Subclass phenotypes in patients with unprovoked venous thromboembolisms using a latent class analysis.
Anticoagulant
Bleeding
Latent class analysis
Recurrence
Venous thromboembolism
Journal
Thrombosis research
ISSN: 1879-2472
Titre abrégé: Thromb Res
Pays: United States
ID NLM: 0326377
Informations de publication
Date de publication:
19 Apr 2024
19 Apr 2024
Historique:
received:
10
01
2024
revised:
18
03
2024
accepted:
17
04
2024
medline:
24
4
2024
pubmed:
24
4
2024
entrez:
23
4
2024
Statut:
aheadofprint
Résumé
Patients with unprovoked venous thromboembolisms (VTEs) can be sub-classified based on the different phenotypes using a latent class analysis (LCA), which might be useful for selecting individual management strategies. In the COMMAND VTE Registry-2 database enrolling 5197 VTE patients, the current derivation cohort consisted of 1556 patients with unprovoked VTEs. We conducted clustering with an LCA, and the patients were classified into subgroups with the highest probability. We compared the clinical characteristics and outcomes among the developed subgroups. This LCA model proposed 3 subgroups based on 8 clinically relevant variables, and classified 592, 813, and 151 patients as Class I, II, and III, respectively. Based on the clinical features, we named Class I the younger, Class II the older with a few comorbidities, and Class III the older with many comorbidities. The cumulative 3-year anticoagulation discontinuation rate was highest in the older with many comorbidities (Class III) (39.9 %, 36.1 %, and 48.4 %, P = 0.02). There was no significant difference in the cumulative 5-year incidence of recurrent VTEs among the 3 classes (12.8 %, 11.1 %, and 4.0 % P = 0.20), whereas the cumulative 5-year incidence of major bleeding was significantly higher in the older with many comorbidities (Class III) (7.8 %, 12.7 %, and 17.8 %, P = 0.04). The current LCA revealed that patients with unprovoked VTEs could be sub-classified into further phenotypes depending on the patient characteristics. Each subclass phenotype could have different clinical outcomes risks especially a bleeding risk, which could have a potential benefit when considering the individual anticoagulation strategies. URL: http://www.umin.ac.jp/ctr/index.htm COMMAND VTE Registry-2: Unique identifier, UMIN000044816 COMMAND VTE Registry: Unique identifier, UMIN000021132.
Sections du résumé
BACKGROUND
BACKGROUND
Patients with unprovoked venous thromboembolisms (VTEs) can be sub-classified based on the different phenotypes using a latent class analysis (LCA), which might be useful for selecting individual management strategies.
METHODS
METHODS
In the COMMAND VTE Registry-2 database enrolling 5197 VTE patients, the current derivation cohort consisted of 1556 patients with unprovoked VTEs. We conducted clustering with an LCA, and the patients were classified into subgroups with the highest probability. We compared the clinical characteristics and outcomes among the developed subgroups.
RESULTS
RESULTS
This LCA model proposed 3 subgroups based on 8 clinically relevant variables, and classified 592, 813, and 151 patients as Class I, II, and III, respectively. Based on the clinical features, we named Class I the younger, Class II the older with a few comorbidities, and Class III the older with many comorbidities. The cumulative 3-year anticoagulation discontinuation rate was highest in the older with many comorbidities (Class III) (39.9 %, 36.1 %, and 48.4 %, P = 0.02). There was no significant difference in the cumulative 5-year incidence of recurrent VTEs among the 3 classes (12.8 %, 11.1 %, and 4.0 % P = 0.20), whereas the cumulative 5-year incidence of major bleeding was significantly higher in the older with many comorbidities (Class III) (7.8 %, 12.7 %, and 17.8 %, P = 0.04).
CONCLUSION
CONCLUSIONS
The current LCA revealed that patients with unprovoked VTEs could be sub-classified into further phenotypes depending on the patient characteristics. Each subclass phenotype could have different clinical outcomes risks especially a bleeding risk, which could have a potential benefit when considering the individual anticoagulation strategies.
CLINICAL TRIAL REGISTRATION
BACKGROUND
URL: http://www.umin.ac.jp/ctr/index.htm COMMAND VTE Registry-2: Unique identifier, UMIN000044816 COMMAND VTE Registry: Unique identifier, UMIN000021132.
Identifiants
pubmed: 38653180
pii: S0049-3848(24)00131-2
doi: 10.1016/j.thromres.2024.04.017
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
27-36Informations de copyright
Copyright © 2024 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest Dr. Yamashita received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo, and grant support from Bayer Healthcare and Daiichi-Sankyo. Dr. Morimoto reports lecturer fees from Bristol-Myers Squibb, Daiichi Sankyo, Japan Lifeline, Kowa, Kyocera, Novartis, and Toray; manuscript fees from Bristol-Myers Squibb, and Kowa, and the advisory board for Sanofi. Dr. Kaneda received lecture fees from Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo. Dr. Nishimoto received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo. Dr. Ikeda N. received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, and Daiichi-Sankyo. Dr. Ikeda S. received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, and Daiichi-Sankyo. Dr. Ogihara received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo, and research funds from Bayer Healthcare and Daiichi-Sankyo. Dr. Koitabashi received lecture fees from Bayer Healthcare and grant support from Pfizer. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.