Abstract

Blood loss during burn excisional surgery remains an important factor as it is associated with significant comorbidity, mortality and longer length of stay. Blood loss is, among others, influenced by length of surgery, burn size, excision size and age. Most literature available is aimed at large burns and little research is available for small burns. Therefore, the goal of this study is to investigate blood loss and develop a prediction model to identify patient at risk for blood loss during burn excisional surgery ≤10% body surface area. This retrospective study included adult patients who underwent burn excisional surgery of ≤10% body surface area in the period 2013-2018. Duplicates, patients with missing data and delayed surgeries were excluded. Primary outcome was blood loss. A prediction model for per-operative blood loss (>250ml) was built using a multivariable logistic regression analysis with stepwise backward elimination. Discriminative ability was assessed by the area under the ROC-curve in conjunction with optimism and calibration. In total 269 patients were included for analysis. Median blood loss was 50ml (0-150) / % body surface area (BSA) excised and 0.28 (0-0.81) ml / cm2. Median burn size was 4% BSA and median excision size was 2% BSA. Blood loss of>250ml was present in 39% of patients. The model can predict blood loss>250ml based on %BSA excised, length of surgery and ASA-score with an AUC of 0.922 (95% CI 0.883 - 0.949) and an AUC after optimism correction of 0.915. The calibration curve showed an intercept of 0.0 (95% CI -0.36 to 0.36) with a slope of 1.0 (95% CI 0.78-1.22). Median blood loss during burn excisional surgery of ≤10% BSA is 50ml / % BSA excised and 0.28ml / cm2 excised. However, a substantial part of patients is at risk for higher blood loss. The prediction model can predict P(blood loss>250ml) with an AUC of 0.922, based on expected length of surgery, ASA-score and size of excision. The model can be used to identify patients at risk for significant blood loss (>250ml).

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.