An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension
ABSTRACT Although right ventricle (RV) dysfunction drives clinical worsening in pulmonary hypertension (PH), information about RV function has not been well integrated in PH risk assessment. The gold standard for assessing RV function and ventriculo‐arterial coupling is the construction of multi‐bea...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Pulmonary Circulation |
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| Online Access: | https://doi.org/10.1002/pul2.70057 |
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| author | Nikita Sivakumar Cindy Zhang Connie Chang‐Chien Pan Gu Yikun Li Yi Yang Darin Rosen Tijana Tuhy Ilton M. Cubero Salazar Matthew Kauffman Rachel L. Damico Casey Overby Taylor Joseph L. Greenstein Steven Hsu Paul M. Hassoun Catherine E. Simpson |
| author_facet | Nikita Sivakumar Cindy Zhang Connie Chang‐Chien Pan Gu Yikun Li Yi Yang Darin Rosen Tijana Tuhy Ilton M. Cubero Salazar Matthew Kauffman Rachel L. Damico Casey Overby Taylor Joseph L. Greenstein Steven Hsu Paul M. Hassoun Catherine E. Simpson |
| author_sort | Nikita Sivakumar |
| collection | DOAJ |
| description | ABSTRACT Although right ventricle (RV) dysfunction drives clinical worsening in pulmonary hypertension (PH), information about RV function has not been well integrated in PH risk assessment. The gold standard for assessing RV function and ventriculo‐arterial coupling is the construction of multi‐beat pressure–volume (PV) loops. PV loops are technically challenging to acquire and not feasible for routine clinical use. Therefore, we aimed to map standard clinically available measurements to emergent PV loop phenotypes. One hundred and one patients with suspected PH underwent right heart catheterization (RHC) with exercise, multi‐beat PV loop measurement, and same‐day cardiac magnetic resonance imaging (CMR). We applied unsupervised k‐means clustering on 10 PV loop metrics to obtain three patient groups with unique RV functional phenotypes and times to clinical worsening. We integrated RHC and CMR measurements to train a random forest classifier that predicts the PV loop patient group with high discrimination (AUC = 0.93). The most informative variable for PV loop phenotype prediction was exercise mean pulmonary arterial pressure (mPAP). Distinct and clinically meaningful PV loop phenotypes exist that can be predicted using clinically accessible hemodynamic and RV‐centric measurements. Exercise mPAP may inform RV pressure–volume relationships. |
| format | Article |
| id | doaj-art-e69d11bfd41546ad9a82f3d6f92c902a |
| institution | DOAJ |
| issn | 2045-8940 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pulmonary Circulation |
| spelling | doaj-art-e69d11bfd41546ad9a82f3d6f92c902a2025-08-20T02:49:40ZengWileyPulmonary Circulation2045-89402025-01-01151n/an/a10.1002/pul2.70057An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary HypertensionNikita Sivakumar0Cindy Zhang1Connie Chang‐Chien2Pan Gu3Yikun Li4Yi Yang5Darin Rosen6Tijana Tuhy7Ilton M. Cubero Salazar8Matthew Kauffman9Rachel L. Damico10Casey Overby Taylor11Joseph L. Greenstein12Steven Hsu13Paul M. Hassoun14Catherine E. Simpson15Institute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USADivision of Cardiology Johns Hopkins University School of Medicine Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USAInstitute for Computational Medicine Johns Hopkins University Baltimore Maryland USADivision of Cardiology Johns Hopkins University School of Medicine Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USADivision of Pulmonary and Critical Care Medicine Baltimore Maryland USAABSTRACT Although right ventricle (RV) dysfunction drives clinical worsening in pulmonary hypertension (PH), information about RV function has not been well integrated in PH risk assessment. The gold standard for assessing RV function and ventriculo‐arterial coupling is the construction of multi‐beat pressure–volume (PV) loops. PV loops are technically challenging to acquire and not feasible for routine clinical use. Therefore, we aimed to map standard clinically available measurements to emergent PV loop phenotypes. One hundred and one patients with suspected PH underwent right heart catheterization (RHC) with exercise, multi‐beat PV loop measurement, and same‐day cardiac magnetic resonance imaging (CMR). We applied unsupervised k‐means clustering on 10 PV loop metrics to obtain three patient groups with unique RV functional phenotypes and times to clinical worsening. We integrated RHC and CMR measurements to train a random forest classifier that predicts the PV loop patient group with high discrimination (AUC = 0.93). The most informative variable for PV loop phenotype prediction was exercise mean pulmonary arterial pressure (mPAP). Distinct and clinically meaningful PV loop phenotypes exist that can be predicted using clinically accessible hemodynamic and RV‐centric measurements. Exercise mPAP may inform RV pressure–volume relationships.https://doi.org/10.1002/pul2.70057cardiac resonance imagingright heart catheterizationright ventricular‐pulmonary arterial couplingunsupervised clustering |
| spellingShingle | Nikita Sivakumar Cindy Zhang Connie Chang‐Chien Pan Gu Yikun Li Yi Yang Darin Rosen Tijana Tuhy Ilton M. Cubero Salazar Matthew Kauffman Rachel L. Damico Casey Overby Taylor Joseph L. Greenstein Steven Hsu Paul M. Hassoun Catherine E. Simpson An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension Pulmonary Circulation cardiac resonance imaging right heart catheterization right ventricular‐pulmonary arterial coupling unsupervised clustering |
| title | An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension |
| title_full | An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension |
| title_fullStr | An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension |
| title_full_unstemmed | An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension |
| title_short | An Unsupervised Approach to Derive Right Ventricular Pressure–Volume Loop Phenotypes in Pulmonary Hypertension |
| title_sort | unsupervised approach to derive right ventricular pressure volume loop phenotypes in pulmonary hypertension |
| topic | cardiac resonance imaging right heart catheterization right ventricular‐pulmonary arterial coupling unsupervised clustering |
| url | https://doi.org/10.1002/pul2.70057 |
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