Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and duration of hospital stay in opioid poisoning, in order to facilitate appropriate clinical decision-making. METHOD AND RESULTS: This study used artificial intelligence technology to predict the maintenance dose and duration of administration by selecting clinical and paraclinical features that were selected by Pearson correlation (filter method) (Stage 1) and then the (wrapper method) Recursive Feature Elimination Cross-Validated (RFECV) (Stage2). The duration of administration was divided into two categories: A (which includes a duration of less than or equal to 24 h of infusion) and B (more than 24 h of naloxone infusion). XGBoost algorithm model with an accuracy rate of 91.04%, a prediction rate of 91.34%, and a sensitivity rate of 91.04% and area under the Curve (AUC) 0.97 was best model for classification patients. Also, the best maintenance dose of naloxone was obtained with XGBoost algorithm with R2 = 0.678. Based on the selected algorithm, the most important features for classifying patients for the duration of treatment were bicarbonate, respiration rate, physical sign, The partial pressure of carbon dioxide (PCO2), diastolic blood pressure, pulse rate, naloxone bolus dose, Blood Creatinine(Cr), Body temperature (T). The most important characteristics for determining the maintenance dose of naloxone were physical signs, bolus dose of 4.5 mg/kg, Glasgow Coma Scale (GCS), Creatine Phosphokinase (CPK) and intensive care unit (ICU) add. CONCLUSION: A predictive model can significantly enhance the decision-making and clinical care provided by emergency physicians in hospitals and medical settings. XGBoost was found to be the superior model.

A Case Series of Variable Manifestations of COVID-19 in Liver Transplant Recipients.

Severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) that is known as COVID-19 is a new emerging respiratory infection attributed to novel coronavirus, firstly introduced in Wuhan, China, at the end of 2019. This infection is still of great concern because of various presentations of the disease, which are not fully understood. The manifestations of this virus among liver transplanted patients would be more challenging in the setting of immunosuppression. The focus of this study is to introduce different presentations of this virus in five liver transplant recipients referred to the gastroenterology ward of Taleghani Hospital, a teaching referral hospital in Tehran, Iran. These patients were started on different types of therapies for coronavirus infection, from only supportive care up to remdisivir infusion and hemoperfusion based on the severity of the disease. Additionally, they were advised to continue all their immunosuppressant agents with adjustment except for CellCept that was withheld.