Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study
Abstract Background Nutritional problems in patients with anorectal malformation (ARM) after anorectoplasty have not received sufficient attention. This study aimed to establish prediction model of postoperative undernutrition for patients with complex ARM. Methods Retrospective review of 104 patien...
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2025-08-01
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| Online Access: | https://doi.org/10.1186/s12876-025-04202-5 |
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| author | Wei Feng Linxiao Fan Jinping Hou Xiaohong Die Yi Wang Rui Jiang |
| author_facet | Wei Feng Linxiao Fan Jinping Hou Xiaohong Die Yi Wang Rui Jiang |
| author_sort | Wei Feng |
| collection | DOAJ |
| description | Abstract Background Nutritional problems in patients with anorectal malformation (ARM) after anorectoplasty have not received sufficient attention. This study aimed to establish prediction model of postoperative undernutrition for patients with complex ARM. Methods Retrospective review of 104 patients with complex ARM was conducted with assessments of clinical data. Lasso-Logistic regression analysis was used to identify independent factors, and then establish a Nomogram model for predicting postoperative undernutrition. Harrell’s concordance index and calibration curves were applied to evaluate the performance of this model. R software was used for statistical analysis. Results Of the 104 patients, the proportion of malnutrition was 28.85% (30/104). Lasso-Logistic regression analysis showed that non-parental caregivers (Odds ratio [OR]: 11.20), undernutrition at anorectoplasty (OR: 4.101), surgery for other systemic malformation (OR: 8.378), and feeding method (artificial, OR: 25.320) were independently associated with postoperative undernutrition (all P < 0.05), and Nomogram model was developed based on these determinants. The area under the receiver operator characteristic curve of this cohort was 0.877 and it still was 0.906 through bootstrapping validation (n = 1000). The H-L goodness-of-fit test showed that there was no significant difference between the predicted and actual incidence of postoperative undernutrition (χ2 = 7.55, P = 0.273). Meanwhile, the calibration curves indicated that the forecast was in good agreement with the actual situation. Decision curve showed that when the probability threshold of Nomogram model predicting malnutrition was >0.03, application of the model would add net benefit compared to either the treat-all strategy or the treat-none strategy. Conclusion This Nomogram model for predicting postoperative undernutrition of complex ARM patients showed desirable accuracy and discrimination for clinicians, aiding the early initiation of preventative interventions for undernutrition. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | BMC Gastroenterology |
| spelling | doaj-art-e8fd8b6bcb2e46c4a09616381f538d4d2025-08-20T04:03:13ZengBMCBMC Gastroenterology1471-230X2025-08-0125111410.1186/s12876-025-04202-5Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory studyWei Feng0Linxiao Fan1Jinping Hou2Xiaohong Die3Yi Wang4Rui Jiang5Department of General & Neonatal Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionDepartment of General & Neonatal Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionDepartment of General & Neonatal Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionDepartment of General & Neonatal Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionDepartment of General & Neonatal Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionDepartment of Anesthesiology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and ReconstructionAbstract Background Nutritional problems in patients with anorectal malformation (ARM) after anorectoplasty have not received sufficient attention. This study aimed to establish prediction model of postoperative undernutrition for patients with complex ARM. Methods Retrospective review of 104 patients with complex ARM was conducted with assessments of clinical data. Lasso-Logistic regression analysis was used to identify independent factors, and then establish a Nomogram model for predicting postoperative undernutrition. Harrell’s concordance index and calibration curves were applied to evaluate the performance of this model. R software was used for statistical analysis. Results Of the 104 patients, the proportion of malnutrition was 28.85% (30/104). Lasso-Logistic regression analysis showed that non-parental caregivers (Odds ratio [OR]: 11.20), undernutrition at anorectoplasty (OR: 4.101), surgery for other systemic malformation (OR: 8.378), and feeding method (artificial, OR: 25.320) were independently associated with postoperative undernutrition (all P < 0.05), and Nomogram model was developed based on these determinants. The area under the receiver operator characteristic curve of this cohort was 0.877 and it still was 0.906 through bootstrapping validation (n = 1000). The H-L goodness-of-fit test showed that there was no significant difference between the predicted and actual incidence of postoperative undernutrition (χ2 = 7.55, P = 0.273). Meanwhile, the calibration curves indicated that the forecast was in good agreement with the actual situation. Decision curve showed that when the probability threshold of Nomogram model predicting malnutrition was >0.03, application of the model would add net benefit compared to either the treat-all strategy or the treat-none strategy. Conclusion This Nomogram model for predicting postoperative undernutrition of complex ARM patients showed desirable accuracy and discrimination for clinicians, aiding the early initiation of preventative interventions for undernutrition.https://doi.org/10.1186/s12876-025-04202-5Complex anorectal malformationUndernutritionNomogram modelLasso-Logistic regression |
| spellingShingle | Wei Feng Linxiao Fan Jinping Hou Xiaohong Die Yi Wang Rui Jiang Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study BMC Gastroenterology Complex anorectal malformation Undernutrition Nomogram model Lasso-Logistic regression |
| title | Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study |
| title_full | Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study |
| title_fullStr | Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study |
| title_full_unstemmed | Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study |
| title_short | Development of nomogram model based on LASSO-Logistic regression for predicting postoperative undernutrition to complex anorectal malformation: a pilot exploratory study |
| title_sort | development of nomogram model based on lasso logistic regression for predicting postoperative undernutrition to complex anorectal malformation a pilot exploratory study |
| topic | Complex anorectal malformation Undernutrition Nomogram model Lasso-Logistic regression |
| url | https://doi.org/10.1186/s12876-025-04202-5 |
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