A registry‐based algorithm to predict ejection fraction in patients with heart failure

Uijl, Alicia; Lund, Lars H.; Vaartjes, Ilonca; Brugts, Jasper J.; Linssen, Gerard C.M.; Asselbergs, Folkert W.; Hoes, Arno W.; Dahlström, Ulf; Koudstaal, Stefan; Savarese, Gianluigi

doi:10.1002/ehf2.12779

Cited by 14 publications

(20 citation statements)

References 29 publications

(73 reference statements)

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“…The higher PPV for HFpEF reported in our study is likely explained by addition of frequent comorbid conditions and demographics in addition to diagnoses codes. A second study by Uijl et al based on data from the Swedish Heart Failure Registry used an approach similar to ours, where 22 predictors including laboratory results such as NT-proBNP, renal function; demographics such as age, sex; and comorbid conditions were used to classify patients into HFpEF and HFrEF [ 10 ]. The authors noted discrimination of 0.73 for this model in an external validation cohort.…”

Section: Discussionmentioning

confidence: 99%

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure

et al. 2021

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Background Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees. Methods Truven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEF<45%). Model performance was reported in terms of overall accuracy, positive predicted values (PPV), and sensitivity for HFrEF and HFpEF. Results A total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80–0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95–0.97) and PPV of 0.81 (95% CI, 0.81–0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30–0.34) and PPV was 0.73 (95%CI, 0.69–0.76). These results were consistent with what was previously published in US Medicare claims data. Conclusions The successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well.

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Section: Discussionmentioning

confidence: 99%

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure

et al. 2021

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“…Few studies attempted to develop methods to predict left ventricular ejection fraction in patients with heart failure. Some used administrative claims from Medicare [ 21 , 22 , 23 ] or a specific database such as the Swedish Heart Failure Registry [ 24 ]. For those using administrative claims, a large number of variables were used in the training sample, identified by the ICD-code.…”

Section: Discussionmentioning

confidence: 99%

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

et al. 2021

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Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group. Conclusion: The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.

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“…Few studies have approached to develop methods for predicting left ventricular ejection fraction in patients with heart failure. Some have used Administrative-Claims from Medicare 21,22,23 or specific data base such is the Swedish Heart Failure Registry 24 . In those using Administrative-claims a large number of variables, have been used in the training sample, identified by the ICD-code, while the treatments.…”

Section: Discussionmentioning

confidence: 99%

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

Lopez

Holgado

Cortés

et al. 2021

Preprint

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Artificial Intelligence are creating a paradigm shift in health care, being phenotyping patients through clustering techniques one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data in Electronic Health Records (EHR). Subjects and methods: 2854 subjects more than 25 years old with diagnose of HF and LVEF measured by echocardiography were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. Performance of the algorithm developed were tested in heart failure patients from Primary Care. To select the most influencing variables, LASSO algorithm setting was used and to tackle the issue of one class exceed the other one by a large proportion we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: Full XGBoost model obtained the maximized accuracy, a high negative predictive value and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence FE value. Applied in the EHR data set with a total 25594 patients with an ICD-code of HF and no regular follow-up in Cardiology clinics, 6170 (21.1%) were identified as those pertaining to the reduced EF group. Conclusion: The algorithm obtained is able to rescue a number of HF patients with reduced ejection fraction that can be take benefit for a protocol with strong recommendation to succeed. Furthermore, the methodology can be used for studies with data extracted from the Electronic Health Records.

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A registry‐based algorithm to predict ejection fraction in patients with heart failure

Cited by 14 publications

References 29 publications

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

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