Diagnostic Accuracy of Renal Angina Index in Predicting Acute Kidney Injury in Pediatric Patients with Sepsis: A Philippine Tertiary Hospital Experience
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引用次数: 2
Abstract
Background: The coexistence of acute kidney injury (AKI) in sepsis contributes significantly to morbidity and mortality rates. Traditional diagnostic markers still pose variable limitations in early AKI prediction. The use of renal angina index (RAI) as a clinical predictive tool for AKI is an emerging concept. Objectives: To determine the diagnostic accuracy of RAI in predicting AKI in patients with sepsis Methodology: This is a five-year retrospective cohort study conducted at the Philippine General Hospital (PGH). Records of eligible patients with sepsis were reviewed. RAI was calculated based on the composite of risk factors and clinical evidence of injury on day 0 of admission stratifying subjects into two groups: RAI (-) and RAI (+) for those with scores ≥ 8. Prediction of AKI with the RAI was analyzed. Results: A total of 222 patients were enrolled. The RAI (+) group (score ≥ 8) consisted 95 patients (43%). AKI incidence rate was 40.5 % (90/222) and 87/90 patients (91.6%) were classified in the RAI (+) group. The use of RAI in predicting AKI has a sensitivity of 96.7%, specificity of 94.0%, positive predictive value (PPV) of 91.6%, negative predictive value (NPV) of 97.7%, positive likelihood ratio (LR) of 15.95, negative LR of 0.04 and area under the curve-receiver operating characteristic (AUC-ROC) of 0.953 (95% CI 0.92-0.98). Conclusions: RAI is a good screening tool in predicting sepsis-associated AKI among pediatric patients. It provides early recognition of AKI and is a practical method which can be used at bedside.
背景:脓毒症并发急性肾损伤(AKI)对脓毒症的发病率和死亡率有重要影响。传统的诊断标记在早期AKI预测中仍然存在不同的局限性。肾性心绞痛指数(RAI)作为AKI的临床预测工具是一个新兴的概念。目的:确定RAI在预测败血症患者AKI中的诊断准确性方法:这是一项在菲律宾总医院(PGH)进行的为期五年的回顾性队列研究。回顾了符合条件的脓毒症患者的记录。根据入院第0天的危险因素和损伤临床证据的综合计算RAI,将评分≥8分的受试者分为两组:RAI(-)和RAI(+)。分析了RAI对AKI的预测。结果:共纳入222例患者。RAI(+)组(评分≥8)95例(43%)。AKI发生率为40.5% (90/222),RAI(+)组87/90(91.6%)。RAI预测AKI的敏感性为96.7%,特异性为94.0%,阳性预测值(PPV)为91.6%,阴性预测值(NPV)为97.7%,阳性似然比(LR)为15.95,阴性LR为0.04,曲线下面积-受试者工作特征(AUC-ROC)为0.953 (95% CI 0.92-0.98)。结论:RAI是预测儿童败血症相关AKI的良好筛查工具。它提供了AKI的早期识别,是一种实用的方法,可以在床边使用。