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CRP (C-reactive protein) in outcome prediction after subarachnoid haemorrhage and the role of machine learning

CRP (C-reactive protein) in outcome prediction after subarachnoid haemorrhage and the role of machine learning
CRP (C-reactive protein) in outcome prediction after subarachnoid haemorrhage and the role of machine learning

Background and Purpose: Outcome prediction after aneurysmal subarachnoid hemorrhage (aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with outcome, but it is unclear if this is independent of other predictors and applies to aSAH of all grades. Therefore, the role of CRP in aSAH outcome prediction models is unknown. The purpose of this study is to assess if CRP is an independent predictor of outcome after aSAH, develop new prognostic models incorporating CRP, and test whether these can be improved by application of machine learning.

Methods: This was an individual patient-level analysis of data from patients within 72 hours of aSAH from 2 prior studies. A panel of statistical learning methods including logistic regression, random forest, and support vector machines were used to assess the relationship between CRP and modified Rankin Scale. Models were compared with the full Subarachnoid Hemmorhage International Trialists’ (SAHIT) prediction tool of outcome after aSAH and internally validated using cross-validation.

Results: One thousand and seventeen patients were included for analysis. CRP on the first day after ictus was an independent predictor of outcome. The full SAHIT model achieved an area under the receiver operator characteristics curve (AUC) of 0.831. Addition of CRP to the predictors of the full SAHIT model improved model performance (AUC, 0.846, P=0.01). This improvement was not enhanced when learning was performed using a random forest (AUC, 0.807), but was with a support vector machine (AUC of 0.960, P <0.001).

Conclusions: CRP is an independent predictor of outcome after aSAH. Its inclusion in prognostic models improves performance, although the magnitude of improvement is probably insufficient to be relevant clinically on an individual patient level, and of more relevance in research. Greater improvements in model performance are seen with support vector machines but these models have the highest classification error rate on internal validation and require external validation and calibration.

C-reactive protein, machine learning, prognosis, subarachnoid hemorrhage, support vector machine
0039-2499
3276-3285
Gaastra, Benjamin
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Barron, Peter
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Newitt, Laura
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Chhugani, Simran
c97445a2-cd47-4364-9e8b-239626c05e85
Turner, Carole
8e4e5fa4-c97b-4f00-9c65-27119cc5945e
Kirkpatrick, Peter
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Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Galea, Ian
66209a2f-f7e6-4d63-afe4-e9299f156f0b
Bulters, Diederik
d6f9644a-a32f-45d8-b5ed-be54486ec21d
Gaastra, Benjamin
c7b7f371-706b-4d59-9150-94e8f254e205
Barron, Peter
2b425830-13ac-45fa-b6cf-28975c20b6ad
Newitt, Laura
698a6e22-a733-4523-8e91-7a9295a9054c
Chhugani, Simran
c97445a2-cd47-4364-9e8b-239626c05e85
Turner, Carole
8e4e5fa4-c97b-4f00-9c65-27119cc5945e
Kirkpatrick, Peter
9ae3c516-4485-49c3-8930-9cc837986311
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Galea, Ian
66209a2f-f7e6-4d63-afe4-e9299f156f0b
Bulters, Diederik
d6f9644a-a32f-45d8-b5ed-be54486ec21d

Gaastra, Benjamin, Barron, Peter, Newitt, Laura, Chhugani, Simran, Turner, Carole, Kirkpatrick, Peter, Macarthur, Benjamin, Galea, Ian and Bulters, Diederik (2021) CRP (C-reactive protein) in outcome prediction after subarachnoid haemorrhage and the role of machine learning. Stroke, 52 (10), 3276-3285. (doi:10.1161/STROKEAHA.120.030950).

Record type: Article

Abstract

Background and Purpose: Outcome prediction after aneurysmal subarachnoid hemorrhage (aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with outcome, but it is unclear if this is independent of other predictors and applies to aSAH of all grades. Therefore, the role of CRP in aSAH outcome prediction models is unknown. The purpose of this study is to assess if CRP is an independent predictor of outcome after aSAH, develop new prognostic models incorporating CRP, and test whether these can be improved by application of machine learning.

Methods: This was an individual patient-level analysis of data from patients within 72 hours of aSAH from 2 prior studies. A panel of statistical learning methods including logistic regression, random forest, and support vector machines were used to assess the relationship between CRP and modified Rankin Scale. Models were compared with the full Subarachnoid Hemmorhage International Trialists’ (SAHIT) prediction tool of outcome after aSAH and internally validated using cross-validation.

Results: One thousand and seventeen patients were included for analysis. CRP on the first day after ictus was an independent predictor of outcome. The full SAHIT model achieved an area under the receiver operator characteristics curve (AUC) of 0.831. Addition of CRP to the predictors of the full SAHIT model improved model performance (AUC, 0.846, P=0.01). This improvement was not enhanced when learning was performed using a random forest (AUC, 0.807), but was with a support vector machine (AUC of 0.960, P <0.001).

Conclusions: CRP is an independent predictor of outcome after aSAH. Its inclusion in prognostic models improves performance, although the magnitude of improvement is probably insufficient to be relevant clinically on an individual patient level, and of more relevance in research. Greater improvements in model performance are seen with support vector machines but these models have the highest classification error rate on internal validation and require external validation and calibration.

Text
Gaastra et al Stroke 2021 inc supp - Accepted Manuscript
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 31 March 2021
e-pub ahead of print date: 9 July 2021
Published date: 1 October 2021
Additional Information: Funding Information: The STASH study (Simvastatin in Aneurysmal Subarachnoid Haemorrhage) was funded by the British Heart Foundation (SP/08/003/24065). Publisher Copyright: © 2021 Lippincott Williams and Wilkins. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: C-reactive protein, machine learning, prognosis, subarachnoid hemorrhage, support vector machine

Identifiers

Local EPrints ID: 449430
URI: http://eprints.soton.ac.uk/id/eprint/449430
ISSN: 0039-2499
PURE UUID: 226624a9-892e-48e6-b318-bfb271d0ce23
ORCID for Benjamin Gaastra: ORCID iD orcid.org/0000-0002-7517-6882
ORCID for Ian Galea: ORCID iD orcid.org/0000-0002-1268-5102
ORCID for Diederik Bulters: ORCID iD orcid.org/0000-0001-9884-9050

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Date deposited: 28 May 2021 16:31
Last modified: 28 Apr 2022 06:16

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Contributors

Author: Peter Barron
Author: Laura Newitt
Author: Simran Chhugani
Author: Carole Turner
Author: Peter Kirkpatrick
Author: Ian Galea ORCID iD
Author: Diederik Bulters ORCID iD

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