A Risk Stratification Model for Unplanned Extubation a Chinese Tertiary Hospital Respiratory Intensive Care Unit: A Retrospective Study Based on Nursing-Related and Ventilation Characteristics.

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.