Network Analysis to Risk Stratify Patients With Exercise Intolerance

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.

Keywords: diagnosis; hypertension, pulmonary; outcome; precision medicine; prognosis; systems biology.

Figure 1. Overall study design and approach to developing the invasive cardiopulmonary exercise testing (iCPET) networks

(A) Flow diagram of the overall study design. Developing the iCPET network was performed to identify novel and functionally related associations between iCPET variables, which then could be used to characterize subpopulations of patients with exercise intolerance. (B) Approach to developing the iCPET network in the derivation cohort. CO, cardiac output, %p, percent predicted.

Figure 2. Exercise network and subnetwork used to select variables for determining exercise pathophenotypes

(A) Data from 738 consecutive patients referred for invasive cardiopulmonary exercise testing (iCPET) were analyzed. Pairwise correlations (|r| > 0.5; this |r| threshold corresponded to P<9.5e⁻⁴⁶ for each correlation) for 73 iCPET variables were used to identify unanticipated relationships between exercise measurements (i.e., the exercise network) after grouping variables by iCPET function (box). A complete list of variable abbreviations and iCPET functional groups is provided in Online Tables I and II, respectively. The exercise network contained 39 nodes and 98 edges, but was too dense for further interpretation. Therefore, a subnetwork was constructed based on relationships between variables and pVO₂, which was a highly connected node in the larger exercise network indicating that this was a potentially important variable. (B) This subnetwork was used for further analyses to determine if specific exercise pathophenotypes could be identified within the iCPET cohort. pVO₂, volume of oxygen consumption at peak exercise; pSV, stroke volume at peak exercise; FVC, forced vital capacity in liters; MVV, maximal voluntary ventilation; pCa-vO₂, arterial to mixed-venous O₂ content difference at peak exercise; pVE, minute ventilation at peak exercise; FEV-1, forced expiratory volume-1 second in liters; pLactate, arterial lactate level at peak exercise; ppH; arterial pH at peak exercise.

Figure 3. Normalized value for each variable in the subnetwork stratified by cluster

Data from 738 patients referred for invasive cardiopulmonary exercise testing (iCPET) were used to classify patients into clusters based on their performance on the 10 variables from the exercise subnetwork. (A) The distribution of patients in each cluster is plotted according to variance for iCPET data from all patients in the study cohort (principal component [PC] 1 and PC 2). (B) The normalized value of each variable in the exercise subnetwork is presented as a function of cluster assignment. The line between variables is used to visually distinguish data belonging to each particular cluster. (C) Heat map illustrating the individual patient level differences by cluster for each subnetwork variable. Scale indicates the range of normalized fold-change in performance. (D) The prevalence of patients meeting standard exercise diagnoses in each iCPET cluster. pVO₂, volume of oxygen consumption at peak exercise; pSV, stroke volume at peak exercise; FVC, forced vital capacity in liters; MVV, maximal voluntary ventilation; pCa-vO₂, arterial to mixed-venous O₂ content difference at peak exercise; pVE, minute ventilation at peak exercise; FEV-1, forced expiratory volume-1 second in liters; pLactate, arterial lactate level at peak exercise; ppH; arterial pH at peak exercise; PVD, pulmonary vascular disease; LHD-noPVD, left heart disease without PVD; LHD+PVD, left heart disease with PVD.

Figure 4. Clinical outcome by subnetwork cluster and exercise risk calculator

Data from 738 patients referred for invasive cardiopulmonary exercise testing (iCPET) were used to classify patients into four clusters based on their performance on the 10 variables from the exercise subnetwork, including peak volume of oxygen consumption (pVO₂). (A) The study cohort was stratified by patient cluster and Kaplan-Meier analysis of the probability of all-cause hospitalization was performed. (B) The iCPET subnetwork, cluster, and outcome data were used to generate an on-line risk stratification calculator. This clinical tool automates patient cluster assignment from performance results on each of the 10 variables in the subnetwork, and reports prognosis based on the corresponding cluster-derived 3-year all-cause hospitalization rate (95% confidence interval) (accessible at: https://icpet.partners.org/). (C) The risk calculator was applied to a validation cohort of 113 iCPET patients referred for exercise intolerance to Medical University in Graz, Austria. (D) These results were similar to findings from the derivation cohort studying patients with exercise intolerance referred to Brigham and Women’s Hospital. The difference in hospitalization rate by cluster between cohorts was not statistically significantly different by 2-way ANOVA (P=0.28).