A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning: a nationwide prospective cohort study.
Sci Bull (Beijing)
; 68(18): 2106-2114, 2023 09 30.
Article
em En
| MEDLINE
| ID: mdl-37599175
ABSTRACT
Rare but critical bleeding events in primary immune thrombocytopenia (ITP) present life-threatening complications in patients with ITP, which severely affect their prognosis, quality of life, and treatment decisions. Although several studies have investigated the risk factors related to critical bleeding in ITP, large sample size data, consistent definitions, large-scale multicenter findings, and prediction models for critical bleeding events in patients with ITP are unavailable. For the first time, in this study, we applied the newly proposed critical ITP bleeding criteria by the International Society on Thrombosis and Hemostasis for large sample size data and developed the first machine learning (ML)-based online application for predict critical ITP bleeding. In this research, we developed and externally tested an ML-based model for determining the risk of critical bleeding events in patients with ITP using large multicenter data across China. Retrospective data from 8 medical centers across the country were obtained for model development and prospectively tested in 39 medical centers across the country over a year. This system exhibited good predictive capabilities for training, validation, and test datasets. This convenient web-based tool based on a novel algorithm can rapidly identify the bleeding risk profile of patients with ITP and facilitate clinical decision-making and reduce the occurrence of adversities.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Trombocitopenia
/
Púrpura Trombocitopênica Idiopática
Tipo de estudo:
Clinical_trials
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Diagnostic_studies
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Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Aspecto:
Patient_preference
Limite:
Humans
Idioma:
En
Revista:
Sci Bull (Beijing)
Ano de publicação:
2023
Tipo de documento:
Article