Identifying clinical phenotype clusters in patients with coronary artery disease

Joris Holtrop; Carl-Emil Lim; Alicia Uijl; Peter Ueda; Tomas Jernberg; Manon G van der Meer; Pim van der Harst; Adriaan O Kraaijeveld; Jan-Willem Balder; Steven H J Hageman; Frank L J Visseren; Jannick A N Dorresteijn

doi:10.1136/heartjnl-2025-325740

Identifying clinical phenotype clusters in patients with coronary artery disease

Joris Holtrop
, Carl-Emil Lim
, Alicia Uijl
, Peter Ueda
, Tomas Jernberg
, Manon G van der Meer
, Pim van der Harst
, Adriaan O Kraaijeveld
, Jan-Willem Balder
, Steven H J Hageman
, Frank L J Visseren
, Jannick A N Dorresteijn^*
,

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

BACKGROUND: Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are predominantly one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. The purpose of this study is to identify such clinical phenotypic clusters in patients with CAD and assess their relationship with the risk of recurrent CV events.

METHODS: Unsupervised machine learning through latent class analysis was performed in patients with CAD from the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry (n=88 894) and Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) cohort (n=5506). Characteristics for clustering were based on availability, missingness and clinical relevance. Clustering was performed in SWEDEHEART and validated in UCC-SMART. Association between clusters and the composite of myocardial infarction, stroke or CV death was assessed using Cox proportional hazard models.

RESULTS: Four phenotypes could be distinguished: cluster 1 (38%, n=33 777) of predominantly younger males with increased body mass index, blood pressure and C-reactive protein, cluster 2 (21%, n=18 775) of smokers with few traditional risk factors, cluster 3 (30%, n=26 501) of older patients with few comorbidities and cluster 4 (11%, n=9841) of patients with multimorbidity. Compared with cluster 1, cluster 4 was at the highest risk (HR 4.38 95% CI (4.01 to 4.78)), followed by cluster 3 (HR 1.78 (1.70 to 1.85)), and cluster 2 (HR 0.97 (0.88 to 1.07)). Validation in UCC-SMART yielded similar results.

CONCLUSION: Four distinct and reproducible phenotypes, with differences in the risk of recurrent CV events, were identified among patients with CAD. These may be relevant in practice and warrant research into specific pathophysiology and differences in treatment effects.

Original language	English
Pages (from-to)	431-436
Number of pages	6
Journal	Heart (British Cardiac Society)
Volume	112
Issue number	8
Early online date	1 Jun 2025
DOIs	https://doi.org/10.1136/heartjnl-2025-325740
Publication status	Published - 25 Mar 2026

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10.1136/heartjnl-2025-325740

Identifying clinical phenotype clusters in patients with coronary artery diseaseFinal published version, 1.23 MBLicence: Taverne

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Holtrop, J., Lim, C.-E., Uijl, A., Ueda, P., Jernberg, T., van der Meer, M. G., van der Harst, P., Kraaijeveld, A. O., Balder, J.-W., Hageman, S. H. J., Visseren, F. L. J., Dorresteijn, J. A. N. (2026). Identifying clinical phenotype clusters in patients with coronary artery disease. Heart (British Cardiac Society), 112(8), 431-436. https://doi.org/10.1136/heartjnl-2025-325740

@article{dc5232023d864ef8a19e775a0b4de72c,

title = "Identifying clinical phenotype clusters in patients with coronary artery disease",

abstract = "BACKGROUND: Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are predominantly one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. The purpose of this study is to identify such clinical phenotypic clusters in patients with CAD and assess their relationship with the risk of recurrent CV events.METHODS: Unsupervised machine learning through latent class analysis was performed in patients with CAD from the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry (n=88 894) and Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) cohort (n=5506). Characteristics for clustering were based on availability, missingness and clinical relevance. Clustering was performed in SWEDEHEART and validated in UCC-SMART. Association between clusters and the composite of myocardial infarction, stroke or CV death was assessed using Cox proportional hazard models.RESULTS: Four phenotypes could be distinguished: cluster 1 (38\%, n=33 777) of predominantly younger males with increased body mass index, blood pressure and C-reactive protein, cluster 2 (21\%, n=18 775) of smokers with few traditional risk factors, cluster 3 (30\%, n=26 501) of older patients with few comorbidities and cluster 4 (11\%, n=9841) of patients with multimorbidity. Compared with cluster 1, cluster 4 was at the highest risk (HR 4.38 95\% CI (4.01 to 4.78)), followed by cluster 3 (HR 1.78 (1.70 to 1.85)), and cluster 2 (HR 0.97 (0.88 to 1.07)). Validation in UCC-SMART yielded similar results.CONCLUSION: Four distinct and reproducible phenotypes, with differences in the risk of recurrent CV events, were identified among patients with CAD. These may be relevant in practice and warrant research into specific pathophysiology and differences in treatment effects.",

author = "Joris Holtrop and Carl-Emil Lim and Alicia Uijl and Peter Ueda and Tomas Jernberg and \{van der Meer\}, \{Manon G\} and \{van der Harst\}, Pim and Kraaijeveld, \{Adriaan O\} and Jan-Willem Balder and Hageman, \{Steven H J\} and Visseren, \{Frank L J\} and Dorresteijn, \{Jannick A N\}",

note = "Publisher Copyright: {\textcopyright} Author(s) (or their employer(s)) 2026. No commercial re-use. See rights and permissions. Published by BMJ Group.",

year = "2026",

month = mar,

day = "25",

doi = "10.1136/heartjnl-2025-325740",

language = "English",

volume = "112",

pages = "431--436",

journal = "Heart (British Cardiac Society)",

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TY - JOUR

T1 - Identifying clinical phenotype clusters in patients with coronary artery disease

AU - Holtrop, Joris

AU - Lim, Carl-Emil

AU - Uijl, Alicia

AU - Ueda, Peter

AU - Jernberg, Tomas

AU - van der Meer, Manon G

AU - van der Harst, Pim

AU - Kraaijeveld, Adriaan O

AU - Balder, Jan-Willem

AU - Hageman, Steven H J

AU - Visseren, Frank L J

AU - Dorresteijn, Jannick A N

PY - 2026/3/25

Y1 - 2026/3/25

N2 - BACKGROUND: Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are predominantly one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. The purpose of this study is to identify such clinical phenotypic clusters in patients with CAD and assess their relationship with the risk of recurrent CV events.METHODS: Unsupervised machine learning through latent class analysis was performed in patients with CAD from the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry (n=88 894) and Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) cohort (n=5506). Characteristics for clustering were based on availability, missingness and clinical relevance. Clustering was performed in SWEDEHEART and validated in UCC-SMART. Association between clusters and the composite of myocardial infarction, stroke or CV death was assessed using Cox proportional hazard models.RESULTS: Four phenotypes could be distinguished: cluster 1 (38%, n=33 777) of predominantly younger males with increased body mass index, blood pressure and C-reactive protein, cluster 2 (21%, n=18 775) of smokers with few traditional risk factors, cluster 3 (30%, n=26 501) of older patients with few comorbidities and cluster 4 (11%, n=9841) of patients with multimorbidity. Compared with cluster 1, cluster 4 was at the highest risk (HR 4.38 95% CI (4.01 to 4.78)), followed by cluster 3 (HR 1.78 (1.70 to 1.85)), and cluster 2 (HR 0.97 (0.88 to 1.07)). Validation in UCC-SMART yielded similar results.CONCLUSION: Four distinct and reproducible phenotypes, with differences in the risk of recurrent CV events, were identified among patients with CAD. These may be relevant in practice and warrant research into specific pathophysiology and differences in treatment effects.

AB - BACKGROUND: Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are predominantly one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. The purpose of this study is to identify such clinical phenotypic clusters in patients with CAD and assess their relationship with the risk of recurrent CV events.METHODS: Unsupervised machine learning through latent class analysis was performed in patients with CAD from the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry (n=88 894) and Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) cohort (n=5506). Characteristics for clustering were based on availability, missingness and clinical relevance. Clustering was performed in SWEDEHEART and validated in UCC-SMART. Association between clusters and the composite of myocardial infarction, stroke or CV death was assessed using Cox proportional hazard models.RESULTS: Four phenotypes could be distinguished: cluster 1 (38%, n=33 777) of predominantly younger males with increased body mass index, blood pressure and C-reactive protein, cluster 2 (21%, n=18 775) of smokers with few traditional risk factors, cluster 3 (30%, n=26 501) of older patients with few comorbidities and cluster 4 (11%, n=9841) of patients with multimorbidity. Compared with cluster 1, cluster 4 was at the highest risk (HR 4.38 95% CI (4.01 to 4.78)), followed by cluster 3 (HR 1.78 (1.70 to 1.85)), and cluster 2 (HR 0.97 (0.88 to 1.07)). Validation in UCC-SMART yielded similar results.CONCLUSION: Four distinct and reproducible phenotypes, with differences in the risk of recurrent CV events, were identified among patients with CAD. These may be relevant in practice and warrant research into specific pathophysiology and differences in treatment effects.

UR - https://www.scopus.com/pages/publications/105007210193

U2 - 10.1136/heartjnl-2025-325740

DO - 10.1136/heartjnl-2025-325740

M3 - Article

C2 - 40451277

SN - 1355-6037

VL - 112

SP - 431

EP - 436

JO - Heart (British Cardiac Society)

JF - Heart (British Cardiac Society)

IS - 8

ER -

Identifying clinical phenotype clusters in patients with coronary artery disease

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