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Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas. [ABSTRACT FROM AUTHOR]

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term. [ABSTRACT FROM AUTHOR]

Introduction: To reduce mortality in hospitalized patients with COVID‐19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID‐19. Methods: This study was conducted on 660 hospitalized patients with CVD and COVID‐19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID‐19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation. Results: Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID‐19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation. Conclusions: Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID‐19. [ABSTRACT FROM AUTHOR]

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu‐like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID‐19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank‐sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C‐reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID‐19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID‐19. To the best of our knowledge, a number of factors associated with mortality due to COVID‐19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID‐19. [ABSTRACT FROM AUTHOR]

Aim: To determine the prevalence of, and reasons for, missed care by nurses and the factors associated with it in Iranian hospitals. Background: Despite providing high‐quality patient care in hospitals, nurses often fail to deliver optimum care, which jeopardizes the safety of patients and increases health care costs. Therefore, identifying missed nursing care is essential if the quality of health services is to be improved. Methods: A cross‐sectional study was conducted among 215 nurses working in the medical–surgical wards of eight public and private hospitals in Tabriz, Iran. The 'MISSCARE' survey tool was used to collect data in relation to the extent of missed care and the related reasons. Logistic regression models were used to assess the association between factors and missed nursing care. Results: Results showed that the mean score of overall missed nursing care was 2.57 and 72.1% of the nurses reported that they missed at least one nursing care item on their last shift. 'Patient discharge planning and teaching', 'emotional support to patient and/or family' and 'attend interdisciplinary care conferences whenever held' were the most common missed items by nurses in Iran. In a 5‐point Likert scale, the most important reasons identified by nurses for missed care were 'human resources', 3.11 (95% CI: 3.03–3.19); 'material resources', 2.7 (95% CI: 2.6–2.8); and 'communication', 2.4 (95% CI: 2.3–2.5), respectively. Missed nursing care was associated with sex (OR for males = 2.83, 95% CI: 1.01–7.89), age (OR = 1.16, 95% CI: 1.01–1.33) and the number of patients under care (OR = 1.11, 95% CI 1.01–1.22). A reverse association was found with the number of patients discharged (OR = 0.89, 95% CI: 0.82–0.96) and satisfaction with teamwork (OR = 0.60, 95% CI: 0.41–0.89). Conclusions: There is a need to identify the factors contributing to the incidence of missed nursing care in hospitals and to develop strategies to address these. Implications for Nursing Management: The results of this study highlighted the importance of addressing missed nursing care in Iranian public and private hospitals. Nursing management should consider improving communication between team members and units. Changes are required regarding task division and payments to the nursing staff. Further research is required to understand the reasons behind missed nursing care, and the development of appropriate policies to address better nursing care in hospitals is recommended. [ABSTRACT FROM AUTHOR]

Objective: This modelling study aimed to estimate the comorbidity burden for four common non-communicable diseases with ischaemic heart diseases (IHD) in Iran during a period of 28 years., Design: Analysis of the burden of comorbidity with IHD based on data included prevalence rates and the disability weight (DW) average for calculating years lived with disability (YLDs) from the Iran population based on the Global Burden of Disease (GBD) study., Setting: Population-based available data in GBD 2017 study of Iran population., Participant: The source of data was the GBD 2017 Study. We evaluated IHD, major depressive disorder (MDD), diabetes mellitus (DM), ischaemic stroke (IS), and osteoarthritis (OA) age-standardised prevalence rates and their DW., Main Outcome Measures: A new formula that modified the GBD calculator was used to measure the comorbidity YLDs. In the new formula, some multipliers were considered, measuring the departure from independence., Result: The contribution of total comorbidity for each combination of IHD with DM, MDD, IS and OA was 2.5%, 2.0%, 1.6% and 2.9%, respectively. The highest YLD rates were observed for IHD_MDD, 16.5 in 1990 and 17.0 in 2017. This was followed by IHD_DM, from 11.5 to 16.9 per 100 000. The YLD rates for IHD_OA changed slightly (6.5-6.7) per 100 000, whereas there was a gradual reduction in the trends of IHD-IS, from 4.0-4.5 per 100 000., Conclusion: Of the four comorbidities studied, the highest burden was due to the coexistence of MDD with IHD. Our results highlight the importance of addressing the burden of comorbidities when studying the burden of IHD or any other non-communicable disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)