Development of a Nomogram to Predict the Risk of Occipital Pressure Injury in Patients With Craniocerebral Injury

ABSTRACT

Objective

To investigate the risk factors associated with occipital pressure injury in patients with craniocerebral injury and to develop a risk prediction model for assessing the risk of occipital pressure injury in patients in this population.

Methods

We conducted a prospective cohort study with descriptive analysis. A cohort of 455 patients diagnosed with craniocerebral injury and treated in our neurosurgery department and NSICU of our hospital, who met the criteria, were selected for this study. We collected the clinical data in the neurosurgery ward and Neurosurgical Intensive Care Unit (NSICU) from October 2022 to September 2024. Using R randomisation, we established: Test group (n = 324), validation group (n = 131). Then we performed both univariate and multivariate regression analysis to identify the independent risk factors of occipital pressure injury and developed a predictive model based on these findings. H-L test (Hosmer-Lemeshow test) was used to evaluate the accuracy and specificity of the model.

Results

Among the 455 patients with craniocerebral injury, 324 were randomly selected into the test group, within which 42 developed occipital pressure injuries, representing a 12.96% incidence rate. Univariate analysis showed that there were significant differences (p < 0.05) between terms of gender, Braden scale score, Glasgow score, APACHE II score, NRS2002, Barthel scale, presence of edema, use of vasoactive drugs, use of mechanical ventilator, ICU stay length, MDRO infection, hair friction, use of head circular dressing, surgical interventions, drainage tube placement, surgery duration, volume of red blood cell infusion and length of using mechanical ventilation. Multivariate logistic regression analysis showed that gender, APACHE II score, length of using mechanical ventilation, and use of head circular dressing were independent risk factors for the development of occipital pressure injuries. A nomogram predictive model was formulated based on these factors and demonstrated excellent discriminative ability (AUC = 0.888).

Conclusion

The occipital pressure injury risk predictive model for patients with craniocerebral injury performed strong predictive capacity in this study. It provides theoretical guidance for early formulation of predictive intervention strategies. We prepared this article in accordance with the STROBE checklist.