# Development and validation of a perioperative risk prediction model for pressure ulcers in neurosurgical procedures: a machine learning approach with protocol compliance metrics

## TL;DR

This study created a machine learning model to predict pressure ulcers in neurosurgical patients, helping doctors identify high-risk patients early.

## Contribution

A novel machine learning-based nomogram with high sensitivity and specificity for predicting pressure ulcers in neurosurgery.

## Key findings

- Eight predictors including diabetes duration, BMI, and albumin were identified as significant for pressure ulcer risk.
- The model achieved 77% sensitivity and 92% specificity in training and validation sets.
- Decision curve analysis confirmed the model's clinical utility across various risk thresholds.

## Abstract

This study aimed to develop and validate a nomogram for predicting pressure ulcer (PU) incidence in neurosurgical patients to enhance postoperative risk management.

A retrospective analysis of 1,020 patients across four tertiary centers (2005–2025) evaluated 20 variables. Propensity score matching (PSM) addressed confounding, while LASSO regression and machine learning identified predictors. Model performance was assessed via AUC-ROC, C-index, and decision curve analysis.

Eight independent predictors of PU were identified: diabetes duration, BMI, albumin, prealbumin, age, hemoglobin, temperature difference, and urinary incontinence. The training set achieved an AUC-ROC of 0.825 (95% CI: 0.797–0.853) with 77% sensitivity and 92% specificity, while the validation set showed an AUC-ROC of 0.800 (95% CI: 0.753–0.847) with 76% sensitivity and 92% specificity. The nomogram demonstrated recalibrated C-indices of 0.833 (training) and 0.826 (validation). Decision curve analysis confirmed significant net benefit across clinical thresholds.

This validated nomogram enables early PU risk stratification, facilitating personalized postoperative interventions. Given its high sensitivity and specificity, the model can be integrated into clinical practice to assist in early identification of high-risk patients, thereby improving patient outcomes through timely interventions.