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Meta-Analysis
. 2022 Dec 11;26(1):382.
doi: 10.1186/s13054-022-04263-y.

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

Simon A Amacher #  1   2 , René Blatter #  2 , Matthias Briel  3   4   5 , Christian Appenzeller-Herzog  6 , Chantal Bohren  2 , Christoph Becker  2   7 , Katharina Beck  2 , Sebastian Gross  2 , Kai Tisljar  1 , Raoul Sutter  1   5 , Stephan Marsch  1   5 , Sabina Hunziker  8   9
Affiliations
Meta-Analysis

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

Simon A Amacher et al. Crit Care. .

Abstract

This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions.

Keywords: CAHP; Cardiac arrest; GO-FAR; Neurological outcome; OHCA; Prediction model; Prognostication.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the search and screening process
Fig. 2
Fig. 2
Meta-analysis of the C-statistic for the OHCA score. CI Confidence interval; KOCAR King’s Out-of-Hospital Cardiac Arrest Registry; OHCA Out-of-Hospital Cardiac Arrest; REML Restricted maximum likelihood; RFH Royal Free Hospital London
Fig. 3
Fig. 3
Meta-analysis of the total observed versus expected (O:E) ratio for the OHCA score. CI Confidence Interval; KOCAR King’s Out-of-Hospital Cardiac Arrest Registry; OHCA Out-of-Hospital Cardiac Arrest; REML Restricted maximum likelihood
Fig. 4
Fig. 4
Meta-analysis of the C-statistic for the CAHP score. CAHP Cardiac Arrest Hospital Prognosis; CI Confidence Interval; KOCAR King’s Out-of-Hospital Cardiac Arrest Registry; REML Restricted maximum likelihood
Fig. 5
Fig. 5
Meta-analysis of the total observed vs. expected (O:E) ratio for the CAHP score. CAHP Cardiac Arrest Hospital Prognosis; CI Confidence Interval; KOCAR King’s Out-of-Hospital Cardiac Arrest Registry; REML Restricted maximum likelihood
Fig. 6
Fig. 6
Meta-analysis of the C-statistic for the GO-FAR score. CI Confidence interval; GO-FAR Good Outcome Following Attempted Resuscitation; REML Restricted maximum likelihood
Fig. 7
Fig. 7
Meta-analysis of the total observed vs. expected (O:E) ratio for the GO-FAR score. CI Confidence interval; GO-FAR Good Outcome Following Attempted Resuscitation; REML Restricted maximum likelihood
See this image and copyright information in PMC

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