Details

Autor(en) / Beteiligte

• Pommerening, Matthew J
• Goodman, Michael D
• Holcomb, John B
• Wade, Charles E
• Fox, Erin E
• del Junco, Deborah J
• Brasel, Karen J
• Bulger, Eileen M
• Cohen, Mitch J
• Alarcon, Louis H
• Schreiber, Martin A
• Myers, John G
• Phelan, Herb A
• Muskat, Peter
• Rahbar, Mohammad
• Cotton, Bryan A

Titel

Clinical gestalt and the prediction of massive transfusion after trauma

Ist Teil von

• Injury, 2015-05, Vol.46 (5), p.807-813

Ort / Verlag

Netherlands: Elsevier Ltd

Erscheinungsjahr

2015

Quelle

MEDLINE

Beschreibungen/Notizen

• Abstract Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question “Is the patient likely to be massively transfused?” 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p < 0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier.