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Heart Diseases

Heart diseases encompass a wide range of conditions affecting the heart and its blood vessels.
This includes coronary artery disease, heart failure, arrhythmias, and congenital heart defects.
Accurate identification and optimization of research protocols are crucial for advancing our understanding and treatment of these complex disorders.
PubComapre.ai leverages AI-driven tools to streamline the process, helping researchers access the most effective heart disease protocols from published literature, preprints, and patents.
By enhancing protocol selection, this innovative approach can improve the accuracy and outcomes of heart disease research, ultimately leading to better patient care.

Most cited protocols related to «Heart Diseases»

1
Predicting Mortality Risk with Methylation Biomarkers
Using the NHANES training data, we applied a Cox penalized regression model—where the hazard of aging-related mortality (mortality from diseases of the heart, malignant neoplasms, chronic lower respiratory disease, cerebrovascular disease, Alzheimer’s disease, Diabetes mellitus, nephritis, nephrotic syndrome, and nephrosis) was regressed on forty-two clinical markers and chronological age to select variables for inclusion in our phenotypic age score. Ten-fold cross-validation was employed to select the parameter value, lambda, for the penalized regression. In order to develop a sparse parsimonious age estimator (fewer biomarker variables preferred to produce robust results) we selected a lambda of 0.0192, which represented a one standard deviation increase over the lambda with minimum mean-squared error during cross-validation (Supplement 1: Fig. S13). Of the forty-two biomarkers included in the penalized Cox regression model, this resulted in ten variables (including chronological age) that were selected for the phenotypic age predictor.
These nine biomarkers and chronological age were then included in a parametric proportional hazards model based on the Gompertz distribution. Based on this model, we estimated the 10-year (120 months) mortality risk of the j-the individual. Next, the mortality score was converted into units of years (Supplement 1). The resulting phenotypic age estimate was regressed on DNA methylation data using an elastic net regression analysis. The penalization parameter was chosen to minimize the cross validated mean square error rate (Supplement 1: Fig. S14), which resulted in 513 CpGs.
Levine M.E., Lu A.T., Quach A., Chen B.H., Assimes T.L., Bandinelli S., Hou L., Baccarelli A.A., Stewart J.D., Li Y., Whitsel E.A., Wilson J.G., Reiner A.P., Aviv A., Lohman K., Liu Y., Ferrucci L, & Horvath S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10(4), 573-591.
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Publication 2018
Biological Markers Cerebrovascular Disorders cytidylyl-3'-5'-guanosine Diabetes Mellitus Dietary Supplements DNA Methylation Heart Diseases Malignant Neoplasms Nephritis Nephrotic Syndrome Phenotype Respiration Disorders
2
Continuous Surveillance of Heart Failure Hospitalization
Beginning in 2005, the ARIC Study conducted continuous, retrospective surveillance of hospital discharges for HF for all residents age 55 years and older in four US communities: Forsyth County, North Carolina; the city of Jackson, Mississippi; eight northwest suburbs of Minneapolis, Minnesota; and Washington County, Maryland. In 2005, there were 31 hospitals serving the four ARIC communities. The combined population in 2005 for these regions was approximately 177,000 men and women 55 years of age or older. Because of the small number of hospitalizations in the sample among race/ethnic groups other than black or white (n=55), we categorized these as white for the purposes of these analyses.
Annual electronic discharge indices were obtained from all hospitals admitting residents from the four ARIC communities. Discharges meeting eligibility criteria were sampled from these files. A hospitalization was considered eligible for validation as a HF event based on its International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code, age, gender, race, and residence in the community surveillance area. Target primary or secondary hospital discharge diagnoses codes included: heart failure (428), rheumatic heart disease (398.91), hypertensive heart disease- with congestive heart failure (402.01, 402.11, 402.91), hypertensive heart disease and renal failure- with CHF (404.01, 404.03, 404.13, 404.91, 404.93), acute cor pulmonale (415.0), chronic pulmonary heart disease, unspecified (416.9), other primary cardiomyopathies (425.4), acute edema of lung, unspecified (518.4), dyspnea and respiratory abnormalities (786.0). Sampling probabilities were created to optimize variance estimates around event rate estimates with a pre-set maximum number of cases to be abstracted in 2005 of 1499 (See Supplemental Methods). This fixed number of abstractions was estimated and set based on a target number (n=500) of hospitalized events that could be investigated and validated considering available resources and time constraints. All analyses were weighted to account for the sampling probabilities.
Rosamond W.D., Chang P.P., Baggett C., Johnson A., Bertoni A.G., Shahar E., Deswal A., Heiss G, & Chambless L.E. (2012). Classification of Heart Failure in the Atherosclerosis Risk in Communities (ARIC) Study: A Comparison of Diagnostic Criteria. Circulation. Heart Failure, 5(2), 152-159.
Publication 2012
Cardiomyopathies, Primary Congenital Abnormality Cor Pulmonale Diagnosis Dyspnea Eligibility Determination Gender Heart Heart Diseases High Blood Pressures Hospitalization Kidney Failure Patient Discharge Pulmonary Edema Racial Groups Respiratory Rate Rheumatic Heart Disease Woman
3
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
4
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
5
Assessing Comorbidities and Outcomes in COVID-19
Demographic, clinical, and outcome data were collected by using a prespecified case report form. Comorbidities were defined according to a modified Charlson comorbidity index.10 (link) Comorbidities collected were chronic cardiac disease, chronic respiratory disease (excluding asthma), chronic renal disease (estimated glomerular filtration rate ≤30), mild to severe liver disease, dementia, chronic neurological conditions, connective tissue disease, diabetes mellitus (diet, tablet, or insulin controlled), HIV or AIDS, and malignancy. These conditions were selected a priori by a global consortium to provide rapid, coordinated clinical investigation of patients presenting with any severe or potentially severe acute infection of public interest and enabled standardisation.
Clinician defined obesity was also included as a comorbidity owing to its probable association with adverse outcomes in patients with covid-19.11 (link)
12 (link) The clinical information used to calculate prognostic scores was taken from the day of admission to hospital.13 (link) A practical approach was taken to sample size requirements.14 We used all available data to maximise the power and generalisability of our results. Model reliability was assessed by using a temporally distinct validation cohort with geographical subsetting, together with sensitivity analyses.
Knight S.R., Ho A., Pius R., Buchan I., Carson G., Drake T.M., Dunning J., Fairfield C.J., Gamble C., Green C.A., Gupta R., Halpin S., Hardwick H.E., Holden K.A., Horby P.W., Jackson C., Mclean K.A., Merson L., Nguyen-Van-Tam J.S., Norman L., Noursadeghi M., Olliaro P.L., Pritchard M.G., Russell C.D., Shaw C.A., Sheikh A., Solomon T., Sudlow C., Swann O.V., Turtle L.C., Openshaw P.J., Baillie J.K., Semple M.G., Docherty A.B., Harrison E.M., Baillie J.K., Semple M.G., Openshaw P.J., Carson G., Alex B., Bach B., Barclay W.S., Bogaert D., Chand M., Cooke G.S., Docherty A.B., Dunning J., da Silva Filipe A., Fletcher T., Green C.A., Harrison E.M., Hiscox J.A., Ho A.Y., Horby P.W., Ijaz S., Khoo S., Klenerman P., Law A., Lim W.S., Mentzer A.J., Merson L., Meynert A.M., Noursadeghi M., Moore S.C., Palmarini M., Paxton W.A., Pollakis G., Price N., Rambaut A., Robertson D.L., Russell C.D., Sancho-Shimizu V., Scott J.T., Sigfrid L., Solomon T., Sriskandan S., Stuart D., Summers C., Tedder R.S., Thomson E.C., Thwaites R.S., Turtle L.C., Zambon M., Hardwick H., Donohue C., Ewins J., Oosthuyzen W., Griffiths F., Norman L., Pius R., Drake T.M., Fairfield C.J., Knight S., Mclean K.A., Murphy D., Shaw C.A., Dalton J., Girvan M., Saviciute E., Roberts S., Harrison J., Marsh L., Connor M., Halpin S., Jackson C., Gamble C., Leeming G., Law A., Hendry R., Scott-Brown J., Greenhalf W., Shaw V., McDonald S., Ahmed K.A., Armstrong J.A., Ashworth M., Asiimwe I.G., Bakshi S., Barlow S.L., Booth L., Brennan B., Bullock K., Catterall B.W., Clark J.J., Clarke E.A., Cole S., Cooper L., Cox H., Davis C., Dincarslan O., Dunn C., Dyer P., Elliott A., Evans A., Fisher L.W., Foster T., Garcia-Dorival I., Greenhalf W., Gunning P., Hartley C., Ho A., Jensen R.L., Jones C.B., Jones T.R., Khandaker S., King K., Kiy R.T., Koukorava C., Lake A., Lant S., Latawiec D., Lavelle-Langham L., Lefteri D., Lett L., Livoti L.A., Mancini M., McDonald S., McEvoy L., McLauchlan J., Metelmann S., Miah N.S., Middleton J., Mitchell J., Moore S.C., Murphy E.G., Penrice-Randal R., Pilgrim J., Prince T., Reynolds W., Ridley P.M., Sales D., Shaw V.E., Shears R.K., Small B., Subramaniam K.S., Szemiel A., Taggart A., Tanianis-Hughes J., Thomas J., Trochu E., van Tonder L., Wilcock E., Zhang J.E., Adeniji K., Agranoff D., Agwuh K., Ail D., Alegria A., Angus B., Ashish A., Atkinson D., Bari S., Barlow G., Barnass S., Barrett N., Bassford C., Baxter D., Beadsworth M., Bernatoniene J., Berridge J., Best N., Bothma P., Brealey D., Brittain-Long R., Bulteel N., Burden T., Burtenshaw A., Caruth V., Chadwick D., Chambler D., Chee N., Child J., Chukkambotla S., Clark T., Collini P., Cosgrove C., Cupitt J., Cutino-Moguel M.T., Dark P., Dawson C., Dervisevic S., Donnison P., Douthwaite S., DuRand I., Dushianthan A., Dyer T., Evans C., Eziefula C., Fegan C., Finn A., Fullerton D., Garg S., Garg S., Garg A., Godden J., Goldsmith A., Graham C., Hardy E., Hartshorn S., Harvey D., Havalda P., Hawcutt D.B., Hobrok M., Hodgson L., Holme A., Hormis A., Jacobs M., Jain S., Jennings P., Kaliappan A., Kasipandian V., Kegg S., Kelsey M., Kendall J., Kerrison C., Kerslake I., Koch O., Koduri G., Koshy G., Laha S., Larkin S., Leiner T., Lillie P., Limb J., Linnett V., Little J., MacMahon M., MacNaughton E., Mankregod R., Masson H., Matovu E., McCullough K., McEwen R., Meda M., Mills G., Minton J., Mirfenderesky M., Mohandas K., Mok Q., Moon J., Moore E., Morgan P., Morris C., Mortimore K., Moses S., Mpenge M., Mulla R., Murphy M., Nagel M., Nagarajan T., Nelson M., Otahal I., Pais M., Panchatsharam S., Paraiso H., Patel B., Pepperell J., Peters M., Phull M., Pintus S., Pooni J.S., Post F., Price D., Prout R., Rae N., Reschreiter H., Reynolds T., Richardson N., Roberts M., Roberts D., Rose A., Rousseau G., Ryan B., Saluja T., Shah A., Shanmuga P., Sharma A., Shawcross A., Sizer J., Smith R., Snelson C., Spittle N., Staines N., Stambach T., Stewart R., Subudhi P., Szakmany T., Tatham K., Thomas J., Thompson C., Thompson R., Tridente A., Tupper-Carey D., Twagira M., Ustianowski A., Vallotton N., Vincent-Smith L., Visuvanathan S., Vuylsteke A., Waddy S., Wake R., Walden A., Welters I., Whitehouse T., Whittaker P., Whittington A., Wijesinghe M., Williams M., Wilson L., Wilson S., Winchester S., Wiselka M., Wolverson A., Wooton D.G., Workman A., Yates B, & Young P. (2020). Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. The BMJ, 370, m3339.
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Publication 2020
Acquired Immunodeficiency Syndrome Asthma Chronic Condition Chronic Kidney Diseases Connective Tissue Diseases COVID 19 Dementia Diabetes Mellitus Diet Disease, Chronic Glomerular Filtration Rate Heart Heart Diseases Hypersensitivity Infection Insulin Liver Diseases Malignant Neoplasms Obesity Patients Respiration Disorders Respiratory Rate Tablet

Most recents protocols related to «Heart Diseases»

1
Benchmarking MCA-Miner against FP-Growth in BRL
Not available on PMC !

Example 1

The MCA-miner method disclosed herein in FIGS. 2A-2C, when used together with BRL, offers the power of rule list interpretability while maintaining the predictive capabilities of already established machine learning methods.

The performance and computational efficiency of the new MCA-miner is benchmarked against the “Titanic” dataset, as well as the following five (5) datasets available in the UCI Machine Learning Repository: “Adult,” “Autism Screening Adult,” “Breast Cancer Wisconsin (Diagnostic),” “Heart Disease,” and “HIV-1 protease cleavage,” which are designated as Adult, ASD, Cancer, Heart, and HIV, respectively. These datasets represent a wide variety of real-world experiments and observations, thus enabling the improvements described herein to be compared against the original BRL implementation using the FP-Growth miner.

All six benchmark datasets correspond to binary classification tasks. The experiments were conducted using the same set up in each of the benchmarks. First, the dataset is transformed into a format that is compatible with the disclosed BRL implementation. Second, all continuous attributes are quantized into either two (2) or three (3) categories, while keeping the original categories of all other variables. It is worth noting that depending on the dataset and how its data was originally collected, the existing taxonomy and expert domain knowledge are prioritized in some instances to generate the continuous variable quantization. A balanced quantization is generated when no other information was available. Third, a model is trained and tested using 5-fold cross-validations, reporting the average accuracy and Area Under the ROC Curve (AUC) as model performance measurements.

Table 1 presents the empirical result of comparing both implementations. The notation in the table follows the definitions above. To strive for a fair comparison between both implementations, the parameters rmax=2 and smin=0:3 are fixed for both methods, and in particular for MCA-miner μmin=0:5 and M=70 are also set. The multi-core implementations for both the new MCA-miner and BRL were executed on six parallel processes, and stopped when the Gelman & Rubin parameter satisfied {circumflex over (R)}≤1.05. All the experiments were run using a single AWS EC2 c5.18×large instance with 72 cores.

TABLE 1
Performance evaluation of FP-Growth against MCA-miner
when used with BRL on benchmark datasets. ttrain is the full training wall time.
FP-GROWTH + BRLMCA-MINER + BRL
DATASETnpΣt-1p|α1|ACCURACYAUCttrain[s]ACCURACYAUCttrain[s]
Adult45.222141110.810.855120.810.85115
ASD24821890.870.901980.870.9016
Cancer569321500.920.971680.920.9422
Heart30313490.820.861170.820.8615
HIV5.84081600.870.884490.870.8836
Titanic2.201380.790.761180.790.7510

It is clear from the experiments in Table 1 that the new MCA-miner matches the performance of FP-Growth in each case, while significantly reducing the computation time required to mine rules and train a BRL model.

US11857322B2. Systems and methods for screening, diagnosing, and stratifying patients (2024-01-02). BlackThorn Therapeutics, Inc. [US]. Inventors: Qingzhu Gao [US], Humberto Andres Gonzalez Cabezas [US], Parvez Ahammad [US], Yuelu Liu [US].
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Patent 2024
Adult Autistic Disorder Cytokinesis Diagnosis Figs Heart Heart Diseases HIV-2 Malignant Neoplasm of Breast Malignant Neoplasms p16 protease, Human immunodeficiency virus 1
2
Mortality Risk Factors Across Causes
Overall mortality was the primary outcome and defined as death due to any cause during follow-up, including diseases of heart (I00-I09, I11, I13, I20-I51), malignant neoplasms (C00-C97), chronic lower respiratory diseases (J40-J47), accidents (unintentional injuries) (V01-X59, Y85-Y86), cerebrovascular diseases (I60-I69), Alzheimer’s disease (G30), Diabetes mellitus (E10-E14), influenza and pneumonia (J09-J18), nephritis, nephrotic syndrome and nephrosis (N00-N07, N17-N19, N25-N27), all other causes (residual). Follow-up commenced at the baseline examination date. CVD mortality was considered as the secondary outcome and included death due to diseases of heart (I00-I09, I11, I13, I20-I51). The comprehensive information on this program and its procedures were published on the NHANES website (https://www.cdc.gov/nchs/nhanes/).
Xiao S., Wang Z., Zuo R., Zhou Y., Yang Y., Chen T, & Liu N. (2023). Association of Systemic Immune Inflammation Index with All-Cause, Cardiovascular Disease, and Cancer-Related Mortality in Patients with Cardiovascular Disease: A Cross-Sectional Study. Journal of Inflammation Research, 16, 941-961.
Publication 2023
Accidental Injuries Accidents Cardiac Death Cerebrovascular Disorders Diabetes Mellitus Heart Diseases Malignant Neoplasms Nephritis Nephrotic Syndrome Pneumonia Respiration Disorders Virus Vaccine, Influenza
3
Preoperative Predictors of Postoperative Pulmonary Complications
The following data were recorded during the preoperative examination: Sex, age, height, body weight, BMI, smoking history, complete blood count (leukocytes, hemoglobin, platelets), liver function tests (liver enzymes, albumin), renal function tests, preoperative oxygen saturation, history of previous surgery, and concomitant diseases (type 2 diabetes, hypertension, pulmonary and cardiac diseases).
The following data were also collected: History and physical examination findings, chest radiographs, computed tomographic examinations of the chest (CT), electrocardiography (ECG) and echocardiography (if required), pulmonary function test results (forced expiratory volume (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio), and arterial blood gases. In patients with lung cancer, the type and stage of malignancy were determined, and flexible bronchoscopy was performed.
During the intraoperative process, the type of endotracheal tube, the duration of anesthesia and surgery, the surgical procedure (VATS, thoracotomy, mediastinoscopy, and others) performed, and complications that required intraoperative treatment were also noted.
PPCs have been defined as complications that occur in the postoperative period and cause clinical conditions.
Eldaabossi S., Al-Ghoneimy Y., Ghoneim A., Awad A., Mahdi W., Farouk A., Soliman H., Kanany H., Antar A., Gaber Y., Shaarawy A., Nabawy O., Atef M., Nour S.O, & Kabil A. (2023). The ARISCAT Risk Index as a Predictor of Pulmonary Complications After Thoracic Surgeries, Almoosa Specialist Hospital, Saudi Arabia. Journal of Multidisciplinary Healthcare, 16, 625-634.
Publication 2023
Albumins Anesthesia Arteries Blood Gas Analysis Blood Platelets Body Weight Bronchoscopy Chest Complete Blood Count concomitant disease Diabetes Mellitus, Non-Insulin-Dependent Echocardiography Electrocardiography Enzymes Exhaling Forced Vital Capacity Heart Diseases Hemoglobin High Blood Pressures Kidney Function Tests Leukocytes Liver Liver Function Tests Lung Lung Cancer Mediastinoscopy Operative Surgical Procedures Oxygen Saturation Patients Physical Examination Radiography, Thoracic Staging, Cancer Tests, Pulmonary Function Thoracic Surgery, Video-Assisted Thoracotomy Training Programs Volumes, Forced Expiratory X-Ray Computed Tomography
4
Cervical Radicular Pain Management Protocol
The following patients were eligible for analysis: (1) CR, the diagnostic criteria: Clinical symptoms, physical examination, and confirmation of the unilateral disc herniation via cervical CT or magnetic resonance imaging (MRI); (2) Patients aged >18 years; (3) Lower cervical radicular pain lasting ≤3 months; (4) Numerical rating scale, NRS≥ 4.
The following patients were excluded from analysis: (1) Severe heart disease; (2) Severe spinal deformity; (3) Hypersensitivity to local anesthetics or hormones; (4) Coagulation dysfunction; (5) Systemic infection or skin infection at the puncture site; (6) Patients with abnormal mental behavior, severe anxiety, or depression; (7) Lactating and pregnant women; (8) History of cervical surgery; (9) Cervical spondylotic myelopathy; (10) Moderate and severe foraminal stenosis.
Ma L., Wang Y., Yao M., Huang B., Deng J, & Wen H. (2023). Evaluating the Extent of Ultrasound-Guided Cervical Selective Nerve Root Block in the Lower Cervical Spine: Evidence Based on Computed Tomography Images. Journal of Pain Research, 16, 669-676.
Publication 2023
Anxiety Cellulitis Coagulation, Blood Congenital Abnormality Diagnosis Heart Diseases Hormones Hypersensitivity Intervertebral Disk Displacement Local Anesthetics Mentally Ill Persons Neck Neck Pain Operative Surgical Procedures Patients Physical Examination Pregnant Women Punctures Sepsis Spinal Cord Diseases Spondylosis, Cervical Stenosis Tooth Root
5
Sociodemographic Factors and USOC/HL Associations
Covariate selection was guided by previous literature on sociodemographic and health characteristics associated with having a USOC or HL (10 (link),11 (link)). These include baseline, age, race/ethnicity (White, Black, Hispanic, and other), sex, marital status (married/living with partner, and single/never married/divorced/widow), education (less than high school, high school diploma or equivalent, and some college or more), household income (under the poverty line, 100%–199% the poverty line, and ≥200% of the poverty line), number of chronic health conditions among heart attack, heart disease, high blood pressure, arthritis, osteoporosis, diabetes, lung disease, stroke, or cancer (0, 1–2, 3–5, or 6+), self-reported health status (Likert scale, 1 = Excellent, …, 5 = Poor), number of activities of daily living (ADLs) for which the respondent reported needing help (none, 1–2 ADLs, and 3≤ ADLs), dementia (probable, possible, and no dementia) (20 ), additional health coverage (Medigap/Medicare supplement, Medicaid, or Tricare), and depression status (based on Patient Health Questionnaire-2 scores ≥3) (21 (link)).
Despite being identified as a risk factor for loss of USOC, experiencing transportation barriers (reporting that a transportation problem restricted any activity participation in the month before the interview) was not included in the main analyses due to data availability, as a total of N = 1 804 participants had missing information.
Garcia Morales E., Assi L., Powell D., Luu K, & Reed N. (2023). The Association Between Self-Reported Hearing Loss and Loss of Usual Source of Health Care Among Older Medicare Beneficiaries: Evidence From the National Health and Aging Trends Study. Innovation in Aging, 7(2), igad002.
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Publication 2023
Arthritis Cerebrovascular Accident Dementia Diabetes Mellitus Dietary Supplements Ethnicity Heart Diseases High Blood Pressures Hispanics Households Insurance, Medigap Lung Diseases Malignant Neoplasms Myocardial Infarction Osteoporosis Training Programs Widow

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Stata 13 is a comprehensive, integrated statistical software package developed by StataCorp. It provides a wide range of data management, statistical analysis, and graphical capabilities. Stata 13 is designed to handle complex data structures and offers a variety of statistical methods for researchers and analysts.

More about "Heart Diseases"

Heart diseases encompass a wide range of cardiovascular conditions, including coronary artery disease, myocardial infarction, heart failure, arrhythmias, and congenital heart defects.
These complex disorders can have significant impacts on an individual's health and well-being.
Accurate identification and optimization of research protocols are crucial for advancing our understanding and treatment of heart diseases.
PubCompare.ai, an innovative AI-driven tool, can help researchers streamline the process of accessing the most effective heart disease protocols from published literature, preprints, and patents.
By enhancing protocol selection, this approach can improve the accuracy and outcomes of heart disease research, ultimately leading to better patient care.
Researchers can leverage PubCompare.ai's powerful comparison tools to locate the best protocols for their studies, whether they are working with SAS version 9.4, Stata version 14, SPSS version 25, or other statistical software.
This can help ensure that their research methods are aligned with the latest advancements in the field, ultimately leading to more robust and reliable findings.
Moreover, the AI-powered approach of PubCompare.ai can help researchers identify and address potential typos or errors in research protocols, ensuring that their work is as accurate and efficient as possible.
This can be particularly important when working with large datasets or complex statistical analyses, such as those involved in the study of heart diseases.
In summary, PubCompare.ai's innovative tools can help researchers enhance the accuracy and outcomes of their heart disease research, leading to improved patient care and a better understanding of these complex cardiovascular conditions.
By streamlining the protocol selection process and leveraging AI-driven insights, researchers can focus on the most effective and impactful areas of their work.