Department of Nephrology and Hypertension, Cleveland Clinic, OH 44195, USA.
Am J Kidney Dis. 2012 Mar;59(3):382-9. doi: 10.1053/j.ajkd.2011.10.046. Epub 2011 Dec 28.
Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables.
Prospective observational cohort.
SETTINGS & PARTICIPANTS: 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008.
Presurgical and combined pre- and intrasurgical variables were used to develop predictive models.
Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery.
Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P < 0.001).
External validation of these predictive models in other cohorts is required before wide-scale application.
We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical management, risk adjustment, and enrichment of clinical trials with high-risk participants.
准确预测心脏手术相关急性肾损伤（AKI）将改善临床决策，并有助于及时诊断和治疗。本研究的目的是使用术前和术前及术中联合变量为心脏手术相关 AKI 建立预测模型。
前瞻性观察队列。
2000-2008 年在克利夫兰诊所接受心脏手术的 25898 名患者。
使用术前和术前及术中联合变量来开发预测模型。
透析治疗和术后 2 周内血清肌酐水平加倍或透析治疗（或更早出院）的复合终点的发生率分别为 1.7%和 4.3%。肾功能参数是所有 4 个模型中独立的强预测因素。术前变量模型中，反映手术复杂性的类型和既往心脏手术史是强有力的预测因素。然而，术中变量的纳入解释了与手术相关的信息的所有差异。预测透析治疗的模型显示出良好的校准和极好的区分度；综合（术前和术中）模型的表现优于单独术前模型（C 统计量分别为 0.910 和 0.875）。预测复合终点的模型也具有极好的区分度，同时使用术前和综合（术前和术中）变量（C 统计量分别为 0.797 和 0.825）。然而，预测复合终点的术前模型校准效果不佳（P < 0.001）。
在广泛应用之前，需要在其他队列中对这些预测模型进行外部验证。
我们开发并内部验证了 4 个新的模型，可准确预测心脏手术相关 AKI。这些模型基于现成的临床信息，可用于患者咨询、临床管理、风险调整和高危患者临床试验的富集。
