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Outcome prediction by quantitative right ventricular function assessment in 575 subjects evaluated for pulmonary hypertension.

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TLDR
Quantitative assessment of RV free-wall systolic strain is feasible and is a powerful predictor of the clinical outcome of patients with known or suspected PH and provided incremental prognostic value over conventional clinical and echocardiographic variables.
Abstract
Background— Although right ventricular (RV) dysfunction is a major determinant of outcome in patients with pulmonary hypertension (PH), the optimal measure of RV function is poorly defined. We hypothesized that RV strain measured by speckle-tracking echocardiography predicts outcome in PH over a broad range of pulmonary pressures. Methods and Results— Prospective peak RV longitudinal systolic strain measurement was performed on 575 patients (mean age, 56±18 years; 63% women) referred for echocardiography for known or suspected PH. Survival status was assessed over a median of 16.5 (interquartile range, 7.6–20.0) months. There were 406 patients with PH (71%) (74% group 1, 14% group 3, and 12% group 4) and 169 patients without evidence of PH (29%). Among patients with PH, 46% were World Health Organization functional class III–IV. The mean RV strain was −21.2±6.7% for all patients. RV strain declined with worse functional class, shorter 6-minute walk distances, higher N-terminal pro-B-type natriuretic peptide levels, and the presence of right heart failure. RV strain predicted outcome across pulmonary pressures and groups of PH. Eighteen-month survival was 92%, 88%, 85%, and 71% according to RV strain quartile ( P <0.001), with a 1.46 higher risk of death (95% confidence interval, 1.05–2.12) per 6.7% decline in RV strain. RV strain predicted survival when adjusted for pulmonary pressure, pulmonary vascular resistance, and right atrial pressure and provided incremental prognostic value over conventional clinical and echocardiographic variables. Conclusions— Quantitative assessment of RV free-wall systolic strain is feasible and is a powerful predictor of the clinical outcome of patients with known or suspected PH.

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Journal ArticleDOI

Special article2015 ESC/ERS Guidelines for the Diagnosis and Treatment of Pulmonary Hypertension

TL;DR: This article is being published concurrently in the European Heart Journal and the European Respiratory Journal and is identical except for minor stylistic and spelling differences in keeping with each journal’s style.
Journal ArticleDOI

Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association.

TL;DR: RHF is a complex syndrome including diverse causes, pathways, and pathological processes and multiple hemodynamic and biochemical markers are associated with worsening RHF and can serve to guide clinical assessment and therapeutic decision making.
References
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Journal ArticleDOI

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors

TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Journal ArticleDOI

Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

TL;DR: Two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables, are introduced that offer incremental information over the AUC and are proposed to be considered in addition to the A UC when assessing the performance of newer biomarkers.
Book ChapterDOI

Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors

TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
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