Intracranial lesion features in moderate-to-severe traumatic brain injury: relation to neurointensive care variables and clinical outcome

医学 颅内压 神经重症监护 蓄水池 创伤性脑损伤 血肿 中线偏移 脑灌注压 蛛网膜下腔出血 开颅术 格拉斯哥结局量表 神经外科 病变 麻醉 格拉斯哥昏迷指数 神经组阅片室 神经学 外科 脑血流 考古 精神科 历史
作者
Teodor Svedung Wettervik,Anders Hånell,Per Enblad,Anders Lewén
出处
期刊:Acta neurochirurgica [Springer Science+Business Media]
卷期号:165 (9): 2389-2398 被引量:3
标识
DOI:10.1007/s00701-023-05743-y
摘要

The primary aim was to determine the association of intracranial hemorrhage lesion type, size, mass effect, and evolution with the clinical course during neurointensive care and long-term outcome after traumatic brain injury (TBI).In this observational, retrospective study, 385 TBI patients treated at the neurointensive care unit at Uppsala University Hospital, Sweden, were included. The lesion type, size, mass effect, and evolution (progression on the follow-up CT) were assessed and analyzed in relation to the percentage of secondary insults with intracranial pressure > 20 mmHg, cerebral perfusion pressure < 60 mmHg, and cerebral pressure autoregulatory status (PRx) and in relation to Glasgow Outcome Scale-Extended.A larger epidural hematoma (p < 0.05) and acute subdural hematoma (p < 0.001) volume, greater midline shift (p < 0.001), and compressed basal cisterns (p < 0.001) correlated with craniotomy surgery. In multiple regressions, presence of traumatic subarachnoid hemorrhage (p < 0.001) and intracranial hemorrhage progression on the follow-up CT (p < 0.01) were associated with more intracranial pressure-insults above 20 mmHg. In similar regressions, obliterated basal cisterns (p < 0.001) were independently associated with higher PRx. In a multiple regression, greater acute subdural hematoma (p < 0.05) and contusion (p < 0.05) volume, presence of traumatic subarachnoid hemorrhage (p < 0.01), and obliterated basal cisterns (p < 0.01) were independently associated with a lower rate of favorable outcome.The intracranial lesion type, size, mass effect, and evolution were associated with the clinical course, cerebral pathophysiology, and outcome following TBI. Future efforts should integrate such granular data into more sophisticated machine learning models to aid the clinician to better anticipate emerging secondary insults and to predict clinical outcome.
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