Network analysis to risk stratify patients with exercise intolerance
Circulation research, 2018•Am Heart Assoc
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 …
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 …
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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