# Real‐Time Predictive Analysis of ICU Ventilator Weaning Failure: A Prospective Validation Study

## TL;DR

This study developed a model to predict ICU ventilator weaning failure, helping doctors make better decisions to avoid reintubation.

## Contribution

A validated nomogram model for predicting ICU ventilator weaning failure using clinical indicators.

## Key findings

- The model achieved high accuracy with an AUC of 0.828 in the training set and 0.823 in the validation set.
- Key predictors included age, APACHE II score, ventilation duration, and sedative use.
- The model showed good calibration and could aid in individualized weaning strategies.

## Abstract

To construct and validate a nomogram model for predicting the risk of ICU ventilator weaning failure (reintubation or death within 48 h after extubation) based on multidimensional clinical indicators.

A total of 485 patients in the ICU who needed ventilator weaning were selected and divided into a training set (n = 340) and a validation set (n = 145) at a ratio of 7:3. Baseline data and weaning‐related indicators of the patients were collected. Weaning failure (reintubation or death within 48 h after weaning) was regarded as the outcome event. Independent risk factors were screened through univariate and multivariate logistic regression. A nomogram model was constructed, and the model's performance was evaluated using the C‐index, AUC, calibration curve, Hosmer–Lemeshow test, and decision curve.

The weaning failure rate in the training set was 28.53% (97/340), and that in the validation set was 29.66% (43/145). Multivariate regression showed that age, APACHE II score, duration of mechanical ventilation, spontaneous breathing frequency, Glasgow Coma Scale score, and the use of sedatives were independent influencing factors (p < 0.05). The C‐index of the nomogram model in the training set and the validation set was 0.829 and 0.826, respectively. The AUC was 0.828 (95% CI: 0.762–0.893) and 0.823 (95% CI: 0.732–0.915), respectively. The sensitivity and specificity were 0.811, 0.751 and 0.662, 0.702, respectively. The calibration curve and Hosmer–Lemeshow test (p = 0.109, 0.402) showed that the model had a good fit.

The nomogram model constructed based on the above indicators can effectively predict the risk of ICU ventilator weaning failure and provide a basis for formulating individualized weaning strategies.

This study developed and validated a nomogram model to predict ICU ventilator weaning failure using age, APACHE II, ventilation duration, and other key clinical factors. The model showed high accuracy (AUC > 0.82) and good calibration, aiding individualized weaning decisions. Results support its utility in reducing reintubation risks and improving ICU outcomes.