Citation

Hernandes Rocha TA, Elahi C, Cristina da Silva N, Sakita FM, Fuller A, Mmbaga BT, Green EP, Haglund MM, Staton CA, Nickenig Vissoci JR. J. Neurosurg. 2019; ePub(ePub): 1-9.

Affiliation

Duke University Global Health Institute, Durham, North Carolina.

Copyright

(Copyright © 2019, American Association of Neurological Surgeons)

DOI

10.3171/2019.2.JNS182098

PMID

31075779

Abstract

OBJECTIVETraumatic brain injury (TBI) is a leading cause of death and disability worldwide, with a disproportionate burden of this injury on low- and middle-income countries (LMICs). Limited access to diagnostic technologies and highly skilled providers combined with high patient volumes contributes to poor outcomes in LMICs. Prognostic modeling as a clinical decision support tool, in theory, could optimize the use of existing resources and support timely treatment decisions in LMICs. The objective of this study was to develop a machine learning-based prognostic model using data from Kilimanjaro Christian Medical Centre in Moshi, Tanzania.

METHODSThis study is a secondary analysis of a TBI data registry including 3138 patients. The authors tested nine different machine learning techniques to identify the prognostic model with the greatest area under the receiver operating characteristic curve (AUC). Input data included demographics, vital signs, injury type, and treatment received. The outcome variable was the discharge score on the Glasgow Outcome Scale-Extended.

RESULTSThe AUC for the prognostic models varied from 66.2% (k-nearest neighbors) to 86.5% (Bayesian generalized linear model). An increasing Glasgow Coma Scale score, increasing pulse oximetry values, and undergoing TBI surgery were predictive of a good recovery, while injuries suffered from a motor vehicle crash and increasing age were predictive of a poor recovery.

CONCLUSIONSThe authors developed a TBI prognostic model with a substantial level of accuracy in a low-resource setting. Further research is needed to externally validate the model and test the algorithm as a clinical decision support tool.

Language: en

Keywords

AUC = area under the receiver operating characteristic curve; ED = emergency department; GCS = Glasgow Coma Scale; GOSE = Glasgow Outcome Scale–Extended; HIC = high-income country; ICU = intensive care unit; KCMC = Kilimanjaro Christian Medical Centre; LMICs; LMICs = low- and middle-income countries; ML = machine learning; MOI = mechanism of injury; PERRLA = pupils equal, round, and reactive to light and accommodation; TBI = traumatic brain injury; machine learning; neurosurgery; predictive analytics; traumatic brain injury