The chronic care model (CCM) uses a systematic approach to restructure health care systems. The aim of this systematic review was to examine studies that evaluated different elements of the CCM in patients with type 2 diabetes mellitus (T2DM) and to assess the influence of the CCM on different clinical outcomes. There view was performed in the Medline and Cochrane Library electronic databases. The search was limited to randomized controlled trials conducted with T2DM patients. Studies were eligible for inclusion if they compared usual care with interventions that use done or more elements of the CCM and assessed the impact on clinical outcomes. After applying the eligibility criteria, 12 studies were included for data extraction. Of these, six showed evidence of effectiveness of the CCM for T2DM management in primary care as well as significant improvements in clinical outcomes. In the other six studies, no improvements regarding clinical outcomes were observed when comparing the intervention and control groups. Some limitations, such as a short follow-up period and a low number of patients, were observed. Some studies showed that the reorganization of health systems can improveT2DM care. However, it is possible that greater benefits could be obtained through combing all 6 elements of CCM.
With short-term treatment, etanercept and adalimumab had higher efficacy results; with long-term treatment, adalimumab appeared to be the most effective. Clinicians should be aware that each of the three drugs has different rates of efficacy and different safety considerations that must be taken into account when selecting the best treatment for an individual with rheumatoid arthritis.
BackgroundVitamins are essential micronutrients with antioxidant potential that may provide a complementary treatment for patients with chronic diseases. Our aim was to assess the effect of vitamin supplementation on the antioxidant status and glycemic index of type 2 diabetes mellitus patients.MethodsWe performed a systematic review with meta-analyses. Electronic searches were conducted in PubMed, Scopus, and Web of Science (December 2017). Randomized controlled trials evaluating the effect of any vitamin or vitamin complex supplementation on antioxidant status as primary outcome were included. The outcomes considered were: reduction of malondialdehyde (MDA); augmentation of glutathione peroxidase (GPx); changes in total antioxidant capacity (TAC), enhance in superoxide dismutase enzyme—SOD, and thiobarbituric acid reactive substances (TBARS). Outcomes of glycemic control were also evaluated. Pairwise meta-analyses were performed using software Review Manager 5.3.ResultsThirty trials fulfilled the inclusion criteria, but only 12 could be included in the meta-analyses of antioxidant outcomes. The most commonly studied vitamins were B, C, D and E. Vitamin E was related to significant reduction of blood glucose as well as glycated hemoglobin compared to placebo, while both vitamins C and E were mainly associated with reducing MDA and TBARS and elevating GPx, SOD and TAC, compared to placebo. However, outcome reports in this field are still inconsistent (e.g. because of a lack of standard measures).ConclusionsSupplementation of vitamin E may be a valuable strategy for controlling diabetes complications and enhancing antioxidant capacity. The effects of other micronutrients should be further investigated in larger and well-designed trials to properly place these complementary therapies in clinical practice.Electronic supplementary materialThe online version of this article (10.1186/s13098-018-0318-5) contains supplementary material, which is available to authorized users.
High-quality evidence shows that alemtuzumab, natalizumab and ocrelizumab present the highest efficacy among DMTs, and other meta-analyses are required regarding adverse events frequency, to better understand the safety of therapies. Based on efficacy profile, guidelines should consider a three-category classification (i.e. high, intermediate and low efficacy).
Objective This study aimed to implement and evaluate machine learning based-models to predict COVID-19’ diagnosis and disease severity. Methods COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients’ laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve). Results The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase. Conclusion Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.
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