Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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Insulin and its secretagogues are essential for some patients with type 2 diabetes (T2D) to maintain good glycemic control (GC), but severe hypoglycemia (SH) is a concern. This network meta-analysis aimed to find optimal glucose-lowering drug treatment regimens in terms of GC and SH in T2D patients. MEDLINE and EMBASE were used to identify trials that compared two or more treatments including insulins and/or sulfonylurea or glinides and that examined both GC and SH. ⋯ Cluster analysis indicated that premixed insulin plus glucagon-like peptide-1 receptor agonist (Mix-ins + GLP1) belonged to the high-efficacy category for GC and glinide plus thiazolidinedione (glinide + TZD) belonged to the relatively high-efficacy category for GC among several high-safety categories regarding SH. In T2D patients, clinicians should consider appropriate combinations of non-insulin glucose-lowering agents (especially glinide + TZD) for reducing SH risk before switching to insulin therapies. If switching, they should be willing to add non-insulin glucose-lowering agents (especially, Mix-ins + GLP1) to insulins to further improve GC.
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Randomized Controlled Trial
Prognostic model for predicting overall survival in patients with glioblastoma: an analysis based on the SEER database.
Predicting the prognosis of glioblastoma (GBM) has always been important for improving survival. An understanding of the prognostic factors for patients with GBM can help guide treatment. Herein, we aimed to construct a prognostic model for predicting overall survival (OS) for patients with GBM. ⋯ The nomogram had a higher areas under the ROC curve value. The nomogram was well validated, which can effectively predict the OS of patients with GBM. Thus, this nomogram could be applied in clinical practice.
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Observational Study
Hematological ratios in coronavirus disease 2019 patients with and without invasive mechanical ventilation.
Patients with the most severe form of coronavirus disease 2019 (COVID-19) often require invasive ventilation. Determining the best moment to intubate a COVID-19 patient is complex decision and can result in important consequences for the patient. Therefore, markers that could aid in clinical decision-making such as hematological indices are highly useful. ⋯ All hematological ratios exhibited significant differences between the control group and COVID-19 patients. NLR, d-NLR, SII, and NPR were higher in the IMV group than they were in the NIMV group. The hematological indices addressed in this study demonstrated high potential for use as auxiliaries in clinical decision-making regarding the need for IMV.
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Observational Study
Comparative admission rates and infection severity of COVID-19 among unvaccinated and vaccinated patients.
Vaccination efforts have limited the burden of the pandemic caused by the coronavirus disease 2019 (COVID-19) with substantial evidence showing reduced hospitalization rates among vaccinated populations. However, few studies have explored correlations between vaccination status and inpatient COVID-19 outcomes. This observational case-control study involved a retrospective chart review of adult patients hospitalized for COVID-19 infection at a medium-sized hospital in Central Michigan between May 1, 2021 and September 30, 2021. ⋯ Despite higher intensive care unit admission rates among unvaccinated patients (39.1% vs 23.9%, OR: 1.83, 90% CI: 0.74-4.64), this difference did not reach statistical significance. Accordingly, immunization status strongly correlates with patient demographics and differences in inpatient treatment. Larger studies are needed to further assess the vaccine's impact on inpatient outcomes outside of our community.
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Retrospective chart review (RCR) studies rely on the collection and analysis of documented clinical data, a process that can be prone to errors. The aim of this study was to develop a defined set of criteria to evaluate RCR datasets for potential data errors. The Data Error Criteria (DEC) were developed by identifying data coding and data entry errors via literature review and then classifying them based on error types. ⋯ Inter-rater agreement was near perfect for all categories. Identifying errors outlined in the DEC can be crucial for the data analysis stage as they can lead to inaccurate calculations and delay study timelines. The DEC offers a framework to evaluate datasets while reducing time and efforts needed to create high-quality RCR-related databases.