急性一氧化碳中毒迟发性神经后遗症预测模型构建与效能验证

doi:10.3969/j.issn.1006-5725.2025.10.015

Abstract

Abstract:

Objective To construct a predictive model for delayed neurological sequelae （DNS） following acute carbon monoxide poisoning （ACMP） and to verify its efficacy. Methods A retrospective analysis of the general data of 183 patients with ACMP was conducted. The factors influencing the occurrence of DNS were analyzed using a multivariate Logistic regression model. A corresponding predictive model was then established and its efficacy was verified. Results The multivariate logistic regression model showed that age， smoking history， severe poisoning， blood lactate， time from poisoning to hyperbaric oxygen therapy， and pulmonary infection were independent risk factors for DNS following ACMP （P < 0.05）. The area under the curve （AUC） of the model for predicting DNS in the development set was 0.933， with a sensitivity of 94.12% and specificity of 89.77%. In the validation set， the AUC was 0.906， with a sensitivity of 90.00% and specificity of 92.68%. The Hosmer-Lemeshow test showed that the predicted probabilities of DNS in both the development and validation sets were not significantly different from the actual probabilities （P > 0.05）. The predictive model achieved clinical net benefit within the risk threshold ranges of 0.11 ~ 0.98 for the development set and 0.12 ~ 0.92 for the validation set. Conclusions Age， smoking history， severe poisoning， blood lactate， time from poisoning to hyperbaric oxygen therapy， and pulmonary infection are independent risk factors for DNS following ACMP. The corresponding predictive model has been verified to have good clinical efficacy.

Key words: acute carbon monoxide poisoning, delayed neurological sequelae, predictive model, nomogram

CLC Number:

R595.1

Shaolin LI,Xiaohong MA,Dehe ZHANG,Peng. SONG. Development and validation of a predictive model for delayed neurological sequelae in acute carbon monoxide poisoning[J]. The Journal of Practical Medicine, 2025, 41(10): 1533-1539.

Figures/Tables 9

Tab.1

Comparison of general information between the two groups"

一般资料	发生组（n = 34）	未发生组（n = 88）	χ² /t/Z值	P值
性别			2.136	0.144
男	22（64.71）	44（50.00）
女	12（35.29）	44（50.00）
年龄（x ± s）/岁	45.26 ± 8.89	41.31 ± 8.03	2.364	0.020
BMI（x ± s）/（kg/m²）	23.32 ± 2.97	22.91 ± 2.88	0.699	0.486
吸烟史			4.242	0.039
是	15（44.12）	22（25.00）
否	19（55.88）	66（75.00）
饮酒史			3.085	0.079
是	14（41.18）	22（25.00）
否	20（58.82）	66（75.00）
独居			4.487	0.034
是	8（23.53）	8（9.09）
否	26（76.47）	80（90.91）
职业类型			3.223	0.073
体力劳动	22（64.71）	41（46.59）
脑力劳动	12（35.29）	47（53.41）
合并基础疾病
糖尿病	6（17.65）	6（6.82）	3.243	0.072
高血压	9（26.47）	11（12.50）	3.492	0.062
冠心病	3（8.82）	5（5.68）	0.395	0.530
高脂血症	19（55.88）	38（43.18）	1.589	0.207
中毒原因			1.657	0.646
取暖	10（29.41）	29（32.95）
做饭、洗澡	18（52.94）	36（40.91）
自杀	2（5.88）	7（7.95）
职业暴露	4（11.76）	16（18.18）
中毒严重程度			6.134	0.047
轻度	4（11.76）	23（26.14）
中度	10（29.41）	34（38.64）
重度	20（58.82）	31（35.23）
入院时GCS评分［M（P₂₅，P₇₅ ）］/分	6（5，10）	5（4，8）	2.404	0.018
接触CO时间（x ± s）/h	15.24 ± 3.03	13.34 ± 2.64	3.418	0.001
昏迷时间（x ± s）/min	27.11 ± 5.61	24.97 ± 4.58	2.169	0.032
血乳酸（x ± s）/（mmol/L）	2.93 ± 0.60	2.61 ± 0.57	2.740	0.007
WBC（x ± s）/（×10⁹/L）	13.68 ± 2.74	12.73 ± 2.61	1.778	0.078
PLT（x ± s）/（×10⁹/L）	91.12 ± 18.23	97.67 ± 19.36	1.702	0.091
Hb（x ± s）/（g/dL）	119.35 ± 26.35	110.24 ± 22.43	1.914	0.058
ALB（x ± s）/（g/L）	26.74 ± 5.39	28.89 ± 5.87	1.854	0.066
中毒至高压氧治疗时间（x ± s）/h	4.00 ± 0.73	3.25 ± 0.84	4.578	< 0.001
中毒后并发症
肺部感染	8（23.53）	7（7.95）	5.517	0.019
心肌酶谱升高	12（35.29）	17（19.32）	3.454	0.063
肝功能异常	10（29.41）	13（14.77）	3.435	0.064
肾功能异常	11（32.35）	15（17.05）	3.427	0.064

Tab.1

Tab.2

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

References 27

1	NAÑAGAS K A， PENFOUND S J， KAO L W. Carbon monoxide toxicity［J］. Emerg Med Clin North Am， 2022， 40（2）：283-312. doi:10.1016/j.emc.2022.01.005 doi: 10.1016/j.emc.2022.01.005
2	AHN C， OH J， KIM C W， et al. Early neuroimaging and delayed neurological sequelae in carbon monoxide poisoning： A systematic review and meta-analysis［J］. Sci Rep， 2022， 12（1）：3529. doi:10.1038/s41598-022-07191-7 doi: 10.1038/s41598-022-07191-7
3	LEE H， OH J， KANG H， et al. Association between early phase serum lactate levels and occurrence of delayed neuropsychiatric sequelae in adult patients with acute carbon monoxide poisoning： A systematic review and meta-analysis［J］. J Pers Med， 2022， 12（4）：651. doi:10.3390/jpm12040651 doi: 10.3390/jpm12040651
4	NAMGUNG M， OH J， AHN C， et al. Association between glasgow coma scale in early carbon monoxide poisoning and development of delayed neurological sequelae： A meta-analysis［J］. J Pers Med， 2022， 12（4）：635. doi:10.3390/jpm12040635 doi: 10.3390/jpm12040635
5	FENG S Y. Magnetic resonance imaging for predicting delayed neurologic sequelae caused by carbon monoxide poisoning： A systematic review and meta-analysis［J］. Medicine （Baltimore）， 2022， 101（47）：e31981. doi:10.1097/md.0000000000031981 doi: 10.1097/md.0000000000031981
6	SILVA-FIGUEROA A M. A nomogram for relapse/death and contemplating adjuvant therapy for parathyroid carcinoma［J］. Surg Oncol Clin N Am， 2023， 32（2）：251-269. doi:10.1016/j.soc.2022.10.003 doi: 10.1016/j.soc.2022.10.003
7	高春锦，葛环，赵立明，等. 一氧化碳中毒临床治疗指南（一）［J］. 中华航海医学与高气压医学杂志， 2012， 19（2）：127-128.
8	ZHANG Y， BAI Y， FENG T， et al. Establishment and application of severity assessment system for patients with delayed encephalopathy caused by carbon monoxide poisoning［J］. Am J Transl Res， 2023， 15（11）：6558-6564.
9	WANKHADE B S， SHAIKH W S， ALRAIS Z F， et al. Neurological sequelae after acute carbon monoxide poisoning［J］. Cureus， 2024， 16（1）：e52840.
10	蒋力，雷蕊绮，彭红艳，等. 核转录因子-κB抑制剂二硫代氨基甲酸吡咯烷对急性一氧化碳中毒大鼠模型中iNOS动态变化的影响［J］. 实用医学杂志， 2020， 36（10）：1311-1317.
11	WANG T， ZHANG Y， GU Y， et al. Neurological sequelae in acute carbon monoxide poisoning： A prospective observational study with MRI data［J］. Acta Neurol Scand， 2022， 145（5）：590-598. doi:10.1111/ane.13587 doi: 10.1111/ane.13587
12	WANG R， LI K， WANG Z， et al. Changes of nuclear factor kappa-B pathway activity in hippocampus after acute carbon monoxide poisoning and its role in nerve cell injury［J］. Mol Neurobiol， 2024， 61（8）：5206-5215. doi:10.1007/s12035-023-03889-5 doi: 10.1007/s12035-023-03889-5
13	DIAS-PEDROSO D， RAMALHO J S， SARDÃO V A， et al. Carbon Monoxide-Neuroglobin Axis Targeting Metabolism Against Inflammation in BV-2 Microglial Cells［J］. Mol Neurobiol， 2022， 59（2）：916-931. doi:10.1007/s12035-021-02630-4 doi: 10.1007/s12035-021-02630-4
14	ARYA A K， SETHURAMAN K， WADDELL J， et al. Inflammatory responses to acute carbon monoxide poisoning and the role of plasma gelsolin［J］. Sci Adv， 2025， 11（6）：eado9751. doi:10.1126/sciadv.ado9751 doi: 10.1126/sciadv.ado9751
15	EL-SARNAGAWY G N， ELGAZZAR F M， GHONEM M M. Development of a risk prediction nomogram for delayed neuropsychiatric sequelae in patients with acute carbon monoxide poisoning［J］. Inhal Toxicol， 2024， 36（6）：406-419. doi:10.1080/08958378.2024.2374394 doi: 10.1080/08958378.2024.2374394
16	OMI T. Cerebrospinal fluid biomarkers for monitoring delayed neurologic sequelae after carbon monoxide poisoning［J］. Neurol India， 2022， 70（4）：1668-1669. doi:10.4103/0028-3886.355093 doi: 10.4103/0028-3886.355093
17	LIU Z B， WANG L C， LIAN J J， et al. Analysis of factors associated with the development of delayed encephalopathy following acute carbon monoxide poisoning［J］. Sci Rep， 2024， 14（1）：14630. doi:10.1038/s41598-024-64424-7 doi: 10.1038/s41598-024-64424-7
18	KIM S H， LEE Y， KANG S， et al. Derivation and validation of a score for predicting poor neurocognitive outcomes in acute carbon monoxide poisoning［J］. JAMA Netw Open， 2022， 5（5）：e2210552. doi:10.1001/jamanetworkopen.2022.10552 doi: 10.1001/jamanetworkopen.2022.10552
19	CROOKS C J， WEST J， MORLING J R， et al. Pulse oximeter measurement error of oxygen saturation in patients with SARS-CoV-2 infection stratified by smoking status［J］. Eur Respir J， 2022， 60（5）：2201190. doi:10.1183/13993003.01190-2022 doi: 10.1183/13993003.01190-2022
20	CROOKS C J， WEST J， MORLING J R， et al. Pulse oximeter measurement error of oxygen saturation in patients with SARS-CoV-2 infection stratified by smoking status［J］. Eur Respir J， 2022， 60（5）：2201190. doi:10.1183/13993003.01190-2022 doi: 10.1183/13993003.01190-2022
21	SUBER T L， TABARY M， BAIN W， et al. Oxidized phospholipid and transcriptomic signatures of THC-related vaping associated lung injury［J］. Sci Rep， 2024， 14（1）：31622. doi:10.1038/s41598-024-79585-8 doi: 10.1038/s41598-024-79585-8
22	ALHARTHY N， ALANAZI A， ALMOQAYTIB A， et al. Demographics and clinical characteristics of carbon monoxide poisoning for patients attending in the emergency department at a tertiary hospital in Riyadh， Saudi Arabia［J］. Int J Emerg Med， 2024， 17（1）：25. doi:10.1186/s12245-024-00600-w doi: 10.1186/s12245-024-00600-w
23	冯顺易，张萌，吕广卫，等. 视神经鞘直径预测急性一氧化碳中毒迟发性脑病的临床价值［J］. 中国中西医结合急救杂志， 2022， 29（2）：159-162.
24	TONG C， MIAO Q， ZHENG J， et al. A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery［J］. Ann Med， 2023， 55（1）：800-807. doi:10.1080/07853890.2022.2160490 doi: 10.1080/07853890.2022.2160490
25	KULKARNI A V， SINGAL A K. Nomogram model for hospitalized patients in the ICU for alcohol-related cirrhosis： A step closer to the continuing search for an ideal prognostic model［J］. Dig Liver Dis， 2023， 55（4）：496-497. doi:10.1016/j.dld.2023.01.164 doi: 10.1016/j.dld.2023.01.164
26	ZHANG D， HU J， LIU Z， et al. Prognostic nomogram in patients with epithelioid sarcoma： A SEER-based study［J］. Cancer Med， 2023， 12（3）：3079-3088. doi:10.1002/cam4.5230 doi: 10.1002/cam4.5230
27	ZOU W， WU D， WU Y， et al. Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer［J］. BMC Gastroenterol， 2023， 23（1）：315. doi:10.1186/s12876-023-02819-y doi: 10.1186/s12876-023-02819-y

自变量	B	SE	Wald χ²	P值	OR	95%CI
年龄	0.591	0.212	7.744	0.005	1.805	1.191 ~ 2.736
吸烟史	0.969	0.249	15.139	< 0.001	2.634	1.617 ~ 4.291
中毒严重程度
轻度	-	-	-	-	1	-
中度	1.396	0.896	2.428	0.119	4.041	0.698 ~ 23.403
重度	0.928	0.334	7.703	0.006	2.529	1.313 ~ 4.870
血乳酸	0.853	0.297	8.283	0.004	2.348	1.313 ~ 4.198
中毒至高压氧治疗时间	0.978	0.341	8.210	0.004	2.658	1.362 ~ 5.189
肺部感染	1.049	0.327	10.295	0.001	2.854	1.504 ~ 5.417
常数项	-18.239	4.174	19.096	< 0.001	< 0.001	-

[1]	Kui DONG,jie WU,Jing YAN,Haitao LIU,Jun WANG,Guan′en. QIAO. Study on risk factors of colorectal adenomatous polyps and construction and validation of prediction model [J]. The Journal of Practical Medicine, 2025, 41(6): 838-845.
[2]	Runfang WANG,Ya DUAN,Liyan DU,Xiaodan LIU,Wenning LIAN,Yan HUO,Dandan. YANG. Epidemiological status and risk factors associated with placental abruption among pregnant women in Hebei Province [J]. The Journal of Practical Medicine, 2025, 41(6): 904-910.
[3]	Dongli LIU,Zilin QUAN,Lingxiu ZHONG,Qiqi CHEN,Wenqiao CAI,Senpei ZHUANG,Ying WEI,Huiyi PAN,Yawen. LIN. Construction and validation of a predictive model for antibiotic-associated diarrhea after surgery in children with congenital heart disease [J]. The Journal of Practical Medicine, 2025, 41(5): 683-690.
[4]	Qiaoying MO,Fangyi ZHU,Cunkui ZHU,Shenglong. MA. Nomogram model of malnutrition risk in patients suffering from chronic heart failure grounded on GNRI score [J]. The Journal of Practical Medicine, 2025, 41(5): 691-698.
[5]	Changhui WU,Zhiping HUANG,Huiping DAI,Huifang QIU,Chun HE,Fang. TANG. Energy efficiency of contrast⁃enhanced ultrasound combined with TERT promoter mutation to construct a nomogram model for the prediction of concomitant cervical lymph node metastasis in PTMC [J]. The Journal of Practical Medicine, 2025, 41(5): 756-765.
[6]	Wei LIU,Jun HOU,Longquan TANG,Peng ZHOU,Yan ZHONG,Qinyan LUO,Xiaoyu KUANG,Hua LIU,Ziqing XIONG,Wei XIONG,Chenggao WU,Aiping. LE. Analysis of influencing factors of blood transfusion in children with traumatic brain injury and construction of prediction model： A multi⁃center retrospective study [J]. The Journal of Practical Medicine, 2025, 41(4): 553-560.
[7]	Zhao WANG,Jiepeng ZUO,Hang CHE,Lingyun REN,Zhe XU,Lei. WANG. Evaluation of plasma PPARγ as a potential diagnostic marker for female interstitial cystitis/painful bladder syndrome and its predictive model [J]. The Journal of Practical Medicine, 2025, 41(2): 258-263.
[8]	Xilin WANG,Chanjuan YANG,Daomeng. CHENG. Construction of a nomogram prediction model for aggressive behavior in patients with bipolar disorder [J]. The Journal of Practical Medicine, 2024, 40(5): 677-681.
[9]	Hang DENG,Hao. ZHANG. Clinical application of hepatocellular carcinoma prediction models： current challenges and future directions [J]. The Journal of Practical Medicine, 2024, 40(24): 3561-3567.
[10]	Xing HU,Qingrong LI,Jiang LI,Wei HE,Ping′an HE,Mei LV,Xu. YANG. The Nomogram model was established for the risk assessment of intestinal colonization with neonatal CRKP [J]. The Journal of Practical Medicine, 2024, 40(2): 231-236.
[11]	Shaojin LI,Shipeng. ZHENG. Relevant preoperative imaging pathological features and tumor markers serve as predictive indicators for the risk of sentinel lymph node metastasis in breast cancer [J]. The Journal of Practical Medicine, 2024, 40(17): 2418-2424.
[12]	Yue MENG,Li ZHENG,Jing ZHOU,Dashan WANG,Jin HU,Die WANG,You LI,Junhua WANG,Ziyun. WANG. Self⁃screening for arteriosclerosis in middle⁃aged and elderly residents and the construction of a primary care initial screening tool [J]. The Journal of Practical Medicine, 2024, 40(14): 1947-1951.
[13]	ZU Xiaoyun, JIN Guoxin.. Preliminary exploration of establishing the risk prediction model of nerve injury after thoracolumbar burst fracture in the elderly based on nomogram [J]. The Journal of Practical Medicine, 2023, 39(5): 597-601.
[14]	Linan GAO,Pengkun YANG,Wenjun CAO,Qian ZHANG. Development and Validation of a Predictive Model for Neonatal Sepsis [J]. The Journal of Practical Medicine, 2023, 39(21): 2789-2795.
[15]	Xiuquan QI,Wei WANG,Xiaokang QIU,Ye. XU. Establishment of Nomogram prediction model for latent blood loss in patients with lumbar disc herniation after operation [J]. The Journal of Practical Medicine, 2023, 39(18): 2300-2305.

Development and validation of a predictive model for delayed neurological sequelae in acute carbon monoxide poisoning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 27

Related Articles 15

Recommended Articles

Metrics

Comments