Development of a bedside tool to predict time to death after withdrawal of life-sustaining therapies in infants and children

2012 
Objectives: To generate a preliminary bedside predictor of rapid time-to-death after withdrawal of support in children to help identify potential candidates for organ donation after circulatory death. Design: Retrospective chart review. Setting: Pediatric intensive care unit of an academic children’s hospital. Patients: All deaths in the pediatric intensive care unit from May 1996 to April 2007. Interventions: None. Measurements and Main Results: Among 1389 deaths, 634 patients underwent withdrawal of support and 518 with complete data regarding demographics, life-supportive therapies, and end-of-life circumstances were analyzed. Three hundred seventy-three (72%) patients died within 30 mins of withdrawal and 452 (87%) died within 60 mins. Using multiple logistic regression, significant predictors of death within 30 or 60 mins (typical cut-off times for organ donation) were identified and a predictor score was generated. Significant predictors included: age 1 month or younger; norepinephrine, epinephrine, or phenylephrine >0.2 µg/kg/min; extracorporeal membrane oxygenation; and positive end-expiratory pressure >10 cmH2O; and spontaneous ventilation. Possible scores for the 30-min predictor ranged from –17 to 67; a score ≤–9 predicted a 37% probability of death ≤30 mins, whereas a score ≥38 predicted an 85% probability of death within 30 mins. For the 60-min predictor, scores ranged from –21 to 38; score ≤–10 predicted a 59% probability of death within 60 mins and a score ≥16 predicted a 98% probability of death within 60 mins. Conclusions: This tool is a reasonable preliminary predictor for death within 30 or 60 mins after withdrawal of support in terminally ill or injured children and might assist in identifying potential pediatric candidates for donation after circulatory death, although prospective validation is required.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    27
    References
    19
    Citations
    NaN
    KQI
    []