Academic pediatrics · 2022
Advanced Automatic Crash Notification Algorithm for Children.
СтатьяПоддержка исследования: вне правительства СШАПоддержка правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Weaver Ashley A, Talton Jennifer W, Barnard Ryan T, Gaffley Michaela, Doud Andrea N, Schoell Samantha L, Petty John K, Martin R Shayn, Meredith J Wayne, Stitzel Joel D
Аннотация
BACKGROUND: Advanced automatic crash notification (AACN) can improve triage decision-making by using vehicle telemetry to alert first responders of a motor vehicle crash and estimate an occupant's likelihood of injury. The objective was to develop an AACN algorithm to predict the risk that a pediatric occupant is seriously injured and requires treatment at a Level I or II trauma center.
METHODS: Based on 3 injury facets (severity; time sensitivity; predictability), a list of Target Injuries associated with a child's need for Level I/II trauma center treatment was determined. Multivariable logistic regression of motor vehicle crash occupants was performed creating the pediatric-specific AACN algorithm to predict risk of sustaining a Target Injury. Algorithm inputs included: delta-v, rollover quarter-turns, belt status, multiple impacts, airbag deployment, and age. The algorithm was optimized to achieve under-triage ≤5% and over-triage ≤50%. Societal benefits were assessed by comparing correctly triaged motor vehicle crash occupants using the AACN algorithm against real-world decisions.
RESULTS: The pediatric AACN algorithm achieved 25% to 49% over-triage across crash modes, and under-triage rates of 2% for far-side, 3% for frontal and near-side, 8% for rear, and 14% for rollover crashes. Applied to real-world motor vehicle crashes, improvements of 59% in under-triage and 45% in over-triage are estimated: more appropriate triage of 32,320 pediatric occupants annually.
CONCLUSIONS: This AACN algorithm accounts for pediatric developmental stage and will aid emergency personnel in correctly triaging pediatric occupants after a motor vehicle crash. Once incorporated into the trauma triage network, it will increase triage efficiency and improve patient outcomes.
METHODS: Based on 3 injury facets (severity; time sensitivity; predictability), a list of Target Injuries associated with a child's need for Level I/II trauma center treatment was determined. Multivariable logistic regression of motor vehicle crash occupants was performed creating the pediatric-specific AACN algorithm to predict risk of sustaining a Target Injury. Algorithm inputs included: delta-v, rollover quarter-turns, belt status, multiple impacts, airbag deployment, and age. The algorithm was optimized to achieve under-triage ≤5% and over-triage ≤50%. Societal benefits were assessed by comparing correctly triaged motor vehicle crash occupants using the AACN algorithm against real-world decisions.
RESULTS: The pediatric AACN algorithm achieved 25% to 49% over-triage across crash modes, and under-triage rates of 2% for far-side, 3% for frontal and near-side, 8% for rear, and 14% for rollover crashes. Applied to real-world motor vehicle crashes, improvements of 59% in under-triage and 45% in over-triage are estimated: more appropriate triage of 32,320 pediatric occupants annually.
CONCLUSIONS: This AACN algorithm accounts for pediatric developmental stage and will aid emergency personnel in correctly triaging pediatric occupants after a motor vehicle crash. Once incorporated into the trauma triage network, it will increase triage efficiency and improve patient outcomes.