Incidence of rhabdomyolysis occurrence in psychoactive substances intoxication: a systematic review and meta-analysis.

Rhabdomyolysis is a potentially life-threatening condition induced by diverse mechanisms including drugs and toxins. We aimed to investigate the incidence of rhabdomyolysis occurrence in intoxicated patients with psychoactive substances. In this review, three databases (PubMed, Scopus, Web of Science) and search engine (Google Scholar) were searched by various keywords. After the screening of retrieved documents, related data of included studies were extracted and analyzed with weighted mean difference (WMD) in random effect model. The highest incidence of rhabdomyolysis was observed in intoxication with heroin (57.2 [95% CI 22.6-91.8]), amphetamines (30.5 [95% CI 22.6-38.5]), and cocaine (26.6 [95% CI 11.1-42.1]). The pooled effect size for blood urea nitrogen (WMD = 8.78, p = 0.002), creatinine (WMD = 0.44, p < 0.001), and creatinine phosphokinase (WMD = 2590.9, p < 0.001) was high in patients with rhabdomyolysis compared to patients without rhabdomyolysis. Our results showed a high incidence of rhabdomyolysis induced by psychoactive substance intoxication in ICU patients when compared to total wards. Also, the incidence of rhabdomyolysis occurrence was high in ICU patients with heroin and amphetamine intoxication. Therefore, clinicians should anticipate this complication, monitor for rhabdomyolysis, and institute appropriate treatment protocols early in the patient's clinical course.

Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods.

Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.

Relationship Between Income Level and Hospitalization Rate in COVID-19 Cases; an Example of Social Factors Affecting Health.

Introduction: Considering the population's socioeconomic status and clinical features is essential in planning and performing interventions related to disease control. The main purpose of this study was to investigate the relationship between income level and hospitalization rate of COVID-19 patients|. Methods: A cross-sectional study was performed on 198,944 hospitalized COVID-19 patients in Tehran province between March 2020 and March 2021. Data of hospitalized COVID-19 patients was obtained from the Hospital Intelligent Management System (HIM). The income data of patients were obtained from the Iranian Database on Targeted Subsidies belonging to the Ministry of Cooperatives, Labor, and Social Welfare. Data analyses were performed using SPSS software. Results: About 2.5% of the inpatients were from the first decile, while 20.6% were from the tenth. The share of the lower three deciles of total hospitalization was about 11%, while the share of the upper three deciles was 50%. There was a big difference between the upper- and lower-income deciles regarding death rates. In the first decile, 30% of inpatients died, while the proportion was 10% in the tenth decile. There was a significant and positive relationship between income decline and hospitalization (r = 0.75; p = 0.02). Also, there was a significant and negative relationship between income decline and death rate (r = -0.90; p = 0.01). Conclusion: Low-income groups use fewer inpatient services, are more prone to severe illness and death from COVID-19|, and treatment in this group has a lower chance of success. Using a systemic approach to address socioeconomic factors in healthcare planning is crucial.