Charlson comorbidity index scores and in-hospital prognosis of patients with severe acute respiratory infections.

BACKGROUND: Respiratory infections are one of the leading causes of mortality, and comorbid conditions play a significant role in the severity and fatality of these infections. AIMS: We evaluated the Charlson Comorbidity Index (CCI) score and possible predictors of mortality in hospitalised patients with severe acute respiratory infection (SARI), aiming to test if the CCI is a valid in-hospital prognostic indicator. METHODS: Patients older than 14 years, hospitalised from 2010 to 2016 due to SARI by viral infection and who were submitted to respiratory virus testing were included. We assessed comorbidity retrospectively through chart review and calculated four variants of the CCI. RESULTS: Of the 291 patients assessed, 72.8% (n = 212) presented comorbidities, and 24% died (n = 70). The most recurrent comorbidities were chronic pulmonary disease (n = 76/212, 36%) and HIV (n = 50/212, 23.6%). The 1994 age-adjusted CCI predicted in-hospital mortality in SARI patients (P = 0.04), and HIV was associated with in-hospital mortality (P = 0.032). CONCLUSIONS: The comorbidity scores used to assess mortality risk in hospitalised patients with SARI displayed poor results, but HIV infection was considered a marker of severity. However, other factors should be considered in order to compose a score system that allows us to specifically assess the risk of mortality in patients with SARI.

High-Flow Nasal Cannula Therapy in Patients With COVID-19: Predictive Response Factors.

BACKGROUND: COVID-19 pneumonia has been responsible for many ICU patients' admissions with hypoxemic respiratory failure, and oxygen therapy is one of the pillars of its treatment. The current pandemic scenario has limited the availability of ICU beds and access to invasive ventilation equipment. High-flow nasal cannula (HFNC) can reduce the need for orotracheal intubation compared with conventional oxygen therapy, providing better results than noninvasive respiratory support. However, HFNC use has been controversial due to concerns about the benefits and risks of aerosol dispersion. In this context, we evaluated the performance of the HFNC therapy in patients with COVID-19 and investigated factors that can predict favorable responses. METHODS: A prospective observational study was conducted, which included hospitalized adult subjects with COVID-19 in the respiratory wards who needed oxygen therapy. Clinical and laboratory parameters were collected to compare HFNC therapy use and the outcomes. RESULTS: In 6 months, 128 subjects were included and the success rate of HFNC therapy was 53%. Logistic regression analysis showed that the Charlson comorbidity score, need for oxygen flow, [Formula: see text], and breathing frequency predicted therapy failure. The mortality rate increased among the non-responders versus the responders (47% vs 3%), 48% of failure occurred in the first 24 h of the HFNC therapy. A ROX (respiratory frequency - oxygenation) index > 4.98 in 6 h and > 4.53 in 24 h predicted success of the HFNC therapy with an area under the curve of 0.7, and a ROX index < 3.47 predicted failure with 88% of specificity. CONCLUSIONS: HFNC in the subjects with COVID-19 was associated with reduced mortality and improved oxygenation in the subjects with respiratory distress. Close monitoring of specific parameters defines eligible patients and rapidly identifies those in need of invasive ventilatory support.

Multivariate generalized linear mixed models for continuous bounded outcomes: Analyzing the body fat percentage data.

We propose a multivariate regression model to handle multiple continuous bounded outcomes. We adopted the maximum likelihood approach for parameter estimation and inference. The model is specified by the product of univariate probability distributions and the correlation between the response variables is obtained through the correlation matrix of the random intercepts. For modeling continuous bounded variables on the interval (0,1) we considered the beta and unit gamma distributions. The main advantage of the proposed model is that we can easily combine different marginal distributions for the response variable vector. The computational implementation is performed using Template Model Builder, which combines the Laplace approximation with automatic differentiation. Therefore, the proposed approach allows us to estimate the model parameters quickly and efficiently. We conducted a simulation study to evaluate the computational implementation and the properties of the maximum likelihood estimators under different scenarios. Moreover, we investigate the impact of distribution misspecification in the proposed model. Our model was motivated by a data set with multiple continuous bounded outcomes, which refer to the body fat percentage measured at five regions of the body. Simulation studies and data analysis showed that the proposed model provides a general and rich framework to deal with multiple continuous bounded outcomes.

Osteosarcopenia and trabecular bone score in patients with type 2 diabetes mellitus.

OBJECTIVE: To evaluate the prevalence of osteosarcopenia and the association of osteosarcopenia with trabecular bone score (TBS) in a group of patients with type 2 diabetes mellitus(T2DMG) compared with a paired control group (CG). METHODS: Cross-sectional study with men and women ≥ 50 years recruited by convenience. Patients in both groups answered questionnaires and underwent evaluation of bone mineral density (BMD), handgrip strength (HGS), and TBS. The T2DMG also underwent a gait speed (GS) test. Sarcopenia was defined as low lean mass plus low HGS or GS according to the Foundation for the National Institute of Health Sarcopenia Project, and osteosarcopenia was deemed present when sarcopenia was associated with osteopenia, osteoporosis, or low-energy trauma fractures. RESULTS: The T2DMG (n = 177) and CG (n = 146) had, respectively, mean ages of 65.1 ± 8.2 years and 68.8 ± 11.0 years and 114 (64.4%) and 80 (54.7%) women. T2DMG versus the CG had higher rates of osteosarcopenia (11.9% versus 2.14%, respectively, p = 0.010), sarcopenia (12.9% versus 5.4%, respectively, p < 0.030), and fractures (29.9% versus 18.5%, respectively, p = 0.019), and lower HGS values (24.4 ± 10.3 kg versus 30.9 ± 9.15 kg, respectively, p < 0.001), but comparable BMD values. Mean TBS values were 1.272 ± 0.11 and 1.320 ± 0.12, respectively (p = 0.001). On multivariate analysis, age, greater waist circumference, fractures, and osteoporosis increased the risk of degraded TBS. Osteosarcopenia was associated with diabetes complications (p = 0.03), calcium and vitamin D supplementation (p = 0.01), and all components of osteosarcopenia diagnosis (p < 0.05). CONCLUSION: Compared with the CG, the T2DMG had a higher prevalence of osteosarcopenia, sarcopenia, and fractures and lower bone quality assessed by TBS.

A prospective cohort study on the challenge to return to work after hospitalisation in Brazil.

After hospital discharge, workers are expected to return to work (RTW). However, the ability to return and any possible limitations after returning are unclear. Will they be able to RTW? Do they face limitations? This is a prospective cohort study with descriptive analysis aimed to investigate the RTW during 1 year following hospital discharge and to identify any limitations faced by the workers. Data collection occurred between February 2017 and July 2018 in an Internal Medicine Unit at a General Hospital. Convenience sampling was used, and participation was voluntary. Participants comprised 170 adults. Participants' RTW and limitations were followed for 1 year after discharge. During hospitalisation, 95.9% of participants planned to RTW. However, only 30% and 34.1% had returned after 1 month and 1 year respectively. Initially, 57.6% did not believe that they would present any limitation after discharge. However, 85.3% experienced restrictions after 1 month, and after 1 year this percentage rose to 88.2%. A correlation was found between young adults and RTW. The presence of limitations increased non-RTW risk. Therefore, it is recommended that the worker role should not be merely replaced by that of a patient, but it deserves attention of the latter in order to RTW activities. The work-return process must focus on understanding, treating and monitoring workers' limitations over time.

Multivariate quasi-beta regression models for continuous bounded data.

We propose a multivariate regression model to deal with multiple continuous bounded data. The proposed model is based on second-moment assumptions, only. We adopted the quasi-score and Pearson estimating functions for estimation of the regression and dispersion parameters, respectively. Thus, the proposed approach does not require a multivariate probability distribution for the variable response vector. The multivariate quasi-beta regression model can easily handle multiple continuous bounded outcomes taking into account the correlation between the response variables. Furthermore, the model allows us to analyze continuous bounded data on the interval [0, 1], including zeros and/or ones. Simulation studies were conducted to investigate the behavior of the NORmal To Anything (NORTA) algorithm and to check the properties of the estimating function estimators to deal with multiple correlated response variables generated from marginal beta distributions. The model was motivated by a data set concerning the body fat percentage, which was measured at five regions of the body and represent the response variables. We analyze each response variable separately and compare it with the fit of the multivariate proposed model. The multivariate quasi-beta regression model provides better fit than its univariate counterparts, as well as allows us to measure the correlation between response variables. Finally, we adapted diagnostic tools to the proposed model. In the supplementary material, we provide the data set and R code.

Double Poisson-Tweedie Regression Models.

In this paper, we further extend the recently proposed Poisson-Tweedie regression models to include a linear predictor for the dispersion as well as for the expectation of the count response variable. The family of the considered models is specified using only second-moments assumptions, where the variance of the count response has the form µ+ϕµp $\mu + \phi \mu^p$, where µ is the expectation, ϕ and p are the dispersion and power parameters, respectively. Parameter estimations are carried out using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions. The performance of the fitting algorithm is investigated through simulation studies. The results showed that our estimating function approach provides consistent estimators for both mean and dispersion parameters. The class of models is motivated by a data set concerning CD4 counting in HIV-positive pregnant women assisted in a public hospital in Curitiba, Paraná, Brazil. Specifically, we investigate the effects of a set of covariates in both expectation and dispersion structures. Our results showed that women living out of the capital Curitiba, with viral load equal or larger than 1000 copies and with previous diagnostic of HIV infection, present lower levels of CD4 cell count. Furthermore, we detected that the time to initiate the antiretroviral therapy decreases the data dispersion. The data set and R code are available as supplementary materials.

Sarcopenia in heart failure with reduced ejection fraction.

AIMS: To evaluate the prevalence of pre-sarcopenia and sarcopenia and their relationship with clinical variables, physical activity, quality of life, and diet in patients with heart failure with reduced left ventricular ejection fraction (HFrEF). METHODS: We performed a cross-sectional study in patients with HFrEF and matched controls. Clinical, laboratory analysis, dual-emission X-ray densitometry, handgrip strength, and physical activity level questionnaire assessments were performed. Echocardiography, quality of life, gait speed, and 24-hour nutritional recall questionnaire were also analyzed. Pre-sarcopenia and sarcopenia were defined according to the European Working Group on Sarcopenia in Older People with the cut-off points of the Foundation for the National Institute of Health. RESULTS: 79 patients and 143 controls were enrolled. Pre-sarcopenia was found in 30.4%, and sarcopenia in 10.1% of the patients. Pre-sarcopenic patients were older and shorter, and had more fractures, higher calcemia, and creatinine (P < 0.05). Sarcopenic patients were older and had higher creatinine and TSH (P < 0.05). After multiple logistic regression analysis, only age was associated with pre-sarcopenia (OR: 1.046; CI 1.004-1.095; P = 0.04) and SP (OR: 1.119; CI 1.039-1.229; P = 0.008). Women with HFrEF presented higher lean mass than controls (P < 0.001), but were weaker (P < 0.001), while men presented lower lean mass (P < 0.001). Low gait speed was associated with right ventricular dysfunction (P = 0.016) and lower left ventricular ejection fraction (P = 0.037). CONCLUSION: Pre-sarcopenia and sarcopenia were associated with aging. Despite having higher lean mass, women with HFrEF were weaker. Low gait speed was associated with biventricular systolic dysfunction.

Procalcitonin, C-Reactive Protein, Albumin, and Blood Cultures as Early Markers of Sepsis Diagnosis or Predictors of Outcome: A Prospective Analysis.

PURPOSE: Sepsis is a condition with high mortality rates and its diagnosis remains a challenge. We assessed epidemiological, clinical data, multiple biomarker profiles, and blood culture with respect to sepsis diagnosis and predictors of outcome. METHODS: In total, 183 patients who were suspected of having sepsis and underwent blood culture collection were followed up for 7 days. Sepsis-related Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II scores were calculated daily; biomarkers and blood culture test results were evaluated. RESULTS: In total, 78 (43%) had sepsis, 50 (27%) had septic shock, and 55 (30%) had no sepsis. Blood culture was positive in 28% and 42% of the sepsis and septic shock groups, respectively (P < .001). Regarding clinical profiles and biomarker values, there were no differences between the sepsis and non-sepsis groups, but significant differences were observed in the septic shock group. Multivariate logistic regression models revealed that age, serum albumin level, APACHE II, and SOFA 1st day scores were the independent variables for death. CONCLUSIONS: The challenge in the diagnosis of sepsis continues as clinical and laboratory differences found between the groups were due to septic shock. Older aged patients with lower albumin levels and higher APACHE II and SOFA 1st day scores have a greater probability of mortality.

Osteosarcopenia and trabecular bone score in patients with type 2 diabetes mellitus

ABSTRACT Objective: To evaluate the prevalence of osteosarcopenia and the association of osteosarcopenia with trabecular bone score (TBS) in a group of patients with type 2 diabetes mellitus(T2DMG) compared with a paired control group (CG). Materials and methods: Cross-sectional study with men and women ≥ 50 years recruited by convenience. Patients in both groups answered questionnaires and underwent evaluation of bone mineral density (BMD), handgrip strength (HGS), and TBS. The T2DMG also underwent a gait speed (GS) test. Sarcopenia was defined as low lean mass plus low HGS or GS according to the Foundation for the National Institute of Health Sarcopenia Project, and osteosarcopenia was deemed present when sarcopenia was associated with osteopenia, osteoporosis, or low-energy trauma fractures. Results: The T2DMG (n = 177) and CG (n = 146) had, respectively, mean ages of 65.1 ± 8.2 years and 68.8 ± 11.0 years and 114 (64.4%) and 80 (54.7%) women. T2DMG versus the CG had higher rates of osteosarcopenia (11.9% versus 2.14%, respectively, p = 0.010), sarcopenia (12.9% versus 5.4%, respectively, p < 0.030), and fractures (29.9% versus 18.5%, respectively, p = 0.019), and lower HGS values (24.4 ± 10.3 kg versus 30.9 ± 9.15 kg, respectively, p < 0.001), but comparable BMD values. Mean TBS values were 1.272 ± 0.11 and 1.320 ± 0.12, respectively (p = 0.001). On multivariate analysis, age, greater waist circumference, fractures, and osteoporosis increased the risk of degraded TBS. Osteosarcopenia was associated with diabetes complications (p = 0.03), calcium and vitamin D supplementation (p = 0.01), and all components of osteosarcopenia diagnosis (p < 0.05). Conclusion: Compared with the CG, the T2DMG had a higher prevalence of osteosarcopenia, sarcopenia, and fractures and lower bone quality assessed by TBS.