Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.
Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.
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DOI：
10.1136/bmjopen-2017-016591
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发表时间：
2017-09-27
期刊：
影响因子：
2.9
通讯作者：
Forni LG
中科院分区：
文献类型：
--
作者：
Hodgson LE;Sarnowski A;Roderick PJ;Dimitrov BD;Venn RM;Forni LG
严格评价一般人群中医院获得性急性肾损伤（HA-AKI）的预测模型。系统评价。Medline、Embase和Web of Science，直到2016年11月。描述在非专科成人医院人群中预测HA-AKI的多变量模型开发的研究。数据提取报告和评估遵循已发布的指南。共筛选参考文献14046篇。在53个HA-AKI预测模型中，11个符合入选标准（普通内科和/或外科人群，474478例患者发作），5个经过外部验证。最常见的预测因子是年龄（n=9个模型）、糖尿病（5）、入院血清肌酐（SCr）（5）、慢性肾脏疾病（CKD）（4）、药物（利尿剂（4）和/或ACE抑制剂/血管紧张素受体阻滞剂（3））、碳酸氢盐和心力衰竭（各4个模型）。确定了用于结局定义的异质结。报告中的缺陷包括对预测因子、缺失数据和样本量的处理。入院SCr通常用于代表基线肾功能。大多数模型被认为具有高偏倚风险。预测HA-AKI的受试者工作特征曲线下面积在推导中范围为0.71-0.80（8/11项研究报告），内部验证研究（n=7）为0.66-0.80，5项外部验证为0.65-0.71。对于校准，在4/11次推导、3/11次内部和3/5次外部验证中提供了Hosmer-Lemeshow检验或校准图。少数型号允许轻松的床边计算和潜在的电子自动化。阿基预测模型可能有助于解决风险评估中的缺陷;然而，在一般医院人群中，很少有外部验证。类似的预测因素反映了老年人口与慢性合并症。报告缺陷反映了更广泛的预测研究，SCr（基线功能和用作预测器）的处理是一个问题。今后的研究应侧重于验证、探索电子联系和影响分析。后者可以将预测模型与阿基警报联合收割机相结合，以解决预防和早期识别演变中的阿基。
Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations. Systematic review. Medline, Embase and Web of Science until November 2016. Studies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal. 14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71–0.80 in derivation (reported in 8/11 studies), 0.66–0.80 for internal validation studies (n=7) and 0.65–0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found. AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.