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Sökning: WFRF:(Sandoval Yader)

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  • Jaffe, Allan S., et al. (författare)
  • Single Troponin Measurement to Rule Out Myocardial Infarction: JACC Review Topic of the Week
  • 2023
  • Ingår i: Journal of the American College of Cardiology. - 0735-1097 .- 1558-3597. ; 82:1, s. 60-69
  • Forskningsöversikt (refereegranskat)abstract
    • The term “single-sample rule-out” refers to the ability of very low concentrations of high-sensitivity cardiac troponin (hs-cTn) on presentation to exclude acute myocardial infarction with high clinical sensitivity and negative predictive value. Observational and randomized studies have confirmed this ability. Some guidelines endorse use of a concentration of hs-cTn at the assay's limit of detection, while other studies have validated the use of higher concentrations, allowing this approach to identify a greater proportion of patients at low risk. In most studies, at least 30% of patients can be triaged with this approach. The concentration of hs-cTn varies according to the assay used and sometimes how regulations permit reporting. It is clear that patients need to be at least 2 hours from the onset of symptoms being evaluated. Caution is warranted, particularly with older patients, women, and patients with underlying cardiac comorbidities.
  • Neumann, Johannes Tobias, et al. (författare)
  • Personalized diagnosis in suspected myocardial infarction
  • 2023
  • Ingår i: Clinical Research in Cardiology. - : Springer. - 1861-0684 .- 1861-0692. ; 112, s. 1288-1301
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hscTn)- based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.Methods: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability ( ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.Results: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline- recommended strategy.Conclusion We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.
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