Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

Aim. Еvaluate the influence of combination therapy with fosinopril or zofenopril on the regulatory-adaptive status (RAS) of patients with diastolic chronic heart failure (CHF). Material and methods. The study includes 80 patients with CHF I-II functional class according to the classification of the New York heart Association with left ventricle ejection fraction ≥50% because of hypertensive disease (HD) of III stage, who were randomized into two groups for treatment with fosinopril (14.7±4.2 mg/day, n=40) or zofenopril (22.5±7.5 mg/day, n=40). As part of combination pharmacotherapy, patients were included nebivolol (7.1±2.0 mg/day and 6.8±1.9 mg/day), in the presence of indications, atorvastatin and acetylsalicylic acid in the intestinal shell were prescribed. Initially and after six months, the following was done: a quantitative evaluation of the RAS (by cardio-respiratory synchronism test), echocardiography, tredmil-test, six-minute walking test, determination of the N-terminal precursor of the natriuretic brain peptide level in blood plasma and subjective evaluation of quality of life. Results. Therapy, using fosinopril, in comparison with zofenopril, more improved RAS (by 66.5%, p

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Clinical significance of urinary cyclic guanosine monophosphate in diagnosis of heart failure

Clinical Chemistry ◽

10.1093/clinchem/40.1.96 ◽

1994 ◽

Vol 40 (1) ◽

pp. 96-100 ◽

Cited By ~ 19

Author(s):

G Jakob ◽

J Mair ◽

K P Vorderwinkler ◽

G Judmaier ◽

P König ◽

...

Keyword(s):

Heart Failure ◽

New York ◽

Renal Impairment ◽

Heart Association ◽

Cyclic Guanosine Monophosphate ◽

Guanosine Monophosphate ◽

Neoplastic Disease ◽

Patients With Heart Failure ◽

Functional Classes ◽

New York Heart Association

Abstract We measured concentrations of guanosine 3',5'-monophosphate (cGMP) in plasma and urine of healthy subjects and patients with congestive heart failure, renal impairment, neoplastic disease, and hepatic cirrhosis. There was no correlation between cGMP concentrations in urine and in plasma. In all patients except those with renal impairment, urinary cGMP concentrations were significantly higher than in healthy persons. Only patients with heart failure or renal impairment showed significantly increased plasma cGMP concentrations. In contrast, cGMP in urine does not relate to the clinically assessed severity of heart failure (New York Heart Association functional classes). Determination of cGMP in plasma results in higher sensitivity and specificity for diagnosing heart failure than measurement of cGMP in urine.

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Reexamining the New York Heart Association Functional Classification of Heart Failure

The American Journal of Cardiology ◽

10.1016/j.amjcard.2007.05.006 ◽

2007 ◽

Vol 100 (5) ◽

pp. 911-912 ◽

Cited By ~ 3

Author(s):

Efstratios Apostolakis ◽

Karolina Akinosoglou

Keyword(s):

Heart Failure ◽

New York ◽

Heart Association ◽

Functional Classification ◽

New York Heart Association

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Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network

Symmetry ◽

10.3390/sym12122019 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2019

Author(s):

Dengao Li ◽

Ye Tao ◽

Jumin Zhao ◽

Hang Wu

Keyword(s):

Heart Failure ◽

New York ◽

Congestive Heart Failure ◽

Positive Predictive Value ◽

Predictive Value ◽

Heart Association ◽

Residual Network ◽

Ecg Signals ◽

Multi Scale ◽

New York Heart Association

Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.

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