Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.
Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.
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DOI：
10.1161/jaha.115.002136
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发表时间：
2015-12-11
影响因子：
5.4
通讯作者：
Matheny ME
中科院分区：
文献类型：
--
作者：
Brown JR;MacKenzie TA;Maddox TM;Fly J;Tsai TT;Plomondon ME;Nielson CD;Siew ED;Resnic FS;Baker CR;Rumsfeld JS;Matheny ME
急性肾损伤（阿基）常发生在心导管插入术和经皮冠状动脉介入治疗后。虽然经皮冠状动脉介入治疗存在临床风险模型，但两种手术均不存在模型，现有模型也未考虑指数入院前的风险因素。我们的目标是开发这样一个模型，用于退伍军人健康管理局的前瞻性自动监测计划。我们收集了2009年1月1日至2013年9月30日在退伍军人健康管理局接受心导管插入术或经皮冠状动脉介入治疗的所有患者的数据，排除了慢性透析、终末期肾病、肾移植和术前和术后肌酐测量缺失的患者。我们在模型开发中使用了4种阿基定义，并纳入了术前1年和就诊时的风险因素。我们使用最小绝对收缩和选择算子（LASSO）开发了术后阿基的预测模型，并使用自举进行了内部验证。我们使用115 633例血管造影术开发模型，并使用来自新英格兰队列的27 905例手术进行外部验证。阿基模型的交叉验证C统计量为0.74（95% CI：0.74-0.75），AKIN 2为0.83（95% CI：0.82-0.84），造影剂肾病为0.74（95% CI：0.74-0.75），透析为0.89（95% CI：0.87-0.90）。我们开发了一个强大的、外部验证的心导管插入术或经皮冠状动脉介入治疗后阿基临床预测模型，用于在退伍军人健康管理局的手术前和手术后立即自动识别高风险患者。目前正在努力将这些模型纳入常规临床实践。
Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end‐stage renal disease, renal transplant, and missing pre‐ and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross‐validated C‐statistics of 0.74 (95% CI: 0.74–0.75) for AKI, 0.83 (95% CI: 0.82–0.84) for AKIN2, 0.74 (95% CI: 0.74–0.75) for contrast‐induced nephropathy, and 0.89 (95% CI: 0.87–0.90) for dialysis. We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high‐risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice.