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Candidemia on presentation to the hospital: development and validation of a risk score

机译：到医院就诊的念珠菌血症：风险评分的制定和验证

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IntroductionCandidemia results in substantial morbidity and mortality, especially if initial antifungal therapy is delayed or is inappropriate; however, candidemia is difficult to diagnose because of its nonspecific presentation.MethodsTo develop a risk score for identifying hospitalized patients with candidemia, we performed a retrospective analysis of a large database of 176 acute-care hospitals in the United States. We studied 64,019 patients with bloodstream infection (BSI) on presentation from 2000 through 2005 (derivation cohort) and 24,685 from 2006 to 2007 (validation cohort). We used recursive partitioning (RPART) to identify the best discriminators for Candida as the cause of BSI. We compared three sets of models (equal-weight, unequal-weight, vs full model with additional variables from logistic regression model) for sensitivity analysis.ResultsThe RPART identified 6 variables as the best discriminators: age 0.10, indicating predicted and observed candidemia rates did not differ significant across the 7 risk stratus). The full model with 16 risk factors had slightly higher AUROCs (0.74 versus 0.73 for derivation versus validation); however, 7 variables were no longer significant in the recalibrated model for the validation cohort, indicating that the additional items did not materially enhance the model.ConclusionsA simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.
机译：前言念珠菌血症会导致大量的发病和死亡，尤其是如果最初的抗真菌治疗被推迟或不合适的话。方法为了建立识别住院念珠菌血症患者的风险评分，我们对美国176家急诊医院的大型数据库进行了回顾性分析。我们研究了从2000年至2005年（派生队列）和2006年至2007年（验证队列）就诊的64,019例血流感染（BSI）患者。我们使用递归分区（RPART）来确定假丝酵母是BSI的最佳原因。我们比较了三组模型（等权重，不等权重，完全模型与logistic回归模型中的其他变量）进行敏感性分析。结果RPART确定6个变量为最佳判别器：年龄为0.10，表明预测和观察到的念珠菌血症发生率在7个风险层次中差异不显着）。具有16个风险因素的完整模型的AUROC稍高（派生与验证的AUROC分别为0.74和0.73）;但是，在重新验证的模型中，有7个变量在验证队列中不再显着，表明其他项目并未对模型产生实质性的改善。结论一个简单的等重风险评分可根据医院就诊时的分级方式区分患者的念珠菌风险。

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