Exercise-based cardio-oncology rehabilitation for cardiotoxicity prevention during breast cancer chemotherapy: The ONCORE randomized controlled trial.

BACKGROUND: Breast cancer (BC) treatment with anthracyclines and/or anti-human epidermal growth factor receptor-2 (HER2) antibodies is associated with an increased risk of cardiovascular disease complications, including cancer therapy-related cardiac dysfunction (CTRCD). While Cardio-Oncology Rehabilitation (CORe) programs including exercise have emerged to minimize these risks, its role in preventing CTRCD is unclear. OBJECTIVES: We investigated the effectiveness of an exercise-based CORe program in preventing CTRCD [left ventricular ejection fraction (LVEF) drop ≥10% to a value <53% or a decrease >15% in global longitudinal strain (GLS)]. Secondary outcomes examined changes in cardiac biomarkers, physical performance including peak oxygen consumption, psychometric and lifestyle outcomes. Safety, adherence, and patient satisfaction were also assessed. METHODS: This is a randomized controlled trial including 122 early-stage BC women receiving anthracyclines and/or anti-HER2 antibodies, randomized to CORe (n = 60) or usual care with exercise recommendation (n = 62). Comprehensive assessments were performed at baseline and after cardiotoxic treatment completion. The average duration of the intervention was 5.8 months. RESULTS: No cases of CTRCD were identified during the study. LVEF decreased in both groups, but was significantly attenuated in the CORe group [-1.5% (-2.9, -0.1); p = 0.006], with no changes detected in GLS or cardiac biomarkers. The CORe intervention led to significant body mass index (BMI) reduction (p = 0.037), especially in obese patients [3.1 kg/m2 (1.3, 4.8)]. Physical performance and quality-of-life remained stable, while physical activity level increased in both groups. No adverse events were detected. CONCLUSIONS: This study suggests that CORe programs are safe and may help attenuate LVEF decline in BC women receiving cardiotoxic therapy and reduce BMI in obese patients.

Health self-perception is associated with life-styles and comorbidities and its effect on mortality is confounded by age. A population based study.

Background: Health self-perception (HSP) is the individual and subjective concept that a person has of their state of health. Despite its simplicity, HSP is considered a valid and relevant indicator employed in epidemiological research and in professional practice as an overall measure of health. Objectives: (1) To describe and analyze the associations between HSP and demographic variables, lifestyle and diseases prevalent in a population and (2) to investigate the relationship between HSP and mortality. Materials and methods: In a primary care setting, we conducted a longitudinal study of a random populational sample of a Galician municipality, stratified by decade of life. A total of 1,516 adults older than 18 years, recruited by the 2013-2015 AEGIS study, were followed-up for more than 5 years. During the clinical interview, data were collected on lifestyle and prevalent diseases. The HSP was grouped into 2 categories (good/poor). The statistical analysis consisted of a logistic regression, Kaplan-Meier curves and Cox regression. Results: A total of 540 (35.6%) participants reported poor HSP. At the end of the follow-up, 78 participants had died (5.1%). The participants with increased age and body mass index and chronic diseases (anxiety, depression, ischemic heart disease, diabetes, and cancer) presented a poorer subjective health. A high level of physical activity and moderate alcohol consumption were associated with better HSP. A poorer HSP was associated with increased mortality, an association that disappeared after adjusting for the rest of the covariates (HR, 0.82; 95% CI 0.50-1.33). Conclusion: (1) Health self-perception is associated with age, lifestyle, and certain prevalent diseases. (2) A poorer HSP is associated with increased mortality, but this predictive capacity disappeared after adjusting for potential confounders such as age, lifestyle, and prevalent diseases.

Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study.

BACKGROUND: The prognosis of patients with COVID-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with COVID-19 infection (Gal-COVID-19 scores). METHODS: This is a retrospective cohort study of patients with COVID-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance. RESULTS: The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair-good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)]. CONCLUSIONS: The Gal-COVID-19 scores provide risk estimates for predicting severity in COVID-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.

Predictors of pre-rehabilitation exercise capacity in elderly European cardiac patients - The EU-CaRE study.

AIMS: Functional capacity is an important endpoint for therapies oriented to older adults with cardiovascular diseases. The literature on predictors of exercise capacity is sparse in the elderly population. In a longitudinal European study on effectiveness of cardiac rehabilitation of seven European countries in elderly (>65 years) coronary artery disease or valvular heart disease patients, predictors for baseline exercise capacity were determined, and reference ranges for elderly cardiac patients provided. METHODS: Mixed models were performed in 1282 patients (mean age 72.9 ± 5.4 years, 79% male) for peak oxygen consumption relative to weight (peak VO2; ml/kg per min) with centre as random factor and patient anthropometric, demographic, social, psychological and nutritional parameters, as well as disease aetiology, procedure, comorbidities and cardiovascular risk factors as fixed factors. RESULTS: The most important predictors for low peak VO2 were coronary artery bypass grafting or valve surgery, low resting forced expiratory volume, reduced left ventricular ejection fraction, nephropathy and peripheral arterial disease. Each cumulative comorbidity or cardiovascular risk factors reduced exercise capacity by 1.7 ml/kg per min and 1.1 ml/kg per min, respectively. Males had a higher peak VO2 per body mass but not per lean mass. Haemoglobin was significantly linked to peak VO2 in both surgery and non-surgery patients. CONCLUSIONS: Surgical procedures, cumulative comorbidities and cardiovascular risk factors were the factors with the strongest relation to reduced exercise capacity in the elderly. Expression of peak VO2 per lean mass rather than body mass allows a more appropriate comparison between sexes. Haemoglobin is strongly related to peak VO2 and should be considered in studies assessing exercise capacity, especially in studies on patients after cardiac surgery.

Identification of Pleural Response Patterns: A Cluster Analysis.

BACKGROUND: Pleural effusion occurs as a response of the pleura to aggressions. The pleura reacts differently according to the type of injury. However, pleural reactions have not yet been characterized. The objective of this study was to identify homogeneous clusters of patients based on the analytical characteristics of their pleural fluid and identify pleural response patterns. METHODS: A prospective study was conducted of consecutive patients seen in our unit for pleural effusion. Principal component and cluster analyses were carried out to identify pleural response patterns based on a combination of pleural fluid biomarkers. RESULTS: A total of 1613 patients were grouped into six clusters, namely: cluster 1 (10.5% of the cohort, primarily composed of patients with malignant pleural effusions); cluster 2 (17.4%, pleural effusions with inflammatory biomarkers); cluster 3 (16.1%, primarily composed of patients with infectious pleural effusions); cluster 4 (2.5%, a subcluster of cluster 3, superinfectious effusions); cluster 5 (23.4%, paucicellular pleural effusions); and cluster 6 (30.1%, miscellaneous). Significant differences were observed across clusters in terms of the analytical characteristics of PF (p<0.001 for all), age (p<0.001), and gender (p=0.016). A direct relationship was found between the type of cluster and the etiology of pleural effusion. CONCLUSION: Pleural response is heterogeneous. The pleura may respond differently to the same etiology or similarly to different etiologies, which hinders diagnosis of pleural effusion.

Diagnosis of infectious pleural effusion using predictive models based on pleural fluid biomarkers.

INTRODUCTION: Diagnosis of pleural infection (PI) may be challenging. The purpose of this paper is to develop and validate a clinical prediction model for the diagnosis of PI based on pleural fluid (PF) biomarkers. METHODS: A prospective study was conducted on pleural effusion. Logistic regression was used to estimate the likelihood of having PI. Two models were built using PF biomarkers. The power of discrimination (area under the curve) and calibration of the two models were evaluated. RESULTS: The sample was composed of 706 pleural effusion (248 malignant; 28 tuberculous; 177 infectious; 48 miscellaneous exudates; and 212 transudates). Areas under the curve for Model 1 (leukocytes, percentage of neutrophils, and C-reactive protein) and Model 2 (the same markers plus interleukin-6 [IL-6]) were 0.896 and 0.909, respectively (not significant differences). However, both models showed higher capacity of discrimination than their biomarkers when used separately (P < 0.001 for all). Rates of correct classification for Models 1 and 2 were 88.2% (623/706: 160/177 [90.4%] with infectious pleural effusion [IPE] and 463/529 [87.5%] with non-IPE) and 89.2% (630/706: 153/177 [86.4%] of IPE and 477/529 [90.2%] of non-IPE), respectively. CONCLUSIONS: The two predictive models developed for IPE showed a good diagnostic performance, superior to that of any of the markers when used separately. Although IL-6 contributes a slight greater capacity of discrimination to the model that includes it, its routine determination does not seem justified.

The Combined Use of Pleural Fluid Parameters Improves Diagnostic Accuracy in Complicated Pleural Infection/Empyema. / La combinación de la determinación de parámetros bioquímicos del líquido pleural mejora la predicción diagnóstica de la infección pleural complicada/empiema.

INTRODUCTION: Identifying infectious pleural effusions (IPE) that will progress to complicated infection or empyema is challenging. The purpose of this study was to determine whether a model based on multiple biochemical parameters in pleural fluid can predict which IPEs will produce empyema. METHODS: A prospective study was performed of all cases of IPEs treated in our unit. IPEs were classified as uncomplicated or complicated (empyema). Logistic regression was used to estimate the risk for complicated pleural infection (empyema). A predictive model was developed using biochemical parameters in pleural fluid. Discriminatory power (areas under the ROC curve), calibration, and diagnostic accuracy of the model were assessed. RESULTS: A total of 177 patients were included in the study (74 with uncomplicated infectious pleural effusion, and 103 with complicated pleural effusion/empyema). The area under the curve (AUC) for the model (pH, lactate dehydrogenase and interleukin 6) was 0.9783, which is significantly superior to the AUC of the individual biochemical parameters alone (0.921, 0.949, and 0.837, respectively; P<.001 using all parameters). The rate of correct classification of infectious pleural effusions was 96% [170/177: 72/74 (97.3%) for uncomplicated and 98/103 (95.1%) for complicated effusion (empyema)]. CONCLUSION: The multiple-marker model showed better diagnostic performance for predicting complicated infectious pleural effusion (empyema) compared to individual parameters alone.