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中华重症医学电子杂志 ›› 2019, Vol. 05 ›› Issue (04) : 346 -352. doi: 10.3877/cma.j.issn.2096-1537.2019.04.010

所属专题: 文献

重症医学研究

数学模型在颅脑损伤患者入院指标与预后指标多因素模型分析中的应用
韦广发1, 郑庆斌1, 孟丽君1, 冯庆玲1, 袁文杰1, 欧金磊1, 王玉荣1, 孙京京1, 刘微丽1,(), 李勇1   
  1. 1. 225000 扬州大学附属医院 扬州市第一人民医院重症医学科
  • 收稿日期:2019-05-30 出版日期:2019-11-28
  • 通信作者: 刘微丽
  • 基金资助:
    江苏省扬州市社会发展项目(YZ2017080)

The application of mathematical model in the analysis of multi-factor models of admission indicators and prognostic indicators for patients with brain injury

Guangfa Wei1, Qingbin Zheng1, Lijun Meng1, Qingling Feng1, Wenjie Yuan1, Jinlei Ou1, Yurong Wang1, Jingjing Sun1, Weili Liu1,(), Yong Li1   

  1. 1. Department of Critical Care Medicine, Affiliated Hospital of Yangzhou University, Yangzhou First People′s Hospital, Yanghzou 225000, China
  • Received:2019-05-30 Published:2019-11-28
  • Corresponding author: Weili Liu
  • About author:
    Corresponding author: Liu Weili, Email:
引用本文:

韦广发, 郑庆斌, 孟丽君, 冯庆玲, 袁文杰, 欧金磊, 王玉荣, 孙京京, 刘微丽, 李勇. 数学模型在颅脑损伤患者入院指标与预后指标多因素模型分析中的应用[J/OL]. 中华重症医学电子杂志, 2019, 05(04): 346-352.

Guangfa Wei, Qingbin Zheng, Lijun Meng, Qingling Feng, Wenjie Yuan, Jinlei Ou, Yurong Wang, Jingjing Sun, Weili Liu, Yong Li. The application of mathematical model in the analysis of multi-factor models of admission indicators and prognostic indicators for patients with brain injury[J/OL]. Chinese Journal of Critical Care & Intensive Care Medicine(Electronic Edition), 2019, 05(04): 346-352.

目的

探讨颅脑损伤患者入院时的多项指标以及指标间交互作用对患者预后指标的影响。

方法

收集2013年1月至2017年8月入住扬州大学附属医院重症医学科的130例颅脑外伤患者的临床资料进行分析,建立预后指标与入院指标的数学模型,进行数学模型相关分析。并收集2017年10月至2018年12月26例颅脑外伤患者临床资料进行建模数据外部验证。

结果

镇静躁动评分(RASS)平方项、格拉斯哥昏迷评分(GCS)与RASS评分交互项、年龄与急性生理和慢性健康状况(APACHEⅡ)评分交互项、心率与乳酸(LAC)交互项等对预后指标有显著影响。

结论

数学模型可以对颅脑损伤患者进行预后指标评估预测。

Objective

To explore the influence of prognosis indicators of multiple indicators and their interactionat hospital admission in patients with brain injury.

Methods

We analyzed the clinical data of 130 patients with craniocerebral trauma who were admitted to the Department of Critical Care Medicine of the Affiliated Hospital of Yangzhou University from January 2013 to August 2017. We performed a mathematical model to identify the correlation between data at hospital admission and outcome. The clinical data of 26 patients with craniocerebral trauma from October 2017 to December 2018 were collected for external validation.

Results

Sedation score (RASS) squared item, Grasse coma (GCS) score and RASS score interaction item, age and acute physiology and chronic health status (APACHEⅡ) score interaction item, heart rate and lactic acid (LAC) interaction term were significantly associated with outcome.

Conclusion

Mathematical models can be used to assess prognostic indicators in patients with brain injury.

表1 临床数据
表2 系数的显著性分析
图1 镇静评分RASS单因素对预后指标影响的曲线图
图2 GCS评分与镇静评分RASS交互作用等值线图
图3 年龄与APACHEⅡ评分交互作用等值线图
图4 心率与乳酸交互作用等值线图
表3 内部验证130例患者的观测值和拟合值
样本 观测值 拟合值 拟合误差
1 1 1.3094 -0.3094
2 5 4.3413 0.6587
3 3 2.1499 0.8501
4 1 1.7955 -0.7955
5 4 4.0702 -0.0702
6 3 2.5322 0.4678
7 2 2.3195 -0.3195
8 4 3.6383 0.3617
9 1 2.2940 -1.2940
10 1 1.5881 -0.5881
11 1 2.2016 -1.2016
12 5 3.0091 1.9909
13 4 4.2750 -0.2750
14 3 2.8244 0.1756
15 1 1.2850 -0.285
16 3 3.2680 -0.268
17 4 3.8662 0.1338
18 1 1.0000 0
19 5 4.4367 0.5633
20 1 1.8224 -0.8224
21 5 5.0000 0
22 5 4.1995 0.8005
23 3 3.6493 -0.6493
24 1 1.0000 1.0000
25 2 3.2827 -1.2827
26 1 2.5245 -1.5245
27 5 4.5873 0.4127
28 3 3.5043 -0.5043
29 2 1.9846 0.0154
30 1 1.0000 0.0000
31 2 1.4150 0.585
32 4 3.4235 0.5765
33 3 2.0516 0.9484
34 1 1.0000 1.0000
35 4 4.0225 -0.0225
36 1 2.0697 -1.0697
37 3 2.6211 0.3789
38 1 2.2449 -1.2449
39 3 2.8299 0.1701
40 4 3.1361 0.8639
41 5 3.7609 1.2391
42 3 3.4085 -0.4085
43 5 3.3069 1.6931
44 3 3.6481 -0.6481
45 3 3.0972 -0.0972
46 2 3.3202 -1.3202
47 1 1.0000 0
48 2 2.3267 -0.3267
49 1 1.0000 0
50 3 3.1241 -0.1241
51 1 1.5364 -0.5364
52 2 3.1129 -1.1129
53 1 1.3995 -0.3995
54 3 2.6472 0.3528
55 4 3.9615 0.0385
56 5 3.9626 1.0374
57 4 4.1137 -0.1137
58 1 1.3217 -0.3217
59 5 5.0000 0
60 1 1.9954 -0.9954
61 2 2.2912 -0.2912
62 3 3.5790 -0.579
63 4 3.2437 0.7563
64 3 2.9375 0.0625
65 5 4.2667 0.7333
66 4 4.5576 -0.5576
67 5 4.4843 0.5157
68 2 2.3068 -0.3068
69 3 1.8361 1.1639
70 1 1.4134 -0.4134
71 1 1.3807 -0.3807
72 1 1.2379 -0.2379
73 2 2.3563 -0.3563
74 2 2.8986 -0.8986
75 5 4.0512 0.9488
76 2 1.2737 0.7263
77 3 3.7541 -0.7541
78 4 2.8553 1.1447
79 4 4.7874 -0.7874
80 3 3.2123 -0.2123
81 5 4.6262 0.3738
82 5 3.8452 1.1548
83 5 5.2690 -0.2690
84 2 1.4489 0.5511
85 1 1.1753 -0.1753
86 5 3.6253 1.3747
87 4 3.5735 0.4265
88 1 1.5942 -0.5942
89 1 1.0000 0
90 1 2.2918 -1.2918
91 4 3.3102 0.6898
92 5 4.3525 0.6475
93 1 1.0000 0
94 3 2.6012 0.3988
95 2 1.7479 0.2521
96 3 2.4006 0.5994
97 3 2.8533 0.1467
98 1 2.5910 -1.5910
99 3 2.7021 0.2979
100 1 2.2252 -1.2252
101 1 1.0199 -0.0199
102 4 4.4977 -0.4977
103 3 2.3896 0.6104
104 1 1.0997 -0.0997
105 1 1.2433 -0.2433
106 1 1.0833 -0.0833
107 1 1.2784 -0.2784
108 1 2.2542 -1.2542
109 5 4.0411 0.9589
110 3 2.4991 0.5009
111 4 4.3917 -0.3917
112 3 2.7506 0.2494
113 1 1.5952 -0.5952
114 5 5.0000 0
115 3 2.6814 0.3186
116 1 2.6136 -1.6136
117 2 2.0309 -0.0309
118 4 3.7946 0.2054
119 4 3.1958 0.8042
120 4 1.8218 2.1782
121 1 1.0000 0
122 1 1.9108 -0.9108
123 2 2.9382 -0.9382
124 4 3.4696 0.5304
125 1 1.4539 -0.4539
126 4 4.2663 -0.2663
127 2 1.9344 0.0656
128 3 2.5227 0.4773
129 5 5.0000 0
130 4 3.6591 0.3409
表4 外部验证26例患者的观测值和拟合值
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