Severe lower limb injuries are potentially devastating and pose some of the most difficult decisions in trauma surgery. The goal is to ensure survival and reconstruct the most functional limb possible. Ideally this is achieved by salvaging the injured limb. However, in certain situations amputation is the safest and most effective method of achieving an optimal outcome. Errors in these decisions may have profound consequences, yet they are frequently based on incomplete information and uncertain risks. Furthermore, most surgeons have limited experience making these decisions, and existing decision-support tools are unhelpful. The aim of this thesis was to improve the understanding of decision-making following severe lower limb trauma, and develop accurate prognostic models that can help identify those patients whose limb can be safely and effectively salvaged, and also identify those for whom attempts at limb salvage would be dangerous or fail. The rationale for amputation decisions was analysed in a cohort of severe lower limb injuries (n = 579). Two prognostic models were designed to support difficult aspects of these decisions. Both models were developed using Bayesian networks that combine existing knowledge with individual patient data. The first provides early and accurate identification (AUROC = 0.927) of patients at risk of Trauma-Induced Coagulopathy, the principal indication for damage-control intervention. The model's performance in new patients, and ability to handle missing predictor information, was prospectively validated. The second model accurately predicts the likely outcome, in terms of viability, of attempted limb salvage. This model outperformed the most widely used decision-support tool, the Mangled Extremity Severity Score (AUROC 0.932 versus 0.723; P < 0.0001). These Bayesian network tools accurately quantify critical risks that make rational judgement on the safety and effectiveness of interventions possible. This information enables individualised and evidence-based decisions, at a time when decision-making is most effective.