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Development of biomarker combinations for postoperative acute kidney injury via Bayesian model selection in a multicenter cohort study

机译：通过多中心队列研究通过贝叶斯模型选择开发用于术后急性肾损伤的生物标志物组合

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BackgroundAcute kidney injury (AKI) is a frequent complication of cardiac surgery. We sought prognostic combinations of postoperative biomarkers measured within 6?h of surgery, potentially in combination with cardiopulmonary bypass time (to account for the degree of insult to the kidney). We used data from a large cohort of patients and adapted methods for developing biomarker combinations to account for the multicenter design of the study. MethodsThe primary endpoint was sustained mild AKI, defined as an increase of 50% or more in serum creatinine over preoperative levels lasting at least 2 days during the hospital stay. Severe AKI (secondary endpoint) was defined as a serum creatinine increase of 100% or more or dialysis during hospitalization. Data were from a cohort of 1219 adults undergoing cardiac surgery at 6 medical centers; among these, 117 developed sustained mild AKI and 60 developed severe AKI. We considered cardiopulmonary bypass time and 22 biomarkers as candidate predictors. We adapted Bayesian model averaging methods to develop center-adjusted combinations for sustained mild AKI by (1) maximizing the posterior model probability and (2) retaining predictors with posterior variable probabilities above 0.5. We used resampling-based methods to avoid optimistic bias in evaluating the biomarker combinations. ResultsThe maximum posterior model probability combination included plasma N-terminal-pro-B-type natriuretic peptide, plasma heart-type fatty acid binding protein, and change in serum creatinine from before to 0–6?h after surgery; the median probability combination additionally included plasma interleukin-6. The center-adjusted, optimism-corrected AUCs for these combinations were 0.80 (95% CI: 0.78, 0.87) and 0.81 (0.78, 0.87), respectively, for predicting sustained mild AKI, and 0.81 (0.76, 0.90) and 0.83 (0.76, 0.90), respectively, for predicting severe AKI. For these data, the Bayesian model averaging methods yielded combinations with prognostic capacity comparable to that achieved by standard frequentist methods but with more parsimonious models. ConclusionsPending external validation, the identified combinations could be used to identify individuals at high risk of AKI immediately after cardiac surgery and could facilitate clinical trials of renoprotective agents.
机译：背景急性肾损伤（AKI）是心脏手术的常见并发症。我们寻求在手术后6小时内测量的术后生物标志物的预后组合，可能与心肺旁路手术时间结合使用（以考虑对肾脏的侮辱程度）。我们使用了来自大量患者的数据并采用了适合的方法来开发生物标志物组合，以说明该研究的多中心设计。方法主要终点为持续轻度AKI，定义为住院期间至少持续2天，血清肌酐比术前水平增加50％或更多。严重的AKI（次要终点）定义为住院期间血清肌酐增加100％或以上或进行透析。数据来自在6个医疗中心接受心脏外科手术的1219名成年人的队列;其中，117例持续性轻度AKI，60例严重度AKI。我们将体外循环时间和22种生物标志物视为候选预测指标。我们采用贝叶斯模型平均方法，通过（1）最大化后验模型概率和（2）保留后验概率大于0.5的预测变量，开发出针对持续性轻度AKI的中心调整组合。我们使用基于重采样的方法来避免在评估生物标志物组合时出现乐观偏见。结果最大的后验模型概率组合包括血浆N端前B型利尿钠肽，血浆心脏型脂肪酸结合蛋白以及血清肌酐从术前至术后6-6h的变化。中位概率组合还包括血浆白介素-6。这些组合经中心调整，乐观校正后的AUC分别为0.80（95％CI：0.78，0.87）和0.81（0.78，0.87），以预测持续的轻度AKI，分别为0.81（0.76，0.90）和0.83（0.76） ，分别为0.90），以预测严重的AKI。对于这些数据，贝叶斯模型平均方法所产生的组合的预后能力与标准的常客方法可比，但具有更多的简约模型。结论在进行外部验证之前，所鉴定的组合可用于鉴定心脏手术后具有高AKI风险的个体，并可促进肾保护剂的临床试验。

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