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.

A pulmonary rehabilitation program reduces hospitalizations in chronic obstructive pulmonary disease patients: A cost-effectiveness study.

BACKGROUND: Although pulmonary rehabilitation (PR) is recommended in patients with chronic obstructive pulmonary disease (COPD), there is a scarcity of data demonstrating the cost-effectiveness and effectiveness of PR in reducing exacerbations. METHODS: A quasi-experimental study in 200 patients with COPD was conducted to determine the number of exacerbations 1 year before and after their participation in a PR program. Quality of life was measured using the COPD assessment test and EuroQol-5D. The costs of the program and exacerbations were assessed the year before and after participation in the PR program. The incremental cost-effectiveness ratio (ICER) was estimated in terms of quality-adjusted life years (QALYs). RESULTS: The number of admissions, length of hospital stay, and admissions to the emergency department decreased after participation in the PR program by 48.2%, 46.6%, and 42.5%, respectively (P < 0.001 for all). Results on quality of life tests improved significantly (P < 0.001 for the two tests). The cost of PR per patient and the cost of pre-PR and post-PR exacerbations were €1867.7 and €7895.2 and €4201.9, respectively. The PR resulted in a cost saving of €1826 (total, €365,200) per patient/year, and the gain in QALYs was+0.107. ICER was -€17,056. The total cost was <€20,000/QALY in 78% of patients. CONCLUSIONS: PR contributes to reducing the number of exacerbations in patients with COPD, thereby slowing clinical deterioration. In addition, it is cost-effective in terms of QALYs.

Development of prognostic models to estimate the probability of lung injury one year after COVID-19-related hospitalization-a prospective study.

Background: Long-term effects of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection still under study. The objectives of this study were to identify persistent pulmonary lesions 1 year after coronavirus disease 2019 (COVID-19) hospitalization and assess whether it is possible to estimate the probability that a patient develops these complications in the future. Methods: A prospective study of ≥18 years old patients hospitalized for SARS-COV-2 infection who develop persistent respiratory symptoms, lung function abnormalities or have radiological findings 6-8 weeks after hospital discharge. Logistic regression models were used to identify prognostic factors associated with a higher risk of developing respiratory problems. Models performance was assessed in terms of calibration and discrimination. Results: A total of 233 patients [median age 66 years [interquartile range (IQR): 56, 74]; 138 (59.2%) male] were categorized into two groups based on whether they stayed in the critical care unit (79 cases) or not (154). At the end of follow-up, 179 patients (76.8%) developed persistent respiratory symptoms, and 22 patients (9.4%) showed radiological fibrotic lesions with pulmonary function abnormalities (post-COVID-19 fibrotic pulmonary lesions). Our prognostic models created to predict persistent respiratory symptoms [post-COVID-19 functional status at initial visit (the higher the score, the higher the risk), and history of bronchial asthma] and post-COVID-19 fibrotic pulmonary lesions [female; FVC% (the higher the FVC%, the lower the probability); and critical care unit stay] one year after infection showed good (AUC 0.857; 95% CI: 0.799-0.915) and excellent performance (AUC 0.901; 95% CI: 0.837-0.964), respectively. Conclusions: Constructed models show good performance in identifying patients at risk of developing lung injury one year after COVID-19-related hospitalization.

Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic markers.

The reference interval is the most widely used medical decision-making, constituting a central tool in determining whether an individual is healthy or not. When the results of several continuous diagnostic tests are available for the same patient, their clinical interpretation is more reliable if a multivariate reference region (MVR) is available rather than multiple univariate reference intervals. MVRs, defined as regions containing 95% of the results of healthy subjects, extend the concept of the reference interval to the multivariate setting. However, they are rarely used in clinical practice owing to difficulties associated with their interpretability and the restrictions inherent to the assumption of a Gaussian distribution. Further statistical research is thus needed to make MVRs more applicable and easier for physicians to interpret. Since the joint distribution of diagnostic test results may well change with patient characteristics independent of disease status, MVRs adjusted for covariates are desirable. The present work introduces a novel formulation for MVRs based on multivariate conditional transformation models (MCTMs). Additionally, we take into account the estimation uncertainty of such MVRs by means of tolerance regions. These conditional MVRs imply no parametric restriction on the response, and potentially nonlinear continuous covariate effects can be estimated. MCTMs allow the estimation of the effects of covariates on the joint distribution of multivariate response variables and on these variables' marginal distributions, via the use of most likely transformation estimation. Our contributions proved reliable when tested with simulated data and for a real data application with two glycemic markers.

Age-Related Changes in Serum N-Glycome in Men and Women-Clusters Associated with Comorbidity.

(1) Aim: To describe, in a general adult population, the serum N-glycome in relation to age in men and women, and investigate the association of N-glycome patterns with age-related comorbidity; (2) Methods: The serum N-glycome was studied by hydrophilic interaction chromatography with ultra-performance liquid chromatography in 1516 randomly selected adults (55.3% women; age range 18-91 years). Covariates included lifestyle factors, metabolic disorders, inflammatory markers, and an index of comorbidity. Principal component analysis was used to define clusters of individuals based on the 46 glycan peaks obtained in chromatograms; (3) Results: The serum N-glycome changed with ageing, with significant differences between men and women, both in individual N-glycan peaks and in groups defined by common features (branching, galactosylation, sialylation, fucosylation, and oligomannose). Through K-means clustering algorithm, the individuals were grouped into a cluster characterized by abundance of simpler N-glycans and a cluster characterized by abundance of higher-order N-glycans. The individuals of the first cluster were older, showed higher concentrations of glucose and glycation markers, higher levels of some inflammatory markers, lower glomerular filtration rate, and greater comorbidity index; (4) Conclusions: The serum N-glycome changes with ageing with sex dimorphism. The N-glycome could be, in line with the inflammaging hypothesis, a marker of unhealthy aging.

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.

Modeling conditional reference regions: Application to glycemic markers.

Many clinical decisions are taken based on the results of continuous diagnostic tests. Usually, only the results of one single test is taken into consideration, the interpretation of which requires a reference range for the healthy population. However, the use of two different tests, can be necessary in the diagnosis of certain diseases. This obliges a bivariate reference region be available for their interpretation. It should also be remembered that reference regions may depend on patient variables (eg, age and sex) independent of the suspected disease. However, few proposals have been made regarding the statistical modeling of such reference regions, and those put forward have always assumed a Gaussian distribution, which can be rather restrictive. The present work describes a new statistical method that allows such reference regions to be estimated with no insistence on the results being normally distributed. The proposed method is based on a bivariate location-scale model that provides probabilistic regions covering a specific percentage of the bivariate data, dependent on certain covariates. The reference region is estimated nonparametrically and the nonlinear effects of continuous covariates via polynomial kernel smoothers in additive models. The bivariate model is estimated using a backfitting algorithm, and the optimal smoothing parameters of the kernel smoothers selected by cross-validation. The model performed satisfactorily in simulation studies under the assumption of non-Gaussian conditions. Finally, the proposed methodology was found to be useful in estimating a reference region for two continuous diagnostic tests for diabetes (fasting plasma glucose and glycated hemoglobin), taking into account the age of the patient.

Offering, participation and adherence to cardiac rehabilitation programmes in the elderly: a European comparison based on the EU-CaRE multicentre observational study.

AIMS: Cardiac rehabilitation (CR) is strongly recommended but participation of elderly patients has not been well characterized. This study aims to analyse current rates and determinants of CR referral, participation, adherence, and compliance in a contemporary European cohort of elderly patients. METHODS AND RESULTS: The EU-CaRE observational study included data from consecutive patients aged ≥ 65 with acute coronary syndrome, revascularization, stable coronary artery disease, or heart valve replacement, recruited in eight European centres. Rates and factors determining offering, participation, and adherence to CR programmes and compliance with training sessions were studied across centres, under consideration of extensive-outpatient vs. intensive-inpatient programmes. Three thousand, four hundred, and seventy-one patients were included in the offering and participation analysis. Cardiac rehabilitation was offered to 80.8% of eligible patients, formal contraindications being the main reason for not offering CR. Mean participation was 68.0%, with perceived lack of usefulness and transport issues being principal barriers. Mean adherence to CR programmes of participants in the EU-CaRE study (n = 1663) was 90.3%, with hospitalization/physical impairment as principal causes of dropout. Mean compliance with training sessions was 86.1%. Older age was related to lower offering and participation, and comorbidity was associated with lower offering, participation, adherence, and compliance. Intensive-inpatient programmes displayed higher adherence (97.1% vs. 85.9%, P < 0.001) and compliance (full compliance: 66.0% vs. 38.8%, P < 0.001) than extensive-outpatient programmes. CONCLUSION: In this European cohort of elderly patients, older age and comorbidity tackled patients' referral and uptake of CR programmes. Intensive-inpatient CR programmes showed higher completion than extensive-outpatient CR programmes, suggesting this formula could suit some elderly patients.

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.

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients.

The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6-25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

Identification of Pleural Response Patterns: A Cluster Analysis / Identificación de patrones de respuesta pleural: un análisis de conglomerados

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

INTRODUCCIÓN: El derrame pleural ocurre como una respuesta de la pleura a las agresiones. La pleura reacciona de manera diferente según el tipo de lesión. Sin embargo, las reacciones pleurales aún no se han clasificado. El objetivo de este estudio fue identificar grupos homogéneos de pacientes basados en las características analíticas de su líquido pleural e identificar patrones de respuesta pleural. MÉTODOS: Se realizó un estudio prospectivo de pacientes consecutivos ingresados en nuestra unidad por presentar derrame pleural. Se llevaron a cabo análisis de componentes principales y análisis de conglomerados para identificar los patrones de respuesta pleural basados en las combinaciones de biomarcadores del líquido pleural. RESULTADOS: Un total de 1.613 pacientes se agruparon en 6 grupos: conglomerado 1 (10,5% de la cohorte, compuesto principalmente por pacientes con derrames pleurales malignos); conglomerado 2 (17,4%, derrames pleurales con biomarcadores inflamatorios); conglomerado 3 (16,1%, compuesto principalmente por pacientes con derrames pleurales infecciosos); conglomerado 4 (2,5%, un subgrupo del conglomerado 3, derrames superinfecciosos); conglomerado 5 (23,4%, derrames pleurales paucicelulares), y el conglomerado 6 (30,1%, miscelánea). Se observaron diferencias significativas entre los grupos en las características analíticas del líquido pleural (p < 0,001 para todos), la edad (p < 0,001) y el género (p = 0,016). Se encontró una relación directa entre el tipo de conglomerado y la etiología del derrame pleural. CONCLUSIONES: La respuesta pleural es heterogénea. La pleura puede responder de manera diferente a una misma etiología o de manera similar en diferentes etiologías, lo que dificulta el diagnóstico de derrame pleural

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.

Improvement in VO2peak predicts readmissions for cardiovascular disease and mortality in patients undergoing cardiac rehabilitation.

BACKGROUND: Improvement in exercise capacity is a main goal of cardiac rehabilitation but the effects are often lost at long-term follow-up and thus also the benefits on prognosis. We assessed whether improvement in VO2peak during a cardiac rehabilitation programme predicts long-term prognosis. METHODS AND RESULTS: We performed a retrospective analysis of 1561 cardiac patients completing cardiac rehabilitation in 2011-2017 in Copenhagen. Mean age was 63.6 (11) years, 74% were male and 84% had coronary artery disease, 6% chronic heart failure and 10% heart valve replacement. The association between baseline VO2peak and improvement after cardiac rehabilitation and being readmitted for cardiovascular disease and/or all-cause mortality was assessed with three different analyses: Cox regression for the combined outcome, for all-cause mortality and a multi-state model. During a median follow-up of 2.3 years, 167 readmissions for cardiovascular disease and 77 deaths occurred. In adjusted Cox regression there was a non-linear decreasing risk of the combined outcome with higher baseline VO2peak and with improvement of VO2peak after cardiac rehabilitation. A similar linear association was seen for all-cause mortality. Applying the multi-state model, baseline VO2peak and change in VO2peak were associated with risk of a cardiovascular disease readmission and with all-cause mortality but not with mortality in those having an intermediate readmission for cardiovascular disease. CONCLUSION: VO2peak as well as change in VO2peak were highly predictive of future risk of readmissions for cardiovascular disease and all-cause mortality. The predictive value did not extend beyond the next admission for a cardiovascular event.

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.

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

Introducción: Predecir cuándo un derrame pleural infeccioso puede evolucionar hacia una infección complicada/empiema es difícil de establecer. Nuestro propósito es analizar si un modelo predictivo construido con parámetros bioquímicos del líquido pleural puede ayudar a identificar estos derrames. Métodos: Se estudió de forma prospectiva a todos los pacientes diagnosticados de derrame pleural infeccioso y se clasificaron en no complicados y complicados/empiemas. Se realizó un análisis de regresión logística para estimar la probabilidad de infección pleural complicada/empiema. Con base en parámetros bioquímicos del líquido pleural, se construyó un modelo predictivo y se determinaron su discriminación (áreas bajo la curva ROC), calibración y precisión diagnóstica. Resultados: Se incluyó a 177 pacientes (74 infecciones pleurales no complicadas y 103 complicadas/empiemas). El área bajo la curva del modelo construido (pH, lactato deshidrogenasa e interleucina 6) fue 0,9783, significativamente mejor que cualquiera de las variables bioquímicas utilizadas de forma individual (0,921, 0,949 y 0,837, respectivamente; p < 0,001 usando todos los parámetros). La tasa de clasificación correcta fue del 96% de los derrames (170/177; 72/74 [97,3%] de los no complicados y 98/103 [95,1%] de los complicados/empiemas). Conclusión: El modelo predictivo analizado tiene una buena rentabilidad para el diagnóstico de las infecciones pleurales complicadas/empiemas, superior a la de cualquiera de las variables individuales que lo componen

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

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.