Chaoyun Jiang, Cheng Yang, Hui Chen, Xiaofang Jiang, Jiahao Zhang, Juan Felipe Alvarez, Haichuan Yu, Yao Zhu, Lianjiu Su, Zhiyong Peng
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Binary logistic regression was employed to identify AKI's independent risk factors and develop a combined prediction model. The predictive value was assessed using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) analyses.</p><p><strong>Results: </strong>337 patients were included in the final analysis, with 109 (32.3%) developing AKI. AKI occurred in 39 (22.2%) stroke patients, 52 (50%) sepsis patients, and 18 (31.6%) post-cardiac surgery patients. [TIMP-2]• [IGFBP7] exhibited predictive value for AKI with an AUC of 0.86 (95% CI 0.75-0.90) in stroke, 0.82 (95% CI 0.74-0.91) in sepsis, and 0.90 (95% CI 0.82-0.98) in post-cardiac surgery. DeLong's test indicated no significant differences in the predictive value of [TIMP-2]• [IGFBP7] between the cardiac surgery group and the stroke (P=0.20) and sepsis (P=0.21) groups.</p><p><strong>Conclusion: </strong>The combined prediction model, which integrates urinary [TIMP-2]• [IGFBP7] concentrations and AKI risk factors, significantly enhances AKI prediction. 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引用次数: 0
摘要
背景:TIMP-2和IGFBP7已被证明是预测急性肾损伤(AKI)的有效生物标志物。然而,尿[TIMP-2]•[IGFBP7]对不同病因的AKI预测能力的差异仍未被探索。本研究旨在评估尿[TIMP-2]•[IGFBP7]在三个不同疾病队列(卒中、败血症和心脏手术)中对AKI的预测能力,这些疾病具有不同的AKI病因。方法:本前瞻性观察性研究评估了尿[TIMP-2]•[IGFBP7]在三个不同AKI病因队列中的预测价值。采用二元logistic回归识别AKI的独立危险因素,建立联合预测模型。采用受试者工作特征(ROC)曲线和曲线下面积(AUC)分析评估预测价值。结果:337例患者纳入最终分析,其中109例(32.3%)发展为AKI。卒中患者39例(22.2%),败血症患者52例(50%),心脏手术后患者18例(31.6%)发生AKI。[TIMP-2]•[IGFBP7]对卒中的AUC为0.86 (95% CI 0.75-0.90),败血症的AUC为0.82 (95% CI 0.74-0.91),心脏手术后AKI的AUC为0.90 (95% CI 0.82-0.98)具有预测价值。DeLong的检验显示,心脏手术组[TIMP-2]•[IGFBP7]的预测值与卒中组(P=0.20)和败血症组(P=0.21)之间无显著差异。结论:结合尿[TIMP-2]•[IGFBP7]浓度与AKI危险因素的联合预测模型可显著提高AKI的预测能力。尿[TIMP-2]•[IGFBP7]对卒中、败血症和心脏手术组AKI的预测价值无显著差异。
Predictive value of urinary [TIMP-2]•[IGFBP7] for AKI among sepsis, stroke, and cardiac surgery cohorts: A prospective study.
Background: TIMP-2 and IGFBP7 have shown effectiveness as biomarkers for predicting Acute Kidney Injury (AKI). However, the variations in the predictive capacity of urinary [TIMP-2]• [IGFBP7] for AKI across different etiologies remain unexplored. This study aimed to assess the predictive capability of urinary [TIMP-2]• [IGFBP7] for AKI in three distinct disease cohorts (stroke, sepsis, and cardiac surgery) characterized by differing AKI etiologies.
Methods: This prospective observational study evaluated the predictive value of urinary [TIMP-2]• [IGFBP7] among three cohorts with varying AKI causes. Binary logistic regression was employed to identify AKI's independent risk factors and develop a combined prediction model. The predictive value was assessed using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) analyses.
Results: 337 patients were included in the final analysis, with 109 (32.3%) developing AKI. AKI occurred in 39 (22.2%) stroke patients, 52 (50%) sepsis patients, and 18 (31.6%) post-cardiac surgery patients. [TIMP-2]• [IGFBP7] exhibited predictive value for AKI with an AUC of 0.86 (95% CI 0.75-0.90) in stroke, 0.82 (95% CI 0.74-0.91) in sepsis, and 0.90 (95% CI 0.82-0.98) in post-cardiac surgery. DeLong's test indicated no significant differences in the predictive value of [TIMP-2]• [IGFBP7] between the cardiac surgery group and the stroke (P=0.20) and sepsis (P=0.21) groups.
Conclusion: The combined prediction model, which integrates urinary [TIMP-2]• [IGFBP7] concentrations and AKI risk factors, significantly enhances AKI prediction. No significant differences were found in the predictive value of urinary [TIMP-2]• [IGFBP7] for AKI among the stroke, sepsis, and cardiac surgery cohorts.
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