TY - JOUR
T1 - Nomogram for Predicting Time to Death after Withdrawal of Life-Sustaining Treatment in Patients with Devastating Neurological Injury
AU - He, X.
AU - Xu, G.
AU - Liang, W.
AU - Liu, B.
AU - Xu, Y.
AU - Luan, Z.
AU - Lu, Y.
AU - Ko, D. S.C.
AU - Manyalich, M.
AU - Schroder, P. M.
AU - Guo, Z.
N1 - Publisher Copyright:
© 2015 The American Society of Transplantation and the American Society of Transplant Surgeons.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - Reliable prediction of time of death after withdrawal of life-sustaining treatment in patients with devastating neurological injury is crucial to successful donation after cardiac death. Herein, we conducted a study of 419 neurocritical patients who underwent life support withdrawal at four neurosurgical centers in China. Based on a retrospective cohort, we used multivariate Cox regression analysis to identify prognostic factors for patient death, which were then integrated into a nomogram. The model was calibrated and validated using data from an external retrospective cohort and a prospective cohort. We identified 10 variables that were incorporated into a nomogram. The C-indexes for predicting the 60-min death probability in the training, external validation and prospective validation cohorts were 0.96 (0.93-0.98), 0.94 (0.91-0.97), and 0.99 (0.97-1.00), respectively. The calibration plots after WLST showed an optimal agreement between the prediction of time to death by the nomogram and the actual observation for all cohorts. Then we identified 22, 26 and 37 as cut-points for risk stratification into four groups. Kaplan-Meier curves indicated distinct prognoses between patients in the different risk groups (p < 0.001). In conclusion, we have developed and validated a nomogram to accurately identify potential cardiac death donors in neurocritical patients in a Chinese population.
AB - Reliable prediction of time of death after withdrawal of life-sustaining treatment in patients with devastating neurological injury is crucial to successful donation after cardiac death. Herein, we conducted a study of 419 neurocritical patients who underwent life support withdrawal at four neurosurgical centers in China. Based on a retrospective cohort, we used multivariate Cox regression analysis to identify prognostic factors for patient death, which were then integrated into a nomogram. The model was calibrated and validated using data from an external retrospective cohort and a prospective cohort. We identified 10 variables that were incorporated into a nomogram. The C-indexes for predicting the 60-min death probability in the training, external validation and prospective validation cohorts were 0.96 (0.93-0.98), 0.94 (0.91-0.97), and 0.99 (0.97-1.00), respectively. The calibration plots after WLST showed an optimal agreement between the prediction of time to death by the nomogram and the actual observation for all cohorts. Then we identified 22, 26 and 37 as cut-points for risk stratification into four groups. Kaplan-Meier curves indicated distinct prognoses between patients in the different risk groups (p < 0.001). In conclusion, we have developed and validated a nomogram to accurately identify potential cardiac death donors in neurocritical patients in a Chinese population.
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U2 - 10.1111/ajt.13231
DO - 10.1111/ajt.13231
M3 - Article
C2 - 25810114
AN - SCOPUS:84937402520
SN - 1600-6135
VL - 15
SP - 2136
EP - 2142
JO - American Journal of Transplantation
JF - American Journal of Transplantation
IS - 8
ER -