Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm
Background Prognostication 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 inf...
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| Published in | Critical care (London, England) Vol. 25; no. 1; pp. 83 - 12 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
25.02.2021
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-8535 1466-609X 1364-8535 1466-609X 1366-609X |
| DOI | 10.1186/s13054-021-03505-9 |
Cover
| Abstract | Background
Prognostication 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.
Methods
We 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.
Results
AUROC 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.
Conclusions
In 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. |
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| AbstractList | Background Prognostication 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. Methods We 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. Results AUROC 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. Conclusions In 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. Background: Prognostication 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. Methods: We 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. Results: AUROC 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. Conclusions: In 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. Abstract Background Prognostication 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. Methods We 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. Results AUROC 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. Conclusions In 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. Prognostication 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.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.We 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.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.AUROC 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.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.In 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.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. Prognostication 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. We 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. AUROC 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. In 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. Background Prognostication 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. Methods We 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. Results AUROC 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. Conclusions In 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. Prognostication 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. We 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. AUROC 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. In 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. Background Prognostication 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. Methods We 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. Results AUROC 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. Conclusions In 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. Keywords: 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 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. |
| ArticleNumber | 83 |
| Audience | Academic |
| Author | Cronberg, Tobias Undén, Johan Johnsson, Jesper Zetterberg, Henrik Dankiewicz, Josef Andersson, Peder Hassager, Christian Kjaergaard, Jesper Wise, Matt P. Björnsson, Ola Lilja, Gisela Blennow, Kaj Frigyesi, Attila Friberg, Hans Stammet, Pascal Nielsen, Niklas |
| Author_xml | – sequence: 1 givenname: Peder orcidid: 0000-0001-8261-9613 surname: Andersson fullname: Andersson, Peder email: peder.andersson@med.lu.se organization: Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Skåne University Hospital, Department of Intensive and Perioperative Care, Skåne University Hospital – sequence: 2 givenname: Jesper surname: Johnsson fullname: Johnsson, Jesper organization: Department of Clinical Sciences Lund, Anesthesia and Intensive Care, Lund University, Helsingborg Hospital – sequence: 3 givenname: Ola surname: Björnsson fullname: Björnsson, Ola organization: Department of Energy Sciences, Faculty of Engineering, Lund University, Centre for Mathematical Sciences, Mathematical Statistics, Lund University – sequence: 4 givenname: Tobias surname: Cronberg fullname: Cronberg, Tobias organization: Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital – sequence: 5 givenname: Christian surname: Hassager fullname: Hassager, Christian organization: Department of Cardiology, Rigshospitalet and Department of Clinical Medicine, University of Copenhagen – sequence: 6 givenname: Henrik surname: Zetterberg fullname: Zetterberg, Henrik organization: Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy At the University of Gothenburg, Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, UK Dementia Research Institute At UCL – sequence: 7 givenname: Pascal surname: Stammet fullname: Stammet, Pascal organization: Medical and Health Directorate, National Fire and Rescue Corps – sequence: 8 givenname: Johan surname: Undén fullname: Undén, Johan organization: Department of Clinical Sciences Malmö, Anaesthesia and Intensive Care, Lund University, Hallands Hospital Halmstad – sequence: 9 givenname: Jesper surname: Kjaergaard fullname: Kjaergaard, Jesper organization: Department of Cardiology, Rigshospitalet and Department of Clinical Medicine, University of Copenhagen – sequence: 10 givenname: Hans surname: Friberg fullname: Friberg, Hans organization: Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Skåne University Hospital – sequence: 11 givenname: Kaj surname: Blennow fullname: Blennow, Kaj organization: Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy At the University of Gothenburg, Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital – sequence: 12 givenname: Gisela surname: Lilja fullname: Lilja, Gisela organization: Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital – sequence: 13 givenname: Matt P. surname: Wise fullname: Wise, Matt P. organization: Adult Critical Care, University Hospital of Wales – sequence: 14 givenname: Josef surname: Dankiewicz fullname: Dankiewicz, Josef organization: Department of Clinical Sciences Lund, Cardiology, Lund University, Skåne University Hospital – sequence: 15 givenname: Niklas surname: Nielsen fullname: Nielsen, Niklas organization: Department of Clinical Sciences Lund, Anesthesia and Intensive Care, Lund University, Helsingborg Hospital – sequence: 16 givenname: Attila surname: Frigyesi fullname: Frigyesi, Attila organization: Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Skåne University Hospital, Centre for Mathematical Sciences, Mathematical Statistics, Lund University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33632280$$D View this record in MEDLINE/PubMed https://gup.ub.gu.se/publication/303220$$DView record from Swedish Publication Index |
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| Cites_doi | 10.3906/sag-1503-64 10.1016/j.amjcard.2016.07.006 10.18637/jss.v061.i08 10.15441/ceem.17.267 10.1016/j.jacc.2015.03.538 10.1016/j.ahj.2012.01.013 10.1148/radiol.2019191293 10.1016/j.resuscitation.2016.04.007 10.1016/S1474-4422(20)30117-4 10.1016/j.resuscitation.2015.10.009 10.1001/jamaneurol.2018.3223 10.1186/s13054-017-1677-2 10.1007/s00134-020-06218-9 10.1016/0735-6757(86)90255-X 10.1007/s00134-020-06080-9 10.1016/j.resuscitation.2009.04.003 10.1089/ther.2015.0033 10.1186/s13054-020-02904-8 10.1016/j.resuscitation.2020.05.016 10.1007/s00134-015-4094-5 10.1001/jamaneurol.2015.0169 10.7326/M14-0697 10.1186/1745-6215-14-300 10.1007/s00134-015-4051-3 10.1186/s40560-019-0393-1 10.1056/NEJMoa1310519 10.1186/s13054-018-2060-7 10.1002/ana.25067 10.1016/j.resuscitation.2019.04.007 10.1186/1471-2105-12-77 10.1186/s13054-020-03103-1 10.1101/2020.04.12.20045146 |
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| CorporateAuthor | Institutioner vid LTH Naturvetenskapliga fakulteten Faculty of Engineering, LTH Lunds Tekniska Högskola Centrum för hjärtstopp Mathematical Statistics Anaesthesiology and Intensive Care Medicine Intensivvårdsepidemiologi Kardiologi Department of Clinical Sciences, Lund Clinical Sciences, Helsingborg Department of Energy Sciences Neurology, Lund Anesthesiology and Intensive Care Institutionen för energivetenskaper Matematikcentrum Section IV Medicinska fakulteten Sektion IV Center for cardiac arrest Cardiology Neurologi, Lund Departments at LTH Matematisk statistik Institutionen för kliniska vetenskaper, Lund Sektion II Kliniska Vetenskaper, Helsingborg Section II Lunds universitet Brain Injury After Cardiac Arrest Förbränningsmotorer Faculty of Science Department of Clinical Sciences, Malmö Lund University Intensive Care Epidemiology Faculty of Medicine Centre for Mathematical Sciences Anestesiologi och intensivvård Combustion Engines Institutionen för kliniska vetenskaper, Malmö |
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| References | PL Myhre (3505_CR14) 2016; 104 N Nielsen (3505_CR21) 2013; 369 M Moseby-Knappe (3505_CR5) 2019; 76 3505_CR6 C Sandroni (3505_CR4) 2018; 22 N Mattsson (3505_CR8) 2017; 82 GS Collins (3505_CR33) 2015; 162 JP Nolan (3505_CR2) 2015; 41 MJ Kim (3505_CR7) 2018; 5 G Ok (3505_CR11) 2016; 46 3505_CR10 G Holmgren (3505_CR17) 2019; 7 3505_CR30 JM Kwon (3505_CR19) 2019; 139 T Kaneko (3505_CR9) 2009; 80 3505_CR36 3505_CR37 J Bro-Jeppesen (3505_CR15) 2016; 98 3505_CR34 T Cronberg (3505_CR1) 2020; 19 3505_CR13 JP Nolan (3505_CR3) 2015; 41 M Annborn (3505_CR12) 2016; 6 P Stammet (3505_CR35) 2015; 65 T Cronberg (3505_CR24) 2015; 72 X Robin (3505_CR31) 2011; 12 M López-Ratón (3505_CR32) 2014; 61 M Frydland (3505_CR16) 2016; 118 3505_CR29 J Johnsson (3505_CR20) 2020; 24 A Majkowska (3505_CR18) 2020; 294 3505_CR27 N Nielsen (3505_CR23) 2013; 14 3505_CR28 3505_CR25 N Nielsen (3505_CR22) 2012; 163 3505_CR26 |
| References_xml | – volume: 46 start-page: 1459 issue: 5 year: 2016 ident: 3505_CR11 publication-title: Turk J Med Sci doi: 10.3906/sag-1503-64 – volume: 118 start-page: 998 issue: 7 year: 2016 ident: 3505_CR16 publication-title: Am J Cardiol doi: 10.1016/j.amjcard.2016.07.006 – ident: 3505_CR26 – ident: 3505_CR28 – ident: 3505_CR30 – volume: 61 start-page: 1 issue: 8 year: 2014 ident: 3505_CR32 publication-title: J Stat Softw doi: 10.18637/jss.v061.i08 – volume: 5 start-page: 211 issue: 4 year: 2018 ident: 3505_CR7 publication-title: Clin Exp Emerg Med doi: 10.15441/ceem.17.267 – volume: 65 start-page: 2104 issue: 19 year: 2015 ident: 3505_CR35 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2015.03.538 – volume: 163 start-page: 541 issue: 4 year: 2012 ident: 3505_CR22 publication-title: Am Heart J. doi: 10.1016/j.ahj.2012.01.013 – volume: 294 start-page: 421 issue: 2 year: 2020 ident: 3505_CR18 publication-title: Radiology doi: 10.1148/radiol.2019191293 – volume: 104 start-page: 12 year: 2016 ident: 3505_CR14 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2016.04.007 – volume: 19 start-page: 611 issue: 7 year: 2020 ident: 3505_CR1 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(20)30117-4 – volume: 98 start-page: 1 year: 2016 ident: 3505_CR15 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2015.10.009 – volume: 76 start-page: 64 issue: 1 year: 2019 ident: 3505_CR5 publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2018.3223 – ident: 3505_CR36 doi: 10.1186/s13054-017-1677-2 – ident: 3505_CR6 doi: 10.1007/s00134-020-06218-9 – ident: 3505_CR25 doi: 10.1016/0735-6757(86)90255-X – ident: 3505_CR27 – ident: 3505_CR34 doi: 10.1007/s00134-020-06080-9 – volume: 80 start-page: 790 issue: 7 year: 2009 ident: 3505_CR9 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2009.04.003 – volume: 6 start-page: 85 issue: 2 year: 2016 ident: 3505_CR12 publication-title: Ther Hypothermia Temp Manag doi: 10.1089/ther.2015.0033 – ident: 3505_CR13 doi: 10.1186/s13054-020-02904-8 – ident: 3505_CR10 doi: 10.1016/j.resuscitation.2020.05.016 – ident: 3505_CR29 – volume: 41 start-page: 2204 issue: 12 year: 2015 ident: 3505_CR3 publication-title: Intensive Care Med doi: 10.1007/s00134-015-4094-5 – volume: 72 start-page: 634 issue: 6 year: 2015 ident: 3505_CR24 publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2015.0169 – volume: 162 start-page: 55 issue: 1 year: 2015 ident: 3505_CR33 publication-title: Ann Int Med doi: 10.7326/M14-0697 – volume: 14 start-page: 300 year: 2013 ident: 3505_CR23 publication-title: Trials doi: 10.1186/1745-6215-14-300 – volume: 41 start-page: 2039 issue: 12 year: 2015 ident: 3505_CR2 publication-title: Intensive Care Med doi: 10.1007/s00134-015-4051-3 – volume: 7 start-page: 44 year: 2019 ident: 3505_CR17 publication-title: J Intensive Care doi: 10.1186/s40560-019-0393-1 – volume: 369 start-page: 2197 issue: 23 year: 2013 ident: 3505_CR21 publication-title: N Engl J Med doi: 10.1056/NEJMoa1310519 – volume: 22 start-page: 150 issue: 1 year: 2018 ident: 3505_CR4 publication-title: Crit Care doi: 10.1186/s13054-018-2060-7 – volume: 82 start-page: 665 issue: 5 year: 2017 ident: 3505_CR8 publication-title: Ann Neurol doi: 10.1002/ana.25067 – volume: 139 start-page: 84 year: 2019 ident: 3505_CR19 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2019.04.007 – volume: 12 start-page: 77 year: 2011 ident: 3505_CR31 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-12-77 – volume: 24 start-page: 474 issue: 1 year: 2020 ident: 3505_CR20 publication-title: Crit Care doi: 10.1186/s13054-020-03103-1 – ident: 3505_CR37 doi: 10.1101/2020.04.12.20045146 |
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Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well... Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as... Background Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well... BackgroundPrognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as... Background: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well... Abstract Background Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables,... |
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| SubjectTerms | Algorithms Ambulance services Artificial intelligence Artificial neural networks Biobanks Biomarkers Brain research Cardiac arrest Care and treatment Cerebral performance category Clinical Medicine Coma Critical care Critical Care Medicine Emergency Medicine Feature selection General & Internal Medicine Heart attacks Intensive Intensive care Klinisk medicin Machine learning Medical and Health Sciences Medical prognosis Medicin och hälsovetenskap Medicine Medicine & Public Health Neural networks Neurologi Neurology Neurosciences Neurovetenskaper Out-of-hospital cardiac arrest Patient outcomes Patients Prediction Prognosis Prognostication Proteins Traumatic brain injury Variables |
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| Title | 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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