External validation of an electrocardiography artificial intelligence-generated algorithm to detect left ventricular systolic function in a general cardiac clinic in Uganda

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C.K Mondo ◽  
Z.I Attia ◽  
E.D Benavente ◽  
P Friedman ◽  
P Noseworthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Left Ventricular Systolic Dysfunction ◽  
External Validation ◽  
Left Ventricular ◽  
Receiver Operating Curve ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

Abstract Background Left ventricular systolic dysfunction (LVSD) is associated with increased morbidity and mortality. Although there are effective treatments for patients with LVSD to prevent mortality, heart failure and to improve symptoms, many patients remain undetected and untreated. We have recently derived a deep learning algorithm to detect LVSD using the electrocardiogram (ECG) which could have an important screening role, particularly in limited resources settings. We evaluated the accuracy of this algorithm for the first time in Africa in a sample of subjects attending a cardiology clinic. Methods We conducted a retrospective study in a general cardiac clinic in Uganda. Consecutive patients ≥18 years who had a digital ECG and echocardiogram done within two days of each other were included. We excluded patients with pacemakers or missing information regarding left ventricular ejection fraction (LVEF). Routine 10-second, twelve-lead surface rest ECG were performed using an Edan PC ECG Model SE-1515, Hamburg, Germany. The probability of LVSD was estimated with the Mayo Clinic artificial intelligence (AI) ECG algorithm. LVEF was calculated by the MMode (Teichholz method) using a Philips Ultrasound system, HD7XE, Bothel, Washington, USA. LVSD was defined as a LVEF≤35%. We assessed the overall diagnostic performance of the algorithm to identify LVSD in this population with the area under the receiver operating curve (AUC), and estimated sensitivity, specificity and accuracy using a pre-specified cut-off based on the probability for LVSD generated by the algorithm. We conducted secondary analyses using different LVEF cutoff values. Results We included 634 subjects, 32% (200) of whom had hypertension and 12% (77) clinical heart failure. Mean age was 57±18.8 years, 58% were women and the overall prevalence of LVSD was 4%. The AI-ECG had an AUC of 0.866 (see figure below), sensitivity 73.08%, specificity 91.10%, negative predictive value 98.75%, positive predictive value 26.03% and an accuracy of 90.96% using the original threshold. Using the optimal cutoff based on the AUCs, the sensitivity was 80.77% and specificity was 81.05% with a negative predictive value of 98.99%. The ROC for the detection of LVEF of 40% or below was 0.821. Conclusion The Mayo AI-ECG algorithm demonstrated good accuracy, sensitivity and specificity to detect LVSD in patients seen in a clinical setting in Uganda. This tool may facilitate the identification of people at a high risk for LVSD in settings with low resources. ROC Funding Acknowledgement Type of funding source: None

scholarly journals Padang Skoring Elektrokardiografi untuk Memprediksi Fraksi Ejeksi Ventrikel Kiri pada Gagal Jantung Kronik

2019 ◽  
Vol 39 (4) ◽  
Author(s):  
Wiza Erlanda ◽  
Hauda El Rasyid ◽  
Masrul Syafri ◽  
Ricvan Dana Nindrea
Keyword(s):  
Heart Failure ◽  
Chronic Heart Failure ◽  
Ejection Fraction ◽  
Left Ventricular ◽  
Receiver Operating Curve ◽  
Left Ventricular Ejection ◽  
Qtc Interval ◽  
Chi Square ◽  
Ventricular Ejection Fraction ◽  
Receiver Operating

ABSTRAK Latar Belakang:  Gagal Jantung dibagi menjadi 3 kelompok yaitu gagal jantung fraksi ejeksi menurun (HfrEF <40%), rentang tengah (HFmrEF 40-49%), dan terpelihara (HfpEF ≥50%). Ekokardiografi masih menjadi pemeriksaan standar saat ini, sayangnya pemeriksaan tersebut masih terbatas dibeberapa pusat kesehatan. Perlu pemeriksaan awal yang lebih sederhana salah satunya elektrokardiografi (EKG) yang mudah digunakan. Penentuan skoring dari EKG diharapkan dapat memudahkan memprediksi fraksi ejeksi dan memberikan terapi yang tepat.  Metode Penelitian: Pendekatan observasional dengan desain potong lintang. Diambil data rekam medis pasien gagal jantung kronik (GJK) di poliklinik Jantung RSUP Dr. M.Djamil Padang bulan Januari-Agustus 2017. Dilakukan analisis bivariat pada varibel EKG terhadap fraksi ejeksi dengan metode chi-square. Analisis multivariate dengan uji regresi binari logistik untuk mendapatkan variabel pada kalkulasi skor dengan uji Hosmer-Lameshow (p<0,25). Skoring dilakukan uji sensitivitas, spesifisitas dan analisis receiver operating curve (ROC).    Hasil Penelitian : 283 subjek GJK dibagi menjadi tiga kelompok. Variabel yang memenuhi persyaratan untuk dilakukan kalkulasi skor adalah pembesaran atrium kiri (LAE) (OR=6,36; p= 0.000) dengan skor 2, QRS lebar (OR=13,06; p= 0.000) dengan skor 3, interval QTc memanjang (OR=2,18; p= 0.065) dengan skor 1 dan perubahan gelombang ST-T (OR=5,05; p= 0.000) dengan skor 2. Subjek dengan HFpEF mempunyai skor <3, HFmrEF mempunyai skor 3-4, dan HFrEF mempunyai skor >4. Sistem skoring EKG memiliki sensitivitas 71,4% dan spesifisitas 88,6% dengan AUC 87,9%  Kesimpulan : Sistem skoring EKG pada penelitian ini dapat digunakan sebagai pedoman awal dalam menentukan fraksi ejeksi ventrikel kiri pada pasien GJK  Kata kunci : elektrokardiografi, gagal jantung kronik, fraksi ejeksi ventrikel kiri Background: Heart failure (HF) are divided into HF reduced ejection fraction (HFrEF<40%), mid range (HFmrEF 40-49%), and preserved (HFpEF ≥50%). Nowadays echocardiography is used as gold standard examination, but it is limited only in several health centers. For this reason, a preliminary examination tools is needed. Electrocardiographic (ECG) examinations tool that available almost at every health center and easy to be used. Calculating the scores from ECG variables to determine the EF will make clinician’s earlier to give initial terapi Method: An observational approach with cross sectional study design. The data was taken from patient’s medical record with chronic heart failure (CHF) who went to the Tepartement of Cardiology at Dr. M. Djamil Padang Hospital in January-August 2017. Bivariate analysis was performed on each ECG variable then correlated with LVEF by chi-square method. Multivariate analysis with logistic binary regression test was conducted to obtain variables that would go into the score calculation stage with the Hosmer-Lameshow test (p <0.25). The sensitivity, specificity test and receiver operating curve (ROC) analysis were performed. Result: 283 subjects of CHF who had been divided into three groups. Obtained variables that met the requirements for calculating scores were left atrial enlargement (LAE) (OR = 6.36; p = 0.000) score was 2, wide QRS (OR = 13.06; p = 0,000) score was 3 , prolonged QTc interval (OR = 2,18; p = 0,065) score was 1 and ST-T change (OR = 5.05; p = 0.000) score was 2. Subjects with HFpEF if the scored were <3, HFmrEF if the scored were 3-4, and HFrEF if the scored were >4. It has sensitivity 71,4%, specificity 88,6% with AUC 87,9%. Conclusion: Electrocardiography scoring system in this study can be used as an initial tools to determining LVEF in patients with CHF. Keywords: electrocardiography, chronic heart failure, left ventricular ejection fraction

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252903
Author(s):  
Mufaddal Mahesri ◽  
Kristyn Chin ◽  
Abheenava Kumar ◽  
Aditya Barve ◽  
Rachel Studer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
External Validation ◽  
Model Performance ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Administrative Claims ◽  
Validation Sample ◽  
Ventricular Ejection Fraction ◽  
Medicare Claims

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.

scholarly journals Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram.

2021 ◽  
Author(s):  
Akhil Vaid ◽  
Kipp W Johnson ◽  
Marcus A Badgeley ◽  
Sulaiman Somani ◽  
Mesude Bicak ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Right Ventricular ◽  
Systolic Dysfunction ◽  
External Validation ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Lv Function ◽  
Ventricular Ejection Fraction ◽  
Left And Right

Background Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECG) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, while ones to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. Objectives This study sought to develop deep learning models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. Methods A multi-center study was conducted with data from five New York City hospitals; four for internal testing and one serving as external validation. We created novel DL models to classify Left Ventricular Ejection Fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. Results We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used Natural Language Processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, Area Under Curve (AUC) at detection of LVEF<=40%, 40%<LVEF<=50%, and LVEF>50% was 0.94 (95% CI:0.94-0.94), 0.82 (0.81-0.83), and 0.89 (0.89-0.89) respectively. For external validation, these results were 0.94 (0.94-0.95), 0.73 (0.72-0.74) and 0.87 (0.87-0.88). For regression, the mean absolute error was 5.84% (5.82-5.85) for internal testing, and 6.14% (6.13-6.16) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (0.84-0.84) in both internal testing and external validation. Conclusions DL on ECG data can be utilized to create inexpensive screening, diagnostic, and predictive tools for both LV/RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography, and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease. Keywords Artificial Intelligence, Deep Learning, Machine Learning, HFrEF, Right Ventricular Dilation, Right Ventricular Systolic Dysfunction, echocardiography, electrocardiogram, ECG, EKG, LVEF, Left Ventricular Ejection Fraction, Left Heart Failure, Right Heart Failure

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

2020 ◽  
Author(s):  
Mufaddal Mahesri ◽  
Kristyn Chin ◽  
Abheenava Kumar ◽  
Aditya Barve ◽  
Rachel Studer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
External Validation ◽  
Model Performance ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Administrative Claims ◽  
Validation Sample ◽  
Ventricular Ejection Fraction ◽  
Medicare Claims

ABSTRACTBACKGROUNDEjection 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.METHODTruven 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.RESULTSA 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.CONCLUSIONSThe 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.

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 Mitral Annular Plane Systolic Excursion (MAPSE) as a Simple Index for Assessing Left Ventricular Function: An Observational Study

2021 ◽  
pp. 163-170
Author(s):  
Jayanti Venkata Balasubramaniyan ◽  
Ashutosh Prasad Tripathi ◽  
J. S. Satyanarayana Murthy
Keyword(s):  
Positive Predictive Value ◽  
Left Ventricular Function ◽  
Ventricular Function ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Lv Function ◽  
Care Hospital ◽  
Ventricular Ejection Fraction

Background: Mitral annular plane systolic excursion (MAPSE) has been proposed as a parameter for assessing left ventricular function. The assessment of LVF has major diagnostic and prognostic implications in patients with cardiovascular diseases. LVF is measured by Left Ventricular Ejection Fraction, however the accuracy of LVEF estimation by two dimensional echocardiography is limited especially in patients with poor image quality. Mitral annular plane systolic excursion (MAPSE) measurement predicts left ventricular function even in conditions with suboptimal echo window. Objective: To assess the correlation of MAPSE derived LVEF with LVEF measured by Modified Simpson’s method. Methods: This is a cross sectional study which included 279 patients admitted at our tertiary care hospital from December 2019 to March 2020 and the patients were divided in two groups. Group A – Patients with LVEF>= 50% and Group B – Patients with LVEF<50%. All patients underwent 2D echocardiographic examination using Modified Simpsons’ method and MAPSE measurement. The VIVID E9, VIVID T8, VIVID E95 and PHILIPS echocardiography machine was used for the non-invasive measurements. MAPSE was recorded at medial and lateral mitral annuli in the apical four-chamber approach. Results: On analysis, a cut off value for average MAPSE-S (medial mitral annuli) was 8.5 was obtained, denoting preserved LV function with sensitivity of 81.7%, specificity of 84.9%, positive predictive value of 91.6% and negative predictive value of 84.9%. The AUC for MAPSE-S was 0.822. Similarly, the cut off value of average MAPSE-L (lateral mitral annuli) was 7.5 denoting impaired LV functions with an AUC of 0.826, sensitivity of 82.8%, specificity of 72.0%, positive predictive value of 85.6% and negative predictive value of 72.0%. The AUC of 82.6% was observed for MAPSE-L. Conclusion: MAPSE reflects longitudinal myocardial shortening. MAPSE is a rapid and sensitive echocardiographic parameter for assessing normal LV function and global LV systolic dysfunction.

scholarly journals Routine Measurements of Cardiac Parasympathetic and Sympathetic Nervous Systems Assists in Primary and Secondary Risk Stratification and Management of Cardiovascular Clinic Patients

2019 ◽  
pp. 27-33 ◽  
Author(s):  
Gary L Murray ◽  
Joseph Colombo
Keyword(s):  
Heart Failure ◽  
Predictive Value ◽  
New Technology ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Cardiac Autonomic Neuropathy ◽  
Ventricular Ejection Fraction ◽  
Sympathetic Nervous Systems ◽  
Cardiovascular Patients ◽  
Coronary Syndromes

Objective: To review our studies of the ease and importance of Parasympathetic and Sympathetic (P&S) measures in managing cardiovascular patients. Background: The autonomic nervous system is responsible for the development or progression of Hypertension (HTN), orthostasis, Coronary Disease (CAD), Congestive Heart Failure (CHF) and arrhythmias. Finally, new technology provides us with rapid, accurate P and S measures critically needed to manage these patients much more successfully. Methods: Using the ANX 3.0 autonomic monitor, P&S activity was recorded in 4 studies: 163 heart failure patients in total, mean follow-up (f/u) 12-24.5 months; 109 orthostasis patients, f/u 2.28 years and 483 patients with risk factors or known HTN, CAD or CHF, f/u 4.92 yrs. All were on guideline-driven therapy. Results: 59% of CHF patients had dangerously high Sympathovagal Balance (SB) or Cardiac Autonomic Neuropathy (CAN) and Ranolazine markedly improved 90% of these, improved left ventricular ejection fraction in 70% of patients on average 11.3 units, and reduced Major Adverse Cardiac Event (MACE) [Acute Coronary Syndromes (ACS), death, acute CHF, Ventricular Tachycardia/Ventricular Fibrillation (VT/VF)] 40%. 66% of orthostatic patients corrected with (r) Alpha Lipoic Acid ([r]ALA); non-responders had the lowest S-tone. In the 483 patient study, SB>2.5 best predicted MACE when compared to nuclear stress and echocardiography (sensitivity 0.59 or 7.03 [CI (Confidence Interval) 4.59-10.78], specificity 0.83, positive predictive value 0.64 and negative predictive value 0.80). Conclusion: Parasympathetic and sympathetic measures significantly improve care of cardiovascular patients.

scholarly journals Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort

2021 ◽  
Vol 15 (12) ◽  
pp. e0009974
Author(s):  
Bruno Oliveira de Figueiredo Brito ◽  
Zachi I. Attia ◽  
Larissa Natany A. Martins ◽  
Pablo Perel ◽  
Maria Carmo P. Nunes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ejection Fraction ◽  
Chagas Disease ◽  
Left Ventricular Systolic Dysfunction ◽  
Systolic Dysfunction ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Systolic Dysfunction ◽  
Ventricular Ejection Fraction ◽  
Male Sex

Background Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. Objective To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. Methodology/principal findings This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3–128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. Conclusion The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

Sign in / Sign up

Export Citation Format

Share Document