Wilson Michelle, Packington Rebecca, Sewell Helen, Bartle Rebecca, McCole Eibhlin, Kurth Mary Jo, Richardson Ciaran, Shaw Sue, Akani Aleli, Banks Rosamonde E, Selby Nicholas M
Clinical and Biomedical Proteomics Group, Leeds Institute of Medical Research, University of Leeds, Leeds.
Department of Renal Medicine, Royal Derby Hospital, Derby.
Am J Kidney Dis. 2022 May;79(5):646-656.e1. doi: 10.1053/j.ajkd.2021.08.017. Epub 2021 Oct 12.
RATIONALE & OBJECTIVE: The effects of acute kidney injury (AKI) on long-term kidney function, cardiovascular disease, and mortality are well documented. We aimed to identify biomarkers for the estimation of risk of new or worsening chronic kidney disease (CKD) following AKI.
Prospective cohort study.
SETTING & PARTICIPANTS: Adults from a single clinical center who experienced AKI between May 2013 and May 2016 and survived until 3 years after the hospitalization during which AKI occurred. Participants included those with and without preexisting CKD.
Panel of 11 plasma biomarkers measured 3 months after hospitalization.
Kidney disease progression, defined as a≥25% decrease in estimated glomerular filtration rate (eGFR) combined with worsening CKD stage, assessed 3 years after the occurrence of AKI.
Associations between biomarkers and kidney disease progression were evaluated in multivariable logistic regression models. Importance of predictor variables was assessed by constructing multiple decision trees, with penalized least absolute shrinkage and selection operator logistic regression for variable selection used to produce multivariable models.
A total of 500 patients were studied. Soluble tumor necrosis factor receptor (sTNFR) 1, sTNFR2, cystatin C, neutrophil gelatinase-associated lipocalin, 3-month eGFR, and urinary albumin-creatinine ratio were independently associated with kidney disease progression and were more important than AKI severity or duration. A multivariable model containing sTNFR1, sTNFR2, cystatin C, and eGFR discriminated between those with and without kidney disease progression (area under the curve, 0.79 [95% CI, 0.70-0.83]). Optimizing the cutoff point to maximize utility as a "rule-out" test to identify those at low risk increased the sensitivity of the model to 95% and its negative predictive value to 92%.
Lack of external validation cohort. Analyses limited to patients who survived for 3 years after AKI. Mixed population of patients with and without baseline CKD.
A panel of plasma biomarkers measured 3 months after discharge from a hospitalization complicated by AKI provides a potential opportunity to identify patients who are at very low risk of incident or worsening CKD. Further study is required to determine its clinical utility through independent prospective validation.
急性肾损伤(AKI)对长期肾功能、心血管疾病及死亡率的影响已有充分记录。我们旨在确定用于评估急性肾损伤后新发或慢性肾脏病(CKD)恶化风险的生物标志物。
前瞻性队列研究。
来自单一临床中心的成年人,于2013年5月至2016年5月期间发生急性肾损伤且存活至急性肾损伤发生后住院3年。参与者包括既往有或无慢性肾脏病的患者。
住院3个月后检测的11种血浆生物标志物组合。
肾病进展,定义为估计肾小球滤过率(eGFR)下降≥25%并伴有慢性肾脏病分期恶化,于急性肾损伤发生3年后评估。
在多变量逻辑回归模型中评估生物标志物与肾病进展之间的关联。通过构建多个决策树评估预测变量的重要性,采用惩罚最小绝对收缩和选择算子逻辑回归进行变量选择以生成多变量模型。
共研究了500例患者。可溶性肿瘤坏死因子受体(sTNFR)1、sTNFR2、胱抑素C、中性粒细胞明胶酶相关脂质运载蛋白、3个月时的eGFR以及尿白蛋白肌酐比值与肾病进展独立相关,且比急性肾损伤的严重程度或持续时间更重要。包含sTNFR1、sTNFR2、胱抑素C和eGFR的多变量模型能够区分有或无肾病进展的患者(曲线下面积,0.79 [95% CI,0.70 - 0.83])。将截断点优化以最大化作为“排除”试验识别低风险患者的效用,可使模型的敏感性提高至95%,阴性预测值提高至92%。
缺乏外部验证队列。分析仅限于急性肾损伤后存活3年的患者。有和无基线慢性肾脏病的患者混合群体。
在因急性肾损伤而住院出院3个月后检测的一组血浆生物标志物,为识别新发或慢性肾脏病恶化风险极低的患者提供了潜在机会。需要进一步研究通过独立的前瞻性验证来确定其临床效用。