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Artificial neural n...
Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care
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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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- 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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- Jakobsson, Andreas (författare)
- Lund University,Lunds universitet,Biomedical Modelling and Computation,Forskargrupper vid Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Cronberg, Tobias (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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- Lilja, Gisela (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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- 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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- Hassager, Christian (författare)
- Copenhagen University Hospital,University of Copenhagen
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- Kjaergard, Jesper (författare)
- Copenhagen University Hospital,University of Copenhagen
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- Wise, Matt (författare)
- University Hospital of Wales
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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,Intensivvårdsepidemiologi,Forskargrupper vid Lunds universitet,Intensive Care Epidemiology,Lund University Research Groups,Skåne University Hospital
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(creator_code:org_t)
- 2020-07-30
- 2020
- Engelska.
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Ingår i: Critical Care. - : Springer Science and Business Media LLC. - 1364-8535. ; 24:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://ccforum.biom...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- BACKGROUND: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). METHODS: Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1-2 and poor functional outcome defined as CPC 3-5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. RESULTS: The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better (p = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33 °C or 36 °C in predefined classes with different risk of a poor outcome. CONCLUSION: A supervised machine learning model using ANN predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information. ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Artificial intelligence
- Artificial neural networks
- Cerebral performance category
- Critical care
- Intensive care
- Machine learning
- Out-of-hospital cardiac arrest
- Prediction
- Prognostication
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas
- Av författaren/redakt...
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Johnsson, Jesper
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Björnsson, Ola
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Andersson, Peder
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Jakobsson, Andre ...
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Cronberg, Tobias
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Lilja, Gisela
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visa fler...
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Friberg, Hans
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Hassager, Christ ...
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Kjaergard, Jespe ...
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Wise, Matt
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Nielsen, Niklas
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Frigyesi, Attila
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visa färre...
- Om ämnet
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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 Anestesi och int ...
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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 Kardiologi
- Artiklar i publikationen
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Critical Care
- Av lärosätet
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Lunds universitet