Supplementary Materials and Analysis Code for: Individualized Nomogram-Based Model for Predicting the Risk of Gingival Recession after Microsurgical Endodontics
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2026
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| _version_ | 1866901468788817920 |
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| author | Qiao, Pan |
| author_facet | Qiao, Pan |
| contents | <p><strong>Abstract</strong> <strong>Background:</strong> 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. <strong>Objective:</strong> To identify key risk factors and develop an individualized nomogram for predicting gingival recession risk following microsurgical endodontics. <strong>Methods:</strong> 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. <strong>Results:</strong> 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). <strong>Conclusion:</strong> 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.</p> <p><strong>Description of supplementary files</strong> This repository contains the analysis code and supplementary tables for the study.</p> <ul> <li> <p><strong>Analysis_Code_R.docx:</strong> The R code scripts used for LASSO regression, multivariate logistic regression, and nomogram construction.</p> </li> <li> <p><strong>Supplementary Table 1.docx:</strong> Verification of statistical assumptions for the final multivariate logistic regression model.</p> </li> <li> <p><strong>Supplementary Table 2.docx:</strong> Internal validation of the final model using Bootstrap method (1,000 resamples).</p> </li> </ul> |
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| id | zenodo_https___doi_org_10_5281_zenodo_18479572 |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | Supplementary Materials and Analysis Code for: Individualized Nomogram-Based Model for Predicting the Risk of Gingival Recession after Microsurgical Endodontics Qiao, Pan <p><strong>Abstract</strong> <strong>Background:</strong> 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. <strong>Objective:</strong> To identify key risk factors and develop an individualized nomogram for predicting gingival recession risk following microsurgical endodontics. <strong>Methods:</strong> 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. <strong>Results:</strong> 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). <strong>Conclusion:</strong> 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.</p> <p><strong>Description of supplementary files</strong> This repository contains the analysis code and supplementary tables for the study.</p> <ul> <li> <p><strong>Analysis_Code_R.docx:</strong> The R code scripts used for LASSO regression, multivariate logistic regression, and nomogram construction.</p> </li> <li> <p><strong>Supplementary Table 1.docx:</strong> Verification of statistical assumptions for the final multivariate logistic regression model.</p> </li> <li> <p><strong>Supplementary Table 2.docx:</strong> Internal validation of the final model using Bootstrap method (1,000 resamples).</p> </li> </ul> |
| title | Supplementary Materials and Analysis Code for: Individualized Nomogram-Based Model for Predicting the Risk of Gingival Recession after Microsurgical Endodontics |
| url | https://doi.org/10.5281/zenodo.18479572 |