Hippisley-Cox Julia, Coupland Carol, Brindle Peter
Division of Primary Care, Nottingham, UK.
Avon Primary Care Research Collaborative, Bristol Clinical Commissioning Group, Bristol, UK.
BMJ Open. 2014 Aug 28;4(8):e005809. doi: 10.1136/bmjopen-2014-005809.
To validate the performance of a set of risk prediction algorithms developed using the QResearch database, in an independent sample from general practices contributing to the Clinical Research Data Link (CPRD).
Prospective open cohort study using practices contributing to the CPRD database and practices contributing to the QResearch database.
The CPRD validation cohort consisted of 3.3 million patients, aged 25-99 years registered at 357 general practices between 1 Jan 1998 and 31 July 2012. The validation statistics for QResearch were obtained from the original published papers which used a one-third sample of practices separate to those used to derive the score. A cohort from QResearch was used to compare incidence rates and baseline characteristics and consisted of 6.8 million patients from 753 practices registered between 1 Jan 1998 and until 31 July 2013.
Incident events relating to seven different risk prediction scores: QRISK2 (cardiovascular disease); QStroke (ischaemic stroke); QDiabetes (type 2 diabetes); QFracture (osteoporotic fracture and hip fracture); QKidney (moderate and severe kidney failure); QThrombosis (venous thromboembolism); QBleed (intracranial bleed and upper gastrointestinal haemorrhage). Measures of discrimination and calibration were calculated.
Overall, the baseline characteristics of the CPRD and QResearch cohorts were similar though QResearch had higher recording levels for ethnicity and family history. The validation statistics for each of the risk prediction scores were very similar in the CPRD cohort compared with the published results from QResearch validation cohorts. For example, in women, the QDiabetes algorithm explained 50% of the variation within CPRD compared with 51% on QResearch and the receiver operator curve value was 0.85 on both databases. The scores were well calibrated in CPRD.
Each of the algorithms performed practically as well in the external independent CPRD validation cohorts as they had in the original published QResearch validation cohorts.
在来自参与临床研究数据链接(CPRD)的全科医疗的独立样本中,验证使用QResearch数据库开发的一组风险预测算法的性能。
前瞻性开放队列研究,使用参与CPRD数据库的医疗机构和参与QResearch数据库的医疗机构。
CPRD验证队列由330万年龄在25 - 99岁之间的患者组成,这些患者于1998年1月1日至2012年7月31日在357家全科医疗诊所登记。QResearch的验证统计数据来自原始发表的论文,这些论文使用了与用于推导分数的诊所不同的三分之一样本。来自QResearch的一个队列用于比较发病率和基线特征,该队列由753家诊所的680万患者组成,这些患者于1998年1月1日至2013年7月31日登记。
与七种不同风险预测分数相关的发病事件:QRISK2(心血管疾病);QStroke(缺血性中风);QDiabetes(2型糖尿病);QFracture(骨质疏松性骨折和髋部骨折);QKidney(中度和重度肾衰竭);QThrombosis(静脉血栓栓塞);QBleed(颅内出血和上消化道出血)。计算了区分度和校准度的指标。
总体而言,CPRD队列和QResearch队列的基线特征相似,尽管QResearch在种族和家族史的记录水平上更高。与QResearch验证队列的已发表结果相比,CPRD队列中每个风险预测分数的验证统计数据非常相似。例如,在女性中,QDiabetes算法在CPRD中解释了50%的变异,而在QResearch中为51%,两个数据库的受试者操作曲线值均为0.85。这些分数在CPRD中校准良好。
每种算法在外部独立CPRD验证队列中的表现与在原始发表的QResearch验证队列中的表现几乎一样好。