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Predicting neurolog...
Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm
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- Andersson, Peder (författare)
- Lund University,Lunds universitet,Anestesiologi och intensivvård,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Anesthesiology and Intensive Care,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Skåne University Hospital
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- Johnsson, Jesper (författare)
- Lund University,Lunds universitet,Kliniska Vetenskaper, Helsingborg,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Clinical Sciences, Helsingborg,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Helsingborg Hospital
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- Björnsson, Ola (författare)
- Lund University,Lunds universitet,Förbränningsmotorer,Institutionen för energivetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,Matematisk statistik,Matematikcentrum,Combustion Engines,Department of Energy Sciences,Departments at LTH,Faculty of Engineering, LTH,Mathematical Statistics,Centre for Mathematical Sciences,Faculty of Engineering, LTH
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- Cronberg, Tobias (författare)
- Lund University,Lunds universitet,Brain Injury After Cardiac Arrest,Forskargrupper vid Lunds universitet,Lund University Research Groups,Skåne University Hospital
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- Hassager, C. (författare)
- University of Copenhagen,Copenhagen University Hospital
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- Zetterberg, Henrik, 1973 (författare)
- University of Gothenburg,Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi,Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry,University College London,Sahlgrenska University Hospital
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- Stammet, P. (författare)
- National Fire and Rescue Corps
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- Undén, Johan (författare)
- Lund University,Lunds universitet,Anestesiologi och intensivvård,Forskargrupper vid Lunds universitet,Anaesthesiology and Intensive Care Medicine,Lund University Research Groups,Halmstad County Hospital
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- Kjaergaard, J. (författare)
- Copenhagen University Hospital,University of Copenhagen
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- Blennow, Kaj, 1958 (författare)
- University of Gothenburg,Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi,Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry,Sahlgrenska University Hospital
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- Lilja, Gisela (författare)
- Lund University,Lunds universitet,Brain Injury After Cardiac Arrest,Forskargrupper vid Lunds universitet,Lund University Research Groups,Skåne University Hospital
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- Wise, M. P. (författare)
- University Hospital of Wales
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- Dankiewicz, Josef (författare)
- Lund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Skåne University Hospital
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- Nielsen, Niklas (författare)
- Lund University,Lunds universitet,Centrum för hjärtstopp,Forskargrupper vid Lunds universitet,Center for cardiac arrest,Lund University Research Groups,Helsingborg Hospital
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- Frigyesi, Attila (författare)
- Lund University,Lunds universitet,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Intensivvårdsepidemiologi,Forskargrupper vid Lunds universitet,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Intensive Care Epidemiology,Lund University Research Groups,Skåne University Hospital
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- Friberg, Hans (författare)
- Lund University,Lunds universitet,Centrum för hjärtstopp,Forskargrupper vid Lunds universitet,Center for cardiac arrest,Lund University Research Groups,Skåne University Hospital
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(creator_code:org_t)
- 2021-02-25
- 2021
- Engelska.
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Ingår i: Critical Care. - : Springer Science and Business Media LLC. - 1364-8535. ; 25:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- BackgroundPrognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers.MethodsWe performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients' background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1-2 whilst a poor outcome was defined as CPC 3-5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets.ResultsAUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% (p<0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions.ConclusionsIn this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Nyckelord
- Machine learning
- Artificial intelligence
- Artificial neural networks
- Neural networks
- Out-of-hospital cardiac arrest
- Cardiac arrest
- Cerebral performance category
- Critical care
- Intensive care
- Prediction
- Prognostication
- General & Internal Medicine
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Andersson, Peder
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Johnsson, Jesper
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Björnsson, Ola
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Cronberg, Tobias
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Hassager, C.
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Zetterberg, Henr ...
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visa fler...
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Stammet, P.
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Undén, Johan
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Kjaergaard, J.
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Blennow, Kaj, 19 ...
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Lilja, Gisela
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Wise, M. P.
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Dankiewicz, Jose ...
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Nielsen, Niklas
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Frigyesi, Attila
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Friberg, Hans
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Medicinska och f ...
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och Neurovetenskaper
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Neurologi
- Artiklar i publikationen
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Critical Care
- Av lärosätet
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Göteborgs universitet
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Lunds universitet