{"id":"861594a86ebd","type":"article","url":"https://hartvaat.nl/2026/03/25/vier-klinische-fenotypes-bij-coronairlijden-met-sterk-verschillend-risico-op-nie/","title":"Vier klinische fenotypes bij coronairlijden met sterk verschillend risico op nieuwe events (SWEDEHEART + UCC-SMART)","title_en":"Identifying clinical phenotype clusters in patients with coronary artery disease","category":"algemeen","category_label":"Algemeen","professions":["cardioloog","huisarts"],"tags":["acuut-hartfalen","biomarkers-cardiovasculair","diabetes-en-hart","fractional-flow-reserve","gepersonaliseerde-geneeskunde","hartrevalidatie","inflammatie","laminopathie","obesitas","ouderen","primaire-preventie","supraventriculaire-tachycardie","vrouwen"],"journal":"Heart","doi":"http://heart.bmj.com/cgi/content/short/112/8/431?rss=1","source_url":"https://doi.org/http://heart.bmj.com/cgi/content/short/112/8/431?rss=1","authors":["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.","on behalf of the UCC-SMART study group","on behalf of the UCC-SMART studygroup","Bongers","van Giele","Vandersteen","Cramer","van der Meer","van der Harst","Nathoe","de Borst","Teraa","Bots","van Smeden","Emmelot-Vonk","de Jong","Lely","Mokhles","Ruigrok","Verhaar","Dorresteijn","Visseren"],"significance":7,"published":"2026-03-25","source_date":"2026-03-25","image":"https://hartvaat.nl/global/img/2ea89016db5e.webp","kennis":["https://hartvaat.nl/kennis/cardiometabool/diabetes-type-1-en-hart/","https://hartvaat.nl/kennis/cardiometabool/diabetes-en-cardiovasculair-risico/"],"congress":"","summary_en":"Guideline recommendations for secondary prevention in coronary artery disease (CAD) are largely one-size-fits-all, while clinically identifiable phenotypes needing specific approaches may exist. Using unsupervised machine learning (latent class analysis), four phenotypes were identified in the Swedish SWEDEHEART registry (88,894 patients) and validated in the Dutch UCC-SMART cohort (Utrecht, 5,506 patients): cluster 1 (38%) younger men with higher BMI, blood pressure and CRP; cluster 2 (21%) smokers with few traditional risk factors; cluster 3 (30%) older patients with few comorbidities; and cluster 4 (11%) patients with multimorbidity. Compared with cluster 1, cluster 4 had the highest risk of a recurrent event (myocardial infarction, stroke or cardiovascular death; HR 4.38), followed by cluster 3 (HR 1.78); cluster 2 did not differ. These four reproducible phenotypes may help target CAD care more precisely.","created":"2026-07-03T10:32:21Z","updated":"2026-07-03T13:31:18Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Richtlijnen voor secundaire preventie bij coronairlijden (CAD) zijn grotendeels 'one-size-fits-all', terwijl er klinisch herkenbare fenotypes kunnen bestaan die een eigen aanpak vragen. Met ongesuperviseerde machine learning (latente-klasse-analyse) werden in het Zweedse SWEDEHEART-register (88.894 patiënten) vier fenotypes geïdentificeerd, gevalideerd in het Nederlandse UCC-SMART-cohort (Utrecht, 5.506 patiënten): cluster 1 (38%) jongere mannen met hoger BMI, bloeddruk en CRP; cluster 2 (21%) rokers met weinig klassieke risicofactoren; cluster 3 (30%) oudere patiënten met weinig comorbiditeit; en cluster 4 (11%) patiënten met multimorbiditeit. Vergeleken met cluster 1 had cluster 4 het hoogste risico op een nieuw event (hartinfarct, beroerte of cardiovasculaire sterfte; HR 4,38), gevolgd door cluster 3 (HR 1,78); cluster 2 verschilde niet. Deze vier reproduceerbare fenotypes kunnen helpen de zorg bij coronairlijden gerichter te maken.","abstract_original":"<sec><st>Background</st>\n<p>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.</p>\n</sec>\n<sec><st>Methods</st>\n<p>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-"}