Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.

Comparison of the efficacy of intravenous tranexamic acid with and without topical administration versus placebo in urgent endoscopy rate for acute gastrointestinal bleeding: A double-blind randomized controlled trial.

BACKGROUND: Tranexamic acid (TXA), a synthetic antifibrinolytic drug, is effective as a treatment for serious hemorrhage, including bleeding arising from major trauma and post-operative interventions. Significant acute gastrointestinal bleeding may have a poor outcome despite routine medical and endoscopic treatments. The aim of this study was to assess whether early intravenous and/or intravenous plus topical administration of TXA reduces the need for urgent endoscopy for acute gastrointestinal bleeding. METHOD: This double-blind randomized clinical trial included 410 patients with proven acute gastrointestinal bleeding. All patients received conventional therapy. The subjects were randomized to three groups: (A) 138 patients received intravenous TXA (1 g q6h); (B) 133 patients received topical TXA (1 g single dose by nasogastric tube) plus systemic TXA; and (C) 139 patients received a placebo (sodium chloride 0.9%) for 24 hours. Subgroup statistical analyses were conducted for urgent endoscopy, mortality, re-bleeding, blood transfusion, endoscopic and/or surgical intervention rates, and health status. RESULTS: The time to endoscopy was significantly shorter in group C (15.58 ± 7.994, p < 0.001). A need for urgent endoscopy was seen in 14.49%, 10.52%, and 30.21% of patients in groups A, B, and C, respectively (p < 0.001). No significant statistical differences were seen between treatment groups regarding mortality, re-bleeding, blood transfusion, and endoscopic and/or surgical intervention rates. No thromboembolic event was documented during the 1-week follow up. CONCLUSIONS: 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.

Combination of Vasopressin -Epinephrine as a Novel Candidate in Patients with Cardiac Arrest.

OBJECTIVES: Out-hospital Cardiac arrest is considered as a global disease, which causes high rate of morbidity and mortality. Although, the return of spontaneous circulation occurs in 10 to 60 percent of cases in OHCA, with variety of treatment, the most patients faced with multiple organ failure and ultimately death. The investigations demonstrated that endogenous vasopressin levels in patients with successful resuscitation is more than died patients. Therefore, it seems the administration of vasopressin during cardiopulmonary resuscitation could be useful. The current study aimed to investigate the administration of vasopressin and epinephrine on neurological surviving of cardiac-respiratory arrest via evaluation of S100b serum factor. METHODS: For this reasons, after collecting of sera from two vasopressin-epinephrine and epinephrine receiving patients, sera were subjected for ELISA to evaluate S100b. RESULTS: The findings demonstrated that the great reductions of S100b in sera of patients that receiving vasopressinepinephrine in comparison with those patients that only got the epinephrine. Although no significant difference was observed between two groups, but survival rates after hospital discharge in group that receiving vasopressin-epinephrine was significantly higher than those patients that only got only epinephrine. CONCLUSION: Today, no advantages of vasopressin over epinephrine have been observed in clinical trials and more studies needed to improve the OHAC patient's surveillance. But, The combination vasopressin-epinephrine in the current study demonstrated that efficacy of this combination should be noted.