Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Intensive Care Unit, The First Affifiliated Hospital, Fujian Medical University, Fuzhou, China.
J Intensive Care Med. 2024 May;39(5):465-476. doi: 10.1177/08850666231214243. Epub 2023 Nov 15.
Sepsis-associated acute kidney injury (SA-AKI) is a critical condition with significant clinical implications, yet there is a need for a predictive model that can reliably assess the risk of its development. This study is undertaken to bridge a gap in healthcare by creating a predictive model for SA-AKI with the goal of empowering healthcare providers with a tool that can revolutionize patient care and ultimately lead to improved outcomes.
A cohort of 615 patients afflicted with sepsis, who were admitted to the intensive care unit, underwent random stratification into 2 groups: a training set (n = 435) and a validation set (n = 180). Subsequently, a multivariate logistic regression model, imbued with nonzero coefficients via LASSO regression, was meticulously devised for the prognostication of SA-AKI. This model was thoughtfully rendered in the form of a nomogram. The salience of individual risk factors was assessed and ranked employing Shapley Additive Interpretation (SHAP). Recursive partition analysis was performed to stratify the risk of patients with sepsis.
Among the panoply of clinical variables examined, hypertension, diabetes mellitus, C-reactive protein, procalcitonin (PCT), activated partial thromboplastin time, and platelet count emerged as robust and independent determinants of SA-AKI. The receiver operating characteristic curve analysis for SA-AKI risk discrimination in both the training set and validation set yielded an area under the curve estimates of 0.843 (95% CI: 0.805 to 0.882) and 0.834 (95% CI: 0.775 to 0.893), respectively. Notably, PCT exhibited the most conspicuous influence on the model's predictive capacity. Furthermore, statistically significant disparities were observed in the incidence of SA-AKI and the 28-day survival rate across high-risk, medium-risk, and low-risk cohorts ( < .05).
The composite predictive model, amalgamating the quintet of SA-AKI predictors, holds significant promise in facilitating the identification of high-risk patient subsets.
脓毒症相关性急性肾损伤（SA-AKI）是一种具有重要临床意义的危急情况，但需要一种能够可靠评估其发生风险的预测模型。本研究旨在通过创建一个用于 SA-AKI 的预测模型来弥合医疗保健中的空白，目标是为医疗保健提供者提供一种工具，从而彻底改变患者护理，并最终带来更好的结果。
对入住重症监护病房的 615 名脓毒症患者进行了一项队列研究，这些患者被随机分为 2 组：训练集（n=435）和验证集（n=180）。随后，通过 LASSO 回归赋予非零系数的多变量逻辑回归模型被精心设计用于预测 SA-AKI。该模型被精心制作成一个列线图。使用 Shapley Additive Interpretation（SHAP）评估和对单个风险因素的重要性进行排名。对脓毒症患者进行递归分区分析以分层风险。
在检查的所有临床变量中，高血压、糖尿病、C 反应蛋白、降钙素原（PCT）、活化部分凝血活酶时间和血小板计数是 SA-AKI 的稳健且独立的决定因素。在训练集和验证集中，SA-AKI 风险判别受试者工作特征曲线分析得到的曲线下面积估计值分别为 0.843（95%CI：0.805 至 0.882）和 0.834（95%CI：0.775 至 0.893）。值得注意的是，PCT 对模型预测能力的影响最为显著。此外，高危、中危和低危队列之间的 SA-AKI 发生率和 28 天生存率存在统计学显著差异（ < .05）。
包含 SA-AKI 预测因素五重奏的综合预测模型具有很大的潜力，可以帮助识别高危患者亚群。
