Development and nomogram validation of a prediction model for aspiration pneumonia in ICU patients receiving mechanical ventilation_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

YU Ying ¹ , ZHOU Linghua ¹ ,  XU Jinhua ¹ , LU Jianli ¹ , HU Hui ²

1. Emergency Intensive Care Unit, Haian People's Hospital Affiliated to Nantong University, Nantong, Jiangsu 226600, P. R. China;
2. Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226600, P. R. China;

Corresponding?author：

XU Jinhua, Email: xujinhua0623@163.com

Keywords：

Mechanical ventilation; aspiration pneumonia; predictive model; nomogram; intensive care unit

DOI：

10.7507/1671-6205.202508017

Video：

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Objective To explore independent risk factors for aspiration pneumonia (AP) in ICU patients receiving mechanical ventilation (MV), develop and validate a nomogram prediction model. Methods A retrospective analysis was conducted on 300 ICU patients undergoing MV at Haian People's Hospital Affiliated to Nantong University from April 2020 to March 2025. The patients were divided into a AP group (n=71) and a Non-AP (n=229) groups based on AP occurrence during treatment. Independent risk factors were identified using multivariate logistic regression. A nomogram prediction model was constructed using R3.5.1, and model performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots. Results Among 300 ICU-MV patients, 71 (23.7%) cases developed AP. Univariate analysis showed that AP patients had significantly higher proportions of advanced age, impaired consciousness, intermittent cuff pressure monitoring, elevated intra-abdominal pressure, poor oral hygiene, and prolonged MV duration, while subglottic secretion drainage was less frequent (P<0.05). Multivariate analysis confirmed these as independent AP risk factors: advanced age (OR=1.047, 95%CI 1.004-1.093), impaired consciousness (OR=1.468, 95%CI 1.148-1.877), intermittent cuff pressure monitoring (OR=2.496, 95%CI 1.300-4.790), elevated intra-abdominal pressure (OR=1.313, 95%CI 1.027-1.678), poor oral hygiene (OR=1.640, 95%CI 1.244-2.161), and prolonged MV duration (OR=1.233, 95%CI 1.116-1.363) (all P<0.05). The final prediction model was logit(P)= –11.242 + 0.046×Age + 0.384×Consciousness + 0.915×Cuff Pressure Monitoring + 0.272 × Intra-abdominal Pressure + 0.495×Oral Hygiene + 0.209×MV Duration. Hosmer-Lemeshow test indicated good fit (χ²=4.136, DF=8, P=0.825). A nomogram-derived risk score>250 predicted>85% AP probability. Model validation showed a C-index of 0.802, AUC of 0.802 (95%CI 0.752-0.845), sensitivity of 79.01%, specificity of 88.17%, cutoff value of 0.287, Youden's index of 0.672, Brier score of 0.117, and calibration slope of 0.613. Conclusions Advanced age, impaired consciousness, intermittent cuff pressure monitoring, intra-abdominal hypertension, poor oral hygiene, and prolonged MV duration significantly increase AP risk in ICU-MV patients. The developed nomogram demonstrates robust predictive accuracy and clinical applicability.

Citation： YU Ying, ZHOU Linghua, XU Jinhua, LU Jianli, HU Hui. Development and nomogram validation of a prediction model for aspiration pneumonia in ICU patients receiving mechanical ventilation. Chinese Journal of Respiratory and Critical Care Medicine, 2026, 25(3): 179-185. doi: 10.7507/1671-6205.202508017 Copy

1.	Ocrospoma S, Restrepo MI. Severe aspiration pneumonia in the elderly. J Intensive Med, 2024, 4(3): 307-317.
2.	Teramoto S. The current definition, epidemiology, animal models and a novel therapeutic strategy for aspiration pneumonia. Respir Investig, 2022, 60(1): 45-55.
3.	Wakabayashi T, Hamaguchi S, Morimoto K, et al. Clinically defined aspiration pneumonia is an independent risk factor associated with long-term hospital stay: a prospective cohort study. BMC Pulm Med, 2023, 23(1): 351.
4.	盧素娟, 林敏, 宋利, 等. 老年腦卒中后吞咽障礙患者繼發吸入性肺炎危險因素及預測模型的建立. 中華全科醫學, 2025, 23(5): 785-788,857.
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6.	Bujang MA. An elaboration on sample size determination for correlations based on effect sizes and confidence interval width: a guide for researchers. Restor Dent Endod, 2024, 49(2): e21.
7.	中華醫學會呼吸病學分會感染學組. 中國成人醫院獲得性肺炎與呼吸機相關性肺炎診斷和治療指南(2018年版). 中華結核和呼吸雜志, 2018, 41(4): 255-280.
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10.	王彩虹, 劉霞琴, 覃雙文, 等. 成人ICU患者顯性誤吸影響因素分析. 護理學雜志, 2024, 39(1): 60-63.
11.	談月蓉, 丁海燕, 李育平, 等. 神經外科ICU患者誤吸的列線圖風險預測模型構建及其診斷效能. 川北醫學院學報, 2023, 38(11): 1551-1554.
12.	Honda Y, Homma Y, Nakamura M, et al. Extremely poor post-discharge prognosis in aspiration pneumonia and its prognostic factors: a retrospective cohort study. Dysphagia, 2024, 39(5): 837-845.
13.	Putot A, Putot S, Manckoundia P. Long-term survival after aspiration pneumonia in older inpatients: a comparative study. J Am Med Dir Assoc, 2023, 24(7): 1088-1091.
14.	Zhang Y, Wang K, Yu H, et al. Incidence and characteristics of aspiration pneumonia in adults in Beijing, China, 2011-2017. Public Health, 2023, 220(1): 65-71.
15.	王甜夢, 曾泓輯, 李彩霞, 等. 腦卒中后吞咽障礙患者吸入性肺炎的影響因素分析及預測模型構建. 中華物理醫學與康復雜志, 2024, 46(7): 618-623.
16.	Zhao T, Zhang Y, Wang K, et al. Identifying risk factors for aspiration in patients hospitalized with community-acquired pneumonia. Int J Clin Pract, 2023, 2023(1): 2198259.
17.	陳菁, 陶鈺, 楊靈梅, 等. 不同算法的老年吞咽功能障礙患者吸入性肺炎風險預測模型比較. 廣西醫科大學學報, 2023, 40(11): 1829-1835.
18.	Chen Q, Yu X, Chen Y, et al. Intermittent versus continuous control of tracheal cuff pressure in patients undergoing mechanical ventilation: a systematic review and meta-analysis. J Clin Nurs, 2023, 32(15-16): 4283-4294.
19.	胡苗苗, 劉麗, 張淑琴. 老年重癥肺炎機械通氣患者脫機失敗的風險預測模型建立與驗證. 中國呼吸與危重監護雜志, 2024, 23(5): 321-326.
20.	Doudakmanis C, Makris D. Ventilator-associated pneumonia in patients with increased intra-abdominal pressure. Cureus, 2025, 17(3): e81370.
21.	Doudakmanis C, Stamatiou R, Makri A, et al. Relationship between intra-abdominal pressure and microaspiration of gastric contents in critically ill mechanically ventilated patients. J Crit Care, 2023, 74(1): 154220.
22.	樊麗文, 王雪靜, 趙彤. 改良口腔護理對RICU機械通氣患者口腔衛生狀況及呼吸機相關肺炎發生率的影響. 河北醫藥, 2023, 45(14): 2226-2229.
23.	鄧偉, 李小珍, 王英, 等. 口咽分泌物微誤吸管理方案在ICU氣管插管患者中的應用研究. 中華護理雜志, 2024, 59(10): 1157-1163.

1. Ocrospoma S, Restrepo MI. Severe aspiration pneumonia in the elderly. J Intensive Med, 2024, 4(3): 307-317.
2. Teramoto S. The current definition, epidemiology, animal models and a novel therapeutic strategy for aspiration pneumonia. Respir Investig, 2022, 60(1): 45-55.
3. Wakabayashi T, Hamaguchi S, Morimoto K, et al. Clinically defined aspiration pneumonia is an independent risk factor associated with long-term hospital stay: a prospective cohort study. BMC Pulm Med, 2023, 23(1): 351.
4. 盧素娟, 林敏, 宋利, 等. 老年腦卒中后吞咽障礙患者繼發吸入性肺炎危險因素及預測模型的建立. 中華全科醫學, 2025, 23(5): 785-788,857.
5. 王靜, 朱真真, 白鈺璇, 等. 腦卒中后吞咽功能障礙繼發吸入性肺炎的危險因素. 昆明醫科大學學報, 2022, 43(2): 141-144.
6. Bujang MA. An elaboration on sample size determination for correlations based on effect sizes and confidence interval width: a guide for researchers. Restor Dent Endod, 2024, 49(2): e21.
7. 中華醫學會呼吸病學分會感染學組. 中國成人醫院獲得性肺炎與呼吸機相關性肺炎診斷和治療指南(2018年版). 中華結核和呼吸雜志, 2018, 41(4): 255-280.
8. Bodien YG, Barra A, Temkin NR, et al. Diagnosing level of consciousness: the limits of the Glasgow coma scale total score. J Neurotrauma, 2021, 38(23): 3295-3305.
9. Wang Y, Lan Y, Jia T, et al. Construction and application of a training program for icu nurses to manage artificial airway gasbags to prevent ventilator-associated pneumonia. J Multidiscip Healthc, 2023, 16(1): 3737-3748.
10. 王彩虹, 劉霞琴, 覃雙文, 等. 成人ICU患者顯性誤吸影響因素分析. 護理學雜志, 2024, 39(1): 60-63.
11. 談月蓉, 丁海燕, 李育平, 等. 神經外科ICU患者誤吸的列線圖風險預測模型構建及其診斷效能. 川北醫學院學報, 2023, 38(11): 1551-1554.
12. Honda Y, Homma Y, Nakamura M, et al. Extremely poor post-discharge prognosis in aspiration pneumonia and its prognostic factors: a retrospective cohort study. Dysphagia, 2024, 39(5): 837-845.
13. Putot A, Putot S, Manckoundia P. Long-term survival after aspiration pneumonia in older inpatients: a comparative study. J Am Med Dir Assoc, 2023, 24(7): 1088-1091.
14. Zhang Y, Wang K, Yu H, et al. Incidence and characteristics of aspiration pneumonia in adults in Beijing, China, 2011-2017. Public Health, 2023, 220(1): 65-71.
15. 王甜夢, 曾泓輯, 李彩霞, 等. 腦卒中后吞咽障礙患者吸入性肺炎的影響因素分析及預測模型構建. 中華物理醫學與康復雜志, 2024, 46(7): 618-623.
16. Zhao T, Zhang Y, Wang K, et al. Identifying risk factors for aspiration in patients hospitalized with community-acquired pneumonia. Int J Clin Pract, 2023, 2023(1): 2198259.
17. 陳菁, 陶鈺, 楊靈梅, 等. 不同算法的老年吞咽功能障礙患者吸入性肺炎風險預測模型比較. 廣西醫科大學學報, 2023, 40(11): 1829-1835.
18. Chen Q, Yu X, Chen Y, et al. Intermittent versus continuous control of tracheal cuff pressure in patients undergoing mechanical ventilation: a systematic review and meta-analysis. J Clin Nurs, 2023, 32(15-16): 4283-4294.
19. 胡苗苗, 劉麗, 張淑琴. 老年重癥肺炎機械通氣患者脫機失敗的風險預測模型建立與驗證. 中國呼吸與危重監護雜志, 2024, 23(5): 321-326.
20. Doudakmanis C, Makris D. Ventilator-associated pneumonia in patients with increased intra-abdominal pressure. Cureus, 2025, 17(3): e81370.
21. Doudakmanis C, Stamatiou R, Makri A, et al. Relationship between intra-abdominal pressure and microaspiration of gastric contents in critically ill mechanically ventilated patients. J Crit Care, 2023, 74(1): 154220.
22. 樊麗文, 王雪靜, 趙彤. 改良口腔護理對RICU機械通氣患者口腔衛生狀況及呼吸機相關肺炎發生率的影響. 河北醫藥, 2023, 45(14): 2226-2229.
23. 鄧偉, 李小珍, 王英, 等. 口咽分泌物微誤吸管理方案在ICU氣管插管患者中的應用研究. 中華護理雜志, 2024, 59(10): 1157-1163.

Chinese Journal of Respiratory and Critical Care Medicine

Development and nomogram validation of a prediction model for aspiration pneumonia in ICU patients receiving mechanical ventilation

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