Abstract / Summary
BackgroundNoninvasive positive pressure ventilation is a key treatment for acute exacerbations of chronic obstructive pulmonary disease and is associated with a facial pressure injury incidence of 20% to 30%. However, current risk assessment still relies on generic tools such as the Braden scale, and there is a lack of risk prediction instruments that incorporate ventilation related respiratory mechanical parameters.ObjectiveBased on the Symptom Science Model 2.0, this study systematically examined factors associated with noninvasive positive pressure ventilation related facial pressure injury in patients with chronic obstructive pulmonary disease and developed and temporally validated a risk prediction model to provide a quantitative tool for early clinical warning and risk stratified prevention.MethodsFrom January 2020 to February 2025, we collected clinical data from 390 patients with chronic obstructive pulmonary disease receiving noninvasive positive pressure ventilation. Participants were divided by enrollment time into a training cohort of 273 patients enrolled between January 2020 and September 2023 for model development and a temporal validation cohort of 117 patients enrolled between October 2023 and February 2025 for model validation. Facial pressure injury was the outcome. Candidate predictors were screened using univariate analysis and least absolute shrinkage and selection operator regression, followed by multivariable logistic regression for model development, and 17 machine learning models were compared using five fold cross validation. Model performance was assessed using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis, and DeLong's test was used to compare area under the curve values. The selected model was further interpreted and visualized using SHAP.ResultsThe overall incidence of noninvasive positive pressure ventilation–related facial pressure injury was 23.33%. Multivariable logistic regression identified leak volume >40 L/min, higher inspiratory pressure, total ventilation time >100 h, single session duration ≥4 h, serum albumin <35 g/L, and a history of noninvasive ventilation use as independent risk factors, whereas BMI ≥18.5 kg/m2 was a protective factor. Among the 17 models, the traditional and parsimonious logistic regression model was recommended, achieving an AUC of 0.785 (95% CI: 0.702–0.868) in the temporal validation cohort; calibration and decision curve analyses also indicated good agreement and favorable clinical net benefit. SHAP analysis showed that serum albumin, total ventilation time, and inspiratory pressure contributed most to the model.ConclusionBased on the Symptom Science Model 2.0, we developed and temporally validated a risk prediction model that integrates noninvasive positive pressure ventilation–specific mechanical parameters. This model may serve as a quantitative tool for early identification of high risk patients and potentially help reduce the incidence of noninvasive positive pressure ventilation–related facial pressure injury.