemic heart disease, 53% heart failure, and 35% both conditions. Mean survival among the 653 (27%) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both heart conditions had the highest mortality rate (45% at 6 years), followed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients in the lowest and highest quartiles of risk differed more than five-fold in their average risk.CONCLUSIONS:Routine clinical data stored in patients’ electronic medical records are capable of predicting mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.