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Background: Disease-modifying drugs (DMDs) are an inseparable part of multiple sclerosis (MS) management, which have dramatically changed the prognosis and course of the disease. A change during DMD therapy, which includes switching or stopping (temporary or permanent) medication, can manipulate the goals and has various causes such as side effects, ineffectiveness of treatment, patient's preference, presence of concurrent diseases and pregnancy. Therefore, we aimed to investigate the patterns and causes of DMD change in patients with MS (PwMS) in Tehran, Iran. The understanding will help us identify opportunities to improve adherence and ultimately patient outcomes and health system efficiency through effective education, and recognition of more tolerable or simpler regimens. The aim of this study is to identify rate and pattern of DMDs among PwMS in Tehran. Material(s) and Method(s): The study population of this cross-sectional was all PwMS in Tehran province who had changed their DMD for any reason in the last 5 years until June 2, 2022. The basic information was extracted through nationwide MS registry of Iran (NMSRI), Tehran, where all MS data including diagnosis had been confirmed by trained neurologists based on the 2017 revisions of the McDonald criteria. Moreover, supplementary unregistered data were gathered through telephone follow-ups carried out by 6 trained physicians with precise quality checks. The questionnaires covered 5 aspects of MS including demographics, disease history, diagnosis, progress and treatment. DMDs were classified into 10 general classes. All participants were asked to attribute the change to distinct categories following a written pre-existing consent. IBM SPSS (version 23) was used for statistical analysis. All the steps taken were in complete adherence with the tenets of the declaration of Helsinki. Result(s): Among 1999 enrolled patients with a mean age of 36.9+/-9.4 and total disease duration of 7.06+/-5.8 years, 1315 experienced change (Group 1) during study period, while 684 did not (Group 2). There was no difference in terms of demographic characteristics between the two groups. On the other hand, Group 1 had longer disease durations and more comorbidities (P <0.001). Getting infected with COVID-19 more than 4 times was observed to be significantly higher in Group 1 (P =0.032). Unlike Patients with PPMS and RRMS, SPMS patients showed higher EDSS scores when experiencing no DMD change. The most widely used DMDs were interferons, while ocrelizumab was the least used drug. Corona virus had the most effect on the change of ocrelizumab. Conclusion(s): DMD change generally occurs independent of socioeconomic level. Since most of the patients (65.8%) experienced DMD change, which serves as the biggest cost component in PwMS, the economic aspects of MS management in this field should be considered in the future.Copyright © 2022
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Fast growing global connectivity and urbanisation increases the risk of spreading worldwide disease. The worldwide SARS-COV-2 disease causes healthcare system strained, especially for the intensive care units. Therefore, prognostic of patients' need for intensive care units is priority at the hospital admission stage for efficient resource allocation. In the early hospitalization, patient chest radiography and clinical data are always collected to diagnose. Hence, we proposed a clinical data structured graph Markov neural network embedding with computed radiography exam features (CGMNN) to predict the intensive care units demand for COVID patients. The study utilized 1,342 patients' chest computed radiography with clinical data from a public dataset. The proposed CGMNN outperforms baseline models with an accuracy of 0.82, a sensitivity of 0.82, a precision of 0.81, and an F1 score of 0.76. © 2022 ACM.
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Background: In December 2019, the Chinese government declared the prevalence of a new epidemic belonging to the coronavirus family;later, this epidemic was called COVID-19. Objectives: To determine the clinical and laboratory factors involved in the mortality of hospitalized COVID-19 patients. Methods: This descriptive-analytic study was performed in Kowsar Hospital of Semnan, Iran, using medical files of 107 patients hospitalized during February-April 2020 with COVID-19 diagnosis with the presence of chest CT findings. The patients’ demographic information, vital signs, clinical symptoms, lab test results, the prescribed medicines during hospitalization, past medical history, and outcomes were analyzed. The relationship between each of the explanatory variables with death outcome was examined using univariate and multivariate logistic regression models, reporting crude and adjusted odds ratios (OR & Adj. OR). Results: The mean age of the patients was 63.36 ± 16.43 years, 67 (62.6%) patients were male, and 85 (79.4%) patients were discharged. Low level of consciousness (Glasgow coma scale < 14) (discharged: 8.2% vs. expired: 40.2%, Adj. OR [95% confidence interval] = 17.9 [3.1, 102.7]), higher body temperature (37.1°C vs. 37.74°C, 3.62 [1.55, 8.43]), and higher blood alkaline phosphatase (177.6 vs. 247.6, 1.01 [1.00, 1.01]) at the time of hospitalization were associated with increased mortality. Also, having the symptom of fatigue or weakness at the time of hospitalization (61.2 vs. 36.4%, 0.06 [0.01, 0.46]), higher red blood cells count (4.67 vs. 4.15, 0.21 [0.08, .55]), and higher blood oxygen saturation levels (89 vs. 82%, 0.92 [0.85, 0.99]) were associated with a lower risk of mortality. Conclusions: Decreased level of consciousness at the time of admission was shown to be a critical and independent predictor of mortality. Several factors are associated with death in patients with COVID-19, which due to the complexity of the relationship between each of them, it is not practical and plausible to make a definite prediction of patients’ prognosis only by noting few factors without considering all the clinical symptoms and laboratory findings. The results of individual studies like ours should be interpreted alongside the results of previous and future clinical studies and not alone. © 2021, Semnan University of Medical Sciences. All rights reserved.
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