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> Disorders > Disease or Syndrome > Congestive Heart Failure

Congestive Heart Failure

Congestive Heart Failre: A chronic and progressive condition where the heart's ability to pump blood effectively is impaired, leading to the buildup of fluid in the lungs and other tissues.
Symptoms may include shortness of breath, fatigue, and swelling in the legs and feet.
Early detection and appropriate treatment are crucial to manage this complex condition and improve patient outcomes.

Most cited protocols related to «Congestive Heart Failure»

1
Estimating Incidence with Competing Risks
We assume that there is a well-defined baseline time in the cohort and that T denotes the time from baseline time until the occurrence of the event of interest. In the absence of competing risks, the survival function, S(t), describes the distribution of event times: S(t) = Pr(Tt). One minus the survival function (ie, the complement of the survival function), F(t) = 1 − S(t) = Pr(T ≤ t) describes the incidence of the event over the duration of follow-up. Two key properties of the survival function are that S(0) = 1 (ie, at the beginning of the study, the event has not yet occurred for any subjects) and (ie, eventually the event of interest occurs for all subjects). In practice, the latter assumption may not be required, because the probability of the event over a restricted follow-up period may be <1.
Estimating the incidence of an event as a function of follow-up time provides important information on the absolute risk of an event. In the absence of competing risks, the Kaplan-Meier estimate of the survival function is frequently used for estimating the survival function. One minus the Kaplan-Meier estimate of the survival function provides an estimate of the cumulative incidence of events over time. In the case study that follows, we examine the incidence of cardiovascular death in patients hospitalized with heart failure. When the complement of the Kaplan-Meier function was used, the estimated incidence of cardiovascular death within 5 years of hospital admission was 43.0%. However, using the Kaplan-Meier estimate of the survival function to estimate the incidence function in the presence of competing risks generally results in upward biases in the estimation of the incidence function.9 (link),10 (link),12 (link) In particular, the sum of the Kaplan-Meier estimates of the incidence of each individual outcome will exceed the Kaplan-Meier estimate of the incidence of the composite outcome defined as any of the event types. Even when the competing events are independent, the Kaplan-Meier estimator yields biases in the probability of the event of interest. The problem here is that the Kaplan-Meier estimator estimates the probability of the event of interest in the absence of competing risks, which is generally larger than that in the presence of competing risks. Furthermore, the hypothetical population in which competing risks do not exist may not be the population of greatest interest for clinical and/or policy making,13 (link) as in the cardiovascular setting where noncardiovascular death may be an important consideration.
The Cumulative Incidence Function (CIF), as distinct from 1 – S(t), allows for estimation of the incidence of the occurrence of an event while taking competing risk into account. This allows one to estimate incidence in a population where all competing events must be accounted for in clinical decision making. The cumulative incidence function for the kth cause is defined as: CIFk(t) = Pr(T ≤ t,D = k), where D is a variable denoting the type of event that occurred. A key point is that, in the competing risks setting, only 1 event type can occur, such that the occurrence of 1 event precludes the subsequent occurrence of other event types. The function CIFk(t) denotes the probability of experiencing the kth event before time t and before the occurrence of a different type of event. The CIF has the desirable property that the sum of the CIF estimates of the incidence of each of the individual outcomes will equal the CIF estimates of the incidence of the composite outcome consisting of all of the competing events. Unlike the survival function in the absence of competing risks, CIFk(t) will not necessarily approach unity as time becomes large, because of the occurrence of competing events that preclude the occurrence of events of type k. In the case study that follows, when using the CIF, the estimated incidence of cardiovascular death within 5 years of hospital admission was 36.8%. This estimate was 6.2% lower than the estimate obtained using the complement of the Kaplan-Meier function. This illustrates the upward bias that can be observed when naively using Kaplan-Meier estimate in the presence of competing risks.
Austin P.C., Lee D.S, & Fine J.P. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation, 133(6), 601-609.
Publication 2016
Cardiovascular System Congestive Heart Failure Patients Population at Risk
2
Gait Speed and Survival in Older Adults
Gait speed was calculated for each participant using distance in meters and time in seconds. All studies used instructions to walk at usual pace and from a standing start. The walk distance varied from 8 ft to 6 m. For 8 ft, we converted to 4-m gait speed by formula.24 (link) For 6 m, we created a conversion formula (4-m speed=−0.0341 + (6-mspeed)×0.9816 withR2=0.93, based on a cohort of 61 individuals with concurrent 4- and 6-m walks). For 15 feet (4.57 m),23 (link) speed was simply meters divided by time. Where available, data on fast gait speed (walk as fast as comfortably able25 (link)) and the Short Physical Performance Battery were obtained.26 (link) Survival for each individual used study monitoring methods, including the National Death Index and individual study follow-up. Time from gait speed baseline to death was calculated in days. Five-year survival status was confirmed for more than 99% of participants.
Additional variables include sex, age, race/ethnicity (white, black, Hispanic, other, defined by participant), height(centimeters), weight(kilograms), body mass index (BMI), calculated as weight in kilograms divided by height in meters squared (<25, 25–30, and >30), smoking (never, past, current), use of mobility aids (none, cane, walker), systolic blood pressure, self-reports of health (excellent or very good vs good, fair, or poor), hospitalization in the past year (yes/no), and physician-diagnosed medical conditions (cancer, arthritis, diabetes, and heart disease, all yes/no). Measures of self-reported functional status were not collected in all studies and varied in content and form. We created a dichotomous variable reflecting dependence in basic activities of daily living (ADLs) based on report of being unable or needing help from another person to perform any basic activity, including eating, toileting, hygiene, transfer, bathing, and dressing. For individuals independent in ADLs, we created a dichotomous variable reflecting difficulty in instrumental ADLs based on report of difficulty or dependence with shopping, meal preparation, or heavy housework due to a health or physical problem. Participants were then classified into 1 of 3 groups; dependent in ADLs, difficulty with instrumental ADLs, or independent. Physical activity data were collected in 6 studies, but time frames and items varied widely. Two studies used the Physical Activity Scale for the Elderly (PASE).27 (link) We dichotomized the PASEs core at 100.28 (link) We created operational definitions of other covariates that were reasonably consistent across studies. Covariates were identical for height, weight, BMI, and systolic blood pressure. Hospitalization within the prior year was determined largely by self-report, and chronic conditions were by self-report of physician diagnosis, with heart disease encompassing angina, coronary artery disease, heart attack, and heart failure.
Studenski S., Perera S., Patel K., Rosano C., Faulkner K., Inzitari M., Brach J., Chandler J., Cawthon P., Connor E.B., Nevitt M., Visser M., Kritchevsky S., Badinelli S., Harris T., Newman A.B., Cauley J., Ferrucci L, & Guralnik J. (2011). Gait Speed and Survival in Older Adults. JAMA : the journal of the American Medical Association, 305(1), 50-58.
Publication 2011
Acquired Immunodeficiency Syndrome Aged Angina Pectoris Arthritis Canes Chronic Condition Congestive Heart Failure Coronary Artery Disease Diabetes Mellitus Diagnosis Ethnicity Foot Heart Diseases Hispanics Hospitalization Index, Body Mass Malignant Neoplasms Myocardial Infarction Neoplasm Metastasis Performance, Physical Physical Examination Physicians Range of Motion, Articular Reading Frames Systolic Pressure Walkers
3
Intensive Blood Pressure Reduction Outcomes
Definitions of study outcomes are outlined in the Supplementary Appendix. A committee whose members were unaware of the study-group assignments adjudicated the clinical outcomes specified in the protocol. The primary hypothesis was that treatment to reach a systolic blood-pressure target of less than 120 mm Hg, as compared with a target of less than 140 mm Hg, would result in a lower rate of the composite outcome of myocardial infarction, acute coronary syndrome not resulting in myocardial infarction, stroke, acute decompensated heart failure, or death from cardiovascular causes. Secondary outcomes included the individual components of the primary composite outcome, death from any cause, and the composite of the primary outcome or death from any cause.
We also assessed renal outcomes, using a different definition for patients with chronic kidney disease (eGFR <60 ml per minute per 1.73 m2) at baseline and those without it. The renal outcome in participants with chronic kidney disease at baseline was a composite of a decrease in the eGFR of 50% or more (confirmed by a subsequent laboratory test) or the development of ESRD requiring long-term dialysis or kidney transplantation. In participants without chronic kidney disease at baseline, the renal outcome was defined by a decrease in the eGFR of 30% or more to a value of less than 60 ml per minute per 1.73 m2. Incident albuminuria, defined for all study participants by a doubling of the ratio of urinary albumin (in milligrams) to creatinine (in grams) from less than 10 at baseline to greater than 10 during follow-up, was also a prespecified renal outcome.
Prespecified subgroups of interest for all outcomes were defined according to status with respect to cardiovascular disease at baseline (yes vs. no), status with respect to chronic kidney disease at baseline (yes vs. no), sex, race (black vs. non-black), age (<75 vs. ≥75 years), and baseline systolic blood pressure in three levels (≤132 mm Hg, >132 to <145 mm Hg, and ≥145 mm Hg). We also planned a comparison of the effects of systolic blood-pressure targets on incident dementia, changes in cognitive function, and cerebral small-vessel ischemic disease; these results are not presented here.
A Randomized Trial of Intensive versus Standard Blood-Pressure Control. (2015). The New England journal of medicine, 373(22), 2103-2116.
Publication 2015
Acute Coronary Syndrome Albumins Cardiovascular Diseases Cardiovascular System Cerebral Small Vessel Diseases Cerebrovascular Accident Chronic Kidney Diseases Cognition Congestive Heart Failure Creatinine Dementia Dialysis EGFR protein, human Kidney Kidney Failure, Chronic Kidney Transplantation Myocardial Infarction Patients Systolic Pressure Urine
4
Validating the Self-Care of Heart Failure Index
Concurrent validity was tested by comparing summary scores on the SCHFI to scores on the European Heart Failure Self-care Behavior Scale23 (link) using data from the sample of 34 HF patients described above. The 12 item European HF Self-Care Behavior Scale uses a 5-point Likert-type scale with 1 equaling ‘I completely agree’ and 5 equaling ‘I don’t agree at all’. Thus, lower scores indicate better self-care. Self-care maintenance was hypothesized to be moderately and negatively (because the scales are reversed) related to the European HF Self-Care Behavior Scale and this hypothesis was supported (r = −.65, p<.001). As the European HF Self-Care Behavior Scale captures self-care maintenance items only, it was not surprising that it was poorly correlated with self-care management (r = −.18, p = .43). Self-care confidence also was poorly correlated with the European HF Self-Care Behavior Scale (r = −.05, p = .76).
Construct validity was tested using confirmatory factor analysis to determine if the individual SCHFI items loaded on the self-care maintenance, management, and confidence scales as expected. Data collected on the first 154 patients who completed the SCHFI in the prospective cohort study described above were used for this analysis, which was done in AMOS 7.0 (SPSS Inc., Chicago, Illinois). The model was specified with self-care management and confidence correlated and with self-care maintenance and confidence correlated, now that two items in the confidence scale speak directly to self-care maintenance.
Overall model fit of the SCHFI was adequate and similar to that found in the previous version of the SCHFI (SCHFI v.4: χ2 = 329.9, SCHFI v.6: χ2 = 356.92) (Figure 2). Two items in the maintenance scale had negative values: item #5 - see your doctor or nurse and item #8 - forget to take one of your medicines. Item #5 is clarified in the instrument example shown in the Appendix as “Keep your doctor or nurse appointments”. The other item is the one reverse scored item, which suggests that patients may have trouble reversing the scale in their minds to give the appropriate response to this item. We will continue to monitor this item in future studies.
Absolute fit indices test how far apart the covariance matrices of the model and the sample data are while incremental fit indices test the hypothesized model against an independent model. We were most interested in the incremental fit indices to validate the data against our existing model. In the SCHFI v.4 model, incremental non-centrality measures (comparative fit index = .73) and independence model comparisons (normed fit index = .67 and nonnormed fit index = .69) were adequate. When tested with the SCHFI v.6 items, these measures were similar to those obtained with v.4 data (comparative fit index = .726; normed fit index = .554; root mean square error of approximation =.07).
Riegel B., Lee C.S., Dickson V.V, & Carlson B. (2009). An Update on the Self-Care of Heart Failure Index. The Journal of cardiovascular nursing, 24(6), 485-497.
Publication 2009
Congestive Heart Failure Europeans Long-Term Care Nurses Patients Pharmaceutical Preparations Physicians Plant Roots Respiratory Diaphragm Self-Management
5
Validating a Metabolic Syndrome Severity Score

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Publication 2013
Adolescent Adult Age Groups BLOOD Cerebrovascular Accident Child Congestive Heart Failure C Reactive Protein Determination, Blood Pressure Diabetes Mellitus Ethnicity Feelings Glucose Heart Disease, Coronary High Density Lipoprotein Cholesterol Hispanics Homeostasis Households Hypolipidemic Agents Insulin Insulin Resistance Lipids Metabolic Syndrome X Mexican Americans Myocardial Infarction Obesity Pharmaceutical Preparations Phlebotomy Plant Roots Plasma Population Group Pressure, Diastolic Racial Groups Sulfur Surrogate Markers Triglycerides Uric Acid Waist Circumference

Most recents protocols related to «Congestive Heart Failure»

1
Personalized Pacemaker for Heart Failure
Not available on PMC !

Example 6

Based on individualized patterns of variability including heart rate variability when the patient was healthy, breathing variability, pulmonary artery pressure variability, or any other individualized patterns, including gene networks, are used for improving heart function and clinical status of a patient with congestive heart failure by implementing into the heart rhythm provided by the pacemaker a personalized variability pattern. The “individualized pacemaker”, rather than providing a pre-determined regular constant rate, provides a heart rate variability within a specific range determined by the variability patterns learned from the subject, other patients with similar diseases, and from response to therapy. The variability introduced by the pacemaker can be dynamic and change over time according to input receives from the patient's variability pattern(s) or other inputs. If the patient already experienced loss of the heart rate variability, introducing a change in therapy is monitored by the partial reversal of the loss of change in one or more of the patterns of variability. These reversals are used as one of the measures for determining the output of the algorithm.

US11881296B2. Identifying and quantifying individualized variability-patterns and implementing them for improved efficacy of systems (2024-01-23). OBERON SCIENCES ILAN LTD. [IL]. Inventors: Yaron Ilan [IL].
Patent 2024
Congestive Heart Failure Gene Regulatory Networks Heart Pacemaker, Artificial Cardiac Patients Pressure Pulmonary Artery Rate, Heart Therapeutics
2
Individualized Heart Rate Variability Therapy
Not available on PMC !

Example 5

In patients with congestive heart failure, introducing number(s)/factor(s) generated based on the heart rate variability into a treatment regimen, such as the ratio between two consecutive heartbeat variabilities, may improve the response to diuretic therapy. If the patient already experienced loss of the heart rate variability, introducing the change in therapy can be followed by monitoring the effect of treatment on reversal of this loss. A use of previous electrocardiogram strips from the subject may enable to generate a “healthier-pattern of variability”. Based on the patient's variability pattern the app determines a dynamic individualized irregularity pattern for a dosing regimen.

US11881296B2. Identifying and quantifying individualized variability-patterns and implementing them for improved efficacy of systems (2024-01-23). OBERON SCIENCES ILAN LTD. [IL]. Inventors: Yaron Ilan [IL].
Patent 2024
Biotin Congestive Heart Failure Diuretics Electrocardiography Medical Devices Patients Pharmaceutical Preparations Pulse Rate Rate, Heart Treatment Protocols
3
Predicting Postoperative Acute Kidney Injury
Cases were stratified into two groups based on the development of AKI postoperatively and assessed for differences in preoperative factors and intraoperative factors. Descriptive statistics were reported for continuous variables as mean ± standard deviation and for categorical variables as frequencies and percentages. Univariate analysis for continuous and categorical variables was conducted by using analysis of variance and chi-squared or Fisher's exact test, as appropriate. Multivariate logistic regression models were used to evaluate the odds of developing AKI postoperatively, adjusted for age, sex, BMI, preoperative laboratory values (creatinine, albumin, and hematocrit), patient comorbidities (diabetes, congestive heart failure, chronic obstructive pulmonary disease, hypertension, and smoking status), and surgical duration for each of the five equations. Results of the multivariate regression model were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Akaike information criterion (AIC) was used to compare the fit of each model in predicting AKI postoperatively, and receiver operating curves (ROC) were generated for each Logistic regression model, with the area under the curve (AUC) calculated for each ROC. Due to the large sample size of this study, an alpha value was accepted at 0.01. Statistical analyses were performed by utilizing Stata software, version 17.0 (StataCorp LLC, College Station, TX, USA).
Mekkawy K.L., Chaudhry Y.P., Rao S.S., Raad M., Amin R.M, & Khanuja H.S. (2023). Comparing five equations to calculate estimated glomerular filtration rate to predict acute kidney injury following total joint arthroplasty. Arthroplasty, 5, 14.
Publication 2023
Albumins Chronic Obstructive Airway Disease Congestive Heart Failure Creatinine Diabetes Mellitus High Blood Pressures Operative Surgical Procedures Patients Volumes, Packed Erythrocyte
4
Venous Ulcer Management in Primary Care
The study will be carried out in 16 primary care health centres in the Region of Madrid.
It will include participants aged 18 or older, with a diagnosis of venous ulcer recorded in the electronic medical record (ABI greater than 0.8 and less than 1.3; diameter of the lesion greater than or equal to 1 cm) and under treatment in primary care nursing consultations. The individuals must be able to walk with or without the aid of devices, understand and answer the questionnaires autonomously, be accessible throughout the duration of the study and have expressed their agreement to participate and signed the consent form.
Those who are unable to commute to the health centre, or who reside outside the area where the research is carried out for more than 6 months per year, will be excluded. People with mixed ulcers, deep vein thrombosis (DVT) in acute phase, decompensated heart failure, dermatitis in acute phase, rheumatoid arthritis, undergoing treatment with antineoplastic drugs or with some absolute contraindication for physical exercise will also be excluded.
Withdrawal criteria are set for patients who, during the course of the trial, present a change in their clinical condition that prevents them from further participation, such as inflammation of the locomotor system (with heat, flushing, pain and functional impotence) or trauma due to a fall during the course of program with or without fracture and/or haematoma at both joint and soft tissue level (muscle and tendons) [24 ], must drop out of the study.
Herraiz-Ahijado B., Folguera-Álvarez C., Verdú-Soriano J., Mori-Vara P, & Rico-Blázquez M. (2023). Active legs: Impact of physical activity as an adjuvant treatment in the healing of venous ulcers in primary care: a RCT protocol study. BMC Nursing, 22, 65.
Publication 2023
Antineoplastic Agents Congestive Heart Failure Deep Vein Thrombosis Dermatitis Diagnosis Erectile Dysfunction Fracture, Bone Hematoma Inflammation Joints Medical Devices Muscle Tissue Musculoskeletal System Pain Patients Primary Health Care Rheumatoid Arthritis Tendons Tissues Ulcer Varicose Ulcer Wounds and Injuries
5
Predicting Kidney Failure Outcomes in Surgery
We identified candidate predictors from the literature and input from clinicians with expertise in kidney failure and perioperative medicine. The final list of variables included demographics of age and sex. Surgeries were categorized into 11 surgery types based on CCI codes, including categories that are specific to people with kidney failure (kidney transplant, peritoneal dialysis catheter insertion, and AV fistula creation). Surgery setting was classified using the administrative data as ambulatory elective, inpatient elective, or inpatient urgent/emergent. We considered comorbidities of previous AMI, cancer, chronic pulmonary disease, dementia, diabetes, heart failure, hypertension, liver disease, obesity, peripheral vascular disease, and stroke. These were defined using validated algorithms of International Statistical Classification of Diseases and Related Health Problems Ninth and Tenth Revision (ICD-9-CM and ICD-10-CA) codes [17 (link)] with an unrestricted lookback period for permanent conditions and 3 years for temporary conditions (Supplementary Tables 3 and 4). Kidney failure treatment modality was categorized as non-dialysis, hemodialysis, or peritoneal dialysis. Preoperative outpatient serum albumin (in g/L) and serum hemoglobin (in g/L) within the year before surgery were included as candidates. There were no missing values for variables except for albumin (15%) and hemoglobin (0.2%), which were imputed using multivariable normal regression with an iterative Markov chain Monte Carlo method.
Harrison T.G., Hemmelgarn B.R., James M.T., Sawhney S., Manns B.J., Tonelli M., Ruzycki S.M., Zarnke K.B., Wilson T.A., McCaughey D, & Ronksley P.E. (2023). Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models. BMC Nephrology, 24, 49.
Publication 2023
Albumins Catheterization Cerebrovascular Accident Congestive Heart Failure Dementia Diabetes Mellitus Disease, Chronic Fistula, Arteriovenous Hemodialysis Hemoglobin High Blood Pressures Inpatient Kidney Kidney Failure Kidney Transplantation Liver Diseases Lung Lung Diseases Malignant Neoplasms Menstruation Disturbances Obesity Operative Surgical Procedures Outpatients Peripheral Vascular Diseases Peritoneal Dialysis Serum Serum Albumin

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More about "Congestive Heart Failure"

Congestive heart failure (CHF) is a chronic and progressive condition where the heart's ability to pump blood effectively is impaired, leading to the buildup of fluid in the lungs and other tissues.
This condition is also known as heart failure, cardiac failure, or left ventricular failure.
Symptoms may include shortness of breath, fatigue, swelling in the legs and feet, and difficulty breathing, especially during physical activity or when lying down.
Early detection and appropriate treatment are crucial to manage this complex condition and improve patient outcomes.
CHF can be caused by a variety of underlying conditions, such as coronary artery disease, high blood pressure, heart valve problems, and cardiomyopathy.
Diagnostic tools like echocardiograms, electrocardiograms (ECGs), and lab tests can help healthcare providers diagnose and monitor CHF.
Treatment options for CHF may include medications, lifestyle changes, and in some cases, surgery or implantable devices.
Medications commonly used to manage CHF include diuretics, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), and beta-blockers.
These medications can help reduce the workload on the heart, improve symptoms, and slow the progression of the disease.
Lifestyle modifications, such as maintaining a healthy diet, exercising regularly, and managing weight and other underlying conditions, can also play a crucial role in managing CHF.
Patients with CHF may benefit from monitoring their fluid intake, sodium intake, and daily weight to help manage their symptoms.
In addition to traditional treatment approaches, researchers and healthcare providers are exploring new technologies and innovative strategies to improve the management of CHF.
For example, some studies have investigated the use of SAS software, Stata, and R software for data analysis and predictive modeling in CHF research.
These statistical software tools can be used to analyze patient data, identify risk factors, and develop personalized treatment plans.
By staying informed about the latest advancements in CHF research and treatment, patients and healthcare providers can work together to develop a comprehensive plan to manage this complex condition and improve overall health outcomes.