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, analytical, 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 1,152 patients presented with Covid-19 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 hours 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 simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.Carlos III Health Institute, Spain, Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER)Instituto de Salud Carlos II

Contributions to flexible bivariate regression models. Applications in Medicine and Environment

This thesis proposes bivariate regression models useful in clinical setting, in medical research, and in other fields. To make these models as widely as possible, no parametric restrictions for the responses variables were contemplated. A probabilistic region covering a specific percentage of the data points can be estimated for these models. This region characterises the bivariate distribution shape depending on the effect of covariates. In practical terms, this identifies which values are most likely to be observed in the general healthy population after adjusting for patient characteristics; a region containing 95% of healthy patients results as conditioned by covariates can thus be obtained for diagnostic purposes. This reference region is a natural extension of the use of reference intervals.2023-11-2

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 patientÓscar Lado-Baleato is funded by a predoctoral grant (ED481A-2018) from the Galician Government (Plan I2C)-Xunta de Galicia. This research was also supported by grants from the Carlos III Health Institute, Spain (PI16/01404 and RD16/0017/0018), and by the project MTM2017-83513-R cofinanced by the Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER). This work was also supported by grants from the Galician Government: RED INBIOEST (ED341D-R2016/032), Grupo de Referencia Competitiva (ED431C 2016-025), and Grupo de Potencial Crecimiento (IN607B 2018-1). Javier Roca-Pardiñas acknowledges financial support by the Grant MTM2017-89422-P (MINECO/AEI/FEDER, UE)S

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

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