Department of Renal Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Health Services and Outcomes Research, National Healthcare Group, Singapore, Singapore.
Nephron. 2024;148(10):678-686. doi: 10.1159/000538822. Epub 2024 Apr 18.
Accurate identification of individuals at risk of developing chronic kidney disease (CKD) may improve clinical care. Nelson et al. developed prediction equations to estimate the risk of incident eGFR of less than 60 mL/min/1.73 m2 in diabetic and non-diabetes patients using data from 34 multinational cohorts. We aim to validate the non-diabetes equation in our local multi-ethnic cohort and develop further prediction models.
Demographics, clinical and laboratory data of hypertensive non-diabetes patients with baseline eGFR ≥60 mL/min/1.73 m2 on follow-up with primary care clinics between 2010 and 2015 were collected. Follow-up was 5 years from entry to study. We validated Nelson's equation and developed our own model which we subsequently validated. The developmental cohort included patients between 2010 and 2014 while the validation cohort included patients in 2015. Variables included age, sex, eGFR, history of cardiovascular disease, ever smoker, body mass index, albuminuria, cholesterol, and treatment. Primary outcome was incident eGFR <60/min/1.73 m2 within 5 years. Model performance was evaluated by C-statistics and calibration was assessed.
In the developmental cohort of 27,800 patients, 2823 (10.2%) developed the outcome during a mean follow-up of 4.4 years while 638 (12.8%) patients developed the outcome in the validation cohort of 4,994 patients. Applicability of Nelson's equation was limited by missing albuminuria, absence of black race, and exclusion of non-hypertensive patients in our cohort. Nonetheless, the modified Nelson's model demonstrated C-statistic of 0.85 (95% CI: 0.84-0.86). The C-statistic of our bespoke model was 0.85 (0.85-0.86) and 0.87 (0.85-0.88) for the developmental cohort and validation cohort, respectively. Calibration was suboptimal as the predicted risk exceeded the observed risk.
The modified Nelson's equation and our locally derived novel model demonstrated high discrimination. Both models may potentially be used in predicting risk of CKD in hypertensive patients who are managed in primary care, allowing for early interventions in high-risk population.
准确识别患有慢性肾脏病（CKD）风险的个体可能会改善临床护理。Nelson 等人使用来自 34 个多国队列的数据，开发了预测方程来估计糖尿病和非糖尿病患者的 eGFR 低于 60 mL/min/1.73 m2 的事件发生率。我们旨在验证我们当地多民族队列中的非糖尿病方程，并开发进一步的预测模型。
收集了 2010 年至 2015 年期间在基层医疗机构进行随访的基线 eGFR≥60 mL/min/1.73 m2 的高血压非糖尿病患者的人口统计学、临床和实验室数据。随访时间为从入组到研究的 5 年。我们验证了 Nelson 的方程并开发了我们自己的模型，然后对其进行了验证。发展队列包括 2010 年至 2014 年的患者，验证队列包括 2015 年的患者。变量包括年龄、性别、eGFR、心血管疾病史、既往吸烟者、体重指数、白蛋白尿、胆固醇和治疗。主要结局是 5 年内 eGFR<60/min/1.73 m2 的事件。通过 C 统计量评估模型性能，并评估校准情况。
在 27800 例患者的发展队列中，2823 例（10.2%）在平均 4.4 年的随访中发生了该结局，而在 4994 例患者的验证队列中，有 638 例（12.8%）发生了该结局。Nelson 方程的适用性受到我们队列中白蛋白尿缺失、缺乏黑人种族和排除非高血压患者的限制。尽管如此，改良的 Nelson 模型的 C 统计量为 0.85（95%CI：0.84-0.86）。我们的定制模型的 C 统计量分别为 0.85（0.85-0.86）和 0.87（0.85-0.88），用于发展队列和验证队列。校准效果不理想，因为预测风险超过了观察风险。
改良的 Nelson 方程和我们本地推导的新模型具有较高的区分度。这两个模型都可以用于预测在基层医疗中接受管理的高血压患者的 CKD 风险，以便对高危人群进行早期干预。
