Dichotomous Relationship Between Age and 30-Day Death or Rehospitalization in Heart Failure Patients Admitted With Acute Decompensated Heart Failure: Results From the ASCEND-HF Trial

Whellan, David J.; Stebbins, Amanda; Hernandez, Adrian F.; McMurray, John J.V.; Mather, Paul; Hasselblad, Vic; O’Connor, Christopher M.

doi:10.1016/j.cardfail.2016.02.011

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Mortality is also associated with the main contextual variable readmissions, whereby a reasonable, logical connection is established between the two main dependent variables, one from each dimension (individual vs. contextual). Thus, in our model, readmission predicts greater probability of mortality, as is attested to and consistent with the previous literature, mainly the CHARM study [17], where death rates clearly increase after hospitalizations for HF even after adjusting for known mortality predictors; according to the literature, this risk increases one month after discharge and continues to rise gradually. The structural model must be supplied with a logical, plausible interpretation such that there is an obvious (but mathematically evident) association where exitus negatively predicts readmissions-for obvious reasons, but accounted for in the model from the mathematical viewpoint.…”

Section: First Level Modelsupporting

confidence: 91%

“…Thus, age is a direct predictor of mortality in HF; this being plainly understood and present in certain statistical models of mortality [16][17][18], and it supports the plausibility of the present model. Similarly, we observed that age predicted greater prevalences of HTN and ARR, as would be expected from the physiopathological point of view [19].…”

Section: First Level Modelsupporting

confidence: 81%

“…The study has found significant, direct, linear predictive relationships for the likelihood of 30-day readmission for HF. Namely, the existence of anemia, RI, diabetes and COPD are objectified as comorbidities that increase the risk of readmission, and this is well known from the prior evidence and the literature [ 17 , 23 , 32 ]. In the same sense, a direct prediction from NDX may be an expression that patients with greater diagnostic complexity and comorbidities have a greater likelihood of readmission, while those who receive greater therapeutic effort (NPR) present lower risk.…”

Section: Discussionmentioning

confidence: 99%

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Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

García-Torrecillas

Lea-Pereira

Alonso-Morillejo

et al. 2023

JPM

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Background: Among the clinical predictors of a heart failure (HF) prognosis, different personal factors have been established in previous research, mainly age, gender, anemia, renal insufficiency and diabetes, as well as mediators (pulmonary embolism, hypertension, chronic obstructive pulmonary disease (COPD), arrhythmias and dyslipidemia). We do not know the role played by contextual and individual factors in the prediction of in-hospital mortality. Methods: The present study has added hospital and management factors (year, type of hospital, length of stay, number of diagnoses and procedures, and readmissions) in predicting exitus to establish a structural predictive model. The project was approved by the Ethics Committee of the province of Almeria. Results: A total of 529,606 subjects participated, through databases of the Spanish National Health System. A predictive model was constructed using correlation analysis (SPSS 24.0) and structural equation models (SEM) analysis (AMOS 20.0) that met the appropriate statistical values (chi-square, usually fit indices and the root-mean-square error approximation) which met the criteria of statistical significance. Individual factors, such as age, gender and chronic obstructive pulmonary disease, were found to positively predict mortality risk. Isolated contextual factors (hospitals with a greater number of beds, especially, and also the number of procedures performed, which negatively predicted the risk of death. Conclusions: It was, therefore, possible to introduce contextual variables to explain the behavior of mortality in patients with HF. The size or level of large hospital complexes, as well as procedural effort, are key contextual variables in estimating the risk of mortality in HF.

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Section: First Level Modelsupporting

confidence: 91%

Section: First Level Modelsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

García-Torrecillas

Lea-Pereira

Alonso-Morillejo

et al. 2023

JPM

View full text Add to dashboard Cite

show abstract

“…The elderly patients were mostly observed to have low degrees of physical activity and depression and anxiety, as well as concomitant chronic diseases, which were likely to result in increasing disease risk and aggravating clinical outcomes 41,42 . Whellan et al pointed out a dichotomous relationship between age and risk of death or readmission, namely, risk of readmission or death decreased as age increased up to 55 years of age, then increased in those older than 55 years 43 .…”

Section: Discussionmentioning

confidence: 99%

Predicting hospital readmissions in patients receiving novel-dose Sacubitril/Valsartan therapy: A competing-risk, causal mediation analysis

Hou¹,

Hao²,

Sun³

et al. 2023

Preprint

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Background: The effects of novel-dose Sacubitril/Valsartan (S/V) in patients with heart failure (HF) in the real world have not been adequately studied. We examined the risk for all-cause re-admission in the patients with HF taking novel-dose S/V and the possible mediator role of left ventricular reverse remodeling (LVRR). Methods and Results: There were 464 patients recruited from December 2017 to September 2021 in our hospital with a median follow-up of 660 days (range, 17-1494). Model 1 and 2 were developed based on the results of univariable competing risk analysis, least absolute shrinkage and selection operator approach, backward stepwise regression and multivariable competing risk analysis. The internal verification (data-splitting method) indicated that Model 1 had better discrimination, calibration, and clinical utility. The corresponding nomogram showed that patients aged 75 years and above or taking the lowest-dose S/V (≤50mg twice a day), or diagnosed with ventricular tachycardia, or valvular heart disease, or chronic obstructive pulmonary disease, or diabetes mellitus were at the highest risk of all-cause readmission. In the causal mediation analysis, LVRR was considered as a critical mediator that negatively affected the difference of novel-dose S/V in readmission. Conclusions: A significant association was detected between novel-dose S/V and all-cause readmission in HF patients, in part negatively mediated by LVRR. The web-based nomogram could provide individual prediction of all-cause readmission in HF patients receiving novel-dose S/V. The effects of different novel-dose S/V are still needed to be explored further in the future.

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“…21,22 Whellan et al pointed out a dichotomous relationship between age and risk of death or readmission, namely, risk of readmission or death decreased as age increased up to 55 years of age, then increased in those older than 55 years. 23 Thus, there might be a tick-shaped relationship between age and all-cause readmission in patients taking novel-dose. Future studies might help more finely identify the high-risk population.…”

Section: Discussionmentioning

confidence: 99%

Predicting Hospital Readmissions in Patients Receiving Novel-Dose Sacubitril/Valsartan Therapy: A Competing-Risk, Causal Mediation Analysis

Hou,

Hao,

Sun

et al. 2023

J Cardiovasc Pharmacol Ther

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Backgrounds: Our study aimed to identify and predict patients with heart failure (HF) taking novel-dose Sacubitril/Valsartan (S/V) at risk for all-cause readmission, as well as investigate the possible role of left ventricular reverse remodeling (LVRR). Methods and results: There were 464 patients recruited from December 2017 to September 2021 in our hospital with a median follow-up of 660 days (range, 17-1494). Competing risk analysis with Gray's Test showed statistically significant differences in all-cause readmission ( p-value< .001) across the three different dose groups. Models 1 and 2 were developed based on the results of univariable competing risk analysis, least absolute shrinkage and selection operator approach, backward stepwise regression, and multivariable competing risk analysis. The internal verification (data-splitting method) indicated that Model 1 had better discrimination, calibration, and clinical utility. The corresponding nomogram showed that patients aged 75 years and above, or taking the lowest-dose S/V (≤50 mg twice a day), or diagnosed with ventricular tachycardia, or valvular heart disease, or chronic obstructive pulmonary disease, or diabetes mellitus were at the highest risk of all-cause readmission. In the causal mediation analysis, LVRR was considered as a critical mediator that negatively affected the difference of novel-dose S/V in readmission. Conclusions: A significant association was detected between novel-dose S/V and all-cause readmission in HF patients, in part negatively mediated by LVRR. The web-based nomogram could provide individual prediction of all-cause readmission in HF patients receiving novel-dose S/V. The effects of different novel-dose S/V are still needed to be explored further in the future.

show abstract

Dichotomous Relationship Between Age and 30-Day Death or Rehospitalization in Heart Failure Patients Admitted With Acute Decompensated Heart Failure: Results From the ASCEND-HF Trial

Cited by 8 publications

References 23 publications

Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure

Predicting hospital readmissions in patients receiving novel-dose Sacubitril/Valsartan therapy: A competing-risk, causal mediation analysis

Predicting Hospital Readmissions in Patients Receiving Novel-Dose Sacubitril/Valsartan Therapy: A Competing-Risk, Causal Mediation Analysis

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