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> Disorders > Disease or Syndrome > Heart Failure

Heart Failure

Heart Failure: A chronic, progressive condition in which the heart is unable to pump enough blood to meet the body's needs.
Symptoms include fatigue, shortness of breath, and swelling in the legs and feet.
Risk factors include coronary artery disease, high blood pressure, and diabetes.
Early detection and proper management are crucial for improving outcomes.
Discover how PubCompare.ai can optimize heart failure research protocols using AI-driven comparisons to enhance accuracy and reproducibility.

Most cited protocols related to «Heart Failure»

1
Survival Analysis in Nuclear Cardiology
In comparing the survival distributions of two or more groups (for example, new therapy vs standard of care), Kaplan-Meier estimation1 and the log-rank test2 are the basic statistical methods of analyses. These are non-parametric methods in that no mathematical form of the survival distributions is assumed. If an investigator is interested in quantifying or investigating the effects of known covariates (e.g., age or race) or predictor variables (e.g., blood pressure), regression models are utilized. As in the conventional linear regression models, survival regression models allow for the quantification of the effect on survival of a set of predictors, the interaction of two predictors, or the effect of a new predictor above and beyond other covariates.
Among the available survival regression models, the Cox proportional hazards model developed by Sir David Cox3 has seen great use in epidemiological and medical studies, and the field of nuclear cardiology is no exception. What follows are some examples of Cox models being used in nuclear cardiology. Xu et al4 (link) looked at how myocardial scarring (assessed with positron emission tomography [PET] or single photon emission computed tomography [SPECT]) and other demographic and medical history factors predicted mortality in patients with advanced heart failure who received cardiac resynchronization therapy. Bourque et al5 (link) looked at how left ventricular ejection fraction (LVEF, assessed with angiography) and nuclear summed rest score (SRS, assessed with SPECT) interacted to change the risk of mortality. Hachamovitch and Berman6 (link) looked at the incremental prognostic value of myocardial perfusion SPECT (MPS) parameters in the prediction of sudden cardiac death. Nakata et al7 (link) looked at how the heart-to-mediastinum ratio (assessed with metaiodobenzylguanidine [MIBG] imaging) predicted cardiac death.
Survival models other than the Cox model have been used in nuclear cardiology as well. For example, in a study of diagnosis strategies for quantifying myocardial perfusion with SPECT, Duvall et al8 (link) utilized a log-normal survival model, a member of the parametric family of regression survival models, since initial data exploration revealed that the proportional hazards assumption of the Cox model was invalid. While this is an excellent example of when to utilize other survival models, it has been more common to see such data presented in conjunction with a Cox model analysis. In earlier studies of MPS-derived predictors of cardiac events, Hachamovitch et al9 (link) used Cox models to identify significant predictors and parametric models, specifically the accelerated failure time (AFT) model, to make estimates of the time to certain percentiles of survival. An identical analysis strategy was used by the research group comprised of Cuocolo, Acampa, Petretta, Daniele et al10 (link)–13 (link) in their research of the impact of various SPECT-derived predictors on the occurrence of cardiac events.
George B., Seals S, & Aban I. (2014). Survival analysis and regression models. Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 21(4), 686-694.
Publication 2014
3-Iodobenzylguanidine Angiography Blood Pressure Cardiac Death Cardiac Events Cardiac Resynchronization Therapy Cardiovascular System Family Member Heart Heart Failure Mediastinum Myocardium Patients Perfusion Positron-Emission Tomography Sudden Cardiac Death Tests, Diagnostic Therapeutics Tomography, Emission-Computed, Single-Photon Ventricular Ejection Fraction
2
PROMIS Item Bank Development and Validation

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Publication 2010
Alcohol Problem Amyotrophic Lateral Sclerosis Anger Angina Pectoris Anxiety Arthritis Asthma Cerebrovascular Accident Chronic Obstructive Airway Disease Cognition Coronary Artery Disease Diabetes Mellitus Disease, Chronic Epilepsy Fatigue Health Care Professionals Heart Heart Diseases Heart Failure High Blood Pressures Kidney Diseases Liver Diseases Lung Malignant Neoplasms Mental Disorders Migraine Disorders Multiple Sclerosis Myocardial Infarction Pain Patients Pharmaceutical Preparations Physical Examination Physicians Psychological Distress Satisfaction Sleep Disorders Spinal Cord Injuries
3
Cohort Study of Korean CKD Patients
Each participating center will enroll approximately 250 consecutive individuals over a 5-year period from 2011 until 2015, totaling 2,450 adult patients with CKD who provide written informed consent. The participating individuals will be monitored for approximately 10 years until death or until ESRD occurs.
The KNOW-CKD will enroll ethnic Korean patients with CKD who range in age between 20 years and 75 years. The CKD stages from 1 to 5 (predialysis), based on the eGFR, is calculated using the four-variable Modification of Diet in Renal Disease (MDRD) equation as follows:
eGFR (ml/min per 1.73 m2) = 175 × [serum Cr (mg/dl)] -1.154 × [age]-0.203 × [0.742 if female] × [1.212 if black], using serum creatinine concentrations measured at a central laboratory and an assay traceable to the international reference material [12 (link)].
Excluded subjects are those who 1) are unable or unwilling to give written consent, 2) have previously received chronic dialysis or organ transplantation, 3) have heart failure (NYHA class 3 or 4) or liver cirrhosis (Child-Pugh class 2 or 3), 4) have a past or current history of malignancy, 5) are currently pregnant, or 6) have a single kidney due to trauma or kidney donation.
We defined and allocated the specific causes of the CKD into four subgroups: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), and polycystic kidney disease (PKD). The definition of the subgroup is defined by the pathologic diagnosis, in the event that the biopsy result is available. Otherwise, the subgroup classification depends on the clinical diagnosis. GN is defined by the presence of glomerular hematuria or albuminuria with or without an underlying systemic disease causing glomerulonephritis. The diagnosis of DN is based on albuminuria in a subject with type 2 diabetes mellitus and the presence of diabetic retinopathy. HTN is defined by the patient’s hypertension history and the absence of a systemic illness associated with renal damage. Unified ultrasound criteria [13 (link)] will be used to diagnose PKD. The other causative diseases will be categorized as ‘unclassified’.
Oh K.H., Park S.K., Park H.C., Chin H.J., Chae D.W., Choi K.H., Han S.H., Yoo T.H., Lee K., Kim Y.S., Chung W., Hwang Y.H., Kim S.W., Kim Y.H., Kang S.W., Park B.J., Lee J, & Ahn C. (2014). KNOW-CKD (KoreaN cohort study for Outcome in patients With Chronic Kidney Disease): design and methods. BMC Nephrology, 15, 80.
Publication 2014
Adult Biological Assay Biopsy Child Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Diabetic Retinopathy Diagnosis Dialysis Diet EGFR protein, human Glomerulonephritis Heart Failure Hematuria High Blood Pressures Hypertensive Nephropathy Kidney Kidney Diseases Kidney Failure, Chronic Kidney Glomerulus Koreans Liver Cirrhosis Malignant Neoplasms Organ Transplantation Patients Polycystic Kidney Diseases Renal Agenesis, Unilateral Serum Ultrasonics Woman Wounds and Injuries
4
Cardiovascular Mortality in Chronic Kidney Disease

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Publication 2010
Antihypertensive Agents Cardiovascular System Cerebrovascular Accident Cholesterol Creatinine Diabetes Mellitus Dietary Modification Glucose Heart Heart Failure High Blood Pressures Hypercholesterolemia Kidney Diseases Myocardial Infarction Non-Smokers Pharmaceutical Preparations Pressure, Diastolic Serum Sudden Cardiac Death Systolic Pressure
5
COPD Randomized Trial Inclusion Criteria
Patients were randomised if they met the following main inclusion criteria: outpatients aged ≥40 years with a history of moderate to very severe COPD (GOLD stage 2–4) [23 ]; post-bronchodilator FEV1 <80% of predicted normal; post-bronchodilator FEV1/forced vital capacity (FVC) <70%; current or ex-smokers with a smoking history of >10 pack–years.
Patients with a significant disease other than COPD were excluded from the trials. Other exclusion criteria included: clinically relevant abnormal baseline laboratory parameters or a history of asthma; myocardial infarction within 1 year of screening; unstable or life-threatening cardiac arrhythmia; known active tuberculosis; clinically evident bronchiectasis; cystic fibrosis or life-threatening pulmonary obstruction; hospitalised for heart failure within the past year; diagnosed thyrotoxicosis or paroxysmal tachycardia; previous thoracotomy with pulmonary resection; regular use of daytime oxygen if patients were unable to abstain during clinic visits; or currently enrolled in a pulmonary rehabilitation programme (or completed in the 6 weeks before screening).
Patients with moderate or severe renal impairment (creatinine clearance ≤50 mL·min−1) were not excluded from the study but were closely monitored by the investigator.
Both studies were performed in accordance with the Declaration of Helsinki, International Conference on Harmonisation Harmonised Tripartite Guideline for Good Clinical Practice and local regulations. The protocols were approved by the authorities and the ethics committees of the respective institutions, and signed informed consent was obtained from all patients.
Buhl R., Maltais F., Abrahams R., Bjermer L., Derom E., Ferguson G., Fležar M., Hébert J., McGarvey L., Pizzichini E., Reid J., Veale A., Grönke L., Hamilton A., Korducki L., Tetzlaff K., Waitere-Wijker S., Watz H, & Bateman E. (2015). Tiotropium and olodaterol fixed-dose combination versus mono-components in COPD (GOLD 2–4). The European Respiratory Journal, 45(4), 969-979.
Publication 2015
Airway Obstruction Asthma Bronchiectasis Bronchodilator Agents Cardiac Conduction System Disease Chronic Obstructive Airway Disease Clinic Visits Conferences Creatinine Cystic Fibrosis Ex-Smokers Forced Vital Capacity Gold Heart Failure Institutional Ethics Committees Lung Myocardial Infarction Outpatients Oxygen Patients Rehabilitation Renal Insufficiency Tachycardia, Paroxysmal Thoracotomy Thyrotoxicosis Tuberculosis

Most recents protocols related to «Heart Failure»

1
Nested Case-Control Study Design
When the effect of time-dependent exposure needs to be investigated in the context of large databases, the nested case–control design is a valid alternative to the cohort design [19 (link)]. The case–control study consists of four steps: (i) cohort selection, (ii) case definition and selection, (iii) for each case, identification of all possible controls, and (iv) random selection of m controls for each case [20 (link)]. In the present study, the cohort involved the oral antidiabetic drug users as described above. Death from any cause was the primary outcome of interest, and cases were thus the cohort members who died during follow-up. For each case patient, all cohort members who survived when the matched case died were identified (i.e. the incidence density sampling method was adopted). For each case patient, one control was randomly selected from the cohort members to be matched for sex, age at index date, clinical status (see below) and date of index prescription.
A secondary outcome was cardiovascular mortality, i.e., death for ischemic heart disease, cerebrovascular disease, or heart failure, which was addressed by another nested case–control study in which patients who died for cardiovascular causes were the cases, and patients matched for age, sex and clinical status and index date were the controls, as described above.
Rea F., Savaré L., Valsassina V., Ciardullo S., Perseghin G., Corrao G, & Mancia G. (2023). Adherence to antidiabetic drug therapy and reduction of fatal events in elderly frail patients. Cardiovascular Diabetology, 22, 53.
Publication 2023
Antidiabetics Cardiovascular System Cerebrovascular Disorders Drug Abuser Heart Failure Myocardial Ischemia Patients
2
Nested Case-Control Study Design
When the effect of time-dependent exposure needs to be investigated in the context of large databases, the nested case–control design is a valid alternative to the cohort design [19 (link)]. The case–control study consists of four steps: (i) cohort selection, (ii) case definition and selection, (iii) for each case, identification of all possible controls, and (iv) random selection of m controls for each case [20 (link)]. In the present study, the cohort involved the oral antidiabetic drug users as described above. Death from any cause was the primary outcome of interest, and cases were thus the cohort members who died during follow-up. For each case patient, all cohort members who survived when the matched case died were identified (i.e. the incidence density sampling method was adopted). For each case patient, one control was randomly selected from the cohort members to be matched for sex, age at index date, clinical status (see below) and date of index prescription.
A secondary outcome was cardiovascular mortality, i.e., death for ischemic heart disease, cerebrovascular disease, or heart failure, which was addressed by another nested case–control study in which patients who died for cardiovascular causes were the cases, and patients matched for age, sex and clinical status and index date were the controls, as described above.
Rea F., Savaré L., Valsassina V., Ciardullo S., Perseghin G., Corrao G, & Mancia G. (2023). Adherence to antidiabetic drug therapy and reduction of fatal events in elderly frail patients. Cardiovascular Diabetology, 22, 53.
Publication 2023
Antidiabetics Cardiovascular System Cerebrovascular Disorders Drug Abuser Heart Failure Myocardial Ischemia Patients
3
Longitudinal Evaluation of PH Progression
Baseline visit details (study and control groups characterizations, inclusion/exclusion criteria of the study) are presented in our previous article [5 (link)]. Patients were clinically followed-up on regular three-month basis, however the first structured (including PET-MRI, echo, 6MWT, biochemical analysis) follow-up visit (FU-1) was done after 24 months from the baseline visits (graphical presentation of the study Fig. 1). PET/MRI scans were done together with other tests during the same hospitalization, in the same patients’ hemodynamic state (Fig. 2). Occurrence of clinical end-points (CEP) was checked between baseline visits and FU-1 visits. CEP was defined as death or WHO class worsening and/or with hospitalisation due to pulmonary hypertension progression or right heart failure (as described previously) [5 (link)]. All CEP( +) patients eventually had PAH therapy escalation within 1 month since CEP. After FU-1 visits we observed study group for next 24 months for occurrence of CEP (defined the same as above), ending in second follow-up visit (FU-2). PET/MRI scans were not repeated after FU-1 visit (during FU-2 only WHO class, laboratory tests, 6MWT and RHC were done). The clinical follow-up lasted in total 48 months.

Graphical presentation of the study

Cardiac 18-fluorodeoxyglucose uptake in pulmonary hypertension patient before A initiation of specific therapy and after B 24 months of treatment

Therapy escalation was defined as initiation of prostacyclin (PGI) treatment (parenteral or oral) or adding second-line oral therapy according to ESC Guidelines [1 (link)]. Right heart catheterization was repeated at FU-1 and FU-2 visits in standard technique within median 6 [2 (link)–9 (link)] days of PET/MRI scans with a use of previously described protocol [5 (link)].
Kazimierczyk R., Szumowski P., Nekolla S.G., Malek L.A., Blaszczak P., Hladunski M., Sobkowicz B., Mysliwiec J, & Kaminski K.A. (2023). The impact of specific pulmonary arterial hypertension therapy on cardiac fluorodeoxyglucose distribution in PET/MRI hybrid imaging–follow-up study. EJNMMI Research, 13, 20.
Publication 2023
Catheterizations, Cardiac Disease Progression ECHO protocol Epoprostenol F18, Fluorodeoxyglucose Heart Failure Hemodynamics MRI Scans Parenteral Nutrition Patients Positron-Emission Tomography Pulmonary Hypertension Therapeutics
4
Validation of AI Model for Predicting Heart Failure
We develop and validate our model using datasets from two different hospitals. Our first dataset consists of 7121 records from 3767 unique patients who underwent cardiac catheterization at Massachusetts General Hospital (Hospital 1). All patients had a diagnosis of HF (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date.
This dataset is split into an 80% development set, used to train predictive models, and a 20% internal holdout test set, used for model evaluation. Datasets are constructed such that no data from a single patient appears in different data splits; i.e., all data splits are done on a per-patient basis. We further split the development set on a per-patient level using an 80–20 split into training and “dev” sets. The training set is used to train the model and the dev set is used to determine when training is completed.
Our second dataset consists of 2725 records from 1249 unique patients who underwent cardiac catheterization at the Brigham and Women’s Hospital (Hospital 2). As with data from MGH, these patients all had a diagnosis of heart failure (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date. We used this entire dataset as an external validation set for model evaluation.
Each record in the datasets consists of: the mean Pulmonary Capillary Wedge Pressure (as measured by cardiac catheterization), a 10-s, 12-lead ECG recorded by the same system (GE Healthcare MUSE) on the same day as the catheterization procedure, and basic demographic information (age/sex). Dataset details are summarized in Table 2.

Model performance (AUROC) on test data. HFNet significantly outperforms the baseline logistic regression (LR) model.

ModelAUROC
Internal test setExternal holdout set
LR0.71 + − 0.010.67 + − 0.01
HFNet0.82 + − 0.01 *0.81 + − 0.01 *

Significant values are in bold.

Key: *: p value < 1e − 10.

Raghu A., Schlesinger D., Pomerantsev E., Devireddy S., Shah P., Garasic J., Guttag J, & Stultz C.M. (2023). ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure. Scientific Reports, 13, 3923.
Publication 2023
Catheterization Catheterizations, Cardiac Diagnosis Electrocardiography, 12-Lead Heart Failure Muse Patients Pulmonary Wedge Pressure Woman
5
COVID-19 Symptom Severity Categorization
Participants in the analytic sample were categorized into four severity groups according to their symptoms: asymptomatic (N = 92, 9.4%), mild (N = 378, 38.5%), moderate (N = 408, 41.5%), and severe (N = 75, 7.6%). This categorization was based on the NIH COVID-19 clinical spectrum updated October 2021 (National Institutes of Health, 2021). The asymptomatic category consisted of responders who reported a positive SARS-CoV-2 virologic test result without any symptoms associated with COVID-19. The mild category included responders with at least one symptom associated with COVID-19 but no shortness of breath or difficulty breathing. Moderate cases were in responders who reported shortness of breath and/or diagnosis of lower respiratory disease (pneumonia/bronchitis) during clinical assessment or imaging. These responders maintained oxygen saturation (SpO2)≥94% on room air at sea level. Mild and moderate cases were medically managed primarily at home, even if they initially visited a healthcare facility for medical treatment and/or testing. Severe cases included responders with SpO2<93% on room air, respiratory rate >30 breaths/min, heart rate greater than 100 beats per minute, acute respiratory distress syndrome, septic shock, cardiac dysfunction, or an exaggerated inflammatory response in addition to pulmonary disease, or severe illness causing cardiac, hepatic, renal, central nervous system, or thrombotic disease during COVID-19. Responders were also categorized as severe if they were admitted to the hospital, or received intensive care or mechanical ventilation, or if they eventually died from COVID-19.
Two complimentary analytic variables were created: an ordinal COVID-19 severity variable on 1–4 scale corresponding to asymptomatic, mild, moderate, and severe symptoms, and a binary COVID-19 severe category variable, with asymptomatic, mild, and moderate patients in one category, and severe patients in the second category.
Waszczuk M.A., Morozova O., Lhuillier E., Docherty A.R., Shabalin A.A., Yang X., Carr M.A., Clouston S.A., Kotov R, & Luft B.J. (2023). Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders. PLOS ONE, 18(3), e0282271.
Publication 2023
Bronchitis Central Nervous System COVID 19 Diagnostic Techniques, Respiratory System Dyspnea Health Services Administration Heart Heart Failure Intensive Care Kidney Mechanical Ventilation Oxygen Saturation Patients Pneumonia Rate, Heart Respiratory Distress Syndrome, Adult Respiratory Rate SARS-CoV-2 Saturation of Peripheral Oxygen Septic Shock Thrombosis

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1
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2
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More about "Heart Failure"

Heart failure (HF) is a chronic and progressive condition where the heart is unable to effectively pump blood to meet the body's needs.
Symptoms of HF include fatigue, shortness of breath, and swelling in the legs and feet.
Risk factors for HF include coronary artery disease, high blood pressure, and diabetes.
Early detection and proper management are crucial for improving outcomes.
Researchers studying HF can utilize various tools and technologies to enhance their research protocols.
For example, the SAS version 9.4 statistical software package can be used for data analysis, while the Stata 15 software is also commonly used in HF research.
The High-Capacity cDNA Reverse Transcription Kit can be used for gene expression analysis, and the Spot Vital Signs LXi device can be used for vital sign monitoring.
Additionally, the Synergy HT multi-mode microplate reader and the CellTiter-Glo luminescent cell viability assay can be employed for cell-based experiments.
Imaging technologies like the Vivid E9 echocardiography system and the Vevo 2100 high-resolution ultrasound imaging system can be used to assess cardiac structure and function in HF models.
Leveraging these tools and technologies, researchers can optimize their HF research protocols using AI-driven comparisons to enhance accuracy and reproducibility.
The PubCompare.ai platform can help researchers identify the best protocols and products for their HF studies by locating relevant information from literature, preprints, and patents, and then using AI-powered comparisons to select the most appropriate options.
This can lead to improved research outcomes and a better understanding of this complex cardiovascular condition.