A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy
Background Postoperative complications are prone to occur in patients after radical pancreaticoduodenectomy (PD). This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD. Methods The clinical data of 360 patients who underwent PD were...
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2024-12-01
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| author | Jiaqi Wang Kangjing Xu Changsheng Zhou Xinbo Wang Junbo Zuo Chenghao Zeng Pinwen Zhou Xuejin Gao Li Zhang Xinying Wang |
| author_facet | Jiaqi Wang Kangjing Xu Changsheng Zhou Xinbo Wang Junbo Zuo Chenghao Zeng Pinwen Zhou Xuejin Gao Li Zhang Xinying Wang |
| author_sort | Jiaqi Wang |
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| description | Background Postoperative complications are prone to occur in patients after radical pancreaticoduodenectomy (PD). This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD. Methods The clinical data of 360 patients who underwent PD were retrospectively collected from two centers between January 2019 and December 2023. Visceral adipose volume (VAV) and subcutaneous adipose volume (SAV) were measured using three-dimensional (3D) computed tomography (CT) reconstruction. According to the Clavien-Dindo classification system, the postoperative complications were graded. Subsequently, a predictive model was constructed based on the results of least absolute shrinkage and selection operator (LASSO) multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally validated by the training and test cohort. The discriminatory ability and clinical utility of the nomogram were evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results The major complications occurred in 13.3% (n = 48) of patients after PD. The nomogram revealed that high VAV/SAV, high system inflammation response index (SIRI), high triglyceride glucose-body mass index (TyG-BMI), low prognostic nutritional index (PNI) and CA199 ≥ 37 were independent risk factors for major complications. The C-index of this model was 0.854 (95%CI [0.800–0.907]), showing excellent discrimination. The calibration curve demonstrated satisfactory concordance between nomogram predictions and actual observations. The DCA curve indicated the substantial clinical utility of the nomogram. Conclusion The model based on clinical and CT indices demonstrates good predictive performance and clinical benefit for major complications in patients undergoing PD. |
| format | Article |
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| spelling | doaj-art-da37f8502847417fac4ab2dac112adba2025-08-20T01:57:08ZengPeerJ Inc.PeerJ2167-83592024-12-0112e1875310.7717/peerj.18753A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomyJiaqi Wang0Kangjing Xu1Changsheng Zhou2Xinbo Wang3Junbo Zuo4Chenghao Zeng5Pinwen Zhou6Xuejin Gao7Li Zhang8Xinying Wang9Department of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaBackground Postoperative complications are prone to occur in patients after radical pancreaticoduodenectomy (PD). This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD. Methods The clinical data of 360 patients who underwent PD were retrospectively collected from two centers between January 2019 and December 2023. Visceral adipose volume (VAV) and subcutaneous adipose volume (SAV) were measured using three-dimensional (3D) computed tomography (CT) reconstruction. According to the Clavien-Dindo classification system, the postoperative complications were graded. Subsequently, a predictive model was constructed based on the results of least absolute shrinkage and selection operator (LASSO) multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally validated by the training and test cohort. The discriminatory ability and clinical utility of the nomogram were evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results The major complications occurred in 13.3% (n = 48) of patients after PD. The nomogram revealed that high VAV/SAV, high system inflammation response index (SIRI), high triglyceride glucose-body mass index (TyG-BMI), low prognostic nutritional index (PNI) and CA199 ≥ 37 were independent risk factors for major complications. The C-index of this model was 0.854 (95%CI [0.800–0.907]), showing excellent discrimination. The calibration curve demonstrated satisfactory concordance between nomogram predictions and actual observations. The DCA curve indicated the substantial clinical utility of the nomogram. Conclusion The model based on clinical and CT indices demonstrates good predictive performance and clinical benefit for major complications in patients undergoing PD.https://peerj.com/articles/18753.pdfPancreaticoduodenectomyPostoperative complicationsThree-dimensional CT reconstructionPredictive model |
| spellingShingle | Jiaqi Wang Kangjing Xu Changsheng Zhou Xinbo Wang Junbo Zuo Chenghao Zeng Pinwen Zhou Xuejin Gao Li Zhang Xinying Wang A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy PeerJ Pancreaticoduodenectomy Postoperative complications Three-dimensional CT reconstruction Predictive model |
| title | A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| title_full | A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| title_fullStr | A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| title_full_unstemmed | A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| title_short | A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| title_sort | novel model based on clinical and computed tomography ct indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy |
| topic | Pancreaticoduodenectomy Postoperative complications Three-dimensional CT reconstruction Predictive model |
| url | https://peerj.com/articles/18753.pdf |
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