Osteoporosis (OP) is one of the most commonly known extra-articular complications of rheumatoid arthritis (RA). Since the prevalence of OP is diverse in different studies and there is no general consensus about it, in this systematic review, we aimed to investigate the global prevalence of OP among RA patients. In this review, three databases including Medline via PubMed, Scopus, and Web of Science (Clarivate analytics) were searched by various keywords. After screening of retrieved papers, the related data of included papers were extracted and analyzed. To assess the risk of methodological bias of included studies, quality assessment checklist for prevalence studies was used. Because of heterogeneity among studies, random-effect model was used to pooled the results of primary studies. In this review, the results of 57 studies were summarized and the total included sample size was 227,812 cases of RA with 64,290 cases of OP. The summary point prevalence of OP among RA was estimated as 27.6% (95%CI 23.9–31.3%). Despite significant advances in prevention, treatment and diagnostic methods in these patients, it still seems that the prevalence of OP in these patients is high and requires better and more timely interventions.

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This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 from the basic medical data on the first day of admission. MethodsThe medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. ResultsA total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. ConclusionDeveloping a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.