基于电子健康记录的心脏风险评估及未满足的预防需求识别。

Electronic health record-based cardiac risk assessment and identification of unmet preventive needs.

作者信息

Persell Stephen D, Dunne Alexis P, Lloyd-Jones Donald M, Baker David W

机构信息

Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.

出版信息

Med Care. 2009 Apr;47(4):418-24. doi: 10.1097/MLR.0b013e31818dce21.

DOI:10.1097/MLR.0b013e31818dce21

PMID:19238100

Abstract

BACKGROUND

Cardiac risk assessment may not be routinely performed. Electronic health records (EHRs) offer the potential to automate risk estimation. We compared EHR-based assessment with manual chart review to determine the accuracy of automated cardiac risk estimation and determination of candidates for antiplatelet or lipid-lowering interventions.

METHODS

We performed an observational retrospective study of 23,111 adults aged 20 to 79 years, seen in a large urban primary care group practice. Automated assessments classified patients into 4 cardiac risk groups or as unclassifiable and determined candidates for antiplatelet or lipid-lowering interventions based on current guidelines. A blinded physician manually reviewed 100 patients from each risk group and the unclassifiable group. We determined the agreement between full review and automated assessments for cardiac risk estimation and identification of which patients were candidates for interventions.

RESULTS

By automated methods, 9.2% of the population were candidates for lipid-lowering interventions, and 8.0% were candidates for antiplatelet medication. Agreement between automated risk classification and manual review was high (kappa = 0.91; 95% confidence interval [CI], 0.88-0.93). Automated methods accurately identified candidates for antiplatelet therapy [sensitivity, 0.81 (95% CI, 0.73-0.89); specificity, 0.98 (95% CI, 0.96-0.99); positive predictive value, 0.86 (95% CI, 0.78-0.94); and negative predictive value, 0.98 (95% CI, 0.97-0.99)] and lipid lowering [sensitivity, 0.92 (95% CI, 0.87-0.96); specificity, 0.98 (95% CI, 0.97-0.99); positive predictive value, 0.94 (95% CI, 0.89-0.99); and negative predictive value, 0.99 (95% CI, 0.98-> or =0.99)].

CONCLUSIONS

EHR data can be used to automatically perform cardiovascular risk stratification and identify patients in need of risk-lowering interventions. This could improve detection of high-risk patients whom physicians would otherwise be unaware.

摘要

背景

心脏风险评估可能未被常规执行。电子健康记录（EHR）提供了自动进行风险评估的可能性。我们比较了基于EHR的评估与人工病历审查，以确定自动心脏风险评估的准确性以及确定抗血小板或降脂干预的候选者。

方法

我们对在一个大型城市初级保健团体诊所就诊的23111名20至79岁的成年人进行了一项观察性回顾性研究。自动评估将患者分为4个心脏风险组或不可分类组，并根据当前指南确定抗血小板或降脂干预的候选者。一名盲法医生对每个风险组和不可分类组中的100名患者进行了人工审查。我们确定了全面审查与自动评估在心脏风险评估以及确定哪些患者是干预候选者方面的一致性。

结果

通过自动方法，9.2%的人群是降脂干预的候选者，8.0%是抗血小板药物的候选者。自动风险分类与人工审查之间的一致性很高（kappa = 0.91；95%置信区间[CI]，0.88 - 0.93）。自动方法准确地识别出抗血小板治疗的候选者[敏感性，0.81（95% CI，0.73 - 0.89）；特异性，0.98（95% CI，0.96 - 0.99）；阳性预测值，0.86（95% CI，0.78 - 0.94）；阴性预测值，0.98（95% CI，0.97 - 0.99）]和降脂治疗的候选者[敏感性，0.92（95% CI，0.87 - 0.96）；特异性，0.98（95% CI，0.97 - 0.99）；阳性预测值，0.94（95% CI，0.89 - 0.99）；阴性预测值，0.99（95% CI，0.98 ->或 = 0.99）]。