心脏手术相关急性肾损伤的临床预测模型：一项系统评价与荟萃分析。
完整摘要 · 中文译文
目的：心脏手术相关急性肾损伤（CSA-AKI）是心脏手术后常见的并发症，与显著的发病率和死亡率相关。临床预测模型（CPM）可识别高危患者，从而能够实施针对性的预防干预并改善预后。本研究旨在识别现有的用于预测发生CSA-AKI风险的临床预测模型，并总结其性能。 方法：对发表在PubMed、Scopus、Cochrane图书馆和Web of Science上的研究进行了系统性检索，纳入在成人患者中开展并经体外循环进行心脏手术的CSA-AKI预测模型的开发或验证研究。由两名独立评审员提取数据。在可行时，使用随机效应模型计算诊断性能的汇总指标。同时进行了校准分析和敏感性分析。 结果：CSA-AKI的汇总发病率为26.3%（95% CI，26.0-26.4），而需要肾脏替代治疗的CSA-AKI（CSA-AKI RRT）发生率为2.2%（95% CI，2.1-2.2）。在经验证的预测模型中，Thakar 2005模型对CSA-AKI RRT显示出最佳的预测性能：汇总AUC为0.80（95% CI，0.77-0.82），灵敏度为0.87，在≥3分的阈值下特异度为0.49。校准分析表明存在风险低估。Mehta 2006和Wijeysundera 2007模型也显示出良好的区分度。对较轻微阶段CSA-AKI的预测区分度较差，但较新的模型如Demirjian 2022模型显示出有前景的性能。 结论：准确 经过充分验证的CPMs有助于促进CSA-AKI的风险分层，这将影响临床实践和研究。Thakar 2005研究显示，其在预测CSA-AKI需行RRT方面具有非常好的区分度，而针对预测较温和阶段CSA-AKI的更多验证研究仍有必要开展。
完整摘要 · 英文原文
PURPOSE: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a frequent postoperative complication associated with substantial morbidity and mortality. Clinical prediction models (CPM) may identify high-risk patients enabling targeted preventive interventions and improved outcomes. The objective of the study was to identify available CPMs for the risk of developing CSA-AKI and summarize their performance. METHODS: A systematic search of studies published in Pubmed, Scopus, the Chocrane Library and Web of Science was conducted, including studies that developed or validated CPMs for CSA-AKI in adult patients undergoing cardiac surgery with cardiopulmonary bypass. Data were extracted by two independent reviewers. Summary measures of diagnostic performance were calculated when feasible using random-effects models. Calibration and sensitivity analyses were also performed. RESULTS: The pooled incidence of CSA-AKI was 26.3% (95% CI, 26.0-26.4), whereas CSA-AKI requiring renal replacement therapy (CSA-AKI RRT) occurred in 2.2% of patients (95% CI, 2.1-2.2). Among validated CPMs, Thakar 2005 demonstrated the best predictive performance for CSA-AKI RRT: pooled AUC 0.80 (95% CI, 0.77-0.82), sensitivity of 0.87, and a specificity of 0.49 at a threshold of ≥ 3 points. Calibration analyses indicated underestimation of risk. The Mehta 2006 and Wijeysundera 2007 models also demonstrated good discrimination. Discrimination for the prediction of milder stages of CSA-AKI was poorer, but newer models such as the Demirjian 2022 model showed promising performance. CONCLUSION: Accurate and well-validated CPMs can facilitate risk stratification for CSA-AKI which can influence clinical practice and research. Thakar 2005 demonstrated very good discrimination for predicting CSA-AKI RRT, whereas more validation studies for the prediction of milder stages of CSA-AKI are warranted.