The American surgeon · 2024
Machine Learning Differentiates Extracorporeal Membrane Oxygenation Mortality Risk Profiles Among Trauma Patients.
СтатьяНазвание и аннотация: Перевод в очереди
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Campbell Bryan R, Rooney Alexandra S, Krzyzaniak Andrea, Calvo Richard Y, Checchi Kyle D, Carroll Alyssa N, Sise Michael J, Bansal Vishal, Krzyzaniak Michael J
Аннотация
BACKGROUND: Extracorporeal membrane oxygenation (ECMO) is resource intensive with high mortality. Identifying trauma patients most likely to derive a survival benefit remains elusive despite current ECMO guidelines. Our objective was to identify unique patient risk profiles using the largest database of trauma patients available.
METHODS: ECMO patients ≥16 years were identified using Trauma Quality Improvement Program data (2010-2019). Machine learning K-median clustering (ML) utilized 101 variables including injury severity, demographics, comorbidities, and hospital stay information to generate unique patient risk profiles. Mortality and patient and center characteristics were evaluated across profiles.
RESULTS: A total of 1037 patients were included with 33% overall mortality, mean age 32 years, and median ISS = 26. The ML identified 3 unique patient risk profile groups. Although mortality rates were equivalent across the 3 groups, groups were distinguished by (Group 1) young (median 25 years), severely injured (ISS = 34) patients with thoracic and head injuries (99%) via blunt mechanism (93%), and a high prevalence of ARDS (77%); (Group 2) relatively young (median 30 years) and moderately injured (ISS = 22) patients with exposure-related injuries (11%); and (Group 3) older (median 46 years) patients with a high proportion of comorbidities (69%) and extremity injuries (100%). There were no differences based on center ECMO volume, teaching status, or ACS-Level across all 3 groups.
CONCLUSION: Machine learning compliments traditional analyses by identifying unique mortality risk profiles for trauma patients receiving ECMO. These details can further inform treatment guidelines, clinical decision making, and institutional criteria for ECMO usage.
METHODS: ECMO patients ≥16 years were identified using Trauma Quality Improvement Program data (2010-2019). Machine learning K-median clustering (ML) utilized 101 variables including injury severity, demographics, comorbidities, and hospital stay information to generate unique patient risk profiles. Mortality and patient and center characteristics were evaluated across profiles.
RESULTS: A total of 1037 patients were included with 33% overall mortality, mean age 32 years, and median ISS = 26. The ML identified 3 unique patient risk profile groups. Although mortality rates were equivalent across the 3 groups, groups were distinguished by (Group 1) young (median 25 years), severely injured (ISS = 34) patients with thoracic and head injuries (99%) via blunt mechanism (93%), and a high prevalence of ARDS (77%); (Group 2) relatively young (median 30 years) and moderately injured (ISS = 22) patients with exposure-related injuries (11%); and (Group 3) older (median 46 years) patients with a high proportion of comorbidities (69%) and extremity injuries (100%). There were no differences based on center ECMO volume, teaching status, or ACS-Level across all 3 groups.
CONCLUSION: Machine learning compliments traditional analyses by identifying unique mortality risk profiles for trauma patients receiving ECMO. These details can further inform treatment guidelines, clinical decision making, and institutional criteria for ECMO usage.