TY - JOUR
T1 - Predicting Vasospasm and Early Mortality in Severe Traumatic Brain Injury
T2 - A Model Using Serum Cytokines, Neuronal Proteins, and Clinical Data
AU - Rindler, Rima S
AU - Robertson, Henry
AU - De Yampert, LaShondra
AU - Khatri, Vivek
AU - Texakalidis, Pavlos
AU - Eshraghi, Sheila
AU - Grey, Scott
AU - Schobel, Seth
AU - Elster, Eric A
AU - Boulis, Nicholas
AU - Grossberg, Jonathan A
N1 - Copyright © 2024 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.
PY - 2024/10/11
Y1 - 2024/10/11
N2 - BACKGROUND AND OBJECTIVES: Prediction of patient outcomes after severe traumatic brain injury (sTBI) is limited with current clinical tools. This study aimed to improve such prognostication by combining clinical data and serum inflammatory and neuronal proteins in patients with sTBI to develop predictive models for post-traumatic vasospasm (PTV) and mortality.METHODS: Fifty-three adult civilian patients were prospectively enrolled in the sTBI arm of the Surgical Critical Care Initiative (SC2i). Clinical, serum inflammatory, and neuronal protein data were combined using the parsimonious machine learning methods of least absolute shrinkage and selection operator (LASSO) and classification and regression trees (CART) to construct parsimonious models for predicting development of PTV and mortality.RESULTS: Thirty-six (67.9%) patients developed vasospasm and 10 (18.9%) died. The mean age was 39.2 years; 22.6% were women. CART identified lower IL9, lower presentation pulse rate, and higher eotaxin as predictors of vasospasm development (full data area under curve (AUC) = 0.89, mean cross-validated AUC = 0.47). LASSO identified higher Rotterdam computed tomography score and lower age as risk factors for vasospasm development (full data AUC 0.94, sensitivity 0.86, and specificity 0.94; cross-validation AUC 0.87, sensitivity 0.79, and specificity 0.93). CART identified high levels of eotaxin as most predictive of mortality (AUC 0.74, cross-validation AUC 0.57). LASSO identified higher serum IL6, lower IL12, and higher glucose as predictive of mortality (full data AUC 0.9, sensitivity 1.0, and specificity 0.72; cross-validation AUC 0.8, sensitivity 0.85, and specificity 0.79).CONCLUSION: Inflammatory cytokine levels after sTBI may have predictive value that exceeds conventional clinical variables for certain outcomes. IL-9, pulse rate, and eotaxin as well as Rotterdam score and age predict development of PTV. Eotaxin, IL-6, IL-12, and glucose were predictive of mortality. These results warrant validation in a prospective cohort.
AB - BACKGROUND AND OBJECTIVES: Prediction of patient outcomes after severe traumatic brain injury (sTBI) is limited with current clinical tools. This study aimed to improve such prognostication by combining clinical data and serum inflammatory and neuronal proteins in patients with sTBI to develop predictive models for post-traumatic vasospasm (PTV) and mortality.METHODS: Fifty-three adult civilian patients were prospectively enrolled in the sTBI arm of the Surgical Critical Care Initiative (SC2i). Clinical, serum inflammatory, and neuronal protein data were combined using the parsimonious machine learning methods of least absolute shrinkage and selection operator (LASSO) and classification and regression trees (CART) to construct parsimonious models for predicting development of PTV and mortality.RESULTS: Thirty-six (67.9%) patients developed vasospasm and 10 (18.9%) died. The mean age was 39.2 years; 22.6% were women. CART identified lower IL9, lower presentation pulse rate, and higher eotaxin as predictors of vasospasm development (full data area under curve (AUC) = 0.89, mean cross-validated AUC = 0.47). LASSO identified higher Rotterdam computed tomography score and lower age as risk factors for vasospasm development (full data AUC 0.94, sensitivity 0.86, and specificity 0.94; cross-validation AUC 0.87, sensitivity 0.79, and specificity 0.93). CART identified high levels of eotaxin as most predictive of mortality (AUC 0.74, cross-validation AUC 0.57). LASSO identified higher serum IL6, lower IL12, and higher glucose as predictive of mortality (full data AUC 0.9, sensitivity 1.0, and specificity 0.72; cross-validation AUC 0.8, sensitivity 0.85, and specificity 0.79).CONCLUSION: Inflammatory cytokine levels after sTBI may have predictive value that exceeds conventional clinical variables for certain outcomes. IL-9, pulse rate, and eotaxin as well as Rotterdam score and age predict development of PTV. Eotaxin, IL-6, IL-12, and glucose were predictive of mortality. These results warrant validation in a prospective cohort.
U2 - 10.1227/neu.0000000000003224
DO - 10.1227/neu.0000000000003224
M3 - Article
C2 - 39471078
SN - 0148-396X
JO - Neurosurgery
JF - Neurosurgery
ER -