Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records
Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records
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DOI：
10.1001/jamacardio.2016.3366
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发表时间：
2016-12-01
期刊：
影响因子：
24
通讯作者：
Darbar, Dawood
中科院分区：
文献类型：
--
作者：
Kolek, Matthew J.;Graves, Amy J.;Darbar, Dawood
重要性心房颤动(AF)导致相当大的发病率、死亡率和医疗费用。对房颤事件的准确预测将加强房颤的管理，并潜在地改善患者的预后。目的验证由心脏和衰老研究队列最初开发的房颤风险预测模型，该模型最初由基因组流行病学-心房颤动(CARGE-AF)研究人员使用大量电子病历(EMR)开发。该预测模型研究的设计、设置和参与者对33 494名40岁或40岁以上、没有房颤病史的白人或非裔美国人确定的EMR进行了回顾和分析。从2005年12月31日到2010年12月31日，参与者在范德比尔特大学医学中心的内科门诊接受了房颤事件的随访。对基线风险的差异进行了调整，将Charge-AF Cox比例风险模型的回归系数应用于EMR队列。没有超声心动图变量的模型的一个简单版本也被评估。分析了2013年10月31日至2014年1月31日的数据。MAIN结果和测量事件房颤。模型中的预测因素包括年龄、种族、身高、体重、收缩压和舒张压、高血压治疗、吸烟状况、2型糖尿病、心力衰竭、心肌梗塞病史、左心室肥厚和PR间期。结果在33494名参与者中，中位年龄为57岁(四分位数范围，49-67岁)；57%的患者为女性，43%的男性，85.7%的白人，14.3%的非裔美国人。在平均4.8(0.9)年的随访期内，2455人(7.3%)发展为房颤。两个模型在电子病历队列中的校正都很差，在低风险个体中对房颤的预测不足，在高危个体中对房颤的预测过高(预测的房颤发生概率的第10和第90个百分位数分别为0.005和0.179)。在我们的队列中，全电荷-房颤模型的C指数为0.708(95%CI，0.699-0.718)。简单模型具有相似的判别力(C指数，0.709；95%CI，0.699-0.718；模型间差异的P=.70)。结论尽管有合理的判别力，Charge-AF模型在该EMR队列中的校正能力较差。这项研究强调了将来自前瞻性队列研究的风险模型应用于EMR队列的困难，并建议在EMR环境中谨慎使用这些房颤风险预测模型。未来的风险模型可能需要在电子病历队列中开发和验证。
IMPORTANCE Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and health care expenditures. Accurate prediction of incident AF would enhance AF management and potentially improve patient outcomes.OBJECTIVE To validate the AF risk prediction model originally developed by the Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) investigators using a large repository of electronic medical records (EMRs).DESIGN, SETTING, AND PARTICIPANTS In this prediction model study, deidentified EMRs of 33 494 individuals 40 years or older who were white or African American and had no history of AF were reviewed and analyzed. The participants were followed up in the internal medicine outpatient clinics at Vanderbilt University Medical Center for incident AF from December 31, 2005, until December 31, 2010. Adjusting for differences in baseline hazard, the CHARGE-AF Cox proportional hazards model regression coefficients were applied to the EMR cohort. A simple version of the model with no echocardiographic variables was also evaluated. Data were analyzed from October 31, 2013, to January 31, 2014.MAIN OUTCOMES AND MEASURES Incident AF. Predictors in the model included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, type 2 diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval.RESULTS Among the 33 494 participants, the median age was 57 (interquartile range, 49-67) years; 57% of patients were women, 43% were men, 85.7% were white, and 14.3% were African American. During the mean (SD) follow-up of 4.8 (0.9) years, 2455 individuals (7.3%) developed AF. Both models had poor calibration in the EMR cohort, with underprediction of AF among low-risk individuals and overprediction of AF among high-risk individuals (10th and 90th percentiles for predicted probability of incident AF, 0.005 and 0.179, respectively). The full CHARGE-AF model had a C index of 0.708 (95% CI, 0.699-0.718) in our cohort. The simple model had similar discrimination (C index, 0.709; 95% CI, 0.699-0.718; P =.70 for difference between models).CONCLUSIONS AND RELEVANCE Despite reasonable discrimination, the CHARGE-AF models showed poor calibration in this EMR cohort. This study highlights the difficulties of applying a risk model derived from prospective cohort studies to an EMR cohort and suggests that these AF risk prediction models be used with caution in the EMR setting. Future risk models may need to be developed and validated within EMR cohorts.