Predicting rLDH after PELD using a temporally validated nomogram

Description

To develop and temporally validate a clinical nomogram for preoperative prediction of rLDH risk. The dataset is provided as an Excel file (.xlsx) and includes patient-level information on demographics, anthropometric measures, radiological parameters, and surgical characteristics. It contains structured clinical data used to develop and validate a predictive model for rLDH. Specifically, the data comprise: Age (years) Sex (male/female) Body mass index (BMI, kg/m²) Imaging-derived parameters (e.g., DHI,sROM) Surgical parameters ([briefly list, e.g., operation time, surgical approach]) Outcome variables ([e.g., rLDH occurrence])

Steps to reproduce

Preoperative and intraoperative parameters were analyzed using Least absolute shrinkage and selection operator regression for variable selection, and a logistic regression-based nomogram was constructed. Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Brier score, calibration curve and decision curve analysis (DCA). All statistical analyses were performed using R software (version 4.6) with the following packages: tidyverse, glmnet, rms, pROC, PRROC, caret, and rmda.

Institutions

• Peking University Third Hospital

  Beijing, Beijing

Categories

Licence