标题： 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 显示英文标题

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

摘要： 目标是评估电子健康记录的数据质量和完整性变化程度以及其对基于常规收集数据的风险预测工具（QRISK3）预测新发心血管疾病（CVD）风险的稳健性的影响。 研究设计是一项纵向队列研究，设置为392个全科诊所（包括360万名患者）并与医院住院数据相链接。 使用Saez稳定性指标评估数据质量的变化，这些指标量化了每个诊所的异常程度。 统计脆弱性模型评估了QRISK3在个体预测和整体风险因素（线性预测器）效应上的准确性是否在不同诊所之间存在差异。 在未被QRISK3解释的CVD发生率方面，各诊所之间存在显著异质性。 在统计脆弱性最低的五分位数中，将女性的QRISK3预测风险设为10%时，考虑到诊所变异性后，该风险范围在7.1%至9.0%之间；在最高的五分位数中，该范围为10.9%至16.4%。 数据质量（使用Saez指标）和完整性在不同统计脆弱性水平之间是相当的。 例如，对于从最低到最高统计脆弱性五分位数的诊所，种族信息缺失记录的比例分别为55.7%、62.7%、57.8%、64.8%和62.1%。 风险因素的影响在不同诊所之间没有显著变化，β系数的统计变异很小。 结论是，各诊所之间CVD发生率的显著未测量异质性并未被数据质量的变化或风险因素的影响所解释。 应结合临床判断和额外风险因素的证据来补充QRISK3的风险预测。 显示英文摘要

摘要： The objective is 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). The study design is a longitudinal cohort study with a setting of 392 general practices (including 3.6 million patients) linked to hospital admission data. Variation in data quality was assessed using Saez 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. 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. In conclusion, 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.