Global Leadership Initiative on Malnutrition criteria for the diagnosis of malnutrition and prediction of mortality in patients awaiting liver transplant: A validation study.

OBJECTIVES: The Global Leadership Initiative on Malnutrition (GLIM) is a framework aiming to standardize malnutrition diagnosis. However, it still needs to be validated, in particular for patients with chronic liver disease. This study aimed to validate the GLIM criteria in patients with liver cirrhosis awaiting liver transplant (LTx). METHODS: This was a retrospective observational study carried out with adult patients on the waiting list for LTx, consecutively evaluated between 2006 and 2021. The phenotypic criteria were unintentional weight loss, low body mass index, and reduced muscle mass (midarm muscle circumference [MAMC]). The etiologic criteria were high Model for End-Stage Liver Disease (MELD) and MELD adjusted for serum sodium (MELD-Na) scores, the Child-Pugh score, low serum albumin, and low food intake and/or assimilation. Forty-three GLIM combinations were tested. Sensitivity (SE), specificity (SP), positive and negative predictive values, and machine learning (ML) techniques were used. Survival analysis with Cox regression was carried out. RESULTS: A total of 419 patients with advanced liver cirrhosis were included (median age, 52.0 y [46-59 y]; 69.2% male; 68.8% malnourished according to the Subjective Global Assessment [SGA]). The prevalence of malnutrition by the GLIM criteria ranged from 3.1% to 58.2%, and five combinations had SE or SP >80%. The MAMC as a phenotypic criterion with MELD and MELD-Na as etiologic criteria were predictors of mortality. The MAMC and the presence of any phenotypic criteria associated with liver disease parameters and low food intake or assimilation were associated with malnutrition prediction in ML analysis. CONCLUSIONS: The MAMC and liver disease parameters were associated with malnutrition diagnosis by SGA and were also predictors of 1-y mortality in patients with liver cirrhosis awaiting LTx.

Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years.

BACKGROUND: Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. METHOD: One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms-random forest, support vector machine (SVM), and XGBoost-were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. RESULTS: The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). CONCLUSIONS: Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.

Adverse events of COVID-19 vaccines in pregnant and postpartum women in Brazil: a cross-sectional study

Background By the fact that pregnant and postpartum women are currently using COVID-19 vaccines, ensure their safety is critical. So, more safety evidence is crucial to include this new technology to their vaccine’s calendar and to develop public policies regarding the support and training of Health Care Personnel. This study aims to describe the adverse events (AE) of COVID-19 vaccines in pregnant and postpartum women in the early stage of vaccination campaign in Brazil. Methods An observational cross-sectional study using data from the Brazilian surveillance information system to characterize the AE of COVID-19 vaccines (Sinovac/Butantan, Pfizer/BioNTech, AstraZeneca and Janssen) in Brazilian pregnant and postpartum women from April to August 2021. Frequency and incidence rate of AE for COVID-19 vaccines were assessed. Results 3,333 AE following immunization were reported for the study population. AE incidence was 309.4/100,000 doses (95% CI 297.23, 321.51). Within the vaccines available, Sinovac/Butantan had the lowest incidence (74.08/100,000 doses; 95% CI 63.47, 84.69). Systemic events were the most frequent notified (82.07%), followed by local (11.93%) and maternal (4.74%), being most of them classified as non-severe (90.65%). Conclusion Our results corroborate the recommendation of vaccination for these groups. Even though, further studies appraising a longer observation time are still needed to provide a broader safety aspect for the vaccines currently under use for this population.

Cost-minimization analysis of teledermatology versus conventional care in the Brazilian National Health System.

Aims: Cost-minimization analysis (CMA) comparing the teledermatology service of the State of Santa Catarina, Brazil with the provision of conventional care, from the societal perspective. Patients & methods: All costs related to direct patient care were considered in calculation of outpatient costs. The evaluation was performed using the parameters avoided referrals and profile of hospitalizations. The economic analysis was developed through a decision tree. Results: Totally, 40% of 79,411 tests performed could be managed in primary care, avoiding commuting and expanding the patients' access. The CMA showed the teledermatology service had a cost per patient of US$196.04, and the conventional care of US$245.66. Conclusion: In this scenario, teledermatology proved to be a cost-saving alternative to conventional care, reducing commuting costs.

Lay abstract Diagnosis and treatment of skin diseases through teledermatology avoid patient referrals, improves accessibility to specialized care, as well as the skin care provided by physicians. This study compared the costs of the teledermatology service of the State of Santa Catarina, Brazil with the costs of the provision of conventional care, to check which of them was more efficient. All costs related to direct patient care were considered in the calculation of costs. Totally, 40% of 79,411 dermatological tests performed could be locally managed in primary care, avoiding commuting, and expanding the patients' access to care. The teledermatology service had a cost per patient of US$196.04, compared with the cost of conventional care of US$245.66. In this evaluation, teledermatology proved to be cheaper than conventional care, reducing commuting costs.

Pilot study GLIM criteria for categorization of a malnutrition diagnosis of patients undergoing elective gastrointestinal operations: A pilot study of applicability and validation.

OBJECTIVES: The Global Leadership Initiative on Malnutrition (GLIM) was proposed to provide a common malnutrition diagnostic framework. The aims of this study were to evaluate the applicability and validity of the GLIM and use machine-learning techniques to help provide the best malnutrition-related variables/combinations to predict complications in patients undergoing gastrointestinal (GI) surgeries. METHOD: This was a prospective cohort study enrolling surgical patients with GI diseases. Malnutrition prevalence was classified by the GLIM, subjective global assessment (SGA), and various anthropometric parameters. The various combination of the phenotypic criteria generated 10 different models. Sensibility (SE) and specificity (SP) were calculated using SGA as the reference criterion. Machine-learning approaches were used to predict complications. P < 0.05 was set as statistically significant. RESULTS: We evaluated 206 patients. Half of the patients were malnourished according SGA, and 16.5% had postoperative complications. The prevalence of malnutrition using GLIM varied from 10.7% to 41.3% among the whole population, 11.7% and 43.6% in the elderly, from 0 to 24% in overweight non-obese and from 0 to 19.6% in obese patients. SE and SP values varied between 61.2% and 100% and 55.3% and 98.1%, respectively, for the general population. Machine-learning models indicated that midarm circumference, one of the GLIM models, and midarm muscle area were the most relevant criteria to predict complications. CONCLUSIONS: The various GLIM combinations provided different rates of malnutrition according to the population. Machine-learning techniques supported the use of common single variables and one GLIM model to predict postoperative complications.

Comparative persistence of anti-tumor necrosis factor therapy in ankylosing spondylitis patients: a multicenter international study.

Objective: To evaluate persistence on conventional DMARDs (cDMARDs) and anti-TNF therapies, and to identify potential determinants of discontinuation among individuals with ankylosing spondylitis (AS) living in Brazil and Quebec, Canada.Methods: We conducted a cohort study of AS patients using health administrative data (2010-2015). One-year and 2-year persistence rates were assessed. Cox regression was used to identify potential determinants of therapy discontinuation.Results: One-year persistence was less likely in Brazil for both anti-TNF and cDMARDs (Brazil: 62.1 and 30.7%, Quebec: 66.9 and 67.0%). The 2-year persistence rates were lower for both anti-TNF and cDMARD, but remained higher in Quebec (Brazil: 47.9 and 18.1%, Quebec: 51.5 and 53.5%). In multivariate Cox regression analysis, age, sex and comorbidities were associated with persistence in both countries. In Quebec, persistence did not differ between rural and urban regions or with socioeconomic status. While in Brazil, patients in regions with higher Human Development Index and those in cities with lower Gini index were less likely to discontinue therapy.Conclusions: Canadian AS patients were more likely to persist on therapy compared to Brazilian patients, although rates were lower at 2 years in both countries. Socioeconomic disparity in persistence was found in Brazil, but not in Quebec.

Building the National Database of Health Centred on the Individual: Administrative and Epidemiological Record Linkage - Brazil, 2000-2015.

INTRODUCTION: In Brazil, the National Health System (SUS) provides healthcare to the public. The system has multiple administrative databases; the major databases record hospital (SIH) and outpatient (SIA) procedures. Epidemiological information is collected for all populations in subsystems, such as mortality (SIM), live births (SINASC) and diseases of compulsory declaration (SINAN). Each subsystem has its own information system, which is able to provide information about consultations, clinical information and medicines dispensed. However, these systems are not linked, thereby preventing individual-centred analysis. OBJECTIVE: To describe the methods and results of parameter setting that are needed to execute the probabilistic deduplication of large administrative and epidemiological databases in Brazil and to create a National Health Database Centred on the individual. METHODS: This paper shows the results of a record linkage model to integrate data from SIH, SIA, SIM, and SINAN, which have different formats and attributes between them and over time. These data consist of 1.3 billion records from 2000-2015. Probabilistic and deterministic record linkages were used to deduplicate these data. The Kappa statistic and clerical review were used to ensure the quality of the linkage. The graph algorithm and depth-first search were used to generate the identifiers. RESULTS: The deterministic deduplication process resulted in a database with 403,113,527 possible unique individuals. After the probabilistic deduplication process of the former database was performed, 159,703,805 unique individuals were identified. This result had an estimated a false positive error rate of 3.3%, and the false negative error was estimated at 12.3%. CONCLUSIONS: The National Health Database centred on the individual was generated and will allow researchers to use real-world evidence to conduct clinical, epidemiological, economic and other studies. This database represents a significant cohort, spanning 15 years of historical data and preserving patient privacy. The success of the process described will allow repeating and appending the data for future years and enable important studies to promote SUS efficiency and provide better treatments for patients.

The Assessment for Disinvestment of Intramuscular Interferon Beta for Relapsing-Remitting Multiple Sclerosis in Brazil.

In Brazil, inclusion and exclusion of health technologies within the Unified Health System (SUS) is the responsibility of the National Committee for Health Technology Incorporation (CONITEC). A recent Cochrane systematic review demonstrated that intramuscular interferon beta 1a (IFN-ß-1a-IM) was inferior to the other beta interferons (IFN-ßs) for multiple sclerosis (MS). As a result, CONITEC commissioned an analysis to review possible disinvestment within SUS. The objective of this paper is to describe the disinvestment process for IFN-ß-1a-IM in Brazil. The first assessment comprised a literature review and mixed treatment comparison meta-analysis. The outcome of interest was the proportion of relapse-free patients in 2 years. This analysis confirmed the inferiority of IFN-ß-1a-IM. Following this, CONITEC recommended disinvestment, with the decision sent for public consultation. More than 3000 contributions were made on CONITEC's webpage, most of them against the preliminary decision. As a result, CONITEC commissioned a study to assess the effectiveness of IFN-ß-1a-IM among Brazilian patients in routine clinical care. The second assessment involved an 11-year follow-up of a non-concurrent cohort of 12,154 MS patients developed by deterministic-probabilistic linkage of SUS administrative databases. The real-world assessment further demonstrated that IFN-ß-1a-IM users had a statistically higher risk of treatment failure, defined as treatment switching or relapse treatment or death, with the assessment showing that IFN-ß-1a-IM was inferior to the other IFN-ßs and to glatiramer acetate in both direct and indirect analysis. In the drug ranking with 40,000 simulations, IFN-ß-1a-IM was the worst option, with a success rate of only 152/40,000. Following this, CONITEC decided to exclude the intramuscular presentation of IFN-ß from the current MS treatment guidelines, giving patients who are currently on this treatment the option of continuing until treatment failure. In conclusion, we believe this is the first example of this new disinvestment process in action, providing an exemplar for other treatments in Brazil as well as other countries.