Pre-operative Carotid Plaque Echolucency Assessment has no Predictive Value for Long-Term Risk of Stroke or Cardiovascular Death in Patients Undergoing Carotid Endarterectomy

Abstract Background Percutaneous mitral valve repairs has been increasingly performed worldwide. The MITRA-UMG registry provides a snapshot of a real-world clinical data and outcomes. Purpose We sought to investigate predictors of clinical outcomes in patients with mitral regurgitation undergoing percutaneous valve repair. Methods The MITRA-UMG registry retrospectively collected data from consecutive patients with symptomatic moderate-to-severe or severe MR underwent MitraClip implantation. The primary endpoint of interest was the composite of cardiovascular death or rehospitalization for HF. Results Between March 2012 and July 2018, a total of 133 consecutive patients admitted to our institution were included. Acute procedural success was obtained in 95.4% of patients, with no intraprocedural death. The composite primary endpoint of cardiovascular death or rehospitalization for heart failure was met in 50 patients (38%) with cumulative incidences of 7%, 25%, at 30 days and 1 year, respectively. In the Cox multivariate model, NYHA functional class IV, left ventricular end-diastolic volume index (LVEDVi), Euroscore II, independently increased the risk of the primary endpoint at long-term follow-up. At Kaplan-Meier analysis, a LVEDVi >92 ml/m2 was associated with an increased incidence of the primary endpoint. Conclusions In searching the ideal phenotype of patients who benefit most of percutaneous mitral valve repair, those presenting with severely dilated ventricles (LVEDVi >92 ml/m2), high operative risk (EUROSCORE II >7%) or advanced heart failure symptoms (NYHA IV) at baseline carried the worst prognosis at long-term. Funding Acknowledgement Type of funding source: None

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Validation of a machine learned model to predict the diagnosis of myocardial infarction

European Heart Journal ◽

10.1093/ehjci/ehaa946.1669 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

D Doudesis ◽

J Yang ◽

A Tsanas ◽

C Stables ◽

A Shah ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Coronary Syndrome ◽

Predictive Value ◽

High Probability ◽

Cardiovascular Death ◽

Gradient Boosting ◽

Funding Source ◽

Coronary Syndrome ◽

Low Probability ◽

One Year

Abstract Introduction The myocardial-ischemic-injury-index (MI3) is a promising machine learned algorithm that predicts the likelihood of myocardial infarction in patients with suspected acute coronary syndrome. Whether this algorithm performs well in unselected patients or predicts recurrent events is unknown. Methods In an observational analysis from a multi-centre randomised trial, we included all patients with suspected acute coronary syndrome and serial high-sensitivity cardiac troponin I measurements without ST-segment elevation myocardial infarction. Using gradient boosting, MI3 incorporates age, sex, and two troponin measurements to compute a value (0–100) reflecting an individual's likelihood of myocardial infarction, and estimates the negative predictive value (NPV) and positive predictive value (PPV). Model performance for an index diagnosis of myocardial infarction, and for subsequent myocardial infarction or cardiovascular death at one year was determined using previously defined low- and high-probability thresholds (1.6 and 49.7, respectively). Results In total 20,761 of 48,282 (43%) patients (64±16 years, 46% women) were eligible of whom 3,278 (15.8%) had myocardial infarction. MI3 was well discriminated with an area under the receiver-operating-characteristic curve of 0.949 (95% confidence interval 0.946–0.952) identifying 12,983 (62.5%) patients as low-probability (sensitivity 99.3% [99.0–99.6%], NPV 99.8% [99.8–99.9%]), and 2,961 (14.3%) as high-probability (specificity 95.0% [94.7–95.3%], PPV 70.4% [69–71.9%]). At one year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability compared to low-probability patients (17.6% [520/2,961] versus 1.5% [197/12,983], P<0.001). Conclusions In unselected consecutive patients with suspected acute coronary syndrome, the MI3 algorithm accurately estimates the likelihood of myocardial infarction and predicts probability of subsequent adverse cardiovascular events. Performance of MI3 at example thresholds Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Medical Research Council

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