Development and Validation of a Nomogram for Hospital-Acquired Pressure Injury in Neurosurgical Patients: A Prospective Cohort Study.

OBJECTIVES

This study aimed to develop and validate a nomogram for predicting hospital-acquired pressure injuries (HAPI) in neurosurgical patients, to support neurosurgeons and clinical teams in early risk stratification and preventive care.

METHODS

We prospectively enrolled a total of 1192 neurosurgical patients and randomly assigned them into a development cohort and a validation cohort at 7:3 ratio. Least absolute shrinkage and selection operator regression and Cox regression were used to screen variables from their demographic data, clinical data, and laboratory data to construct the nomogram model. Internal validation was performed using the receiver operating characteristic (ROC) curve, area under the ROC curve, concordance index (C-index), calibration curve, and decision curve analysis. The study was reported in strict accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement and was registered at the Chinese Clinical Trial Registry, registration number: ChiCTR2300070879.

RESULTS

Among 1165 patients analyzed, the overall HAPI incidence was 3.6%, with most cases stage 1 or 2. Five independent predictors were included in the nomogram model: administration of vasoactive medications (Hazard ratio [HR] = 5.95, 95% confidence interval [CI] 2.66-13.31, P < 0.001), hemiplegia (HR = 6.58, 95% CI 3.02-14.33, P < 0.001), edema (HR = 3.09, 95% CI 1.32-7.26, P = 0.010), nourishment-deprived days (HR = 1.06, 95% CI 1.04-1.09, P < 0.001), and physical restriction duration (HR = 1.04, 95% CI 1.01-1.07, P = 0.016). The C-index was 0.89 (95% CI 0.81-0.97) and 0.94 (95% CI 0.88-1.00) in the development cohort and validation cohort, respectively.

CONCLUSIONS

The nomogram provides an accurate, practical tool for estimating HAPI risk in neurosurgical patients. Integration into clinical workflows may help identify high-risk individuals and enable timely, targeted interventions to improve outcomes.