Prediction Of Congestive Heart Failure Research Articles

Retrospective evaluation of the utility of shock index to determine the presence of congestive heart failure in dogs with myxomatous mitral valve disease (2019-2021): 98 cases.

To calculate the shock index (SI) in dogs with myxomatous mitral valve disease (MMVD) and to evaluate its use to predict the presence of congestive heart failure (CHF). Retrospective study. Small animal university veterinary teaching hospital. Ninety-eight dogs with MMVD and 20 healthy dogs as part of a control group. Heart rate (HR) and systolic blood pressure (SBP) were recorded, and SI was calculated by dividing HR by SBP for each dog. The mean (SD) HR, SBP, and SI were 123/min (32.6), 147mmHg (21.5), and 0.86 (0.3), respectively, for dogs with MMVD and 98/min (20.9), 145mmHg (18.7), and 0.68 (0.13), respectively, for control dogs. Dogs with MMVD had a significantly higher HR compared with control dogs (P<0.01), and an elevation in HR was seen as the severity of MMVD increased. Dogs in stage B2 and C/D MMVD had a significantly higher SI value compared with control dogs (P=0.04 and P<0.01, respectively). SI was significantly higher in dogs in stage C/D MMVD compared with dogs in stage B2 MMVD (P<0.01). Ten of 98 (10%) dogs had an arrhythmia. HR, SBP, and SI were not significantly different between dogs with and without arrhythmias (P=0.13, P=0.57, and P=0.07, respectively), but significantly more dogs with CHF had an arrhythmia (P=0.01). SI (area under the curve [AUC]: 0.98) and HR (AUC: 0.95) were excellent indicators for the presence of CHF. An optimal SI cutoff value ≥1.1 had 92% sensitivity and 95% specificity for predicting the presence of CHF, and an optimal HR cutoff value of ≥157/min had 92% sensitivity and 93% specificity for the prediction of CHF. When there are compatible clinical signs, SI values ≥1.1 may suggest the presence of CHF in dogs with MMVD.

Prediction of congestive heart failure in patients using artificial intelligence: proof of concept

Abstract Introduction Due to the aging of the population we are confronting ourselves with an increased number of patients with chronic heart failure which stands a prevalence of over 37,7 million cases worldwide, being a major public health issue, first by the substantial morbidity and mortality that carries and second by the economic burden it brings annually upon the health-care system. The main purpose of this study is to test a machine learning algorithm which can predict, through voice analysis the acutisation of heart failure, considering the particularities of the patient's voice. Methods The data we have used has been collected from a total of 16 patients, 9 men and 7 women, ages between 65 and 91 years old, who have agreed to take part in the study. The selective criteria of inclusion has been the cause of hospitalization, selecting only the patients presented with cardiogenic acute pulmonary edema, regardless the precipitation cause or other known cardiovascular comorbidities. With the same electronic device we have recorded each patient's voice, twice a day, starting from the day one of hospitalization, when their general status was critical, until the day of discharge, when they were clinically stable. Each voice recording containing specific keywords has been associated to the most used classification system for heart failure, the New York Heart Association Functional Classification and introduced into the machine learning algorithm. Results After integrating the information from 15 patients, the algorithm has classified correctly the 16th patient into the third NYHA stage, based only on his voice recording. Conclusion Voice is a cheap and easy way to monitor a patient's health status. The algorithm we have used for analyzing the voice provides high accuracy results, but for a larger dataset it might not be the best choice as it is computationally expensive. We are looking to obtain larger datasets and to compute more complex voice analyzer algorithms. Funding Acknowledgement Type of funding sources: None.

Diagnosis of Congestive Heart Failure from HRV signal using SVM classifier and Patient Specific cross validation

Congestive heart failure (CHF) is popularly known fatal cardiac disease that occurs when pumping action of heart is lower than normal case. The purpose of this study is to the accurate diagnosis of CHF by improving classifier performance with effective features extraction and cross validation approach. The identification of significant features in electrocardiogram is highly important to detect congestive heart failure. Therefore ,this paper introduces a classifier based automated detection scheme with a novel approach of feature extraction from Heart rate Variability (HRV) signal for early prediction of CHF. The dynamical characteristics of HRV signal is analysed by computation of Largest Lyapunov Exponent. The statistical features are also evaluated to capture crucial variation in HRV signal to distinguish the abnormal and normal heart condition. The extracted features are subjected to Support Vector machine (SVM) classifier for automated discrimination of CHF from normal ECG signal. Experimental results evaluate the performance of extracted features and estimate the accuracy of the classification using the 10 fold cross validation and patient specific cross validation approach. Our experiment is validated by ECG data of normal and CHF subjects from Physionet database. The proposed system is efficient to the detect CHF with an average accuracy of 98.75%,, sensitivity 98.38%, values and 98.94%. Based on comparative study with the existing scientific research work to diagnose CHF, our proposed approach is found to be reliable and efficient for CHF diagnosis

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.

Abstract 15191: Left Atrial Volume as an Independent Predictor of Congestive Heart Failure in Patients With Atrial Fibrillation

Backgound: Left atrial (LA) enlargement has been proposed as a barometer of diastolic dysfunction and a predictor of congestive heart failure (CHF) in patients with sinus rhythm. Whether LA volume predicts CHF in patients with atrial fibrillation (AF) is not well known. Methods: To determine the clinical importance of LA volume in the prediction of CHF in patients with AF, AF patients referred for clinically-indicated echocardiogram, without a history of significant mitral valve disease, congenital heart disease, pacemaker, or cardiac surgery, in 2007-2008 were prospectively included and followed forward them up to September 2014. LA volume was measured using the biplane area-length formula. CHF was ascertained using the Framingham criteria. Cox proportional hazards modeling was used to assess the risk factors of CHF development. Results: Of 456 AF patients who met all study criteria (mean 70 ± 10 year-old, 67% men, 62% hypertension, 26% diabetes, LV ejection fraction 68 ± 13%, LA volume 52 ± 24 mL/m 2 ), 46 (10%) developed CHF events during a mean follow-up of 44 ± 31 months. CHF events were significantly increased with advancing age (HR 1.4, 95%CI 1.0-2.0, P&lt;0.05), but not with sex. In a multivariate Cox proportional hazards model, greater indexed LA volume (per 10 mL/m 2 ; HR 1.2, 95% CI 1.1-1.3, P&lt;0.01) was independent of age (HR 1.04, 95% CI 1.01-1.07, P=0.03), sex (P=0.77), history of CHF (P=0.58), hypertension (P=0.38), diabetes (P=0.89), and LV ejection fraction (HR 0.95, 95% CI 0.93-0.96, P&lt;0.001) for the prediction of CHF development. The Kaplan-Meier estimate of cumulative CHF-free survival by indexed LA volume was shown (Fig.). Conclusions: In our cohort with AF, LA volume predicted CHF developments, independent of LV systolic function and other cardiovascular comorbidities, which appears to be clinically useful information for risk stratification.

Electrocardiographic Versus Echocardiographic Left Ventricular Hypertrophy in Prediction of Congestive Heart Failure in the Elderly.

Left ventricular hypertrophy (LVH) is an established risk factor for heart failure (HF) and is a component of the Framingham Heart Failure Risk Score (FHFRS). Whether LVH detected by electrocardiogram (ECG-LVH) is equally predictive of HF as LVH detected by echocardiography (echo-LVH) is unclear. ECG-LVH and echo-LVH are equally predictive of HF. This analysis included 4543 participants (85% white; 41% male) age ≥ 65 years from the Cardiovascular Health Study who were free of HF at baseline. Incident HF was identified during a median follow-up of 12 years. ECG-LVH was defined by the Cornell criteria. Echo-LVH was defined as left ventricular mass > 95th percentile (male, > 212 g; female, > 175 g). Cox proportional hazard regression was used to examine the association between ECG-LVH and echo-LVH, separately with incident HF. Harrell's concordance C-index was calculated for the FHFRS with inclusion of ECG-LVH and echo-LVH, separately. At baseline, 168 participants had ECG-LVH and 226 had echo-LVH. A total of 1380 incident HF events occurred during follow-up. Both ECG-LVH and echo-LVH were predictive of incident HF (for ECG-LVH, hazard ratio: 1.39, 95% confidence interval [CI]: 1.08-1.77; for echo-LVH, hazard ratio: 1.52, 95% CI: 1.22-1.89). The ability of the FHFRS to predict HF was similar when ECG-LVH (C-index: 0.772, 95% CI: 0.726-0.815) and echo-LVH (C-index: 0.772, 95% CI: 0.727-0.814) were included into the model separately. Both LVH-ECG and echo-LVH are equally predictive of incident HF and can be used interchangeably in HF risk-prediction models.

Prediction of Congestive Heart Failure in Patients with Non Valvular Atrial Fibrillation

Patients with atrial fibrillation (AF) have an increased risk of congestive heart failure (CHF) as well as ischemic stroke. The aim of this study was to investigate the clinical predictors of CHF in patients with non-valvular AF (NVAF). Three hundred and forty-seven patients (derivation cohort) with NVAF were retrospectively evaluated between 2004 and 2005. The associations between potential risk factors and CHF were tested using a Cox proportional hazards analysis, and a risk score for predicting CHF was created. The model was then validated in 161 patients (validation cohort) enrolled between 2008 and 2010. During the follow-up period, 41 patients in the derivation cohort developed CHF requiring hospitalization due to New York Heart Association (NYHA) class III or IV disease. Four independent risk factors were identified, each of which was assigned a number of points as follows: Age ≥72 years (1 point), heart rate ≥80 bpm (1 point), hypertension (1 point), and a previous history of congestive heart failure (2 points). The patients were grouped into one of three risk categories according to the calculated risk score (ARC2H score): low risk (0 points), intermediate risk (1-3 points) and high risk (4-5 points). In the derivation cohort, the annual rates of CHF in these risk categories were 0%, 2.5% and 18% per year respectively. In the validation cohort, the corresponding rates were 0.8%, 8% and 35% per year respectively. A simple clinical risk score, the ARC2H score, was developed to predict CHF in patients with NVAF and validated in an independent cohort.

Detection of Congestive Heart Failure in Dogs by Doppler Echocardiography

Echocardiographic prediction of congestive heart failure (CHF) in dogs has not been prospectively evaluated. CHF can be predicted by Doppler echocardiographic (DE) variables of left ventricular (LV) filling in dogs with degenerative mitral valve disease (MVD) and dilated cardiomyopathy (DCM). Sixty-three client-owned dogs. Prospective clinical cohort study. Physical examination, thoracic radiography, analysis of natriuretic peptides, and transthoracic echocardiography were performed. Diagnosis of CHF was based upon clinical and radiographic findings. Presence or absence of CHF was predicted using receiver-operating characteristic (ROC) curve, multivariate logistic and stepwise regression, and best subsets analyses. Presence of CHF secondary to MVD or DCM could best be predicted by E:isovolumic relaxation time (IVRT) (area under the ROC curve [AUC]=0.97, P<.001), respiration rate (AUC=0.94, P<.001), Diastolic Functional Class (AUC=0.93, P<.001), and a combination of Diastolic Functional Class, IVRT, and respiration rate (R2=0.80, P<.001) or Diastolic Functional Class (AUC=1.00, P<.001), respiration rate (AUC=1.00, P<.001), and E:IVRT (AUC=0.99, P<.001), and a combination of Diastolic Functional Class and E:IVRT (R2=0.94, P<.001), respectively, whereas other variables including N-terminal pro-brain natriuretic peptide, E:Ea, and E:Vp were less useful. Various DE variables can be used to predict CHF in dogs with MVD and DCM. Determination of the clinical benefit of such variables in initiating, modulating, and assessing success of treatments for CHF needs further study.

Prediction of Congestive Heart Failure as Adverse Outcome of Non-ST-Elevation Myocardial Infarction

Background: Non ST segment elevation myocardial infarction (NSTEMI) as the initial presentation of coronary artery disease (CAD) often leads to development of congestive heart failure (CHF). The incidence of CHF after NSTEMI is about 4.9 %. Our objective is to create prediction model for developing CHF as an adverse outcome of NSTEMI using Random Forest tool. Methods: The database was collected 2002-2004 for the CRUSADE registry (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes).

A research in the standard of uric acid and the healing up prediction of congestive heart failure

目的 探讨血尿酸(UA)水平对充血性心力衰竭(CHF)的预后预测价值.方法 60例CHF患者符合NYHA分级标准入选后,经心电图(ECG)、超声心动图(UCG)等检查,并分别在入选次日清晨空腹取静脉血2 ml,以ELASA法测定UA,尔后在病房或门诊每隔2个月重复检测,观察12个月.结果 18例UA升高组(≥500 μmol/L)与42例UA正常组(≥100 μmol/L)比较,左室射血分数(LVEF)下降,死亡率、心脏同步化重建治疗(CRT)差异均有显著意义(均P＜0.01).结论血尿酸升高与充血性心力衰竭关系密切。

Impact of age, race, and sex on the ability of B-type natriuretic peptide to aid in the emergency diagnosis of heart failure: results from the Breathing Not Properly (BNP) multinational study

Background B-type natriuretic peptide (BNP) is secreted from the cardiac ventricles in response to increased wall tension. Methods The Breathing Not Properly Multinational Study was a 7-center, prospective study of 1586 patients who presented to the Emergency Department with acute dyspnea and had BNP measured with a point-of-care assay upon arrival. The gold standard for congestive heart failure (CHF) was adjudicated by two independent cardiologists, blinded to BNP results, who reviewed all clinical data and standardized scores. The current study explores the effect of these variables on BNP decision statistics as well as the impact that changing cutoffs might have on the cost-effectiveness of diagnostic decisions that use BNP information. Results Significant differences in CHF rates were found on the basis of age ( P < .001) and racial group ( P = .020) but not sex ( P = .424). BNP levels increased with increasing age ( P < .001). To evaluate potential differences in the diagnostic utility of BNP levels as a function of demographic variables, separate receiver operating characteristic curves were performed. BNP was a stronger predictor in younger subjects than in older subjects and slightly weaker for female patients than for male patients (area under the curve = 0.918 and 0.870, respectively). An even smaller difference was noted between the white and black racial groups (area under the curve = 0.888 and 0.903, respectively). The differences in specificity as a function of age are larger than other differences in specificity or sensitivity. When logistic regression was used in a multivariate approach to combine the demographic variables with BNP information in the prediction of CHF, only BNP contributed significantly to the prediction of acute CHF. When the model was expanded to include terms for the interaction of each of the demographic variables with log 10 BNP, a significant interaction was found for sex. Since the relative consequences of false-positives and false-negatives are unlikely to be equivalent, the BNP cut-points that would be selected based on the current data as a function of relative costs are presented. Sharply rising consequences are seen for BNP cut-points >100 pg/mL. Conclusions If one assumes that failing to treat cases of CHF is worse than treating negative cases, then relatively low BNP cut-points (eg, not >100 pg/mL) should be used in patients presenting to the Emergency Department with a chief complaint of dyspnea, regardless of age, sex, or ethnicity.