检查电子健康记录的数据质量和完整性对预测患者心血管疾病风险的影响。

Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease.

机构信息

Health e-Research Centre, Farr Institute, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre (MAHSC), Oxford Road, Manchester, M13 9PL, UK.

Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, UK; NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

出版信息

Int J Med Inform. 2020 Jan;133:104033. doi: 10.1016/j.ijmedinf.2019.104033. Epub 2019 Nov 11.

DOI:10.1016/j.ijmedinf.2019.104033

PMID:31785526

Abstract

OBJECTIVE

To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3).

DESIGN

Longitudinal cohort study.

SETTINGS

392 general practices (including 3.6 million patients) linked to hospital admission data.

METHODS

Variation in data quality was assessed using Sáez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices.

RESULTS

There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients.

CONCLUSIONS

The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.

摘要

目的

使用基于常规收集数据（QRISK3）的风险预测工具，评估电子健康记录的数据质量和完整性的变化程度，以及对新发心血管疾病（CVD）风险预测的稳健性的影响。

设计

纵向队列研究。

设置

392 家全科诊所（包括 360 万患者）与住院数据相关联。

方法

使用 Sáez 的稳定性指标评估数据质量的变化，该指标量化了每个实践的异常程度。统计脆弱性模型评估了 QRISK3 在个体预测上的准确性以及总体风险因素（线性预测器）的效果是否在实践之间存在差异。

结果

在 QRISK3 无法解释的 CVD 发生率方面，实践之间存在很大的异质性。在最低五分位的统计脆弱性中，当将实践变异性纳入统计脆弱性模型时，QRISK3 预测女性 10%的风险范围在 7.1%至 9.0%之间；对于最高五分位，这是 10.9%-16.4%。数据质量（使用 Saez 指标）和完整性在不同的统计脆弱性水平上是可比的。例如，在最低五分位至最高五分位的实践中，记录种族缺失信息的比例分别为 55.7%、62.7%、57.8%、64.8%和 62.1%。风险因素的影响在实践之间没有差异，β系数的统计变化很小。