The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial

Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospec...
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Autor*in:	Thomas Sonnweber [verfasserIn] Piotr Tymoszuk [verfasserIn] Regina Steringer-Mascherbauer [verfasserIn] Elisabeth Sigmund [verfasserIn] Stephanie Porod-Schneiderbauer [verfasserIn] Lisa Kohlbacher [verfasserIn] Igor Theurl [verfasserIn] Irene Lang [verfasserIn] Günter Weiss [verfasserIn] Judith Löffler-Ragg [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension

Übergeordnetes Werk:	In: BMC Pulmonary Medicine - BMC, 2003, 23(2023), 1, Seite 12
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12890-023-02427-2

Katalog-ID:	DOAJ089690249

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520			\|a Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH.
650		4	\|a Pulmonary arterial hypertension
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650		4	\|a Right-heart failure
650		4	\|a Atypical pulmonary arterial hypertension
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allfields	10.1186/s12890-023-02427-2 doi (DE-627)DOAJ089690249 (DE-599)DOAJ1595d4134c9b42869adc63fa0917e02b DE-627 ger DE-627 rakwb eng RC705-779 Thomas Sonnweber verfasserin aut The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH. Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension Diseases of the respiratory system Piotr Tymoszuk verfasserin aut Regina Steringer-Mascherbauer verfasserin aut Elisabeth Sigmund verfasserin aut Stephanie Porod-Schneiderbauer verfasserin aut Lisa Kohlbacher verfasserin aut Igor Theurl verfasserin aut Irene Lang verfasserin aut Günter Weiss verfasserin aut Judith Löffler-Ragg verfasserin aut In BMC Pulmonary Medicine BMC, 2003 23(2023), 1, Seite 12 (DE-627)335489125 (DE-600)2059871-3 14712466 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b kostenfrei https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/toc/1471-2466 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 12
spelling	10.1186/s12890-023-02427-2 doi (DE-627)DOAJ089690249 (DE-599)DOAJ1595d4134c9b42869adc63fa0917e02b DE-627 ger DE-627 rakwb eng RC705-779 Thomas Sonnweber verfasserin aut The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH. Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension Diseases of the respiratory system Piotr Tymoszuk verfasserin aut Regina Steringer-Mascherbauer verfasserin aut Elisabeth Sigmund verfasserin aut Stephanie Porod-Schneiderbauer verfasserin aut Lisa Kohlbacher verfasserin aut Igor Theurl verfasserin aut Irene Lang verfasserin aut Günter Weiss verfasserin aut Judith Löffler-Ragg verfasserin aut In BMC Pulmonary Medicine BMC, 2003 23(2023), 1, Seite 12 (DE-627)335489125 (DE-600)2059871-3 14712466 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b kostenfrei https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/toc/1471-2466 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 12
allfields_unstemmed	10.1186/s12890-023-02427-2 doi (DE-627)DOAJ089690249 (DE-599)DOAJ1595d4134c9b42869adc63fa0917e02b DE-627 ger DE-627 rakwb eng RC705-779 Thomas Sonnweber verfasserin aut The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH. Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension Diseases of the respiratory system Piotr Tymoszuk verfasserin aut Regina Steringer-Mascherbauer verfasserin aut Elisabeth Sigmund verfasserin aut Stephanie Porod-Schneiderbauer verfasserin aut Lisa Kohlbacher verfasserin aut Igor Theurl verfasserin aut Irene Lang verfasserin aut Günter Weiss verfasserin aut Judith Löffler-Ragg verfasserin aut In BMC Pulmonary Medicine BMC, 2003 23(2023), 1, Seite 12 (DE-627)335489125 (DE-600)2059871-3 14712466 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b kostenfrei https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/toc/1471-2466 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 12
allfieldsGer	10.1186/s12890-023-02427-2 doi (DE-627)DOAJ089690249 (DE-599)DOAJ1595d4134c9b42869adc63fa0917e02b DE-627 ger DE-627 rakwb eng RC705-779 Thomas Sonnweber verfasserin aut The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH. Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension Diseases of the respiratory system Piotr Tymoszuk verfasserin aut Regina Steringer-Mascherbauer verfasserin aut Elisabeth Sigmund verfasserin aut Stephanie Porod-Schneiderbauer verfasserin aut Lisa Kohlbacher verfasserin aut Igor Theurl verfasserin aut Irene Lang verfasserin aut Günter Weiss verfasserin aut Judith Löffler-Ragg verfasserin aut In BMC Pulmonary Medicine BMC, 2003 23(2023), 1, Seite 12 (DE-627)335489125 (DE-600)2059871-3 14712466 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b kostenfrei https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/toc/1471-2466 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 12
allfieldsSound	10.1186/s12890-023-02427-2 doi (DE-627)DOAJ089690249 (DE-599)DOAJ1595d4134c9b42869adc63fa0917e02b DE-627 ger DE-627 rakwb eng RC705-779 Thomas Sonnweber verfasserin aut The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH. Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension Diseases of the respiratory system Piotr Tymoszuk verfasserin aut Regina Steringer-Mascherbauer verfasserin aut Elisabeth Sigmund verfasserin aut Stephanie Porod-Schneiderbauer verfasserin aut Lisa Kohlbacher verfasserin aut Igor Theurl verfasserin aut Irene Lang verfasserin aut Günter Weiss verfasserin aut Judith Löffler-Ragg verfasserin aut In BMC Pulmonary Medicine BMC, 2003 23(2023), 1, Seite 12 (DE-627)335489125 (DE-600)2059871-3 14712466 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b kostenfrei https://doi.org/10.1186/s12890-023-02427-2 kostenfrei https://doaj.org/toc/1471-2466 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 12
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callnumber-first	R - Medicine
author	Thomas Sonnweber
spellingShingle	Thomas Sonnweber misc RC705-779 misc Pulmonary arterial hypertension misc Risk assessment misc Biomarkers misc Mortality misc Right-heart failure misc Atypical pulmonary arterial hypertension misc Diseases of the respiratory system The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
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topic_title	RC705-779 The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial Pulmonary arterial hypertension Risk assessment Biomarkers Mortality Right-heart failure Atypical pulmonary arterial hypertension
topic	misc RC705-779 misc Pulmonary arterial hypertension misc Risk assessment misc Biomarkers misc Mortality misc Right-heart failure misc Atypical pulmonary arterial hypertension misc Diseases of the respiratory system
topic_unstemmed	misc RC705-779 misc Pulmonary arterial hypertension misc Risk assessment misc Biomarkers misc Mortality misc Right-heart failure misc Atypical pulmonary arterial hypertension misc Diseases of the respiratory system
topic_browse	misc RC705-779 misc Pulmonary arterial hypertension misc Risk assessment misc Biomarkers misc Mortality misc Right-heart failure misc Atypical pulmonary arterial hypertension misc Diseases of the respiratory system
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title	The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
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title_full	The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
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author_browse	Thomas Sonnweber Piotr Tymoszuk Regina Steringer-Mascherbauer Elisabeth Sigmund Stephanie Porod-Schneiderbauer Lisa Kohlbacher Igor Theurl Irene Lang Günter Weiss Judith Löffler-Ragg
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title_sort	combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
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title_auth	The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
abstract	Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH.
abstractGer	Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH.
abstract_unstemmed	Abstract Background Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. Conclusion Supervised and unsupervised learning algorithms such as Elastic Net regression and medoid clustering are powerful tools for automated mortality risk prediction and clinical phenotyping in PAH.
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title_short	The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial
url	https://doi.org/10.1186/s12890-023-02427-2 https://doaj.org/article/1595d4134c9b42869adc63fa0917e02b https://doaj.org/toc/1471-2466
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Machine learning may improve risk management and harness clinical variability in PAH. Methods We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Results Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95%CI: 0.75 – 0.89], test cohort: 0.77 [0.66 – 0.88]). The Elastic Net signature demonstrated superior prognostic accuracy as compared with five established risk scores. The signature factors defined two clusters of PAH patients with distinct risk profiles. The high-risk/poor prognosis cluster was characterized by advanced age at diagnosis, poor cardiac output, increased red cell distribution width, higher pulmonary vascular resistance, and a poor six-minute walking test performance. 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The combination of supervised and unsupervised learning based risk stratification and phenotyping in pulmonary arterial hypertension—a long-term retrospective multicenter trial

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