Therapeutic management, adherence, and clinical outcomes of heart failure in Andalucía. ANDALIC Protocol. / Manejo terapéutico, adherencia y resultados clínicos de la insuficiencia cardiaca en Andalucía. Protocolo ANDALIC.

Heart failure is a prevalent syndrome with high mortality rates, representing a significant economic burden in terms of healthcare. The lack of systematic information about the treatment and adherence of patients with heart failure limits the understanding of these aspects and potentially the improvement of clinical outcomes. OBJECTIVE: To describe the clinical characteristics, therapeutic management, adherence, persistence and clinical results, as well as the association between these variables, in a cohort of patients with heart failure in Andalusia. DESIGN: This study will be an observational, population-based, retrospective cohort study. Data of patients discharged from an Andalusian hospital with a diagnosis of heart failure between 2014 and 2023 will be extracted from the Andalusian population health database. ANALYSIS: The statistical analysis will incorporate the following strategies: 1) Descriptive analysis of the characteristics of the population cohort, adherence measures, and clinical outcomes. 2) Bivariate analyses to study the association of covariates with adherence, persistence and clinical results. 3) Multivariate logistic regression and Cox regression analysis including relevant covariates. 4) To evaluate changes over time, multivariate Poisson regression models will be used. By conducting this comprehensive study, we aim to gain valuable insights into the clinical characteristics, treatment management, and adherence of heart failure patients in Andalusia, as well as to identify factors that may influence clinical outcomes. These findings could be critical both for the development of optimized strategies that improve medical care and quality of life of patients and for mitigating the health burden of HF in the region.

Analysis of the impact of social determinants and primary care morbidity on population health outcomes by combining big data: A research protocol.

Background: In recent years, different tools have been developed to facilitate analysis of social determinants of health (SDH) and apply this to health policy. The possibility of generating predictive models of health outcomes which combine a wide range of socioeconomic indicators with health problems is an approach that is receiving increasing attention. Our objectives are twofold: (1) to predict population health outcomes measured as hospital morbidity, taking primary care (PC) morbidity adjusted for SDH as predictors; and (2) to analyze the geographic variability of the impact of SDH-adjusted PC morbidity on hospital morbidity, by combining data sourced from electronic health records and selected operations of the National Statistics Institute (Instituto Nacional de Estadística/INE). Methods: The following will be conducted: a qualitative study to select socio-health indicators using RAND methodology in accordance with SDH frameworks, based on indicators published by the INE in selected operations; and a quantitative study combining two large databases drawn from different Spain's Autonomous Regions (ARs) to enable hospital morbidity to be ascertained, i.e., PC electronic health records and the minimum basic data set (MBDS) for hospital discharges. These will be linked to socioeconomic indicators, previously selected by geographic unit. The outcome variable will be hospital morbidity, and the independent variables will be age, sex, PC morbidity, geographic unit, and socioeconomic indicators. Analysis: To achieve the first objective, predictive models will be used, with a test-and-training technique, fitting multiple logistic regression models. In the analysis of geographic variability, penalized mixed models will be used, with geographic units considered as random effects and independent predictors as fixed effects. Discussion: This study seeks to show the relationship between SDH and population health, and the geographic differences determined by such determinants. The main limitations are posed by the collection of data for healthcare as opposed to research purposes, and the time lag between collection and publication of data, sampling errors and missing data in registries and surveys. The main strength lies in the project's multidisciplinary nature (family medicine, pediatrics, public health, nursing, psychology, engineering, geography).

Estimating the Population Health Impact of Recently Introduced Modified Risk Tobacco Products: A Comparison of Different Approaches.

INTRODUCTION: Various approaches have been used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP). AIMS AND METHODS: We aimed to compare and contrast aspects of models considering effects on mortality that were known to experts attending a meeting on models in 2018. RESULTS: Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the "null scenario" or joint smoking and MRTP habits in an "alternative scenario". The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between scenarios. Estimating effects on population health depends on frequency of use of the MRTP and smoking, and the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized. CONCLUSIONS: Despite methodological differences, most modellers have assumed the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, to be very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction. IMPLICATIONS: There is a need to estimate the population health impact of introducing modified risk nicotine-containing products for smokers unwilling or unable to quit. This paper reviews a variety of modeling methodologies proposed to do this, and discusses the implications of the different approaches. It should assist modelers in refining and improving their models, and help toward providing authorities with more reliable estimates.

Functional insights into the cellular response triggered by a bile-acid platinum compound conjugated to biocompatible ferric nanoparticles using quantitative proteomic approaches.

At present, bioferrofluids are employed as powerful multifunctional tools for biomedical applications such as drug delivery, among others. The present study explores the cellular response evoked when bile-acid platinum derivatives are conjugated with bioferrofluids by testing the biological activity in osteosarcoma (MG-63) and T-cell leukemia (Jurkat) cells. The aim of this work is to evaluate the biocompatibility of a bile-acid platinum derivative conjugated with multi-functional polymer coated bioferrofluids by observing the effects on the protein expression profiles and in intracellular pathways of nanoparticle-stimulated cells. To this end, a mass spectrometry-based approach termed SILAC has been applied to determine in a high-throughput manner the key proteins involved in the cellular response process (including specific quantitatively identified proteins related to the vesicular transport, cellular structure, cell cycle, biosynthetic process, apoptosis and regulation of the cell cycle). Finally, biocompatibility was evaluated and validated by conventional strategies also (such as flow cytometry, MTT, etc.).

Formant Frequencies and Bandwidths in Relation to Clinical Variables in an Obstructive Sleep Apnea Population.

OBJECTIVES: We investigated whether differences in formants and their bandwidths, previously reported comparing small sample population of healthy individuals and patients with obstructive sleep apnea (OSA), are detected on a larger population representative of a clinical practice scenario. We examine possible indirect or mediated effects of clinical variables, which may shed some light on the connection between speech and OSA. STUDY DESIGN: In a retrospective study, 241 male subjects suspected to suffer from OSA were examined. The apnea-hypopnea index (AHI) was obtained for every subject using overnight polysomnography. Furthermore, the clinical variables usually reported as predictors of OSA, body mass index (BMI), cervical perimeter, height, weight, and age, were collected. Voice samples of sustained phonations of the vowels /a/, /e/, /i/, /o/, and /u/ were recorded. METHODS: Formant frequencies F1, F2, and F3 and bandwidths BW1, BW2, and BW3 of the sustained vowels were determined using spectrographic analysis. Correlations among AHI, clinical parameters, and formants and bandwidths were determined. RESULTS: Correlations between AHI and clinical variables were stronger than those between AHI and voice features. AHI only correlates poorly with BW2 of /a/ and BW3 of /e/. A number of further weak but significant correlations have been detected between voice and clinical variables. Most of them were for height and age, with two higher values for age and F2 of /o/ and F2 of /u/. Only few very weak correlations were detected between voice and BMI, weight and cervical perimeter, wich are the clinical variables more correlated with AHI. CONCLUSIONS: No significant correlations were detected between AHI and formant frequencies and bandwidths. Correlations between voice and other clinical factors characterizing OSA are weak but highlight the importance of considering indirect or mediated effects of such clinical variables in any research on speech and OSA.