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Validation and update of the minimal risk tool in patients suspected of chronic coronary syndrome.

Laust Dupont RasmussenLouise NissenJelmer WestraLars Lyhne KnudsenLene Helleskov MadsenNiels Ramsing HolmEvald Høj ChristiansenHans Erik BøtkerMorten BøttcherSimon Winther
Published in: The international journal of cardiovascular imaging (2020)
Risk stratification in patients with suspected coronary artery disease (CAD) is important. Recently, the minimal-risk-tool (MRT) was developed to identify individuals with low CAD risk despite symptoms in order to avoid unnecessary testing. We aimed to validate and update the MRT-model in a contemporary cohort. The Dan-NICAD trial cohort, consisting of 1675 consecutive patients referred for coronary computed tomography angiography (CTA), was used to calculate the MRT-score based on the published fitted variable coefficients from the PROMISE and SCOT-HEART trials. Minimal risk was defined as zero calcium score, no coronary atherosclerosis at coronary CTA, and no cardiovascular events in the follow-up period. We tested an updated MRT-model by pooling the fitted variable coefficients from all three trials. A total of 1544 patients fulfilling the inclusion criteria were followed for 3.1 [2.7-3.4] years. In 710 (46%) patients, the criteria for minimal risk were fulfilled. Despite substantial coefficient variation, the MRTs based on the PROMISE, the SCOT-HEART and the updated MRT variables showed similar moderate to high discriminative performance for minimal risk estimation. Although all three models tended to underestimate minimal risk, the updated MRT had the best performance. Using a 75% minimal risk cut-off, the updated MRT showed a sensitivity of 11.6% (95% CI 9.3-14.2%) and specificity of 99.3% (95% CI 98.6-99.8%). An updated MRT model based on three large studies increased calibration compared to the existing MRT models, whereas discrimination was similar despite substantial coefficient variation. The updated MRT might supplement currently recommended pre-test probability models.
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