A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism
Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machi...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL) |
| Repositorio: | r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante |
| OAI Identifier: | oai:isabial.fundanetsuite.com:p11386 |
| Acceso en línea: | https://isabial.portalinvestigacion.com/publicaciones11386 https://doi.org/10.1038/s41598-025-14893-1 |
| Access Level: | acceso abierto |
| Palabra clave: | Pulmonary embolism Venous thromboembolism Machine learning Prediction Dyspnea Pulmonary arterial hypertension |
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oai:isabial.fundanetsuite.com:p11386 |
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| dc.title.none.fl_str_mv |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| title |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| spellingShingle |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism Nopp, S Pulmonary embolism Venous thromboembolism Machine learning Prediction Dyspnea Pulmonary arterial hypertension |
| title_short |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| title_full |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| title_fullStr |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| title_full_unstemmed |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| title_sort |
A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism |
| dc.creator.none.fl_str_mv |
Nopp, S Spielvogel, C Bikdeli, B Alberich-Conesa, A Hernández-Blasco, L Peris, ML Otero, R Jimenez, D Monreal, M Ay, C de Blas, PA Aibar, J Alberich-Conesa, A Alda-Lozano, A Alfonso, J Alonso-Carrillo, J Alvarez-Vega, P Amado, C Angelina-Garcia, M Arcelus, JI Ballaz, A Barba, R Barbagelata, C Barrón, M Barrón-Andres, B Bascuñana, J Blanco-Molina, A Bustos-Carpio, J Casado, I Chasco, L Claver, G De Juana-Izquierdo, C Del Toro, J Demelo-Rodriguez, P Diaz-Brasero, AM Diaz-Pedroche, MC Diaz-Peromingo, JA Dubois-Silva, A Escribano, JC Fernández-Capitán, C Fernández-Jimenez, B Fernández-Reyes, JL Fidalgo, MA Francisco, I Gabara, C Galeano-Valle, F Garcia-Bragado, F Garcia-González, C Garcia-Ortega, A Gavin-Sebastián, O de Gómez, MAG Gil-Diaz, A Gómez-Cepeda, C Gómez-Cuervo, C González-Martinez, J González-Munera, A Gorostidi, J Grau, E Guirado, L Gutierrez-Guisado, J Hernández-Blasco, L Jara-Palomares, L Jimenez, D Jou, I Joya, MD Láinez-Justo, S Lecumberri, R Lobo, JL López-Jimenez, L López-Miguel, P López-Núñez, JJ López-Ruiz, A López-Sáez, JB Lorenzo, A Madridano, O Maestre, A Marchena, PJ Marcos, M Pozo, MMD Martin-Martos, F Maza, JM Mercado, MI Molino, A Monreal, M Monzón, L Navas, MS Nieto, JA Núñez-Fernández, MJ Ordieres, L Ortiz, O Otálora, S Otero, R Pacheco-Gómez, N Pagán, J Parra-Caballero, P Pedrajas, JM Perez-Amorós, J Perez-Cabezas, A Perez-Ductor, C Perez-Pinar, M Peris, ML Pesce, ML Porras, JA Puchades, R Puche, G Rivas, A Rivera-Civico, F Rodriguez-Cobo, A Romero-Bruguera, M Salgueiro, G Sánchez-Juez, A Sancho, T Sendin, V Sigüenza, P Soler, S Sota-Yoldi, LA Suárez-Fernández, S Tirado, R Torrents-Vilar, A Torres, MI Trujillo-Santos, J Uresandi, F Valle, R Varona, JF Vázquez, E Villalobos, A Villarejo, C Villares, P Ay, C Nopp, S Pabinger, I Vanassche, T Verhamme, P Verstraete, A Rocha, AT Yoo, HHB Montenegro, AC Morales, SN Roa, J Hirmerova, J Maly, R Acassat, S Bertoletti, L Brehon, M Bura-Riviere, A Catella, J Chopard, R Couturaud, F Espitia, O Le Mao, R Leclercq, B Mahe, I Moustafa, F Plaisance, L Poenou, G Quere, I Sarlon-Bartoli, G Suchon, P Versini, E Schellong, S Rashidi, F Sadeghipour, P Tahmasbi, F Brenner, B Kennet, G Tzoran, I Barillari, G Basaglia, M Bilora, F Bissacco, D Brandolin, B Casana, R Ciammaichella, MM Colaizzo, D Di Micco, P Giorgi-Pierfranceschi, M Grandone, E Lambertenghi-Deliliers, D Marcon, C Poz, A Prandoni, P Simioni, P Siniscalchi, C Taflaj, B Tufano, A Visonà, A Zalunardo, B Skride, A Tazi-Mezalek, Z Fonseca, S Marques, R Meireles, J Pinto, S Bosevski, M Zdraveska, M Barco, S Bounameaux, H Keller, S Mazzolai, L Porceddu, E Aujayeb, A Angiolillo, DJ Bikdeli, B Caprini, JA Khalil, A Ortega-Paz, L Tafur, J Weinberg, I Bui, HM |
| author |
Nopp, S |
| author_facet |
Nopp, S Spielvogel, C Bikdeli, B Alberich-Conesa, A Hernández-Blasco, L Peris, ML Otero, R Jimenez, D Monreal, M Ay, C de Blas, PA Aibar, J Alda-Lozano, A Alfonso, J Alonso-Carrillo, J Alvarez-Vega, P Amado, C Angelina-Garcia, M Arcelus, JI Ballaz, A Barba, R Barbagelata, C Barrón, M Barrón-Andres, B Bascuñana, J Blanco-Molina, A Bustos-Carpio, J Casado, I Chasco, L Claver, G De Juana-Izquierdo, C Del Toro, J Demelo-Rodriguez, P Diaz-Brasero, AM Diaz-Pedroche, MC Diaz-Peromingo, JA Dubois-Silva, A Escribano, JC Fernández-Capitán, C Fernández-Jimenez, B Fernández-Reyes, JL Fidalgo, MA Francisco, I Gabara, C Galeano-Valle, F Garcia-Bragado, F Garcia-González, C Garcia-Ortega, A Gavin-Sebastián, O de Gómez, MAG Gil-Diaz, A Gómez-Cepeda, C Gómez-Cuervo, C González-Martinez, J González-Munera, A Gorostidi, J Grau, E Guirado, L Gutierrez-Guisado, J Jara-Palomares, L Jou, I Joya, MD Láinez-Justo, S Lecumberri, R Lobo, JL López-Jimenez, L López-Miguel, P López-Núñez, JJ López-Ruiz, A López-Sáez, JB Lorenzo, A Madridano, O Maestre, A Marchena, PJ Marcos, M Pozo, MMD Martin-Martos, F Maza, JM Mercado, MI Molino, A Monzón, L Navas, MS Nieto, JA Núñez-Fernández, MJ Ordieres, L Ortiz, O Otálora, S Pacheco-Gómez, N Pagán, J Parra-Caballero, P Pedrajas, JM Perez-Amorós, J Perez-Cabezas, A Perez-Ductor, C Perez-Pinar, M Pesce, ML Porras, JA Puchades, R Puche, G Rivas, A Rivera-Civico, F Rodriguez-Cobo, A Romero-Bruguera, M Salgueiro, G Sánchez-Juez, A Sancho, T Sendin, V Sigüenza, P Soler, S Sota-Yoldi, LA Suárez-Fernández, S Tirado, R Torrents-Vilar, A Torres, MI Trujillo-Santos, J Uresandi, F Valle, R Varona, JF Vázquez, E Villalobos, A Villarejo, C Villares, P Pabinger, I Vanassche, T Verhamme, P Verstraete, A Rocha, AT Yoo, HHB Montenegro, AC Morales, SN Roa, J Hirmerova, J Maly, R Acassat, S Bertoletti, L Brehon, M Bura-Riviere, A Catella, J Chopard, R Couturaud, F Espitia, O Le Mao, R Leclercq, B Mahe, I Moustafa, F Plaisance, L Poenou, G Quere, I Sarlon-Bartoli, G Suchon, P Versini, E Schellong, S Rashidi, F Sadeghipour, P Tahmasbi, F Brenner, B Kennet, G Tzoran, I Barillari, G Basaglia, M Bilora, F Bissacco, D Brandolin, B Casana, R Ciammaichella, MM Colaizzo, D Di Micco, P Giorgi-Pierfranceschi, M Grandone, E Lambertenghi-Deliliers, D Marcon, C Poz, A Prandoni, P Simioni, P Siniscalchi, C Taflaj, B Tufano, A Visonà, A Zalunardo, B Skride, A Tazi-Mezalek, Z Fonseca, S Marques, R Meireles, J Pinto, S Bosevski, M Zdraveska, M Barco, S Bounameaux, H Keller, S Mazzolai, L Porceddu, E Aujayeb, A Angiolillo, DJ Caprini, JA Khalil, A Ortega-Paz, L Tafur, J Weinberg, I Bui, HM |
| author_role |
author |
| author2 |
Spielvogel, C Bikdeli, B Alberich-Conesa, A Hernández-Blasco, L Peris, ML Otero, R Jimenez, D Monreal, M Ay, C de Blas, PA Aibar, J Alda-Lozano, A Alfonso, J Alonso-Carrillo, J Alvarez-Vega, P Amado, C Angelina-Garcia, M Arcelus, JI Ballaz, A Barba, R Barbagelata, C Barrón, M Barrón-Andres, B Bascuñana, J Blanco-Molina, A Bustos-Carpio, J Casado, I Chasco, L Claver, G De Juana-Izquierdo, C Del Toro, J Demelo-Rodriguez, P Diaz-Brasero, AM Diaz-Pedroche, MC Diaz-Peromingo, JA Dubois-Silva, A Escribano, JC Fernández-Capitán, C Fernández-Jimenez, B Fernández-Reyes, JL Fidalgo, MA Francisco, I Gabara, C Galeano-Valle, F Garcia-Bragado, F Garcia-González, C Garcia-Ortega, A Gavin-Sebastián, O de Gómez, MAG Gil-Diaz, A Gómez-Cepeda, C Gómez-Cuervo, C González-Martinez, J González-Munera, A Gorostidi, J Grau, E Guirado, L Gutierrez-Guisado, J Jara-Palomares, L Jou, I Joya, MD Láinez-Justo, S Lecumberri, R Lobo, JL López-Jimenez, L López-Miguel, P López-Núñez, JJ López-Ruiz, A López-Sáez, JB Lorenzo, A Madridano, O Maestre, A Marchena, PJ Marcos, M Pozo, MMD Martin-Martos, F Maza, JM Mercado, MI Molino, A Monzón, L Navas, MS Nieto, JA Núñez-Fernández, MJ Ordieres, L Ortiz, O Otálora, S Pacheco-Gómez, N Pagán, J Parra-Caballero, P Pedrajas, JM Perez-Amorós, J Perez-Cabezas, A Perez-Ductor, C Perez-Pinar, M Pesce, ML Porras, JA Puchades, R Puche, G Rivas, A Rivera-Civico, F Rodriguez-Cobo, A Romero-Bruguera, M Salgueiro, G Sánchez-Juez, A Sancho, T Sendin, V Sigüenza, P Soler, S Sota-Yoldi, LA Suárez-Fernández, S Tirado, R Torrents-Vilar, A Torres, MI Trujillo-Santos, J Uresandi, F Valle, R Varona, JF Vázquez, E Villalobos, A Villarejo, C Villares, P Pabinger, I Vanassche, T Verhamme, P Verstraete, A Rocha, AT Yoo, HHB Montenegro, AC Morales, SN Roa, J Hirmerova, J Maly, R Acassat, S Bertoletti, L Brehon, M Bura-Riviere, A Catella, J Chopard, R Couturaud, F Espitia, O Le Mao, R Leclercq, B Mahe, I Moustafa, F Plaisance, L Poenou, G Quere, I Sarlon-Bartoli, G Suchon, P Versini, E Schellong, S Rashidi, F Sadeghipour, P Tahmasbi, F Brenner, B Kennet, G Tzoran, I Barillari, G Basaglia, M Bilora, F Bissacco, D Brandolin, B Casana, R Ciammaichella, MM Colaizzo, D Di Micco, P Giorgi-Pierfranceschi, M Grandone, E Lambertenghi-Deliliers, D Marcon, C Poz, A Prandoni, P Simioni, P Siniscalchi, C Taflaj, B Tufano, A Visonà, A Zalunardo, B Skride, A Tazi-Mezalek, Z Fonseca, S Marques, R Meireles, J Pinto, S Bosevski, M Zdraveska, M Barco, S Bounameaux, H Keller, S Mazzolai, L Porceddu, E Aujayeb, A Angiolillo, DJ Caprini, JA Khalil, A Ortega-Paz, L Tafur, J Weinberg, I Bui, HM |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Pulmonary embolism Venous thromboembolism Machine learning Prediction Dyspnea Pulmonary arterial hypertension |
| topic |
Pulmonary embolism Venous thromboembolism Machine learning Prediction Dyspnea Pulmonary arterial hypertension |
| description |
Pulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machine learning model to identify patients at risk for long-term consequences after PE. Using data from the RIETE registry, the largest prospective international PE registry, we developed supervised machine learning models to identify patients at increased risk of CTEPH and post-PE syndrome. Our approach involved data preprocessing, model training via random forest algorithm, and validation through Monte-Carlo cross-validation. The performance of the CTEPH prediction model was benchmarked against an existing score. Of the 57,981 PE patients in the RIETE registry, 5,217 were eligible for inclusion. Median age was 68 years, with 50.6% men. Machine learning was based on 111 predictor variables, with 171 patients (3.3%) developing CTEPH. The CTEPH model demonstrated good performance with an AUC of 0.74 (95%CI: 0.73-0.75), significantly outperforming the existing CTEPH prediction score (0.57; 0.54-0.61). Additionally, 1,310 (25.1%) patients were defined as having post-PE syndrome six months after index PE. The post-PE syndrome model showed poorer performance with an AUC of 0.62 (0.61-0.62). Key predictor variables across both models included chest pain at presentation, PE location, troponin, side of clot, and dyspnea at presentation. Machine learning models show promise in predicting CTEPH but are less effective for post-PE syndrome. Future refinement, including integrating imaging data, is necessary to improve predictive performance and clinical utility. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://isabial.portalinvestigacion.com/publicaciones11386 https://doi.org/10.1038/s41598-025-14893-1 |
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https://isabial.portalinvestigacion.com/publicaciones11386 https://doi.org/10.1038/s41598-025-14893-1 |
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Inglés |
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Inglés |
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info:eu-repo/semantics/openAccess |
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openAccess |
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NATURE PORTFOLIO |
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NATURE PORTFOLIO |
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Scientific Reports ISSN: 20452322 reponame:r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante instname:Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL) |
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Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL) |
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r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante |
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r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante |
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1869423066817232896 |
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A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolismNopp, SSpielvogel, CBikdeli, BAlberich-Conesa, AHernández-Blasco, LPeris, MLOtero, RJimenez, DMonreal, MAy, Cde Blas, PAAibar, JAlberich-Conesa, AAlda-Lozano, AAlfonso, JAlonso-Carrillo, JAlvarez-Vega, PAmado, CAngelina-Garcia, MArcelus, JIBallaz, ABarba, RBarbagelata, CBarrón, MBarrón-Andres, BBascuñana, JBlanco-Molina, ABustos-Carpio, JCasado, IChasco, LClaver, GDe Juana-Izquierdo, CDel Toro, JDemelo-Rodriguez, PDiaz-Brasero, AMDiaz-Pedroche, MCDiaz-Peromingo, JADubois-Silva, AEscribano, JCFernández-Capitán, CFernández-Jimenez, BFernández-Reyes, JLFidalgo, MAFrancisco, IGabara, CGaleano-Valle, FGarcia-Bragado, FGarcia-González, CGarcia-Ortega, AGavin-Sebastián, Ode Gómez, MAGGil-Diaz, AGómez-Cepeda, CGómez-Cuervo, CGonzález-Martinez, JGonzález-Munera, AGorostidi, JGrau, EGuirado, LGutierrez-Guisado, JHernández-Blasco, LJara-Palomares, LJimenez, DJou, IJoya, MDLáinez-Justo, SLecumberri, RLobo, JLLópez-Jimenez, LLópez-Miguel, PLópez-Núñez, JJLópez-Ruiz, ALópez-Sáez, JBLorenzo, AMadridano, OMaestre, AMarchena, PJMarcos, MPozo, MMDMartin-Martos, FMaza, JMMercado, MIMolino, AMonreal, MMonzón, LNavas, MSNieto, JANúñez-Fernández, MJOrdieres, LOrtiz, OOtálora, SOtero, RPacheco-Gómez, NPagán, JParra-Caballero, PPedrajas, JMPerez-Amorós, JPerez-Cabezas, APerez-Ductor, CPerez-Pinar, MPeris, MLPesce, MLPorras, JAPuchades, RPuche, GRivas, ARivera-Civico, FRodriguez-Cobo, ARomero-Bruguera, MSalgueiro, GSánchez-Juez, ASancho, TSendin, VSigüenza, PSoler, SSota-Yoldi, LASuárez-Fernández, STirado, RTorrents-Vilar, ATorres, MITrujillo-Santos, JUresandi, FValle, RVarona, JFVázquez, EVillalobos, AVillarejo, CVillares, PAy, CNopp, SPabinger, IVanassche, TVerhamme, PVerstraete, ARocha, ATYoo, HHBMontenegro, ACMorales, SNRoa, JHirmerova, JMaly, RAcassat, SBertoletti, LBrehon, MBura-Riviere, ACatella, JChopard, RCouturaud, FEspitia, OLe Mao, RLeclercq, BMahe, IMoustafa, FPlaisance, LPoenou, GQuere, ISarlon-Bartoli, GSuchon, PVersini, ESchellong, SRashidi, FSadeghipour, PTahmasbi, FBrenner, BKennet, GTzoran, IBarillari, GBasaglia, MBilora, FBissacco, DBrandolin, BCasana, RCiammaichella, MMColaizzo, DDi Micco, PGiorgi-Pierfranceschi, MGrandone, ELambertenghi-Deliliers, DMarcon, CPoz, APrandoni, PSimioni, PSiniscalchi, CTaflaj, BTufano, AVisonà, AZalunardo, BSkride, ATazi-Mezalek, ZFonseca, SMarques, RMeireles, JPinto, SBosevski, MZdraveska, MBarco, SBounameaux, HKeller, SMazzolai, LPorceddu, EAujayeb, AAngiolillo, DJBikdeli, BCaprini, JAKhalil, AOrtega-Paz, LTafur, JWeinberg, IBui, HMPulmonary embolismVenous thromboembolismMachine learningPredictionDyspneaPulmonary arterial hypertensionPulmonary embolism (PE) can result in long-term sequelae, such as post-PE syndrome, including persistent dyspnea and chronic thromboembolic pulmonary hypertension (CTEPH). Existing prediction tools for severe post-PE complications lack sensitivity and specificity. This study aimed to develop a machine learning model to identify patients at risk for long-term consequences after PE. Using data from the RIETE registry, the largest prospective international PE registry, we developed supervised machine learning models to identify patients at increased risk of CTEPH and post-PE syndrome. Our approach involved data preprocessing, model training via random forest algorithm, and validation through Monte-Carlo cross-validation. The performance of the CTEPH prediction model was benchmarked against an existing score. Of the 57,981 PE patients in the RIETE registry, 5,217 were eligible for inclusion. Median age was 68 years, with 50.6% men. Machine learning was based on 111 predictor variables, with 171 patients (3.3%) developing CTEPH. The CTEPH model demonstrated good performance with an AUC of 0.74 (95%CI: 0.73-0.75), significantly outperforming the existing CTEPH prediction score (0.57; 0.54-0.61). Additionally, 1,310 (25.1%) patients were defined as having post-PE syndrome six months after index PE. The post-PE syndrome model showed poorer performance with an AUC of 0.62 (0.61-0.62). Key predictor variables across both models included chest pain at presentation, PE location, troponin, side of clot, and dyspnea at presentation. Machine learning models show promise in predicting CTEPH but are less effective for post-PE syndrome. Future refinement, including integrating imaging data, is necessary to improve predictive performance and clinical utility.NATURE PORTFOLIO2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://isabial.portalinvestigacion.com/publicaciones11386https://doi.org/10.1038/s41598-025-14893-1Scientific ReportsISSN: 20452322reponame:r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicanteinstname:Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL)Inglésinfo:eu-repo/semantics/openAccessoai:isabial.fundanetsuite.com:p113862026-06-12T10:20:37Z |
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15,811543 |