Prediction of acute renal failure after cardiac surgery: retrospective cross-validation of a clinical algorithm
Prediction of acute renal failure after cardiac surgery: retrospective cross-validation of a clinical algorithm
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DOI：
10.1093/ndt/18.1.77
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发表时间：
2003-01-01
影响因子：
6.1
通讯作者：
Solberg, S
中科院分区：
文献类型：
--
作者：
Eriksen, BO;Hoff, KRS;Solberg, S
背景。心脏手术后急性肾功能衰竭（ARF）与高费用和不良预后相关。根据美国一项大型研究的结果，已经开发出一种从术前危险因素预测ARF的算法。本研究的目的是在欧洲患者群体中交叉验证该算法，并评估其作为临床工具的有效性。所有5年内接受过冠状动脉搭桥和瓣膜手术的患者均被纳入研究。回顾性地从医院数据库和医疗记录中获得所有发生透析依赖性ARF患者和随机抽样无ARF患者的术前危险因素数据。对于每位患者，根据该算法计算ARF的风险评分。评估评分预测ARF能力的敏感性、特异性、阳性预测值和阴性预测值以及受试者工作特征（ROC）曲线下面积。纳入2037例患者。ARF的风险为1.9%，ROC曲线下面积为0.71。风险评分在6分及以上时，敏感性为0.53，特异性为0.71，阳性预测值为0.03，阴性预测值为0.99。该算法的有效性在一个种群中得到了证实，该种群在几个方面与开发该算法的美国种群不同。虽然它对于估计患者群体中ARF的风险是有用的，但ARF的低风险限制了算法预测个体患者结果的能力。
Background. Acute renal failure (ARF) after cardiac surgery is associated with high costs and a poor prognosis. Based on the results of a large US study, an algorithm has been developed for predicting ARF from pre-operative risk factors. The aim of this study was to cross-validate this algorithm in a patient population from Europe, and to assess its usefulness as a clinical tool.Methods. All coronary bypass and valvular surgery patients from a 5-year period were included. Data on pre-operative risk factors for all patients who developed dialysis-dependent ARF and for a random sample of patients without ARF were retrospectively obtained from hospital databases and medical records. For each patient, a risk score for ARF was calculated on the basis of the algorithm. The sensitivity, specificity, positive and negative predictive values and area under receiver operating characteristic (ROC) curve of the score's ability to predict ARF were estimated.Results. 2037 patients were included. The risk of ARF was 1.9%, and the area under the ROC curve 0.71. For a risk score of 6 or higher, the sensitivity was 0.53, the specificity 0.71, the positive predictive value 0.03 and the negative predictive value 0.99.Conclusions. The validity of the algorithm was confirmed in a population differing in several aspects from the US populations where it was developed. Although it is useful for estimating the risk of ARF in groups of patients, the low risk of ARF limits the algorithm's ability to predict the outcome for individual patients.