Development and external validation of a prognostic model for ischaemic stroke after surgery.


Journal

British journal of anaesthesia
ISSN: 1471-6771
Titre abrégé: Br J Anaesth
Pays: England
ID NLM: 0372541

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 11 01 2021
revised: 10 05 2021
accepted: 14 05 2021
pubmed: 26 7 2021
medline: 17 11 2021
entrez: 25 7 2021
Statut: ppublish

Résumé

There is an under-recognised patient cohort at elevated risk of postoperative ischaemic stroke. We aimed to develop and validate a prognostic model for the identification of such patients at high risk of ischaemic stroke within 1 yr after noncardiac surgery. This was a hospital registry study of adult patients undergoing noncardiac surgery between 2005 and 2017 at two independent healthcare networks in Massachusetts, USA without a preoperative indication for therapeutic anticoagulation. Logistic regression was used to fit a model from a priori defined candidate predictors for the outcome 1 yr postoperative ischaemic stroke. To enhance clinical applicability, the model was simplified to a scoring system and externally validated. In the development (n=107 756) and validation (n=141 724) cohorts, 1.4% and 0.5% of patients had an ischaemic stroke up to 1 yr postoperatively. The final model included 13 variables (patient characteristics, comorbidities, procedural factors), considering sub-models conditional on a previous history of ischaemic stroke. Areas under the curve were 0.89 (95% confidence interval 0.89-0.90) and 0.88 (95% confidence interval 0.86-0.89) in the development and validation cohorts. Decision curve analysis indicated positive net benefits superior to other prediction instruments. Stroke after surgery (STRAS) screening can reliably identify patients with a high risk for ischaemic stroke during the first year after surgery. A STRAS-guided risk stratification may inform the recruitment to future randomised trials testing the efficacy of treatments for the prevention of postoperative ischaemic stroke.

Sections du résumé

BACKGROUND BACKGROUND
There is an under-recognised patient cohort at elevated risk of postoperative ischaemic stroke. We aimed to develop and validate a prognostic model for the identification of such patients at high risk of ischaemic stroke within 1 yr after noncardiac surgery.
METHODS METHODS
This was a hospital registry study of adult patients undergoing noncardiac surgery between 2005 and 2017 at two independent healthcare networks in Massachusetts, USA without a preoperative indication for therapeutic anticoagulation. Logistic regression was used to fit a model from a priori defined candidate predictors for the outcome 1 yr postoperative ischaemic stroke. To enhance clinical applicability, the model was simplified to a scoring system and externally validated.
RESULTS RESULTS
In the development (n=107 756) and validation (n=141 724) cohorts, 1.4% and 0.5% of patients had an ischaemic stroke up to 1 yr postoperatively. The final model included 13 variables (patient characteristics, comorbidities, procedural factors), considering sub-models conditional on a previous history of ischaemic stroke. Areas under the curve were 0.89 (95% confidence interval 0.89-0.90) and 0.88 (95% confidence interval 0.86-0.89) in the development and validation cohorts. Decision curve analysis indicated positive net benefits superior to other prediction instruments.
CONCLUSIONS CONCLUSIONS
Stroke after surgery (STRAS) screening can reliably identify patients with a high risk for ischaemic stroke during the first year after surgery. A STRAS-guided risk stratification may inform the recruitment to future randomised trials testing the efficacy of treatments for the prevention of postoperative ischaemic stroke.

Identifiants

pubmed: 34303492
pii: S0007-0912(21)00373-1
doi: 10.1016/j.bja.2021.05.035
pii:
doi:

Types de publication

Journal Article Multicenter Study Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

713-721

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2021 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

Auteurs

Katharina Platzbecker (K)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Stephanie D Grabitz (SD)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Dana Raub (D)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Maíra I Rudolph (MI)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Sabine Friedrich (S)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Nathan Vinzant (N)

Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Mayo Clinic, Rochester, MN, USA.

Tobias Kurth (T)

Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Christian Weimar (C)

Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany; BDH-clinic Elzach, Elzach, Germany.

Deepak L Bhatt (DL)

Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Ala Nozari (A)

Department of Anesthesia, Boston Medical Center, Boston, MA, USA.

Timothy T Houle (TT)

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Xinling Xu (X)

Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.

Matthias Eikermann (M)

Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA; Klinik für Anästhesiologie, Universitätsklinikum Essen, Essen, Germany. Electronic address: meikermann@montefiore.org.

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Classifications MeSH