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A registry-based al...
A registry-based algorithm to predict ejection fraction in patients with heart failure
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- Uijl, Alicia (author)
- Karolinska Inst, Sweden; Univ Utrecht, Netherlands; UCL, England
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- Lund, Lars H. (author)
- Karolinska Institutet,Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden
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- Vaartjes, Ilonca (author)
- Univ Utrecht, Netherlands; Univ Utrecht, Netherlands
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- Brugts, Jasper J. (author)
- Erasmus MC, Netherlands
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- Linssen, Gerard C. (author)
- Hosp Grp Twente, Netherlands
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- Asselbergs, Folkert W. (author)
- UCL, England; Univ Utrecht, Netherlands
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- Hoes, Arno W. (author)
- Univ Utrecht, Netherlands
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- Dahlström, Ulf, 1946- (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Kardiologiska kliniken US
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- Koudstaal, Stefan (author)
- UCL, England; Univ Utrecht, Netherlands
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- Savarese, Gianluigi (author)
- Karolinska Institutet,Karolinska Inst, Sweden
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(creator_code:org_t)
- 2020-06-17
- 2020
- English.
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In: ESC Heart Failure. - : WILEY PERIODICALS, INC. - 2055-5822. ; 7:5, s. 2388-2397
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https://doi.org/10.1...
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Abstract
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- Aims Left ventricular ejection fraction (EF) is required to categorize heart failure (HF) [i.e. HF with preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) EF] but is often not captured in population-based cohorts or non-HF registries. The aim was to create an algorithm that identifies EF subphenotypes for research purposes. Methods and results We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression models including 22 variables to predict (i) EF >= vs. <50% and (ii) EF >= vs. <40%. In the secondary analysis, we performed a multivariable multinomial analysis with 22 variables to create a model for all three separate EF subphenotypes: HFrEF vs. HFmrEF vs. HFpEF. The models were validated in the database from the CHECK-HF study, a cross-sectional survey of 10 627 patients from the Netherlands. The C-statistic (discrimination) was 0.78 [95% confidence interval (CI) 0.77-0.78] for EF >= 50% and 0.76 (95% CI 0.75-0.76) for EF >= 40%. Similar results were achieved for HFrEF and HFpEF in the multinomial model, but the C-statistic for HFmrEF was lower: 0.63 (95% CI 0.63-0.64). The external validation showed similar discriminative ability to the development cohort. Conclusions Routine clinical characteristics could potentially be used to identify different EF subphenotypes in databases where EF is not readily available. Accuracy was good for the prediction of HFpEF and HFrEF but lower for HFmrEF. The proposed algorithm enables more effective research on HF in the big data setting.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Keyword
- Electronic health records; Heart failure; Ejection fraction; Prediction; HFrEF; HFmrEF; HFpEF
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Uijl, Alicia
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Lund, Lars H.
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Vaartjes, Ilonca
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Brugts, Jasper J ...
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Linssen, Gerard ...
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Asselbergs, Folk ...
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Hoes, Arno W.
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Dahlström, Ulf, ...
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Koudstaal, Stefa ...
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Savarese, Gianlu ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Cardiac and Card ...
- Articles in the publication
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ESC Heart Failur ...
- By the university
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Linköping University
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Karolinska Institutet