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

2021 ◽  
Vol 8 (6) ◽  
pp. 85
Author(s):  
Cristina Lopez ◽  
Jose Luis Holgado ◽  
Raquel Cortes ◽  
Inma Sauri ◽  
Antonio Fernandez ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Acute Myocardial Infarct ◽  
Health Records ◽  
Ventricular Ejection Fraction ◽  
Electronic Health

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.

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

2021 ◽  
Author(s):  
Cristina Lopez ◽  
Jose Luis Holgado ◽  
Raquel Cortes ◽  
Inma Sauri ◽  
Antonio Fernandez ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Data Set ◽  
Acute Myocardial Infarct ◽  
Health Records ◽  
Electronic Health

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.

scholarly journals Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Alvin Chandra ◽  
Steven T Philips ◽  
Ambarish Pandey ◽  
Mujeeb Basit ◽  
Vaishnavi Kannan ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Practice ◽  
Electronic Health Records ◽  
Real Time ◽  
Cancer Patients ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Health Records ◽  
Electronic Health ◽  
Cardiovascular Care

BACKGROUND Professional society guidelines are emerging for cardiovascular care in cancer patients. How effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice remains unclear. As EHRs are now widely used in clinical practice, we tested the hypothesis whether an EHR-based cardio-oncology registry can address these questions. OBJECTIVE To develop an electronic health records (EHR)-based pragmatic cardio-oncology registry and, as proof of principle, to investigate care gaps in cardiovascular care of cancer patients. METHODS We generated programmatically a de-identified, real-time, EHR-based cardio-oncology registry from all patients in our institutional Cancer Population Registry (n=8275, 2011-2017). We investigated: 1) left ventricular ejection fraction (LVEF) assessment before and after treatment with potentially cardiotoxic agents, and 2) guideline-directed medical therapy (GDMT) for left ventricular dysfunction (LVD), defined as LVEF<50%, and symptomatic heart failure with reduced LVEF (HFrEF), defined as LVEF<50% and problem list documentation of systolic congestive heart failure or dilated cardiomyopathy. RESULTS Rapid development of an EHR-based cardio-oncology registry was feasible. Identification of tests and outcomes was similar by EHR-based cardio-oncology registry and manual chart abstraction (98% sensitivity and 92% specificity for LVD). LVEF was documented prior to initiation of cancer therapy in 20% of patients. Prevalence of post-chemotherapy LVD and HFrEF was relatively low (9% and 2.5%, respectively). Among patients with post-chemotherapy LVD or HFrEF, those referred to cardiology had significantly higher prescription of GDMT. CONCLUSIONS EHR data can efficiently populate a real-time, pragmatic cardio-oncology registry as a byproduct of clinical care for healthcare delivery investigation.

Heart Failure with Preserved Ejection Fraction – A Review

2012 ◽  
Vol 9 (1) ◽  
pp. 90-95 ◽  
Author(s):  
Otto A Smiseth ◽  
Anders Opdahl ◽  
Espen Boe ◽  
Helge Skulstad
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Diastolic Heart Failure ◽  
The Elderly ◽  
Left Ventricular ◽  
Filling Pressure ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Reduced Ejection Fraction ◽  
Objective Evidence

Heart failure with preserved left ventricular ejection fraction (HF-PEF), sometimes named diastolic heart failure, is a common condition most frequently seen in the elderly and is associated with arterial hypertension and left ventricular (LV) hypertrophy. Symptoms are attributed to a stiff left ventricle with compensatory elevation of filling pressure and reduced ability to increase stroke volume by the Frank-Starling mechanism. LV interaction with stiff arteries aggravates these problems. Prognosis is almost as severe as for heart failure with reduced ejection fraction (HF-REF), in part reflecting co-morbidities. Before the diagnosis of HF-PEF is made, non-cardiac etiologies must be excluded. Due to the non-specific nature of heart failure symptoms, it is essential to search for objective evidence of diastolic dysfunction which, in the absence of invasive data, is done by echocardiography and demonstration of signs of elevated LV filling pressure, impaired LV relaxation, or increased LV diastolic stiffness. Antihypertensive treatment can effectively prevent HF-PEF. Treatment of HF-PEF is symptomatic, with similar drugs as in HF-REF.

ACCESSMENT OF RIGHT VENTRICULAR SYSTOLIC FUNCTION USING TAPSE

2011 ◽  
pp. 62-70
Author(s):  
Lien Nhut Nguyen ◽  
Anh Vu Nguyen
Keyword(s):  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Right Ventricular ◽  
Systolic Function ◽  
Systolic Heart Failure ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

Background: The prognostic importance of right ventricular (RV) dysfunction has been suggested in patients with systolic heart failure (due to primary or secondary dilated cardiomyopathy - DCM). Tricuspid annular plane systolic excursion (TAPSE) is a simple, feasible, reality, non-invasive measurement by transthoracic echocardiography for evaluating RV systolic function. Objectives: To evaluate TAPSE in patients with primary or secondary DCM who have left ventricular ejection fraction ≤ 40% and to find the relation between TAPSE and LVEF, LVDd, RVDd, RVDd/LVDd, RA size, severity of TR and PAPs. Materials and Methods: 61 patients (36 males, 59%) mean age 58.6 ± 14.4 years old with clinical signs and symtomps of chronic heart failure which caused by primary or secondary DCM and LVEF ≤ 40% and 30 healthy subject (15 males, 50%) mean age 57.1 ± 16.8 were included in this study. All patients and controls were underwent echocardiographic examination by M-mode, two dimentional, convensional Dopler and TAPSE. Results: TAPSE is significant low in patients compare with the controls (13.93±2.78 mm vs 23.57± 1.60mm, p<0.001). TAPSE is linearly positive correlate with echocardiographic left ventricular ejection fraction (r= 0,43; p<0,001) and linearly negative correlate with RVDd (r= -0.39; p<0.01), RVDd/LVDd (r=-0.33; p<0.01), RA size (r=-0.35; p<0.01), TR (r=-0.26; p<0.05); however, no correlation was found with LVDd and PAPs. Conclusions: 1. Decreased RV systolic function as estimated by TAPSE in patients with systolic heart failure primary and secondary DCM) compare with controls. 2. TAPSE is linearly positive correlate with LVEF (r= 0.43; p<0.001) and linearly negative correlate with RVDd (r= -0.39; p<0.01), RVDd/LVDd (r=-0.33; p<0.01), RA size (r=-0.35; p<0.01), TR (r=-0.26; p<0.05); however, no correlation is found with LVDd and PAPs. 3. TAPSE should be used routinely as a simple, feasible, reality method of estimating RV function in the patients systolic heart failure DCM (primary and secondary).

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