脓毒症相关急性肾损伤的预测模型：一项系统评价与荟萃分析。
完整摘要 · 中文译文
目的：本研究旨在系统评估脓毒症相关性急性肾损伤（SA-AKI）风险模型的预测性能。此外，我们探讨了影响这些模型在临床环境中有效发挥作用的具体因素。 方法：我们系统检索了PubMed、Cochrane图书馆、Web of Science和Embase，以识别截至2026年3月30日发表的队列研究。这些研究聚焦于SA-AKI预测模型的开发与验证。为确保证据质量，采用预测模型研究偏倚风险评估工具（PROBAST）评估偏倚风险。数据合成采用随机效应模型，用以合并C统计量及其95%置信区间（CI）。最后，通过亚组分析探讨异质性的来源。 结果：本荟萃分析共纳入15项研究，涉及46,490例患者。合并C统计量为0.817（95% CI：0.781-0.847），表明对SA-AKI具有中等至良好的区分能力。然而，观察到显著的异质性（I² = 92.8%，τ² = 0.143）。亚组分析进一步阐明了对这种变异的驱动因素。我们发现，尽管亚洲地区开发的模型其合并C统计量高于北美地区（0.845 vs. 0.777），但该差异无统计学意义（交互作用P = 0.076）。同样，低偏倚风险的研究相较于高风险研究显示出更优的预测性能（0.847 vs. 0.762；交互作用P = 0.010）。值得注意的是，模型性能（在原文中此句似乎未完整，按原文逐句保留） 根据交互作用检验，验证类型（内部验证与外部验证）或发表语言并未显著影响其效能。然而，外部验证亚组显示出异常宽的95%置信区间，这凸显了由于纳入研究数量有限，该汇总估计存在高度不确定性。使用留一法进行的敏感性分析证明了汇总估计的稳健性，剔除后的结果保持在狭窄范围内（0.805-0.826）且稳定。最后，漏斗图的目视检查及Egger检验提示不存在潜在的发表偏倚（P = 0.1891）。 结论：当前用于SA-AKI的预测模型显示出中等至良好的预测性能；然而，纳入的研究之间存在高度异质性。未来的研究应优先进行外部验证，并着重降低偏倚风险。此外，我们建议研究者探索稳健的、针对特定人群的模型，以提高临床实用性和泛化能力。 系统评价注册：https://www.crd.york.ac.uk/prospero/，标识号CRD420261347810。
完整摘要 · 英文原文
OBJECTIVES: This study aims to systematically evaluate the predictive performance of risk models for sepsis-associated acute kidney injury (SA-AKI). Furthermore, we explore the specific factors that influence how effectively these models perform in clinical settings. METHODS: We systematically searched PubMed, The Cochrane Library, Web of Science, and Embase to identify cohort studies published up to 30 March 2026. These studies focused on the development and validation of SA-AKI prediction models. To ensure the quality of the evidence, the risk of bias was assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Data synthesis involved a random-effects model, which we used to pool C-statistics and their 95% confidence intervals (CIs). Finally, sources of heterogeneity were explored through subgroup analysis. RESULTS: A total of 15 studies involving 46,490 patients were included in this meta-analysis. The pooled C-statistic was 0.817 (95% CI: 0.781-0.847), indicating a moderate-to-good discriminative ability for SA-AKI. However, substantial heterogeneity was observed (I 2 = 92.8%, τ 2 = 0.143). Subgroup analyses further elucidated the drivers of this variation. We found that while models developed in Asian regions showed a higher pooled C-statistic than those from North America (0.845 vs. 0.777), this difference was not statistically significant (P for interaction = 0.076). Similarly, studies with a low risk of bias yielded superior predictive performance compared to those at high risk (0.847 vs. 0.762; P for interaction = 0.010). Notably, model performance was not significantly influenced by the type of validation (internal vs. external) or the language of publication based on the interaction test. However, the external validation subgroup displayed an exceptionally wide 95% CI, underscoring a high degree of uncertainty in this pooled estimate due to the limited number of contributing studies. Sensitivity analysis using the leave-one-out method demonstrated the robustness of the pooled estimate, as the omitted results remained stable within a narrow range (0.805-0.826). Finally, visual inspection of the funnel plot and Egger's test suggested no potential publication bias (P = 0.1891). CONCLUSION: Current prediction models for SA-AKI demonstrate moderate-to-good predictive performance; however, high heterogeneity was observed across the included studies. Future research should prioritize external validation and emphasize the reduction of bias risk. Furthermore, we recommend that investigators explore robust, population-specific models to enhance clinical utility and generalizability. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier CRD420261347810.