To construct a Nomogram prediction model for intraoperative acquired pressure injury in elderly patients with brain tumors based on limb muscle strength.

A total of 536 elderly patients with brain tumors who underwent surgical treatment in our hospital from January 2022 to June 2025 were enrolled and randomly divided into a training set(n=366) and a validation set(n=170) at a 7∶3 ratio.Based on the occurrence of intraoperative acquired pressure injury,patients in the training set were further classified into an injury group and a non-injury group.Clinical data and limb muscle strength data were collected.Univariate analysis was performed.The least absolute shrinkage and selection operator(LASSO) regression algorithm was used to identify characteristic variables associated with intraoperative acquired pressure injury.Multivariate Logistic regression analysis was conducted to determine independent predictors of intraoperative acquired pressure injury.A Nomogram prediction model was then constructed based on these independent predictors.The calibration of the model was evaluated by using the Hosmer-Lemeshow test and calibration curves.Clinical utility was assessed through decision curve analysis.Predictive performance was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve.External validation was performed by using the validation set.

Logistic regression analysis results showed that diabetes,operative time,intraoperative temperature variation,albumin level,C-reactive protein level,and limb muscle strength grade were independent predictive factors of intraoperative acquired pressure injury in elderly patients with brain tumors.The calibration curves for both the training and validation sets showed good fit with the ideal curve.The Hosmer-Lemeshow test indicated good consistency between predicted and observed probabilities,demonstrating satisfactory model accuracy(training set:χ²=6.697,P=0.570;validation set:χ²=5.464,P=0.707).Decision curve analysis revealed that within a threshold probability range of 0 to 1,the Nomogram model provided a higher net clinical benefit compared to any single predictor in both the training and validation sets,indicating strong clinical utility.The Area under receiver operating characteristic curves for the training set was 0.883(95%CI 0.845-0.920) and for the validation set was 0.879(95%CI 0.820-0.937),indicating excellent predictive.

The Nomogram prediction model for intraoperative acquired pressure injury in elderly patients with brain tumors,incorporating diabetes,operative time,intraoperative temperature variation,albumin level,C-reactive protein level,and limb muscle strength grade,demonstrates good calibration,clinical utility,and predictive performance.The Nomogram prediction model of intraoperative acquired pressure injury in elderly patients with brain tumors based on diabetes,operation time,intraoperative temperature variation,albumin level,C-reactive protein,and limb muscle strength has good calibration,clinical practicality,and prediction efficiency.This model holds significant clinical value for identifying high-risk patients and guiding preventive nursing interventions.