Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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SHR Neuro Krebs Kardio Lipid

Oldham, WM; Oliveira, RKF; Wang, RS; Opotowsky, AR; Rubins, DM; Hainer, J; Wertheim, BM; Alba, GA; Choudhary, G; Tornyos, A; MacRae, CA; Loscalzo, J; Leopold, JA; Waxman, AB; Olschewski, H; Kovacs, G; Systrom, DM; Maron, BA.
Network Analysis to Risk Stratify Patients With Exercise Intolerance
CIRC RES. 2018; 122(6): 864-+. [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG


Autor/innen der Med Uni Graz:
Kovacs Gabor
Olschewski Horst
Tornyos Adrienn

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Plum Analytics:
Number of Figures: 4
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Rationale: Current methods assessing clinical risk due to exercise intolerance in cardiopulmonary disease patients rely on a small subset of traditional variables. Alternative strategies incorporating the spectrum of factors underlying prognosis in at-risk patients may be useful clinically, but are lacking. Objective: Use unbiased analyses to identify variables that correspond to clinical risk in patients with exercise intolerance. Methods and Results: Data from 738 consecutive patients referred for invasive cardiopulmonary exercise testing (iCPET) at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of iCPET parameters was assembled using |r|>0.5. From an exercise network of 39 variables (i.e., nodes) and 98 correlations (i.e., edges) corresponding to P<9.5e-46for each correlation, we focused on a subnetwork containing peak rate of oxygen consumption (pVO2) and 9 linked nodes. K-mean clustering based on these ten variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements (P<0.01). Compared to a probabilistic model including 23 independent predictors of pVO2and pVO2itself, the network model was less redundant and identified clusters that were more distinct. Cluster assignment from the network model was predictive of subsequent clinical events. For example, a 4.3-fold (P<0.0001; 95% CI, 2.2-8.1) and 2.8-fold (P=0.0018; 95% CI, 1.5-5.2) increase in hazard for age- and pVO2-adjusted all-cause 3-year hospitalization, respectively, were observed between the highest vs. lowest risk clusters. Using these data, we developed the first risk-stratification calculator for patients with exercise intolerance. When applying the risk calculator to patients in twoindependent iCPET cohorts (Boston, USA and Graz, Austria), we observed a clinical risk profile that paralleled the derivation cohort. Conclusions: Network analyses were used to identify novel exercise groups and develop a point-of-care risk calculator. These data expand the range of useful clinical variables beyond pVO2that predict hospitalization in patients with exercise intolerance.

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