Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images

doi:10.12116/j.issn.1004-5619.2021.410809

Abstract

Abstract: Objective

To study the correlation between CT imaging features of acceleration and deceleration brain injury and injury degree.

Methods

A total of 299 cases with acceleration and deceleration brain injury were collected and divided into acceleration brain injury group and deceleration brain injury group according to the injury mechanism. Subarachnoid hemorrhage （SAH） and Glasgow coma scale （GCS）, combined with skull fracture, epidural hematoma （EDH）, subdural hematoma （SDH） and brain contusion on the same and opposite sides of the stress point were selected as the screening indexes. χ² test was used for primary screening, and binary logistic regression analysis was used for secondary screening. The indexes with the strongest correlation in acceleration and deceleration injury mechanism were selected.

Results

χ² test showed that skull fracture and EDH on the same side of the stress point; EDH, SDH and brain contusion on the opposite of the stress point; SAH, GCS were correlated with acceleration and deceleration injury （P<0.05）. According to binary logistic regression analysis, the odds ratio （OR） of EDH on the same side of the stress point was 2.697, the OR of brain contusion on the opposite of the stress point was 0.043 and the OR of GCS was 0.238, suggesting there was statistically significant （P<0.05）.

Conclusion

EDH on the same side of the stress point, brain contusion on the opposite of the stress point and GCS can be used as key indicators to distinguish acceleration and deceleration injury mechanism. In addition, skull fracture on the same side of the stress point, EDH and SDH on the opposite of the stress point and SAH were relatively weak indicators in distinguishing acceleration and deceleration injury mechanism.

Key words: forensic medicine, acceleration brain injury, deceleration brain injury, logistic regression, computed tomography （CT）, injury manners

CLC Number:

DF795.1

Xue-yang SUN, Qi-fan YANG, Yun-liang ZHU, Yan-bin WANG, He-wen DONG, Ming-zhen YANG, Zhi-ling TIAN, Lei WAN, Dong-hua ZOU, Xiao-tian YU, Ning-guo LIU. Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images[J]. Journal of Forensic Medicine, 2022, 38(2): 217-222.

Figures/Tables 5

Tab. 1 Basic information of 299 cases of brain injury

组别	性别		年龄/（ $x ˉ$ ±s，岁）	合计
组别	男性	女性	年龄/（ $x ˉ$ ±s，岁）	合计
加速性颅脑损伤
徒手打击	106	3	50.2±14.5	109
钝器打击（棍棒、砖石以及车辆直接撞击）	85	9	50.6±14.9	94
减速性颅脑损伤
摔跌及低处坠落（高度≤1 m）	32	18	47.7±14.3	50
交通事故	27	19	47.8±13.7	46

Tab. 1 Basic information of 299 cases of brain injury

组别	性别		年龄/（ $x ˉ$ ±s，岁）	合计
组别	男性	女性	年龄/（ $x ˉ$ ±s，岁）	合计
加速性颅脑损伤
徒手打击	106	3	50.2±14.5	109
钝器打击（棍棒、砖石以及车辆直接撞击）	85	9	50.6±14.9	94
减速性颅脑损伤
摔跌及低处坠落（高度≤1 m）	32	18	47.7±14.3	50
交通事故	27	19	47.8±13.7	46

Tab. 2 Quantification and assignment for indexes

指标	变量	量化和赋值
受力点同侧
颅骨骨折	x₁	无骨折=0，线形骨折=1，凹陷性骨折=2
EDH	x₂	无=0，有=1
SDH	x₃	无=0，有=1
脑挫伤	x₄	无=0，有=1
受力点对侧
颅骨骨折	x₅	无骨折=0，线形骨折=1，凹陷性骨折=2
EDH	x₆	无=0，有=1
SDH	x₇	无=0，有=1
脑挫伤	x₈	无=0，有=1
SAH	x₉	无=0，有=1
GCS	x₁₀	13~15分=0，3~12分=1
致伤方式	y	加速性=1，减速性=0

Tab. 3 Distribution of the indexes in the acceleration and deceleration brain injury groups

指标	加速性颅脑损伤组/［N=203，n（%）］	减速性颅脑损伤组/［N=96，n（%）］	合计/例
受力点同侧
颅骨凹陷性骨折^1）	66（32.5）	9（9.4）	75
颅骨线形骨折^1）	57（28.1）	42（43.8）	99
EDH	88（43.3）	24（25.0）	112
SDH	41（20.2）	19（19.8）	60
脑挫伤	61（30.0）	28（29.2）	89
受力点对侧
颅骨凹陷性骨折	0	0	0
颅骨线形骨折	0	0	0
EDH^1）	4（2.0）	9（9.4）	13
SDH^1）	10（4.9）	26（27.1）	36
脑挫伤^1）	13（6.4）	60（62.5）	73
SAH^1）	62（30.5）	67（69.8）	129
GCS^1）
3~12分	30（14.8）	60（62.5）	90
13~15分	173（85.2）	36（37.5）	209

Fig. 1 Indexes with statistically significant difference of acceleration and deceleration brain injury groups

Tab. 4 Results of logistic model

指标	χ²	B	OR	95%置信区间
受力点同侧EDH（x₂）	7.641	0.992	2.697	1.335~5.450
受力点对侧脑挫伤（x₈）	70.105	-3.165	0.043	0.020~0.089
GCS（x₁₀）	15.269	-1.437	0.238	0.116~0.489
常数项	51.025	1.693	/	/

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