Prearrest prediction of favourable neurological survival following in-hospital cardiac arrest: The Prediction of outcome for In-Hospital Cardiac Arrest (PIHCA) score.


Journal

Resuscitation
ISSN: 1873-1570
Titre abrégé: Resuscitation
Pays: Ireland
ID NLM: 0332173

Informations de publication

Date de publication:
10 2019
Historique:
received: 22 01 2019
revised: 02 08 2019
accepted: 06 08 2019
pubmed: 15 8 2019
medline: 18 9 2020
entrez: 15 8 2019
Statut: ppublish

Résumé

A prearrest prediction tool can aid clinicians in consolidating objective findings with clinical judgement and in balance with the values of the patient be a part of the decision process for do-not-attempt-resuscitation (DNAR) orders. A previous prearrest prediction tool for in-hospital cardiac arrest (IHCA) have not performed satisfactory in external validation in a Swedish cohort. Therefore our aim was to develop a prediction model for the Swedish setting. Model development was based on previous external validation of The Good Outcome Following Attempted Resuscitation (GO-FAR) score, with 717 adult IHCAs. It included redefinition and reduction of predictors, and addition of chronic comorbidity, to create a full model of 9 predictors. Outcome was favourable neurological survival defined as Cerebral Performance Category score 1-2  at discharge. The likelihood of favourable neurological survival was categorised into very low (<1%), low (1-3%) and above low (>3%). We called the model the Prediction of outcome for In-Hospital Cardiac Arrest (PIHCA) score. The AUROC was 0.808 (95% CI 0.807-0.810) and calibration was satisfactory. With a cutoff of 3% likelihood of favourable neurological survival sensitivity was 99.4% and specificity 8.4%. Although specificity was limited, predictive value for classification into ≤3% likelihood of favorable neurological survival was high (97.4%) and false classification into ≤3% likelihood of favourable neurological survival was low (0.6%). The PIHCA score has the potential to be used as an objective tool in prearrest prediction of outcome after IHCA, as part of the decision process for a DNAR order.

Sections du résumé

BACKGROUND
A prearrest prediction tool can aid clinicians in consolidating objective findings with clinical judgement and in balance with the values of the patient be a part of the decision process for do-not-attempt-resuscitation (DNAR) orders. A previous prearrest prediction tool for in-hospital cardiac arrest (IHCA) have not performed satisfactory in external validation in a Swedish cohort. Therefore our aim was to develop a prediction model for the Swedish setting.
METHODS
Model development was based on previous external validation of The Good Outcome Following Attempted Resuscitation (GO-FAR) score, with 717 adult IHCAs. It included redefinition and reduction of predictors, and addition of chronic comorbidity, to create a full model of 9 predictors. Outcome was favourable neurological survival defined as Cerebral Performance Category score 1-2  at discharge. The likelihood of favourable neurological survival was categorised into very low (<1%), low (1-3%) and above low (>3%).
RESULTS
We called the model the Prediction of outcome for In-Hospital Cardiac Arrest (PIHCA) score. The AUROC was 0.808 (95% CI 0.807-0.810) and calibration was satisfactory. With a cutoff of 3% likelihood of favourable neurological survival sensitivity was 99.4% and specificity 8.4%. Although specificity was limited, predictive value for classification into ≤3% likelihood of favorable neurological survival was high (97.4%) and false classification into ≤3% likelihood of favourable neurological survival was low (0.6%).
CONCLUSION
The PIHCA score has the potential to be used as an objective tool in prearrest prediction of outcome after IHCA, as part of the decision process for a DNAR order.

Identifiants

pubmed: 31412292
pii: S0300-9572(19)30568-4
doi: 10.1016/j.resuscitation.2019.08.010
pii:
doi:

Types de publication

Journal Article Multicenter Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

92-99

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Eva Piscator (E)

Center for Resuscitation Science, Department of Medicine Solna, Karolinska Institutet and Function of Emergency Medicine Solna, Karolinska University Hospital, Stockholm, Sweden. Electronic address: eva.piscator@ki.se.

Katarina Göransson (K)

Department of Medicine Solna, Karolinska Institutet and Function of Emergency Medicine, Karolinska University Hospital, Stockholm, Sweden.

Sune Forsberg (S)

Center for Resuscitation Science, Department of Medicine Solna, Karolinska Institutet and Department of Anaesthesiology and Intensive Care, Norrtälje Hospital, Sweden.

Matteo Bottai (M)

Unit of Biostatistics, Department of Environmental Medicine (IMM), Karolinska Institutet, Stockholm, Sweden.

Mark Ebell (M)

Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, USA.

Johan Herlitz (J)

Center of Prehospital Research, Faculty of Caring Science, Work-life and Welfare, University of Borås and Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden.

Therese Djärv (T)

Center for Resuscitation Science, Department of Medicine Solna, Karolinska Institutet and Function of Emergency Medicine Solna, Karolinska University Hospital, Stockholm, Sweden.

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Classifications MeSH