Predicting hemorrhagic transformation in patients not submitted to reperfusion therapies.


Journal

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
ISSN: 1532-8511
Titre abrégé: J Stroke Cerebrovasc Dis
Pays: United States
ID NLM: 9111633

Informations de publication

Date de publication:
Aug 2020
Historique:
received: 14 01 2020
revised: 22 04 2020
accepted: 05 05 2020
entrez: 22 7 2020
pubmed: 22 7 2020
medline: 31 10 2020
Statut: ppublish

Résumé

Well studied in patients with ischemic stroke after reperfusion therapies (RT), hemorrhagic transformation (HT) is also common in patients not treated with RT and can lead to disability even in initially asymptomatic cases. The best predictors of HT in patients not treated with RT are not well established. Therefore, we aimed to identify predictors of HT in patients not submitted to RT and create a user-friendly predictive score (PROpHET). Patients admitted to a Comprehensive Stroke Center from 2015 to 2017 were prospectively evaluated and randomly selected to the derivation cohort. A multivariable logistic regression modeling was built to produce a predictive grading score for HT. The external validation was assessed using datasets from 7 Comprehensive Stroke Centers using the area under the receiver operating characteristic curve (AUROC). In the derivation group, 448 patients were included in the final analysis. The validation group included 2,683 patients. The score derived from significant predictors of HT in the multivariate logistic regression analysis was male sex (1 point), ASPECTS ≤ 7 (2 points), presence of leukoaraiosis (1 point), hyperdense cerebral middle artery sign (1 point), glycemia at admission ≥180 mg/dL (1 point), cardioembolism (1 point) and lacunar syndrome (-3 points) as a protective factor. The grading score ranges from -3 to 7. A Score ≥3 had 78.2% sensitivity and 75% specificity, and AUROC of 0.82 for all cases of HT. In the validation cohort, our score had an AUROC of 0.83. The PROpHET is a simple, quick, cost-free, and easy-to-perform tool that allows risk stratification of HT in patients not submitted to RT. A cost-free computerized version of our score is available online with a user-friendly interface.

Sections du résumé

BACKGROUND BACKGROUND
Well studied in patients with ischemic stroke after reperfusion therapies (RT), hemorrhagic transformation (HT) is also common in patients not treated with RT and can lead to disability even in initially asymptomatic cases. The best predictors of HT in patients not treated with RT are not well established. Therefore, we aimed to identify predictors of HT in patients not submitted to RT and create a user-friendly predictive score (PROpHET).
MATERIAL AND METHODS METHODS
Patients admitted to a Comprehensive Stroke Center from 2015 to 2017 were prospectively evaluated and randomly selected to the derivation cohort. A multivariable logistic regression modeling was built to produce a predictive grading score for HT. The external validation was assessed using datasets from 7 Comprehensive Stroke Centers using the area under the receiver operating characteristic curve (AUROC).
RESULTS RESULTS
In the derivation group, 448 patients were included in the final analysis. The validation group included 2,683 patients. The score derived from significant predictors of HT in the multivariate logistic regression analysis was male sex (1 point), ASPECTS ≤ 7 (2 points), presence of leukoaraiosis (1 point), hyperdense cerebral middle artery sign (1 point), glycemia at admission ≥180 mg/dL (1 point), cardioembolism (1 point) and lacunar syndrome (-3 points) as a protective factor. The grading score ranges from -3 to 7. A Score ≥3 had 78.2% sensitivity and 75% specificity, and AUROC of 0.82 for all cases of HT. In the validation cohort, our score had an AUROC of 0.83.
CONCLUSIONS CONCLUSIONS
The PROpHET is a simple, quick, cost-free, and easy-to-perform tool that allows risk stratification of HT in patients not submitted to RT. A cost-free computerized version of our score is available online with a user-friendly interface.

Identifiants

pubmed: 32689629
pii: S1052-3057(20)30346-3
doi: 10.1016/j.jstrokecerebrovasdis.2020.104940
pii:
doi:

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

104940

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors state not disclosures related to the main subject of this research.

Auteurs

Joao Brainer Clares de Andrade (JBC)

Universidade Federal de São Paulo, Sao Paulo, SP, Brazil; Columbia University, Doris and Stanley Tananbaum Stroke Center, 710 W 168th St. Neurological Institute of New York. 6TH Floor. NI 614. ZIP 10032. New York City, NY, USA. Electronic address: joao.brainer@unifesp.br.

Jay P Mohr (JP)

Columbia University, Doris and Stanley Tananbaum Stroke Center, 710 W 168th St. Neurological Institute of New York. 6TH Floor. NI 614. ZIP 10032. New York City, NY, USA.

Fabricio Oliveira Lima (FO)

Universidade de Fortaleza, Fortaleza, Ceará, Brazil; Hospital Geral de Fortaleza, Ceara, Brazil.

Joao José de Freitas Carvalho (JJF)

Hospital Geral de Fortaleza, Ceara, Brazil.

Victor Aguiar Evangelista de Farias (VAE)

Universidade Federal do Ceara, Brazil.

Jamary Oliveira-Filho (J)

Universidade Federal da Bahia, Brazil.

Octavio Marques Pontes-Neto (OM)

Universidade de São Paulo, Ribeirão Preto, Brazil.

Rodrigo Bazan (R)

Universidade Estadual Paulista, Brazil.

Kristel Larisa Back Merida (KLB)

Hospital Instituto de Neurologia de Curitiba, Brazil.

Luisa Franciscato (L)

Universidade de São Paulo, Ribeirão Preto, Brazil.

Matheus Mendes Pires (MM)

Universidade Federal da Bahia, Brazil.

Gabriel Pinheiro Modolo (GP)

Universidade Estadual Paulista, Brazil.

Mayara Silva Marques (MS)

Hospital Instituto de Neurologia de Curitiba, Brazil.

Renata Carolina Acri Nunes Miranda (RCAN)

Universidade Federal de São Paulo, Sao Paulo, SP, Brazil.

Gisele Sampaio Silva (GS)

Universidade Federal de São Paulo, Sao Paulo, SP, Brazil.

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Classifications MeSH