Prediction models for aspiration risk in neurological intensive care unit patients: a meta-analysis

doi:10.3877/cma.j.issn.1674-6880.2026.03.006

Abstract

Abstract:

Objective

To systematically evaluate the predictive value of aspiration risk models in neurological intensive care unit patients.

Methods

A computerized search was conducted in databases such as CNKI, VIP, Wanfang, CBM, Web of Science, the Cochrane Library, PubMed, and Embase for studies on prediction models for aspiration risk in neurological intensive care unit patient from the inception of each database to December 30, 2025. Two researchers independently conducted data retrieval and material extraction. The prediction model risk of bias assessment tool (PROBAST) was used to evaluate the quality of the included literature. A meta-analysis was conducted using RevMan 5.4 and MedCalc software.

Results

A total of 21 articles were included. The total sample size ranged from 103 to 3 408 patients, and the number of aspiration patients with severe neuropathy ranged from 24 to 448. All the 21 studies were rated as having a high risk of bias. The meta-analysis showed that the area under the receiver operating characteristic curve of the model was 0.879 [95% confidence interval (CI) (0.849, 0.909), Z = 57.892, P < 0.001]. A Glasgow coma scale (GCS) score ≥ 8 [odds ratio (OR) = 2.43, 95%CI (1.29, 4.57), Z = 2.75, P = 0.006], a high National Institutes of Health stroke scale (NIHSS) score [OR = 1.96, 95%CI (1.49, 2.60), Z = 4.74, P < 0.001], history of aspiration [OR = 3.26, 95%CI (1.40, 7.61), Z = 2.74, P = 0.006], age ≥ 60 years [OR = 1.35, 95%CI (1.19, 1.52), Z = 4.79, P < 0.001], Kubota water swallowing test ≥ level 3 [OR = 4.74, 95%CI (2.50, 9.01), Z = 4.76, P < 0.001], gastric residual volume ≥ 150 mL [OR = 2.31, 95%CI (1.69, 3.16), Z = 5.26, P < 0.001], brainstem infarction [OR = 2.85, 95%CI (2.14, 3.80), Z = 7.14, P < 0.001], and mechanical ventilation [OR = 5.31, 95%CI (1.76, 16.05), Z = 2.96, P = 0.003] were all risk factors for aspiration in neurological intensive care unit patients.

Conclusion

The aspiration risk prediction model for neurological intensive care unit patients demonstrates good performance but carries a high risk of overall bias.

Key words: Neurological intensive care unit, Aspiration, Prediction models, Meta-analysis

Yunjuan Xu, Rong Lu, Ping Jia, Yan Li, Danyang Guo, Qingyun Jiang, Zaichun Pu, Qin Zhang. Prediction models for aspiration risk in neurological intensive care unit patients: a meta-analysis[J]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2026, 19(03): 234-240.

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References 45

1	郑爽，张昕彤，陈晨，等.神经重症患者气管插管拔除失败的风险因素分析[J/OL].中华危重症医学杂志（电子版），2024，17（3）：211-218.
2	中华医学会神经病学分会，中华医学会神经病学分会脑血管病学组.中国重症卒中管理指南2024[J].中华神经科杂志，2024，57（7）：698-714.
3	Miao P, Zhang Y, Zhong A. Risk factors of aspiration occurrence with different feeding patterns in elderly intensive care unit patients: a cross-sectional study [J]. J Thorac Dis, 2023, 15 (5): 2585-2600.
4	高学莉，牛意柔，任志方，等.医院神经科普通病房患者误吸防治与管理的最佳证据总结[J].中国初级卫生保健，2024，38（10）：93-97.
5	邵金洁，杜超群，杨晓英，等.高血压脑出血术后肠内营养病人发生误吸的影响因素及风险预测模型的构建[J].循证护理，2025，11（15）：3172-3175.
6	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist [J]. PLoS Med, 2014, 11 (10): e1001744.
7	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration[J]. Ann Intern Med, 2019, 170 (1): W1-W33.
8	蒲在春，贾平，刘娟，等.肝硬化门静脉血栓形成风险预测模型的Meta分析[J].循证护理，2024，10（16）：2869-2874.
9	Chen L, Li J, Tian F, et al. A simple nomogram for predicting aspiration associated with dysphagia in hospitalized patients after stroke [J]. Neurol Sci, 2024, 45 (6): 2729-2736.
10	Park D, Son SI, Kim MS, et al. Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke [J]. Sci Rep, 2023, 13 (1): 7835.
11	Wang Y, Feng W, Peng J, et al. Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke[J]. J Clin Neurosci, 2024, 124: 60-66.
12	Xu YY, Wei Y, Sha L. Validation of an aspiration risk prediction model for Parkinson's disease based on nomogram: a single-center study [J]. Peer J, 2025, 13: e20443.
13	蔡晓调，陆艳.脑卒中吞咽障碍患者IOE风险评估及模型构建[J].中国现代医生，2024，62（13）：15-20.
14	陈蓓香，黄志文，刘乐乐.神经外科ICU患者胃内容物反流误吸风险预测模型的构建与验证[J].河南医学高等专科学校学报，2025，37（2）：217-220.
15	李好正，李梦媛，肖抒情，等.脑肿瘤患者术后误吸风险预测模型的构建[J].军事护理，2022，39（8）：54-57.
16	李梦媛.脑肿瘤患者术后误吸危险因素分析及风险预测模型的构建与验证研究[D].天津：天津医科大学，2021.
17	彭宇，沙丽艳，刘子龙，等.重症脑出血患者肠内营养支持发生误吸风险预测模型的构建及验证[J].中国护理管理，2022，22（9）：1391-1397.
18	邵金洁，杜超群，杨晓英，等.高血压脑出血术后肠内营养病人发生误吸的影响因素及风险预测模型的构建[J].循证护理，2025，11（15）：3172-3175.
19	石欢，柳月，袁媛.颅脑胶质瘤术后患者重症监护病房误吸风险及预测模型建立[J].中华老年心脑血管病杂志，2024，26（10）：1201-1204.
20	石娟，刘荣.神经外科老年昏迷患者鼻饲反流误吸调查及其危险因素分析[J].老年医学与保健，2023，29（6）：1253-1257.
21	谈月蓉，丁海燕，李育平，等.神经外科ICU患者误吸的列线图风险预测模型构建及其诊断效能[J].川北医学院学报，2023，38（11）：1551-1554.
22	田雅琦，邱立欣，谷媛媛，等.脑卒中吞咽困难病人吞咽造影特点及其对误吸的预测价值研究[J].中西医结合心脑血管病杂志，2023，21（23）：4421-4428.
23	王宇馨.脑卒中鼻饲患者误吸风险预测模型构建与验证[D].天津：天津中医药大学，2023.
24	谢小磊.急诊脑梗死住院患者肠内营养误吸风险预测模型的建立与验证[J].国际护理学杂志，2024，43（14）：2536-2541.
25	徐金娟，吴丽萍.卒中误吸风险分析及预测模型的构建与验证[J].重庆医学，2023，52（23）：3638-3642.
26	余灵芝，林兴，朱秀梅，等.基于列线图构建重症颅脑损伤患者肠内营养误吸风险预测模型研究[J].创伤与急危重病医学，2022，10（6）：467-470.
27	俞玲英，王晓磊，沈晓芳，等.缺血性脑卒中患者住院期间误吸的危险因素及预测模型构建[J].实用临床医药杂志，2024，28（9）：103-108.
28	张珊，蒋林芷，张盼.脑出血患者早期肠内营养并发误吸的危险因素分析及预测模型构建[J].现代医学，2025，53（5）：804-809.
29	邹宛霖.缺血性脑卒中吞咽功能障碍患者发生误吸的风险预测模型构建及验证的研究[D].吉林：北华大学，2025.
30	蒋美丽，陈友芬，许季祥，等.基于机器学习算法构建鼻胃管患者发生误吸的风险预测模型[J].浙江医学，2024，46（22）：2400-2404.
31	汪海霞，何飞，朱丛梅，等. ICU患者早期肠内营养治疗发生误吸的列线图风险预测模型建立与验证[J].天津医药，2025，53（10）：1037-1042.
32	Lesch H, Wittayer M, Dias M, et al. Clinical features and voxel-based-symptom-lesion mapping of silent aspiration in acute infratentorial stroke [J]. Dysphagia, 2024, 39 (2): 289-298.
33	Tran KH, Akhtar N, Ali A, et al. Impact of stroke severity on aspiration pneumonia risks in the medical ward versus the stroke unit: a 10-year retrospective cohort study[J]. BMJ Open, 2025, 15 (3): e093328.
34	景英霞，吴云龙.鼻饲患者误吸风险预测模型的开发和验证[J].全科医学临床与教育，2023，21（12）：1131-1134.
35	Wang J, Wang Y, Wang P, et al. Construction and evaluation of a nomogram prediction model for aspiration pneumonia in patients with acute ischemic stroke[J]. Heliyon, 2023, 9 (11): e22048.
36	葛佩，勇琴歌，章洁.老年人误吸危险因素与风险评估的研究进展[J].中华老年多器官疾病杂志，2023，22（11）：873-876.
37	张晶，丁小容，窦丽君，等.神经外科重症病人肠内营养误吸筛查的研究进展[J].护理研究，2024，38（13）：2365-2370.
38	Labeit B, Michou E, Hamdy S, et al. The assessment of dysphagia after stroke: state of the art and future directions[J]. Lancet Neurol, 2023, 22 (9): 858-870.
39	Hu X, Wang M, Wang Z, et al. Predictors and lesion patterns of dysphagia and swallowing outcomes after acute intracerebral hemorrhage [J]. Ther Adv Neurol Disord, 2025, 18: 17562864241311130.
40	张洋，刁海清，李梦月，等.神经重症患者误吸影响因素的Meta分析[J].实用临床医药杂志，2025，29（1）：118-124.
41	宋海瑞，唐姿燕，孟宪梅，等.脑卒中误吸风险预测模型研究进展[J].中国科技期刊数据库医药，2024，1：80-83.
42	Goelen N, Janssen P, Tack J, et al. Continuous assessment of gastric motility and its relation to gastric emptying in adult critically ill patients [J]. JPEN J Parenter Enteral Nutr, 2021, 45 (8): 1779-1784.
43	张骄莹，蔡莲珠，汪梅贵.脑卒中吞咽障碍患者发生隐性误吸的危险因素及对策[J].西藏医药，2025，46（1）：67-68.
44	Abd-Elhamid MH, Aldugiem MM, Salameh B, et al. Complementary therapies in ICU: the effect on aspiration and physiological parameters in mechanically ventilated patients[J]. Crit Care Nurs Q, 2025, 48 (3): 325-334.
45	Zhang H, Li J, Deng S, et al. Risk prediction models for enteral nutrition aspiration in adult inpatients: a systematic review and critical appraisal[J]. J Clin Nurs, 2026, 35 (3): 1125-1141.

纳入研究	研究类型	样本量（例）	误吸发生数（例）	验证方法	校准方法	预测因子
Chen等^[9]	病例对照	412	184	内部+外部	校准曲线、H-L检验、临床决策曲线	①②
Park等^[10]	队列研究	3 408	448	内部	-	①
Wang等^[11]	病例对照	528	89	内部+外部	校准曲线、H-L检验、临床决策曲线	③
Xu等^[12]	病例对照	160	80	内部+外部	校准曲线、临床决策曲线	④
蔡晓调和陆艳^[13]	病例对照	146	29	-	校准曲线、H-L检验	②③④⑤
陈蓓香等^[14]	病例对照	103	31	-	-	⑤⑥⑦
李好正等^[15]	病例对照	228	24	-	H-L检验	①④⑤⑧
李梦媛^[16]	巢式病例对照	228	24	内部+外部	H-L检验、校准图	④⑤⑧
彭宇等^[17]	回顾性队列研究	421	147	内部+外部	校准曲线	②④⑤⑥
邵金洁等^[18]	病例对照	410	53	内部+外部	H-L检验	⑧
石欢等^[19]	病例对照	379	78	外部	-	⑦
石娟和刘荣^[20]	病例对照	320	79	-	校准曲线、H-L检验	①⑤⑥⑧
谈月蓉等^[21]	病例对照	998	297	-	-	②⑥⑦⑧
田雅琦等^[22]	病例对照	235	89	内部	校准曲线、H-L检验、临床决策曲线	②③
王宇馨^[23]	病例对照	521	149	内部+外部	H-L检验、临床决策分析	⑤⑦⑧
谢小磊^[24]	病例对照	250	56	外部	H-L检验	②⑤⑥⑧
徐金娟和吴丽萍^[25]	病例对照	110	55	内部	H-L检验、校准曲线	①②③
余灵芝等^[26]	病例对照	212	102	内部	H-L检验、校准曲线	①⑧
俞玲英等^[27]	病例对照	316	89	内部	校准曲线、临床决策曲线	①②④
张珊等^[28]	病例对照	158	61	-	H-L检验、校准曲线	⑧
邹宛霖^[29]	病例对照	236	107	内部	H-L检验	①②③

纳入研究	研究类型	样本量（例）	误吸发生数（例）	验证方法	校准方法	预测因子
Chen等^[9]	病例对照	412	184	内部+外部	校准曲线、H-L检验、临床决策曲线	①②
Park等^[10]	队列研究	3 408	448	内部	-	①
Wang等^[11]	病例对照	528	89	内部+外部	校准曲线、H-L检验、临床决策曲线	③
Xu等^[12]	病例对照	160	80	内部+外部	校准曲线、临床决策曲线	④
蔡晓调和陆艳^[13]	病例对照	146	29	-	校准曲线、H-L检验	②③④⑤
陈蓓香等^[14]	病例对照	103	31	-	-	⑤⑥⑦
李好正等^[15]	病例对照	228	24	-	H-L检验	①④⑤⑧
李梦媛^[16]	巢式病例对照	228	24	内部+外部	H-L检验、校准图	④⑤⑧
彭宇等^[17]	回顾性队列研究	421	147	内部+外部	校准曲线	②④⑤⑥
邵金洁等^[18]	病例对照	410	53	内部+外部	H-L检验	⑧
石欢等^[19]	病例对照	379	78	外部	-	⑦
石娟和刘荣^[20]	病例对照	320	79	-	校准曲线、H-L检验	①⑤⑥⑧
谈月蓉等^[21]	病例对照	998	297	-	-	②⑥⑦⑧
田雅琦等^[22]	病例对照	235	89	内部	校准曲线、H-L检验、临床决策曲线	②③
王宇馨^[23]	病例对照	521	149	内部+外部	H-L检验、临床决策分析	⑤⑦⑧
谢小磊^[24]	病例对照	250	56	外部	H-L检验	②⑤⑥⑧
徐金娟和吴丽萍^[25]	病例对照	110	55	内部	H-L检验、校准曲线	①②③
余灵芝等^[26]	病例对照	212	102	内部	H-L检验、校准曲线	①⑧
俞玲英等^[27]	病例对照	316	89	内部	校准曲线、临床决策曲线	①②④
张珊等^[28]	病例对照	158	61	-	H-L检验、校准曲线	⑧
邹宛霖^[29]	病例对照	236	107	内部	H-L检验	①②③

预测因子	纳入研究（篇）	异质性检验结果		效应模型	Meta分析结果
预测因子	纳入研究（篇）	I²值（%）	P值	效应模型	OR值	95%CI	Z值	P值
GCS评分≥ 8分	9^{[15,16,18,20,21,23,24,26,28]}	93	< 0.001	随机效应	2.43	1.29 ~ 4.57	2.75	0.006
NIHSS评分	9^{[9,13,17,21,22,24,25,27,29]}	90	< 0.001	随机效应	1.96	1.49 ~ 2.60	4.74	< 0.001
误吸史	8^{[13,14,15,16,17,20,23,24]}	85	< 0.001	随机效应	3.26	1.40 ~ 7.61	2.74	0.006
年龄≥ 60岁	8^{[9,10,15,20,25,26,27,29]}	92	< 0.001	随机效应	1.35	1.19 ~ 1.52	4.79	< 0.001
洼田饮水试验≥ 3级	6^{[12,13,15,16,17,27]}	82	< 0.001	随机效应	4.74	2.50 ~ 9.01	4.76	< 0.001
胃残留量≥ 150 mL	5^{[14,17,20,21,24]}	0	0.49	固定效应	2.31	1.69 ~ 3.16	5.26	< 0.001
脑干梗死	5^{[11,13,22,25,29]}	0	0.81	固定效应	2.85	2.14 ~ 3.80	7.14	< 0.001
机械通气	4^{[14,19,21,23]}	81	0.001	随机效应	5.31	1.76 ~ 16.05	2.96	0.003

预测因子	纳入研究（篇）	异质性检验结果		效应模型	Meta分析结果
预测因子	纳入研究（篇）	I²值（%）	P值	效应模型	OR值	95%CI	Z值	P值
GCS评分≥ 8分	9^{[15,16,18,20,21,23,24,26,28]}	93	< 0.001	随机效应	2.43	1.29 ~ 4.57	2.75	0.006
NIHSS评分	9^{[9,13,17,21,22,24,25,27,29]}	90	< 0.001	随机效应	1.96	1.49 ~ 2.60	4.74	< 0.001
误吸史	8^{[13,14,15,16,17,20,23,24]}	85	< 0.001	随机效应	3.26	1.40 ~ 7.61	2.74	0.006
年龄≥ 60岁	8^{[9,10,15,20,25,26,27,29]}	92	< 0.001	随机效应	1.35	1.19 ~ 1.52	4.79	< 0.001
洼田饮水试验≥ 3级	6^{[12,13,15,16,17,27]}	82	< 0.001	随机效应	4.74	2.50 ~ 9.01	4.76	< 0.001
胃残留量≥ 150 mL	5^{[14,17,20,21,24]}	0	0.49	固定效应	2.31	1.69 ~ 3.16	5.26	< 0.001
脑干梗死	5^{[11,13,22,25,29]}	0	0.81	固定效应	2.85	2.14 ~ 3.80	7.14	< 0.001
机械通气	4^{[14,19,21,23]}	81	0.001	随机效应	5.31	1.76 ~ 16.05	2.96	0.003

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