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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|&gt;0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding toP &lt;9.5e−46 for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo 2 ) 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 &lt;0.01). Compared with a probabilistic model, including 23 independent predictors of pVo 2 and pVo 2 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 &lt;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 pVo 2 -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 pVo 2 that predict hospitalization in patients with exercise intolerance.

Network Analysis to Risk Stratify Patients With Exercise Intolerance