Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford.
School of Medicine, Keele University, Keele; Institute of Applied Health Research, University of Birmingham, Birmingham.
Br J Gen Pract. 2023 Jul 27;73(733):e605-e614. doi: 10.3399/BJGP.2022.0389. Print 2023 Aug.
Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks.
To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment.
Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England.
People aged ≥40 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD ( = 1 772 618), using a Fine-Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum ( = 3 805 322).
The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk.
This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case.
降压药可降低心血管疾病风险，但也与包括急性肾损伤（AKI）在内的危害有关。目前几乎没有数据可以指导降压治疗相关风险的临床决策。
开发一种预测模型，估算有降压治疗指征的人群发生 AKI 的风险。
利用英格兰临床实践研究数据链（CPRD）中的常规初级保健数据进行观察性队列研究。
纳入年龄≥40 岁、血压在 130mmHg 至 179mmHg 之间至少测量过一次的患者。结局为 1 年、5 年和 10 年内因 AKI 住院或死亡。采用 CPRD GOLD 数据（n=1772618）进行模型推导，采用 Fine-Gray 竞争风险方法，并使用伪值进行后续重新校准。使用 CPRD Aurum 数据（n=3805322）进行外部验证。
参与者的平均年龄为 59.4 岁，52%为女性。最终模型包含 27 个预测因素，在 1 年、5 年和 10 年时均具有良好的区分度（10 年风险的 C 统计量为 0.821，95%置信区间[CI]为 0.818 至 0.823）。在最高预测概率时存在一定程度的过度预测（10 年风险的观测与预期事件概率比为 0.633，95%CI 为 0.621 至 0.645），影响风险最高的患者。大多数患者（>95%）1 年至 5 年 AKI 风险较低，10 年后仅有 0.1%的人群 AKI 风险高且 CVD 风险低。
该临床预测模型可帮助全科医生准确识别 AKI 风险高的患者，从而辅助治疗决策。由于绝大多数患者的风险较低，此类模型可能提供有用的保证，即大多数降压治疗是安全且适当的，同时也可识别出不适合降压治疗的少数患者。
