Our results showed that the antifibrinolytic properties of TXA can aid in changing an urgent endoscopy to an elective procedure, with better outcomes for both physicians and patients.

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Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

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Aims and objectivesThe present study aimed to investigate the effects of interprofessional education on healthcare providers' intentions to engage in an interprofessional shared decision‐making (IP‐SDM) process at emergency department and exploring the affecting factors on their intentions.MethodsWe conducted the study through a sequential explanatory mixed method (quantitative‐qualitative) design. All ED residents and nurses from two university hospitals were invited and assigned to the intervention and control groups. The intervention group was exposed to case‐based learning sessions conducted by applying interprofessional strategies. The intentions of the participants engaged in IP‐SDM were assessed before and 2 weeks after the intervention by a questionnaire designed based on the theory of planned behaviour. The questionnaire scores were compared between the intervention and control groups using analysis of covariance (ANCOVA). Partial eta‐squared (η2) was used for effect size calculations in ANCOVA. Subsequently, to explore the affecting factors in engagement in IP‐SDM, qualitative data were collected through semi‐structured individual interviews. The inductive content analysis approach by Elo and Kyngas was employed to analyze the qualitative data.ResultsOut of 117 potentially eligible healthcare professionals, 113 completed the study in the intervention (n = 55) and control (n = 58) groups. The results showed that the difference between the mean scores of the learners in the intervention (1.41 ± 0.27) and control (0.80 ± 0.52) groups was statistically significant (P‐value = .00001). The main effect of the intervention and a large educational effect size for the intervention were found to be statistically significant F (1, 11) = 180.54, P‐value = .00001, η2 = 0.62. The qualitative data analysis showed two main categories of “team‐based facilitators” and “contextual challenges” as the main affecting factors in the engagement of participant in IP‐SDM.ConclusionOur findings suggested that applying interprofessional education strategies could improve the learners' intention to engage in IP‐SDM. Moreover, the results showed that the interprofessional collaboration among team members, adherence to the team‐based care principles, and administrative support at different levels could be the influential factors the intentions of the participants to engage in IP‐SDM.