USING DATA TO DRIVE IMPROVEMENT: IMPACT OF REPORTING PROCESS COMPLIANCE RATES ON HEART FAILURE RE-HOSPITALIZATIONS AND 30 DAY MORTALITY

ObjectiveMountain districts normally have tougher geographic conditions than plain districts, which might worsen heart failure (HF) conditions in patients. Also, those places frequently are associated with social problems of ageing, underpopulation and fewer medical services, which might cause delay in detection of disease progression and require more admissions. We investigated the association of residence altitude with readmission in patients with HF.MethodsWe followed 452 patients with HF to determine all-cause readmissions over a median of 1.1 years. The altitude of patient residences, population, proportion of the elderly and number of hospitals or clinics in a minor administrative district (Cho-Aza district) located at the residences were examined using data from the 2010 census and Google Maps.ResultsAll-cause readmissions were observed in 269 (60%) patients. The altitude of ≥200 m was significantly associated with readmissions (HR, 1.49; 95 % CI 1.12 to 1.96; p=0.006) after adjustment for physical and haemodynamic parameters, left ventricular ejection fraction, brain natriuretic peptide and components of the established score for predicting readmission for HF. Altitude was significantly associated with ageing, underpopulation, fewer hospitals or clinics and lower temperature (all p<0.01), with an increased tendency for readmission during the winter season; however, it was not associated with patient clinical parameters.ConclusionsHigh altitude residence may be an important predictor for readmission in patients with HF. This relationship may be confounded by unfavourable sociogeographic conditions at higher altitudes.

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Development and psychometric evaluation of the Thirst Distress Scale for patients with heart failure

European Journal of Cardiovascular Nursing ◽

10.1177/1474515117728624 ◽

2017 ◽

Vol 17 (3) ◽

pp. 226-234 ◽

Cited By ~ 5

Author(s):

Nana Waldréus ◽

Tiny Jaarsma ◽

Martje HL van der Wal ◽

Naoko P Kato

Keyword(s):

Heart Failure ◽

Psychometric Properties ◽

Intraclass Correlation ◽

Psychometric Testing ◽

Psychometric Evaluation ◽

Weighted Kappa ◽

Patients With Heart Failure ◽

Using Data ◽

Test Retest Reliability ◽

Distress Scale

Background: Patients with heart failure can experience thirst distress. However, there is no instrument to measure this in patients with heart failure. The aim of the present study was to develop the Thirst Distress Scale for patients with Heart Failure (TDS-HF) and to evaluate psychometric properties of the scale. Methods and results: The TDS-HF was developed to measure thirst distress in patients with heart failure. Face and content validity was confirmed using expert panels including patients and healthcare professionals. Data on the TDS-HF was collected from patients with heart failure at outpatient heart failure clinics and hospitals in Sweden, the Netherlands and Japan. Psychometric properties were evaluated using data from 256 heart failure patients (age 72±11 years). Concurrent validity of the scale was assessed using a thirst intensity visual analogue scale. Patients did not have any difficulties answering the questions, and time taken to answer the questions was about five minutes. Factor analysis of the scale showed one factor. After psychometric testing, one item was deleted. For the eight item TDS-HF, a single factor explained 61% of the variance and Cronbach’s alpha was 0.90. The eight item TDS-HF was significantly associated with the thirst intensity score ( r=0.55, p<0.001). Regarding test-retest reliability, the intraclass correlation coefficient was 0.88, and the weighted kappa values ranged from 0.29–0.60. Conclusion: The eight-item TDS-HF is valid and reliable for measuring thirst distress in patients with heart failure.

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Real-world presentation with heart failure in primary care: do patients selected to follow diagnostic and management guidelines have better outcomes?

Open Heart ◽

10.1136/openhrt-2018-000935 ◽

2018 ◽

Vol 5 (2) ◽

pp. e000935

Author(s):

Alex Bottle ◽

Dani Kim ◽

Paul P Aylin ◽

F Azeem Majeed ◽

Martin R Cowie ◽

...

Keyword(s):

Heart Failure ◽

Primary Care ◽

Practice Research ◽

Clinical Practice Research Datalink ◽

Initial Management ◽

Risk Of Death ◽

Specialist Referral ◽

Partial Completion ◽

General Practices ◽

Using Data

ObjectiveTo describe associations between initial management of people presenting with heart failure (HF) symptoms in primary care, including compliance with the recommendations of the National Institute for Health and Care Excellence (NICE), and subsequent unplanned hospitalisation for HF and death.MethodsThis is a retrospective cohort study using data from general practices submitting records to the Clinical Practice Research Datalink. The cohort comprised patients diagnosed with HF during 2010–2013 and presenting to their general practitioners with breathlessness, fatigue or ankle swelling.Results13 897 patients were included in the study. Within the first 6 months, only 7% had completed the NICE-recommended pathway; another 18.6% had followed part of it (B-type natriuretic peptide testing and/or echocardiography, or specialist referral). Significant differences in hazards were seen in unadjusted analysis in favour of full or partial completion of the NICE-recommended pathway. Covariate adjustment attenuated the relations with death much more than those for HF admission. Compared with patients placed on the NICE pathway, treatment with HF medications had an HR of 1.16 (95% CI 1.05 to 1.28, p=0.003) for HF admission and 1.03 (95% CI 0.90 to 1.17, p= 0.674) for death. Patients who partially followed the NICE pathway had similar hazards to those who completed it. Patients on no pathway had the highest hazard for HF admission at 1.30 (95% 1.18 to 1.43, p<0.001) but similar hazard for death.ConclusionsPatients not put on at least some elements of the NICE-recommended pathway had significantly higher risk of HF admission but non-significant higher risk of death than other patients had.

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Prognostic Impact of In‐Hospital and Postdischarge Heart Failure in Patients With Acute Myocardial Infarction: A Nationwide Analysis Using Data From the Cardiovascular Disease in Norway (CVDNOR) Project

Journal of the American Heart Association ◽

10.1161/jaha.116.005277 ◽

2017 ◽

Vol 6 (3) ◽

Cited By ~ 5

Author(s):

Gerhard Sulo ◽

Jannicke Igland ◽

Ottar Nygård ◽

Stein Emil Vollset ◽

Marta Ebbing ◽

...

Keyword(s):

Myocardial Infarction ◽

Heart Failure ◽

Cardiovascular Disease ◽

Acute Myocardial Infarction ◽

Prognostic Impact ◽

Using Data

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Rural–urban differentials in 30-day and 1-year mortality following first-ever heart failure hospitalisation in Western Australia: a population-based study using data linkage

BMJ Open ◽

10.1136/bmjopen-2013-004724 ◽

2014 ◽

Vol 4 (5) ◽

pp. e004724 ◽

Cited By ~ 18

Author(s):

Tiew-Hwa Katherine Teng ◽

Judith M Katzenellenbogen ◽

Joseph Hung ◽

Matthew Knuiman ◽

Frank M Sanfilippo ◽

...

Keyword(s):

Heart Failure ◽

Western Australia ◽

Data Linkage ◽

Population Based ◽

Population Based Study ◽

Heart Failure Hospitalisation ◽

Using Data

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Exercise Motivation and Self-Efficacy Vary Among Patients with Heart Failure – An Explorative Analysis Using Data from the HF-Wii Study

Patient Preference and Adherence ◽

10.2147/ppa.s314414 ◽

2021 ◽

Vol Volume 15 ◽

pp. 2353-2362

Author(s):

Leonie Klompstra ◽

Tiny Jaarsma ◽

Anna Strömberg ◽

Lorraine S Evangelista ◽

Martje HL van der Wal

Keyword(s):

Heart Failure ◽

Self Efficacy ◽

Exercise Motivation ◽

Patients With Heart Failure ◽

Using Data

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The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques

Journal of Soft Computing and Data Mining ◽

10.30880/jscdm.2021.02.02.009 ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Nurul Farhana Hamzah ◽

◽

Nazri Mohd Nawi ◽

Abdulkareem A. Hezam ◽

◽

...

Keyword(s):

Heart Failure ◽

Congestive Heart Failure ◽

Machine Learning Techniques ◽

Support Vector ◽

Classification Algorithms ◽

Learning Techniques ◽

Boosted Decision Tree ◽

Failure Classification ◽

Artery Disease ◽

Using Data

Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.

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Abstract P168: The Association of Longitudinal Changes in Metabolic Syndrome With Incident Heart Failure: The Atherosclerosis Risk in Communities (ARIC) Study

Circulation ◽

10.1161/circ.137.suppl_1.p168 ◽

2018 ◽

Vol 137 (suppl_1) ◽

Author(s):

Tolulope A Adesiyun ◽

Lucia Kwak ◽

Kavita Sharma ◽

Vijay Nambi ◽

Erin D Michos ◽

...

Keyword(s):

Heart Failure ◽

Metabolic Syndrome ◽

Cox Regression ◽

Positive Association ◽

Atherosclerosis Risk In Communities ◽

Low Hdl ◽

Using Data ◽

Aric Study ◽

Number Of Individuals ◽

Over Time

Background: Metabolic syndrome (MS) is a risk factor for the development of heart failure (HF). However, little is known about how changes in MS over time are associated with HF risk. Hypothesis: We hypothesized that increasing MS components over time and a longer duration of MS would be associated with greater HF risk. Methods: We studied 8,104 participants at ARIC Visit 4 (1996-98) without baseline HF, coronary heart disease or diabetes. MS components were defined using AHA/NHLBI criteria for waist circumference, hyperglycemia, elevated blood pressure, low HDL-C and hypertriglyceridemia, and MS was diagnosed if ≥ 3 criteria were present. Using data on MS components from Visit 1 (1987-89) through 4, we used multivariate Cox regression models to evaluate associations of changes in MS components over time and duration of MS with incident HF occurring after Visit 4. Results: The mean age was 63 years (+/-6), with 58% female. Over a median follow-up of 16 years, there were 902 HF events. Compared to those without MS at Visits 1 and 4, those with MS at both time points had a hazard ratio (HR) for HF of 1.87 (95% CI 1.60-2.19), while those with no MS at Visit 1 but MS at Visit 4 (HR 1.38; 95% CI 1.16-1.64) and those with MS at Visit 1 but not at Visit 4 (1.51; 95% CI 1.13-2.00) had more modest risk associations. Among those without MS at Visit 1, those with 0 MS components at both Visits 1 and 4 had lowest HF risk (reference), with progressively higher risk seen for those who increased to 1-2 (HR 1.66; 95% CI 1.06-2.61), 3 (HR 2.15; 95% CI 1.37-3.38) and 4-5 (HR 2.55; 95% CI 1.58-4.13) MS components by Visit 4. Duration of MS had a positive association with HF risk (Figure), with a HR of 1.08 (95% CI 1.06-1.10) per year of MS duration. Conclusions: Progression in MS components over time and a longer duration of MS are associated with increased HF risk. Given the cardiovascular implications of these findings, particularly for the growing number of individuals developing MS components at an early age, strategies to prevent MS onset and progression should be implemented widely.

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Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

Journal of Clinical Medicine ◽

10.3390/jcm8091298 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1298 ◽

Cited By ~ 3

Author(s):

Giulia Lorenzoni ◽

Stefano Santo Sabato ◽

Corrado Lanera ◽

Daniele Bottigliengo ◽

Clara Minto ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Health Care ◽

Predictive Value ◽

Machine Learning Techniques ◽

Data Imputation ◽

Missing Data Imputation ◽

Heart Failure Patients ◽

Learning Techniques ◽

Using Data

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

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