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Updated: Jul 25, 2025
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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
138
Structural Model of Biomedical and Contextual Factors Predicting In-Hospital Mortality due to Heart Failure.
Juan Manuel García-Torrecillas1,2,3, María Carmen Lea-Pereira4, Enrique Alonso-Morillejo5
1Emergency and Research Unit, Torrecárdenas University Hospital, 04009 Almería, Spain.
Journal of Personalized Medicine
|June 28, 2023
Summary
Hospital size and procedural volume significantly impact heart failure (HF) patient mortality risk, alongside individual factors like age and chronic obstructive pulmonary disease (COPD). Understanding these contextual elements improves HF prognosis prediction.
Area of Science:
- Cardiology
- Health Services Research
- Public Health
Background:
- Established clinical predictors for heart failure (HF) prognosis include individual factors like age, gender, anemia, renal insufficiency, and diabetes.
- Mediating factors such as pulmonary embolism, hypertension, chronic obstructive pulmonary disease (COPD), arrhythmias, and dyslipidemia also influence HF prognosis.
- The role of contextual and individual factors in predicting in-hospital mortality for HF patients remains underexplored.
Purpose of the Study:
- To establish a structural predictive model for in-hospital mortality in heart failure patients.
- To investigate the influence of both individual and contextual (hospital/management) factors on HF mortality.
- To determine key variables for improving the estimation of mortality risk in HF.
Main Methods:
- Utilized databases from the Spanish National Health System, encompassing 529,606 subjects.
- Employed correlation analysis (SPSS 24.0) and structural equation modeling (SEM) analysis (AMOS 20.0) to construct a predictive model.
- Assessed statistical significance using chi-square, fit indices, and root-mean-square error approximation.
Main Results:
- Individual factors including age, gender, and chronic obstructive pulmonary disease (COPD) were identified as positive predictors of mortality risk.
- Contextual factors, specifically larger hospital size (number of beds) and a higher number of procedures performed, negatively predicted the risk of death.
- The developed predictive model demonstrated appropriate statistical values and significance.
Conclusions:
- Contextual variables can be effectively incorporated to explain mortality patterns in heart failure patients.
- Hospital size and procedural volume are critical contextual factors for estimating mortality risk in heart failure.
- This study highlights the importance of considering healthcare system characteristics in HF prognosis.
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