Trauma resuscitation and the damage control approach

[Koblenz algorithm for open abdomen management]

The issue of laparostomy treatment is still controversial, since there are insufficient evidence-based data. German military surgeons have developed and implemented the «Koblenz algorithm» of laparostomy treatment into everyday practice. The algorithm was developed at the Bundeswehr Central Hospital in Koblenz (Germany). Today, approximately 50% of German civilian hospitals use the «Koblenz algorithm». The database for laparostomy treatment was created on the basis of international platform European Registry of Abdominal wall Hernias (EuraHS) in May 2015. These data will be valuable for further multipla-center studies. This manuscript is devoted to analysis of clinical effectiveness of the «Koblenz algorithm» in the treatment of patients with laparostomy. Searching of Russian, English and German studies devoted to «Koblenz algorithm» in the treatment of patients with laparostomy was carried out in the eLIBRARY, Elektronische Zeitschriftenbibliothek, the Cochrane Library and the PubMed databases. The authors comprehensively described «Koblenz algorithm». Mortality in the group of VAC - therapy was 57% (31/54), in case of «Koblenz algorithm» - 33% (33/100). Between-group differences were significant (OR 0.36, 95% CI 0.18-0.72, p=0.003). However, an efficacy of «Koblenz algorithm» should be confirmed in further multiple-center studies including national evidence-based trials.

Massive transfusion prediction in patients with multiple trauma by decision tree: a retrospective analysis

Early initial massive transfusion protocol and blood transfusion can reduce patient mortality, however accurately identifying the risk of massive transfusion (MT) remains a major challenge in severe trauma patient therapy. We retrospectively analyzed clinical data of severe trauma patients with and without MT. Based on analysis results, we established a MT prediction model of clinical and laboratory data by using the decision tree algorithm in patients with multiple trauma. Our results demonstrate that shock index, injury severity score, international normalized ratio, and pelvis fracture were the most significant risk factors of MT. These four indexes were incorporated into the prediction model, and the model was validated by using the testing dataset. Moreover, the sensitivity, specificity, accuracy and area under curve values of prediction model for MT risk prediction were 60%, 92%, 90% and 0.85. Our study provides an easy and understandable classification rules for identifying risk factors associated with MT that may be useful for promoting trauma management.