Predicting the Outcome of Limb Revascularization in Patients With Lower-extremity Arterial Trauma: Development and External Validation of a Supervised Machine-learning Algorithm to Support Surgical Decisions.

2020-10-01
Perkins, Zane B.
Yet, Barbaros
Sharrock, Anna
Rickard, Rory
Marsh, William
Rasmussen, Todd E.
Tai, Nigel R M
Objectives: Estimating the likely success of limb revascularization in patients with lower-extremity arterial trauma is central to decisions between attempting limb salvage and amputation. However, the projected outcome is often unclear at the time these decisions need to be made, making them difficult and threatening sound judgement. The objective of this study was to develop and validate a prediction model that can quantify an individual patient's risk of failed revascularization. Methods: A BN prognostic model was developed using domain knowledge and data from the US joint trauma system. Performance (discrimination, calibration, and accuracy) was tested using ten-fold cross validation and externally validated on data from the UK Joint Theatre Trauma Registry. BN performance was compared to the mangled extremity severity score. Results: Rates of amputation performed because of nonviable limb tissue were 12.2% and 19.6% in the US joint trauma system (n = 508) and UK Joint Theatre Trauma Registry (n = 51) populations respectively. A 10-predictor BN accurately predicted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, calibration slope 1.96, Brier score (BS) 0.05, and Brier skill score 0.50. The model maintained excellent performance in an external validation population: AUROC 0.97, calibration slope 1.72, Brier score 0.08, Brier skill score 0.58, and had significantly better performance than mangled extremity severity score at predicting the need for amputation [AUROC 0.95 (0.92–0.98) vs 0.74 (0.67–0.80); P < 0.0001]. Conclusions: A BN (https://www.traumamodels.com) can accurately predict the outcome of limb revascularization at the time of initial wound evaluation. This information may complement clinical judgement, support rational and shared treatment decisions, and establish sensible treatment expectations.

Citation Formats
Z. B. Perkins et al., “Predicting the Outcome of Limb Revascularization in Patients With Lower-extremity Arterial Trauma: Development and External Validation of a Supervised Machine-learning Algorithm to Support Surgical Decisions.,” Annals of surgery, vol. 272, no. 4, pp. 564–572, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56818.