Time trends in first hospitalization for heart failure in a community-based population

Lorenzoni, Giulia; Azzolina, Danila; Lanera, Corrado; Brianti, Giorgio; Gregori, Darío; Vanuzzo, Diego; Baldi, Ileana

doi:10.1016/j.ijcard.2018.05.132

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Of concern, in this study both absolute numbers and age-adjusted hospitalization rates for cirrhosis were found to be increasing over time. This trend contrasts with the slower growth or decrease in hospitalization rates for some other common chronic conditions [2], [22], [23], [24], [25], where there has been a shift in health care utilization from inpatient to ambulatory care. Our data on in-hospital case-fatality rate of cirrhosis-related admissions is comparable to studies from Europe and the US where the mortality rate of patients admitted for cirrhosis complications varied from 12% to 7.4% [4], [26], [27] and has declined over the last decade [4], [27], [28].…”

Section: Discussionmentioning

confidence: 99%

Increasing Hospitalization Rates for Cirrhosis: Overrepresentation of Disadvantaged Australians

et al. 2019

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Background Limited information is available about hospitalization rates for cirrhosis in Australia. Methods Using information on all hospital episodes of care for patients admitted to Queensland hospitals during 2008–2016, we report age-standardized hospitalization rates/10,000 person-years, in-hospital case-fatality rate among these admissions (n = 30,327), and examine the factors associated with hospital deaths using logistic regression analyses. Findings Hospitalization rates increased from 8.50/10,000 (95% confidence interval (CI) 8.18–8.82) to 11.21/10,000 (95%CI 10.87–11.54) between 2008 and 2016, and peaked in men aged 55–59 years (34.03/10,000) and in Indigenous Australians (32.79/10,000). The number of admissions increased by 61.7% from 2701 admissions in 2008 to 4367 in 2016. During the same period, the percentage increase varied by socioeconomic disadvantage (3.2%/year in the most affluent vs. 9.4%/year in the most disadvantaged quintile; p < 0.001). Alcohol misuse was a contributing factor for cirrhosis in 55.1% of admissions, and socioeconomic disadvantage in 26.8%. The overall in-hospital case-fatality rate was 9.7% for males and 9.3% for females, and decreased in males (p < 0.001). Predictors of in-hospital mortality included hepatorenal syndrome (adjusted odds ratio (AOR) = 7.24, 95%CI 5.99–8.75), HCC (AOR = 2.53, 95%CI 2.20–2.91), hepatic encephalopathy (AOR = 1.94, 95%CI 1.61–2.34), acute peritonitis (AOR = 1.93, 95%CI 1.61–2.33), jaundice (AOR = 1.82, 95%CI 1.20–2.75), age ≥ 70 years (AOR = 1.63, 95%CI 1.38–1.92), a higher comorbidity index (p = 0.021), and residence outside of a “major city” (p < 0.001). Interpretation The increasing healthcare use by Australians with cirrhosis has resource and economic implications. Our data highlight the disproportionate impact of cirrhosis on Indigenous Australians and people from the most socioeconomically disadvantaged areas. Funding .

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Section: Discussionmentioning

confidence: 99%

Increasing Hospitalization Rates for Cirrhosis: Overrepresentation of Disadvantaged Australians

et al. 2019

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“…[148] Despite the advancement in medical therapy for the treatment of chronic HF, this syndrome continues to have an enormous impact (64.3 million people are suffering from HF worldwide with a prevalence of 1-2% in the general adult population of developed countries [149,150]) and is still associated with a significant increase in the total annual cases [149,150]. Chronic HF is a complex and multifactorial disease, where several co-existing causative factors can lead to the syndrome and various comorbidity conditions increasing the severity of HF, considered as the chronic stage of any disease leading to cardiac functional impairment, as well as the final clinical event of CVDs [150,151].…”

Section: Cardiometabolic Adaptations In Heart Failure and Chronic Carmentioning

confidence: 99%

Cardiometabolism as an Interlocking Puzzle between the Healthy and Diseased Heart: New Frontiers in Therapeutic Applications

Pasqua

Rocca

Giglio

et al. 2021

JCM

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Cardiac metabolism represents a crucial and essential connecting bridge between the healthy and diseased heart. The cardiac muscle, which may be considered an omnivore organ with regard to the energy substrate utilization, under physiological conditions mainly draws energy by fatty acids oxidation. Within cardiomyocytes and their mitochondria, through well-concerted enzymatic reactions, substrates converge on the production of ATP, the basic chemical energy that cardiac muscle converts into mechanical energy, i.e., contraction. When a perturbation of homeostasis occurs, such as an ischemic event, the heart is forced to switch its fatty acid-based metabolism to the carbohydrate utilization as a protective mechanism that allows the maintenance of its key role within the whole organism. Consequently, the flexibility of the cardiac metabolic networks deeply influences the ability of the heart to respond, by adapting to pathophysiological changes. The aim of the present review is to summarize the main metabolic changes detectable in the heart under acute and chronic cardiac pathologies, analyzing possible therapeutic targets to be used. On this basis, cardiometabolism can be described as a crucial mechanism in keeping the physiological structure and function of the heart; furthermore, it can be considered a promising goal for future pharmacological agents able to appropriately modulate the rate-limiting steps of heart metabolic pathways.

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“…Heart failure (HF) represents a clear example of chronic cardiovascular disease requiring lifelong management [4,5]. It is strongly related to the aging process [6], and it is associated with high healthcare resource utilization [7] so that the improvement of HF management should be one of the primary goals of current health organizations [8].…”

Section: Introductionmentioning

confidence: 99%

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

Lorenzoni

Sabato²,

Lanera

et al. 2019

JCM

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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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Time trends in first hospitalization for heart failure in a community-based population

Cited by 14 publications

References 30 publications

Increasing Hospitalization Rates for Cirrhosis: Overrepresentation of Disadvantaged Australians

Increasing Hospitalization Rates for Cirrhosis: Overrepresentation of Disadvantaged Australians

Cardiometabolism as an Interlocking Puzzle between the Healthy and Diseased Heart: New Frontiers in Therapeutic Applications

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

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