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Development and val...
Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction : a nationwide population-based study
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- Mohammad, Moman A. (författare)
- Lund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Molekylär kardiologi,Forskargrupper vid Lunds universitet,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Molecular Cardiology,Lund University Research Groups,Skåne University Hospital,Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, S-22185 Lund, Sweden.
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- Olesen, Kevin K.W. (författare)
- Aarhus University Hospital,Aarhus Univ Hosp, Dept Cardiol, Aarhus, Denmark.
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- Koul, Sasha (författare)
- Lund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Molekylär kardiologi,Forskargrupper vid Lunds universitet,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Molecular Cardiology,Lund University Research Groups,Skåne University Hospital,Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, S-22185 Lund, Sweden.
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- Gale, Chris P. (författare)
- Leeds School of Medicine,Univ Leeds, Leeds Inst Cardiovasc & Metab Med, Leeds, W Yorkshire, England.
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- Rylance, Rebecca (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,Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, S-22185 Lund, Sweden.
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- Jernberg, Tomas (författare)
- Karolinska Institutet,Karolinska Inst, Danderyd Univ Hosp, Dept Clin Sci, Div Cardiovasc Med, Stockholm, Sweden.
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- Baron, Tomasz (författare)
- Uppsala universitet,Uppsala University,Uppsala kliniska forskningscentrum (UCR),Institutionen för medicinska vetenskaper
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- Spaak, Jonas (författare)
- Karolinska Institutet,Karolinska Inst, Danderyd Univ Hosp, Dept Clin Sci, Div Cardiovasc Med, Stockholm, Sweden.
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- James, Stefan, 1964- (författare)
- Uppsala universitet,Uppsala University,Kardiologi,Uppsala kliniska forskningscentrum (UCR)
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- Lindahl, Bertil, 1957- (författare)
- Uppsala universitet,Uppsala University,Kardiologi,Uppsala kliniska forskningscentrum (UCR)
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- Maeng, Michael (författare)
- Aarhus University Hospital,Aarhus Univ Hosp, Dept Cardiol, Aarhus, Denmark.
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- Erlinge, David (författare)
- Lund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Molekylär kardiologi,Forskargrupper vid Lunds universitet,Lärare vid läkarprogrammet,Avdelningen för läkarprogrammets kursadministration,Utbildningsenheten,Kansli M,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine,Molecular Cardiology,Lund University Research Groups,Teachers at the Medical Programme,Division of Course Administration for the Medical Programme,The Education Office,Faculty Office - BMC,Skåne University Hospital,Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, S-22185 Lund, Sweden.
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: The Lancet Digital Health. - : Elsevier. - 2589-7500. ; 4:1, s. 37-45
- 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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https://uu.diva-port... (primary) (Raw object)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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http://kipublication...
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https://urn.kb.se/re...
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Abstract
Ämnesord
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- Background: Patients have an estimated mortality of 15–20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing the risk of long-term mortality. We aimed to establish the accuracy of an artificial neural network (ANN) algorithm in predicting 1-year mortality and admission to hospital for heart failure after myocardial infarction. Methods: In this nationwide population-based study, we used data for all patients admitted to hospital for myocardial infarction and discharged alive from a coronary care unit in Sweden (n=139 288) between Jan 1, 2008, and April 1, 2017, from the Swedish Web system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) nationwide registry; these patients were randomly divided into training (80%) and testing (20%) datasets. We developed an ANN using 21 variables (including age, sex, medical history, previous medications, in-hospital characteristics, and discharge medications) associated with the outcomes of interest with a back-propagation algorithm in the training dataset and tested it in the testing dataset. The ANN algorithm was then validated in patients with incident myocardial infarction enrolled in the Western Denmark Heart Registry (external validation cohort) between Jan 1, 2008, and Dec 31, 2016. The predictive ability of the model was evaluated using area under the receiver operating characteristic curve (AUROC) and Youden's index was established as a means of identifying an empirical dichotomous cutoff, allowing further evaluation of model performance. Findings: 139 288 patients who were admitted to hospital for myocardial infarction in the SWEDEHEART registry were randomly divided into a training dataset of 111 558 (80%) patients and a testing dataset of 27 730 (20%) patients. 30 971 patients with myocardial infarction who were enrolled in the Western Denmark Heart Registry were included in the external validation cohort. A first event, either all-cause mortality or admission to hospital for heart failure 1 year after myocardial infarction, occurred in 32 308 (23·2%) patients in the testing and training cohorts only. For 1-year all-cause mortality, the ANN had an AUROC of 0·85 (95% CI 0·84–0·85) in the testing dataset and 0·84 (0·83–0·84) in the external validation cohort. The AUROC for admission to hospital for heart failure within 1 year was 0·82 (0·81–0·82) in the testing dataset and 0·78 (0·77–0·79) in the external validation dataset. With an empirical cutoff the ANN algorithm correctly classified 73·6% of patients with regard to all-cause mortality and 61·5% of patients with regard to admission to hospital for heart failure in the external validation cohort, ruling out adverse outcomes with 97·1–98·7% probability in the external validation cohort. Interpretation: Identifying patients at a high risk of developing heart failure or death after myocardial infarction could result in tailored therapies and monitoring by the allocation of resources to those at greatest risk. Funding: The Swedish Heart and Lung Foundation, Swedish Scientific Research Council, Swedish Foundation for Strategic Research, Knut and Alice Wallenberg Foundation, ALF Agreement on Medical Education and Research, Skane University Hospital, The Bundy Academy, the Märta Winkler Foundation, the Anna-Lisa and Sven-Eric Lundgren Foundation for Medical Research.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
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Mohammad, Moman ...
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Olesen, Kevin K. ...
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Koul, Sasha
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Gale, Chris P.
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Rylance, Rebecca
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Jernberg, Tomas
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Baron, Tomasz
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Spaak, Jonas
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James, Stefan, 1 ...
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Lindahl, Bertil, ...
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Maeng, Michael
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Erlinge, David
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