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Curr Opin Crit Care ; 29(6): 682-688, 2023 12 01.
Article in English | MEDLINE | ID: mdl-37909372

ABSTRACT

PURPOSE OF REVIEW: While MESS has historically influenced limb salvage versus amputation decisions, its universal applicability remains uncertain. With trauma systems expanding and advancements in trauma care, the need for a nuanced understanding of limb salvage has become paramount. RECENT FINDINGS: Recent literature reflects a shift in the management of mangled extremities. Vascular surgery, plastic surgery, and technological advancements have garnered attention. The MESS's efficacy in predicting amputation postvascular reconstruction has been questioned. Machine learning techniques have emerged as a means to predict peritraumatic amputation, incorporating a broader set of variables. Additionally, advancements in socket design, such as automated adjustments and bone-anchored prosthetics, show promise in enhancing prosthetic care. Surgical strategies to mitigate neuropathic pain, including targeted muscle reinnervation (TMR), are evolving and may offer relief for amputees. Predicting the long-term course of osteomyelitis following limb salvage is challenging, but it significantly influences patient quality of life. SUMMARY: The review underscores the evolving landscape of limb salvage decision-making, emphasizing the need for personalized, patient-centered approaches. The Ganga Hospital Score (GHS) introduces a nuanced approach with a 'grey zone' for patients requiring individualized assessments. Future research may leverage artificial intelligence (AI) and predictive models to enhance decision support. Overall, the care of mangled extremities extends beyond a binary choice of limb salvage or amputation, necessitating a holistic understanding of patients' injury patterns, expectations, and abilities for optimal outcomes.


Subject(s)
Artificial Intelligence , Limb Salvage , Humans , Limb Salvage/methods , Quality of Life , Extremities/injuries , Amputation, Surgical , Retrospective Studies , Injury Severity Score
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