William M Oldham1, Rudolf K F Oliveira1, Rui-Sheng Wang1, Alexander R Opotowsky1, David M Rubins1, Jon Hainer1, Bradley M Wertheim1, George A Alba1, Gaurav Choudhary1, Adrienn Tornyos1, Calum A MacRae1, Joseph Loscalzo1, Jane A Leopold1, Aaron B Waxman1, Horst Olschewski1, Gabor Kovacs1, David M Systrom1, Bradley A Maron2. 1. From the Department of Medicine (W.M.O., R.K.F.O., R.-S.W., D.M.R., B.M.W., C.A.M., J.L., A.B.W., D.M.S., J.A.L.), Division of Pulmonary and Critical Care Medicine (W.M.O., B.M.W., A.B.W., D.M.S.), Division of Cardiovascular Medicine (A.R.O., C.A.M., J.L., J.A.L., B.A.M.), and Department of Radiology (J.H.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Respiratory Diseases, Department of Medicine, Federal University of São Paulo (UNIFESP), Brazil (R.K.F.O.); Department of Cardiology, Boston Children's Hospital and Harvard Medical School, MA (A.R.O.); Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston (G.A.A.); Division of Cardiology, Department of Medicine, Providence Veterans Affairs Medical Center and Alpert Medical School of Brown University, Providence, RI (G.C.); Department of Pulmonology, Medical University of Graz, Austria (A.T., H.O., G.K.); Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria (A.T., H.O., G.K.); and Department of Cardiology, Boston VA Healthcare System, MA (B.A.M.). 2. From the Department of Medicine (W.M.O., R.K.F.O., R.-S.W., D.M.R., B.M.W., C.A.M., J.L., A.B.W., D.M.S., J.A.L.), Division of Pulmonary and Critical Care Medicine (W.M.O., B.M.W., A.B.W., D.M.S.), Division of Cardiovascular Medicine (A.R.O., C.A.M., J.L., J.A.L., B.A.M.), and Department of Radiology (J.H.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Division of Respiratory Diseases, Department of Medicine, Federal University of São Paulo (UNIFESP), Brazil (R.K.F.O.); Department of Cardiology, Boston Children's Hospital and Harvard Medical School, MA (A.R.O.); Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston (G.A.A.); Division of Cardiology, Department of Medicine, Providence Veterans Affairs Medical Center and Alpert Medical School of Brown University, Providence, RI (G.C.); Department of Pulmonology, Medical University of Graz, Austria (A.T., H.O., G.K.); Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria (A.T., H.O., G.K.); and Department of Cardiology, Boston VA Healthcare System, MA (B.A.M.). bmaron@partners.org.
Abstract
RATIONALE: Current methods assessing clinical risk because of exercise intolerance in patients with cardiopulmonary disease 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 at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of invasive cardiopulmonary exercise testing parameters was assembled using |r|>0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding to P<9.5e-46 for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo2) and 9 linked nodes. K-mean clustering based on these 10 variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements (P<0.01). Compared with a probabilistic model, including 23 independent predictors of pVo2 and pVo2 itself, 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 versus 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 2 independent invasive cardiopulmonary exercise testing cohorts (Boston 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 pVo2 that predict hospitalization in patients with exercise intolerance.
RATIONALE: Current methods assessing clinical risk because of exercise intolerance in patients with cardiopulmonary disease 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 at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of invasive cardiopulmonary exercise testing parameters was assembled using |r|>0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding to P<9.5e-46 for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo2) and 9 linked nodes. K-mean clustering based on these 10 variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements (P<0.01). Compared with a probabilistic model, including 23 independent predictors of pVo2 and pVo2 itself, 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 versus 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 2 independent invasive cardiopulmonary exercise testing cohorts (Boston 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 pVo2 that predict hospitalization in patients with exercise intolerance.
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