Raw Data for: Individualized Nomogram-Based Model for Predicting the Risk of Gingival Recession after Microsurgical Endodontics

Abstract Background: Microsurgical endodontic surgery is a standard tooth-preserving treatment. However, postoperative gingival recession, particularly in aesthetically critical areas, remains a common complication. A reliable tool for preoperative risk assessment is currently lacking. Objective: To identify key risk factors and develop an individualized nomogram for predicting gingival recession risk following microsurgical endodontics. Methods: This retrospective study analyzed 200 patients, including 50 with postoperative recession. Potential predictors were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and significant variables were incorporated into a multivariate logistic regression model to construct a nomogram. The model’s discrimination was assessed by the C-index, calibration was evaluated via calibration curves and the Hosmer-Lemeshow test, and internal validation was performed using 1,000 bootstrap resamples. Results: The study identified seven independent predictors: thin gingival biotype (OR=5.74), sulcular incision (OR=3.55), presence of restoration (OR=3.29), smoking (OR=2.96), and advanced age (OR=1.05). Conversely, greater keratinized gingiva width (OR=0.57) and the use of floss or water flosser (OR=0.35) were identified as protective factors. The final nomogram demonstrated excellent discrimination with a C-index of 0.87 (95% CI: 0.81; 0.92) and good calibration, supported by a Nagelkerke R2 of 0.46, a non-significant Hosmer-Lemeshow test (P=0.917), and high agreement in calibration curves (Mean Absolute Error=0.026). Conclusion: A validated nomogram integrating anatomical, surgical, and behavioral factors was developed. This tool facilitates preoperative identification of high-risk patients, enabling targeted preventive strategies to optimize aesthetic outcomes. Description of dataset This repository contains the raw data for the study. Raw_Dataset.xlsx: The original dataset containing clinical parameters of the 200 patients analyzed in the study.