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The development of COVID-19 pandemic is the serious challenge for trauma care systems across the world. Recommendations on general principles of trauma care in the settings of pandemic, medical stuff protection and prevention of the spread of infection based on the data from international centers are presented in the article. Delay of all elective cases, restriction of surgical treatment of fractures with relative indications for surgery are recommended. The segregation of cases into COVID-19 confirmed or suspected and COVID-19 negative patients is important. When possible before the admission to in-patient department the patient shall be tested on COVID-19. When the COVID-19 status of the patient is unclear all possible protection measures shall be used: patient isolation and medical stuff protection. To prevent the spread of infection it is recommended to split the stuff of the traumacenter into 2 or 3 groups. Each of the groups is working during the week with subsequent period of the self-isolation (remote work) for the period of 1-2 weeks (according to the duration of incubation period of the COVID-19). During the surgical treatment the most dangerous stages of the surgery are endotracheal intubation / extubation and also aerosol-generative procedures: electrocoagulation, pulsed wound lavage, drilling, the use of oscillating saw and medullary reaming. The stuff in the OP-theatre has to be accordingly equipped (personal protection equipment). The risk factors for the medical stuff are fatigue from overwork, the absence of real-time training in infection-prevention measures and non-compliance with PPE.

The Trauma and Injury Severity Score (TRISS) is the current “gold” standard of screening patient’s condition for purposes of predicting survival probability. More than 40 years of TRISS practice revealed a number of problems, particularly, 1) unexplained fluctuation of predicted values caused by aggregation of screening tests, and 2) low accuracy of uncertainty intervals estimations. We developed a new method made it available for practitioners as a web calculator to reduce negative effect of factors given above. The method involves Bayesian methodology of statistical inference which, being computationally expensive, in theory provides most accurate predictions. We implemented and tested this approach on a data set including 571,148 patients registered in the US National Trauma Data Bank (NTDB) with 1–20 injuries. These patients were distributed over the following categories: (1) 174,647 with 1 injury, (2) 381,137 with 2–10 injuries, and (3) 15,364 with 11–20 injuries. Survival rates in each category were 0.977, 0.953, and 0.831, respectively. The proposed method has improved prediction accuracy by 0.04%, 0.36%, and 3.64% (p-value