Network Analysis to Risk Stratify Patients With Exercise Intolerance

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dc.citation.issue6
dc.citation.volumev. 122
dc.contributor.authorOldham, William M.
dc.contributor.authorOliveira, Rudolf Krawczenko Feitoza de [UNIFESP]
dc.contributor.authorWang, Rui-Sheng
dc.contributor.authorOpotowsky, Alexander R.
dc.contributor.authorRubins, David M.
dc.contributor.authorHainer, Jon
dc.contributor.authorWertheim, Bradley M.
dc.contributor.authorAlba, George A.
dc.contributor.authorChoudhary, Gaurav
dc.contributor.authorTornyos, Adrienn
dc.contributor.authorMacRae, Calum A.
dc.contributor.authorLoscalzo, Joseph
dc.contributor.authorLeopold, Jane A.
dc.contributor.authorWaxman, Aaron B.
dc.contributor.authorOlschewski, Horst
dc.contributor.authorKovacs, Gabor
dc.contributor.authorSystrom, David M.
dc.contributor.authorMaron, Bradley A.
dc.coveragePhiladelphia
dc.date.accessioned2020-07-20T16:31:14Z
dc.date.available2020-07-20T16:31:14Z
dc.date.issued2018
dc.description.abstractRationale: 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 vertical bar r vertical bar>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 (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<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<0.0001en
dc.description.abstract95% CI, 2.2-8.1) and 2.8-fold (P=0.0018en
dc.description.abstract95% 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.en
dc.description.affiliationBrigham & Womens Hosp, Dept Med, 75 Francis St, Boston, MA 02115 USA
dc.description.affiliationBrigham & Womens Hosp, Div Pulm & Crit Care Med, 75 Francis St, Boston, MA 02115 USA
dc.description.affiliationBrigham & Womens Hosp, Div Cardiovasc Med, 77 Ave Louis Pasteur,NRB Room 0630-N, Boston, MA 02115 USA
dc.description.affiliationBrigham & Womens Hosp, Dept Radiol, 75 Francis St, Boston, MA 02115 USA
dc.description.affiliationHarvard Med Sch, 77 Ave Louis Pasteur,NRB Room 0630-N, Boston, MA 02115 USA
dc.description.affiliationFed Univ Sao Paulo UNIFESP, Dept Med, Div Resp Dis, Sao Paulo, Brazil
dc.description.affiliationBoston Childrens Hosp, Dept Cardiol, Boston, MA USA
dc.description.affiliationMassachusetts Gen Hosp, Div Pulm & Crit Care Med, Boston, MA 02114 USA
dc.description.affiliationBrown Univ, Providence Vet Affairs Med Ctr, Dept Med, Div Cardiol, Providence, RI 02912 USA
dc.description.affiliationBrown Univ, Alpert Med Sch, Providence, RI 02912 USA
dc.description.affiliationMed Univ Graz, Dept Pulmonol, Graz, Austria
dc.description.affiliationLudwig Boltzmann Inst Lung Vasc Res, Graz, Austria
dc.description.affiliationBoston VA Healthcare Syst, Dept Cardiol, Boston, MA USA
dc.description.affiliationUnifespFed Univ Sao Paulo UNIFESP, Dept Med, Div Resp Dis, Sao Paulo, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipNational Institutes of Health (NIH)
dc.description.sponsorshipAmerican Heart Association
dc.description.sponsorshipPulmonary Hypertension Association
dc.description.sponsorshipCardiovascular Medical Research and Education Fund
dc.description.sponsorshipKlarman Foundation at Brigham and Women's Hospital
dc.description.sponsorshipAmerican Lung Association
dc.description.sponsorshipSao Paulo Research Foundation (FAPESP)
dc.description.sponsorshipBrazilian National Council for Scientific and Technological Development (CNPq)
dc.description.sponsorshipDunlevie Family Fund
dc.description.sponsorshipRoche Diagnostics
dc.description.sponsorshipActelion
dc.description.sponsorshipAmerican Thoracic Society Foundation, Inc
dc.description.sponsorshipIDNIH: 1K08HL11207-01A1
dc.description.sponsorshipIDNIH: 1R56HL131787-01A1
dc.description.sponsorshipIDNIH: 1R01HL139613-01
dc.description.sponsorshipIDNIH: 1K08HL128802-01A1
dc.description.sponsorshipIDNIH: HL061795
dc.description.sponsorshipIDNIH: HG007690
dc.description.sponsorshipIDNIH: HL108630
dc.description.sponsorshipIDNIH: GM107618
dc.description.sponsorshipIDNIH: U01HL125215
dc.description.sponsorshipIDAHA: 15GRNT25080016
dc.description.sponsorshipIDFAPESP: 2014/12212-5
dc.description.sponsorshipIDCNPq: 232643/2014-8
dc.format.extent864-876
dc.identifierhttp://dx.doi.org/10.1161/CIRCRESAHA.117.312482
dc.identifier.citationCirculation Research. Philadelphia, v. 122, n. 6, p. 864-876, 2018.
dc.identifier.doi10.1161/CIRCRESAHA.117.312482
dc.identifier.issn0009-7330
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/55810
dc.identifier.wosWOS:000429102200014
dc.language.isoeng
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofCirculation Research
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectdiagnosisen
dc.subjecthypertension, pulmonaryen
dc.subjectoutcomeen
dc.subjectprecision medicineen
dc.subjectprognosisen
dc.subjectsystems biologyen
dc.titleNetwork Analysis to Risk Stratify Patients With Exercise Intoleranceen
dc.typeinfo:eu-repo/semantics/article
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