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缺血风险评分的开发与验证。

Development and validation of ischemia risk scores.

作者信息

Miller Robert J H, Rozanski Alan, Slomka Piotr J, Han Donghee, Gransar Heidi, Hayes Sean W, Friedman John D, Thomson Louise E J, Berman Daniel S

机构信息

Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, Canada.

出版信息

J Nucl Cardiol. 2023 Feb;30(1):324-334. doi: 10.1007/s12350-022-02976-9. Epub 2022 Apr 28.

Abstract

BACKGROUND

The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making.

METHODS

Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models.

RESULTS

During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score  < 0.100).

CONCLUSIONS

We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.

摘要

背景

心肌灌注成像中缺血的可能性是医生选择检查时决策的核心,但目前缺乏专门的风险评分。我们推导并验证了两种新的缺血风险评分,以支持医生的决策。

方法

使用15186例患者推导风险评分,并在来自不同中心的2995例患者中进行验证。采用逻辑回归评估与缺血的关联,以得出基于点数的缺血评分和计算得出的缺血评分。使用受试者工作特征曲线下面积(AUC)评估缺血的预测性能,并与CAD联盟的基础模型和临床模型进行比较。

结果

在推导过程中,计算得出的缺血风险评分(0.801)的AUC高于基于点数的评分(0.786,p<0.001)。在验证过程中,计算得出的缺血评分(0.716,95%CI 0.684-0.748)的AUC高于基于点数的缺血评分(0.699,95%CI 0.666-0.732,p=0.016)和临床CAD模型(AUC 0.667,95%CI 0.633-0.701,p=0.002)。在两个群体中,两种缺血评分的校准效果均良好(Brier评分<0.100)。

结论

我们开发了两种新的风险评分,用于预测心肌灌注成像中缺血的概率,在模型推导和外部测试中均显示出高准确性。这些评分可为医生关于诊断测试策略的决策提供支持。

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