Establishment of a prediction model for extubation failure risk in ICU patients using bedside ultrasound technology

Mechanical ventilation (MV) is crucial for managing critically ill patients; however, extubation failure, associated with adverse outcomes, continues to pose a significant challenge.

The purpose of this prospective observational study was to develop and validate a predictive numerical model utilizing bedside ultrasound to forecast extubation outcomes in ICU patients.

We enrolled 300 patients undergoing MV, from whom clinical variables, biomarkers, and ultrasound parameters were collected. Patients were randomly assigned to two groups at a 6:4 ratio: the derivation cohort (n = 180) and the validation cohort (n = 120). A nomogram prediction model was developed using significant predictors identified through multivariate analysis and its performance was assessed and validated by evaluating its discrimination, calibration, and clinical utility.

A total of 300 patients (mean age 72 years; 57.3 % male) were included, with an extubation failure rate of 26.7 %. The model, including diaphragm thickening fraction (OR: 0.890, P = 0.009), modified lung ultrasound score (OR: 1.371, P < 0.001), peak relaxation velocity (OR: 1.515, P = 0.015), and APACHE II (OR: 1.181, P = 0.006), demonstrated substantial discriminative capability, as indicated by an area under the receiver operating characteristic curve (AUC) of 0.886 (95 % CI: 0.830-0.942) for the derivation cohort and 0.846 (95 % CI: 0.827-0.945) for the validation cohort. Hosmer-Lemeshow tests yielded P-values of 0.224 and 0.212 for the derivation and validation cohorts.

We have established a risk prediction model for extubation failure in mechanically ventilated ICU patients. This risk model base on bedside ultrasound parameters provides valuable insights for identifying high-risk patients and preventing extubation failure.