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Updated: Aug 21, 2026
A Structured Approach to Extubation in Mechanically Ventilated Rats
Published on: July 18, 2025
A Risk Stratification Model for Unplanned Extubation a Chinese Tertiary Hospital Respiratory Intensive Care Unit: A
Wenqing Xu1, Ying Yang1, Chen Wang1
1Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital), Shanghai, 200001, People's Republic of China.
Aim:
To develop a risk prediction model for unplanned extubation (UE) in invasively ventilated patients in the respiratory intensive care unit (RICU) using nursing-related and ventilation characteristics, and to perform risk stratification.
Methods:
This single-center retrospective study included adult patients receiving invasive mechanical ventilation in the RICU. Nursing-related variables (RASS score, delirium, physical restraint use, nurse-to-patient ratio) along with baseline and treatment data were collected. Univariate and multivariate logistic regression was employed to identify UE-associated factors and build a nomogram. Model performance was evaluated using ROC curve, Hosmer-Lemeshow test, calibration curve, decision curve analysis, and bootstrap internal validation. Risk stratification was performed based on predicted probability.
Results:
Among 1120 patients, 102 (9.11%) experienced UE. Multivariate analysis identified RASS category, nurse-to-patient ratio, and FiO2 as independent factors. Compared with agitation, awake/mild sedation (OR = 0.262, P = 0.002) and deep sedation (OR = 0.071, P < 0.001) were protective. A nurse-to-patient ratio ≥ 1:4 was an independent risk factor (OR = 3.257, P = 0.001). FiO2 was protective (OR = 0.037, P = 0.009). The model achieved an AUC of 0.781 (95% CI: 0.738-0.824), sensitivity 0.760, specificity 0.700, Brier score 0.076, Hosmer-Lemeshow P 0.301, and calibration slope 1.000. UE incidence in low-, medium-, and high-risk groups was 2.80%, 15.60%, and 25.00%, respectively.
Conclusion:
The UE risk prediction model based on nursing-related characteristics has good discrimination and calibration. RASS category, nurse-to-patient ratio, and FiO2 are key independent factors. This model enables effective risk stratification and supports early identification of high-risk patients and optimized nursing management; however, its generalizability should be interpreted cautiously because of the single-center retrospective design and lack of external validation.
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