Bell Samira, James Matthew T, Farmer Chris K T, Tan Zhi, de Souza Nicosha, Witham Miles D
Renal Unit, Ninewells Hospital, Dundee, UK.
Division of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.
Clin Kidney J. 2020 Jul 16;13(3):402-412. doi: 10.1093/ckj/sfaa072. eCollection 2020 Jun.
Improving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data.
Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine-based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration.
The risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80-0.81] in the development cohort and 0.71 (95% CI 0.70-0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75-0.76) in the Canadian validation cohort.
We have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
提高对社区中急性肾损伤(AKI)风险增加患者的识别能力,可能有助于更早地发现并实施积极的预防措施,从而减轻AKI的影响。本研究的目的是开发并外部验证一种实用的风险评分,以使用常规收集的数据预测医院或社区环境中AKI的风险。
来自苏格兰泰赛德的常规收集的关联数据集用于开发风险评分,来自英国肯特和加拿大艾伯塔的数据集用于外部验证。AKI采用基于改善全球肾脏病预后组织血清肌酐的标准进行定义。以1年内发生AKI作为因变量进行多变量逻辑回归分析。通过评估辨别力(C统计量)和校准来确定模型性能。
风险评分在来自苏格兰泰赛德地区的273450名患者中开发,并在两个人群中进行外部验证:来自英国肯特的218091名个体和来自加拿大艾伯塔的1173607名个体。通过逻辑回归分析,四个变量是AKI的独立预测因素:年龄较大、基线估计肾小球滤过率较低、糖尿病和心力衰竭。包含这四个变量的风险评分具有良好的预测性能,在开发队列中的C统计量为0.80[95%置信区间(CI)0.80 - 0.81],在英国肯特外部验证队列中为0.71(95%CI 0.70 - 0.72),在加拿大验证队列中为0.76(95%CI 0.75 - 0.76)。
我们从常规收集的数据中设计并外部验证了一个简单的风险评分,可帮助初级和二级护理医生识别AKI高风险患者。