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Prediction of Life-Threatening Intracranial Hypertension During the Acute Phase of Traumatic Brain Injury Using Machine Learning

Authors
Lee, Hack-JinKim, HakseungKim, Young-TakWon, KangheeCzosnyka, MarekKim, Dong-Joo
Issue Date
Oct-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Predictive models; Machine learning; Brain modeling; Monitoring; Windows; Standards; Hypertension; Clinical outcome; Intracranial hypertension; Traumatic brain injury; Neuromonitoring; Machine learning
Citation
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.25, no.10, pp 3967 - 3976
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume
25
Number
10
Start Page
3967
End Page
3976
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/136213
DOI
10.1109/JBHI.2021.3085881
ISSN
2168-2194
2168-2208
Abstract
Intracranial hypertension (IH) following acute phase traumatic brain injury (TBI) is associated with high mortality. Objective: This study proposes a novel parameter that may identify a potentially life-threatening IH (LTH) event and designs a machine learning model to predict LTH. Continuous recordings of intracranial pressure (ICP) and arterial blood pressure (ABP) from 273 TBI patients were used as the development dataset. The pressure-time dose (PTD) and pressure reactivity index (PRx) were calculated for each IH event, and an IH event with PRx > 0 and PTD > 5 was considered an LTH event. The association between the LTH parameters accumulated over five days and mortality was analyzed. A categorical boosting (CatBoost) model was employed to predict the occurrence of a future LTH event from the onset of IH using the ABP- and ICP-related parameters. Training and validation were performed on a total of 5,938 IH events. External performance evaluation was performed in 307 IH events included in the Cerebral Haemodynamic Autoregulatory Information System (CHARIS) database. The performance of the proposed model was evaluated through the area under the receiver operating characteristic curve (AUROC). The LTH parameters were able to distinguish between the deceased and surviving patients (AUROC > 0.7, p < 0.001). The CatBoost model predicted LTH with an AUROC = 0.7 on the external test dataset. This study demonstrated that the proposed LTH prediction model has a reasonable predictive capacity for mortality. The CatBoost model anticipates whether an IH event will develop into an LTH event. The findings of this study support the usefulness of ICP monitoring.
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