What question did this study set out to answer?

The aim is to create a stroke-specific risk prediction tool for successful extubation in mechanically ventilated patients.

March 8, 2026

Development and validation of a nomogram for predicting the probability of successful extubation among patients with severe stroke receiving mechanical ventilation.

Key Points

The aim is to create a stroke-specific risk prediction tool for successful extubation in mechanically ventilated patients.
Retrospective enrollment of stroke patients from intensive care units.
Training cohort of 324 and validation cohort of 81 patients.
Utilized least absolute shrinkage and selection operator for factor screening.
Constructed a nomogram using multivariate logistic regression.
Evaluated model performance through various statistical curves.
Extubation failure rates: 46.6% in training and 48.1% in validation cohorts.
Identified 7 independent predictors of extubation failure.
Nomogram demonstrated good prediction accuracy (AUC of 0.789 and 0.745).
Sensitivity and specificity: 74.8%/82.1% in training, 70.5%/77.6% in validation.
Decision curve analysis showed a clinical net benefit for optimal prediction.

Abstract

To address the lack of stroke-specific risk prediction tools for extubation failure, this study aimed to develop and validate a multidimensional nomogram integrating neurological function, respiratory parameters, and systemic status, to provide a basis for individualized clinical decision-making regarding extubation. We retrospectively enrolled 324 mechanically ventilated stroke patients admitted to the intensive care unit of Jining Medical University Affiliated Hospital from January 2022 to May 2024 as the training cohort, and 81 patients from June to December 2024 as the temporal validation cohort. The least absolute shrinkage and selection operator regression was used to screen risk factors from 43 candidate predictors. A nomogram was constructed using multivariate logistic regression. Model performance was evaluated using the receiver operating characteristic curve, calibration curve, decision curve analysis, and clinical impact curve. The developed nomogram integrates neurological function and dynamic respiratory parameters and can effectively identify intensive care unit stroke patients at high risk of extubation failure, potentially providing a tool for optimizing respiratory support strategies. The extubation failure rates were 46.6% (151/324) in the training cohort and 48.1% (39/81) in the validation cohort. Baseline data were well matched between cohorts. Least absolute shrinkage and selection operator regression identified 7 independent predictors. Multivariate logistic regression showed that the National Institutes of Health Stroke Scale score (odds ratio OR = 1.09, 95% confidence interval CI: 1.03-1.15, P = .003), Acute Physiology and Chronic Health Evaluation II score (OR = 1.08, 95% CI: 1.03-1.13, P = .001), duration of mechanical ventilation (OR = 1.06, 95% CI: 1.02-1.10, P = .001), fraction of inspired oxygen (FiO2) (OR = 1.06, 95% CI: 1.02-1.11, P = .004), hemoglobin level (OR = 0.98, 95% CI: 0.97-0.99, P = .005), ischemic stroke type (OR = 0.47, 95% CI: 0.26-0.85, P = .012), and history of neurological disease (OR = 0.46, 95% CI: 0.27-0.77, P = .004) were independent influencing factors for extubation failure. The nomogram demonstrated areas under the curve of 0.789 (95% CI: 0.740-0.837) and 0.745 (95% CI: 0.639-0.851) in the training and validation cohorts, with sensitivities/specificities of 74.8%/82.1% and 70.5%/77.6%, respectively. The calibration curve showed minimal deviation (Hosmer-Lemeshow test P = .325), and decision curve analysis indicated a clinical net benefit across a threshold probability range of 15%-98%.

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Development and validation of a nomogram for predicting the probability of successful extubation among patients with severe stroke receiving mechanical ventilation.

Key Points

Abstract

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