Validated contemporary risk model of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the National Cardiovascular Data Registry Cath-PCI Registry.
Validated contemporary risk model of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the National Cardiovascular Data Registry Cath-PCI Registry.
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DOI：
10.1161/jaha.114.001380
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发表时间：
2014-12
影响因子：
5.4
通讯作者：
Spertus JA
中科院分区：
文献类型：
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作者：
Tsai TT;Patel UD;Chang TI;Kennedy KF;Masoudi FA;Matheny ME;Kosiborod M;Amin AP;Weintraub WS;Curtis JP;Messenger JC;Rumsfeld JS;Spertus JA
我们开发了预测经皮冠状动脉介入治疗 (PCI) 后急性肾损伤 (AKI) 和需要透析的 AKI (AKI-D) 的风险模型，以支持质量评估和预防策略的使用。 AKI 定义为血清肌酐绝对增加 ≥0.3 mg/dL 或相对增加 50%（AKIN 1 期或更高），AKI-D 是 PCI 后透析的新要求。 6/09 至 7/11 期间参与 NCDR Cath/PCI 登记的 947 012 名连续 PCI 患者和 1253 个站点的数据用于开发该模型，其中 70% 随机分配到派生队列，30% 进行验证。 7.33% 的推导和验证队列中发生 AKI。 11 个变量与 AKI 相关：年龄较大、基线肾功能不全（分为轻度、中度和重度）、既往脑血管疾病、既往心力衰竭、既往 PCI、就诊情况（非 ACS 与 NSTEMI 与 STEMI）、糖尿病、慢性肺病、高血压、心脏骤停、贫血、就诊时心力衰竭、球囊泵使用和心源性休克。 STEMI 表现、心源性休克和严重基线 CKD 是 AKI 的最强预测因素。完整模型在推导和验证队列中表现出良好的区分度（c 统计量分别为 0.72 和 0.71）和相同的校准（校准线斜率 = 1.01）。 AKI-D 模型具有更好的辨别力（c 统计量=0.89）和良好的校准（校准线斜率=0.99）。 NCDR AKI 预测模型可以成功对接受 PCI 的患者进行风险分层。该工具在帮助临床医生就 PCI 风险向患者提供咨询、确定患者的预防策略以及支持当地质量改进工作方面的潜力应进行前瞻性测试。
We developed risk models for predicting acute kidney injury (AKI) and AKI requiring dialysis (AKI‐D) after percutaneous coronary intervention (PCI) to support quality assessment and the use of preventative strategies. AKI was defined as an absolute increase of ≥0.3 mg/dL or a relative increase of 50% in serum creatinine (AKIN Stage 1 or greater) and AKI‐D was a new requirement for dialysis following PCI. Data from 947 012 consecutive PCI patients and 1253 sites participating in the NCDR Cath/PCI registry between 6/09 and 7/11 were used to develop the model, with 70% randomly assigned to a derivation cohort and 30% for validation. AKI occurred in 7.33% of the derivation and validation cohorts. Eleven variables were associated with AKI: older age, baseline renal impairment (categorized as mild, moderate, and severe), prior cerebrovascular disease, prior heart failure, prior PCI, presentation (non‐ACS versus NSTEMI versus STEMI), diabetes, chronic lung disease, hypertension, cardiac arrest, anemia, heart failure on presentation, balloon pump use, and cardiogenic shock. STEMI presentation, cardiogenic shock, and severe baseline CKD were the strongest predictors for AKI. The full model showed good discrimination in the derivation and validation cohorts (c‐statistic of 0.72 and 0.71, respectively) and identical calibration (slope of calibration line=1.01). The AKI‐D model had even better discrimination (c‐statistic=0.89) and good calibration (slope of calibration line=0.99). The NCDR AKI prediction models can successfully risk‐stratify patients undergoing PCI. The potential for this tool to aid clinicians in counseling patients regarding the risk of PCI, identify patients for preventative strategies, and support local quality improvement efforts should be prospectively tested.