Predicting the risk of Chronic Kidney Disease in Men and Women in England and Wales: prospective derivation and external validation of the QKidney® Scores
Predicting the risk of Chronic Kidney Disease in Men and Women in England and Wales: prospective derivation and external validation of the QKidney® Scores
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DOI：
10.1186/1471-2296-11-49
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发表时间：
2010-06-21
影响因子：
2.9
通讯作者：
Coupland, Carol
中科院分区：
文献类型：
--
作者：
Hippisley-Cox, Julia;Coupland, Carol
背景：慢性肾脏病是发病的主要原因，目前存在可以降低风险的干预措施。我们试图开发和验证两种新的风险算法（QKidney（R）评分），用于估计（a）中重度CKD的个体5年风险和（B）初级保健人群中发展终末期肾衰竭的个体5年风险。方法：我们进行了一项前瞻性开放队列研究，使用来自368个QResearch R全科实践的数据来开发评分。我们使用两组独立的实践来验证分数-188个独立的QResearch实践和364个贡献于THIN数据库的实践。我们研究了QResearch r衍生队列中年龄在35-74岁之间的775，091名女性和799，658名男性，他们分别贡献了4，068，643和4，121，926人-年的观察结果。我们有两个主要结果：（a）中重度CKD（定义为基于以下任何一项中最早的CKD证据：肾移植;肾透析;肾病诊断;持续性蛋白尿;或肾小球滤过率< 45 mL/min）和（B）终末期肾衰竭。我们得出了男性和女性的独立风险方程。我们计算的校准和歧视措施，使用两个单独的验证cohols.Results：我们的最终模型中重度CKD包括：年龄，种族，剥夺，吸烟，体重指数，收缩压，糖尿病，类风湿性关节炎，心血管疾病，治疗高血压，充血性心力衰竭，外周血管疾病，非甾体抗炎药的使用和肾脏疾病家族史。此外，它还包括SLE和女性肾结石。终末期肾衰竭的最终模型相似，但不包括NSAID的使用。在两个验证队列中，每种风险预测算法在所有测量中均表现良好。对于THIN队列，预测中重度CKD的模型解释了女性总变异的56.38%和男性的57.49%。D统计值较高，女性为2.33，男性为2.38。ROC统计值为0.875为妇女和0.876为man.Conclusions：这些新的算法有可能确定高风险的患者谁可能受益于更详细的评估，更密切的监测或干预措施，以减少他们的风险。
Background: Chronic Kidney Disease is a major cause of morbidity and interventions now exist which can reduce risk. We sought to develop and validate two new risk algorithms (the QKidney (R) Scores) for estimating (a) the individual 5 year risk of moderate-severe CKD and (b) the individual 5 year risk of developing End Stage Kidney Failure in a primary care population.Methods: We conducted a prospective open cohort study using data from 368 QResearch r general practices to develop the scores. We validated the scores using two separate sets of practices -188 separate QResearch r practices and 364 practices contributing to the THIN database. We studied 775,091 women and 799,658 men aged 35-74 years in the QResearch r derivation cohort, who contributed 4,068,643 and 4,121,926 person-years of observation respectively. We had two main outcomes (a) moderate-severe CKD (defined as the first evidence of CKD based on the earliest of any of the following: kidney transplant; kidney dialysis; diagnosis of nephropathy; persistent proteinuria; or glomerular filtration rate of < 45 mL/min) and (b) End Stage Kidney Failure. We derived separate risk equations for men and women. We calculated measures of calibration and discrimination using the two separate validation cohorts.Results: Our final model for moderate-severe CKD included: age, ethnicity, deprivation, smoking, BMI, systolic blood pressure, diabetes, rheumatoid arthritis, cardiovascular disease, treated hypertension, congestive cardiac failure; peripheral vascular disease, NSAID use and family history of kidney disease. In addition, it included SLE and kidney stones in women. The final model for End Stage Kidney Failure was similar except it did not include NSAID use. Each risk prediction algorithms performed well across all measures in both validation cohorts. For the THIN cohort, the model to predict moderate-severe CKD explained 56.38% of the total variation in women and 57.49% for men. The D statistic values were high with values of 2.33 for women and 2.38 for men. The ROC statistic was 0.875 for women and 0.876 for men.Conclusions: These new algorithms have the potential to identify high risk patients who might benefit from more detailed assessment, closer monitoring or interventions to reduce their risk.