Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration
ObjectiveSplenectomy is a vital treatment method for hypersplenism with portal hypertension. However, portal venous system thrombosis (PVST) is a serious problem after splenectomy. Therefore, constructing an effective visual risk prediction model is important for preventing, diagnosing, and treating...
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1103223/full |
| _version_ | 1797896516316168192 |
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| author | Zhou Zheng Zhou Zheng Qingsheng Yu Qingsheng Yu Hui Peng Hui Peng Long Huang Long Huang Wanzong Zhang Wanzong Zhang Yi Shen Yi Shen Hui Feng Hui Feng Wenshan Jing Wenshan Jing Qi Zhang Qi Zhang |
| author_facet | Zhou Zheng Zhou Zheng Qingsheng Yu Qingsheng Yu Hui Peng Hui Peng Long Huang Long Huang Wanzong Zhang Wanzong Zhang Yi Shen Yi Shen Hui Feng Hui Feng Wenshan Jing Wenshan Jing Qi Zhang Qi Zhang |
| author_sort | Zhou Zheng |
| collection | DOAJ |
| description | ObjectiveSplenectomy is a vital treatment method for hypersplenism with portal hypertension. However, portal venous system thrombosis (PVST) is a serious problem after splenectomy. Therefore, constructing an effective visual risk prediction model is important for preventing, diagnosing, and treating early PVST in hepatolenticular degeneration (HLD) surgical patients.MethodsBetween January 2016 and December 2021, 309 HLD patients were selected. The data were split into a development set (215 cases from January 2016 to December 2019) and a validation set (94 cases from January 2019 to December 2021). Patients’ clinical characteristics and laboratory examinations were obtained from electronic medical record system, and PVST was diagnosed using Doppler ultrasound. Univariate and multivariate logistic regression analyses were used to establish the prediction model by variables filtered by LASSO regression, and a nomogram was drawn. The area under the curve (AUC) of receiver operating characteristic (ROC) curve and Hosmer–Lemeshow goodness-of-fit test were used to evaluate the differentiation and calibration of the model. Clinical net benefit was evaluated by using decision curve analysis (DCA). The 36-month survival of PVST was studied as well.ResultsSeven predictive variables were screened out using LASSO regression analysis, including grade, POD14D-dimer (Postoperative day 14 D-dimer), POD7PLT (Postoperative day 7 platelet), PVD (portal vein diameter), PVV (portal vein velocity), PVF (portal vein flow), and SVD (splenic vein diameter). Multivariate logistic regression analysis revealed that all seven predictive variables had predictive values (P < 0.05). According to the prediction variables, the diagnosis model and predictive nomogram of PVST cases were constructed. The AUC under the ROC curve obtained from the prediction model was 0.812 (95% CI: 0.756–0.869) in the development set and 0.839 (95% CI: 0.756–0.921) in the validation set. Hosmer–Lemeshow goodness-of-fit test fitted well (P = 0.858 for development set; P = 0.137 for validation set). The nomogram model was found to be clinically useful by DCA. The 36-month survival rate of three sites of PVST was significantly different from that of one (P = 0.047) and two sites (P = 0.023).ConclusionThe proposed nomogram-based prediction model can predict postoperative PVST. Meanwhile, an earlier intervention should be performed on three sites of PVST. |
| first_indexed | 2024-04-10T07:44:08Z |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-10T07:44:08Z |
| publishDate | 2023-02-01 |
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| series | Frontiers in Medicine |
| spelling | doaj.art-1e593191648549cfb39ed0e0e74aa2682023-02-23T11:00:25ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-02-011010.3389/fmed.2023.11032231103223Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degenerationZhou Zheng0Zhou Zheng1Qingsheng Yu2Qingsheng Yu3Hui Peng4Hui Peng5Long Huang6Long Huang7Wanzong Zhang8Wanzong Zhang9Yi Shen10Yi Shen11Hui Feng12Hui Feng13Wenshan Jing14Wenshan Jing15Qi Zhang16Qi Zhang17The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaThe First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Chinese Medicine Surgery, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaObjectiveSplenectomy is a vital treatment method for hypersplenism with portal hypertension. However, portal venous system thrombosis (PVST) is a serious problem after splenectomy. Therefore, constructing an effective visual risk prediction model is important for preventing, diagnosing, and treating early PVST in hepatolenticular degeneration (HLD) surgical patients.MethodsBetween January 2016 and December 2021, 309 HLD patients were selected. The data were split into a development set (215 cases from January 2016 to December 2019) and a validation set (94 cases from January 2019 to December 2021). Patients’ clinical characteristics and laboratory examinations were obtained from electronic medical record system, and PVST was diagnosed using Doppler ultrasound. Univariate and multivariate logistic regression analyses were used to establish the prediction model by variables filtered by LASSO regression, and a nomogram was drawn. The area under the curve (AUC) of receiver operating characteristic (ROC) curve and Hosmer–Lemeshow goodness-of-fit test were used to evaluate the differentiation and calibration of the model. Clinical net benefit was evaluated by using decision curve analysis (DCA). The 36-month survival of PVST was studied as well.ResultsSeven predictive variables were screened out using LASSO regression analysis, including grade, POD14D-dimer (Postoperative day 14 D-dimer), POD7PLT (Postoperative day 7 platelet), PVD (portal vein diameter), PVV (portal vein velocity), PVF (portal vein flow), and SVD (splenic vein diameter). Multivariate logistic regression analysis revealed that all seven predictive variables had predictive values (P < 0.05). According to the prediction variables, the diagnosis model and predictive nomogram of PVST cases were constructed. The AUC under the ROC curve obtained from the prediction model was 0.812 (95% CI: 0.756–0.869) in the development set and 0.839 (95% CI: 0.756–0.921) in the validation set. Hosmer–Lemeshow goodness-of-fit test fitted well (P = 0.858 for development set; P = 0.137 for validation set). The nomogram model was found to be clinically useful by DCA. The 36-month survival rate of three sites of PVST was significantly different from that of one (P = 0.047) and two sites (P = 0.023).ConclusionThe proposed nomogram-based prediction model can predict postoperative PVST. Meanwhile, an earlier intervention should be performed on three sites of PVST.https://www.frontiersin.org/articles/10.3389/fmed.2023.1103223/fullhepatolenticular degenerationsplenectomyportal vein system thrombosisnomogrampredictive variables |
| spellingShingle | Zhou Zheng Zhou Zheng Qingsheng Yu Qingsheng Yu Hui Peng Hui Peng Long Huang Long Huang Wanzong Zhang Wanzong Zhang Yi Shen Yi Shen Hui Feng Hui Feng Wenshan Jing Wenshan Jing Qi Zhang Qi Zhang Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration Frontiers in Medicine hepatolenticular degeneration splenectomy portal vein system thrombosis nomogram predictive variables |
| title | Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| title_full | Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| title_fullStr | Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| title_full_unstemmed | Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| title_short | Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| title_sort | nomogram based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration |
| topic | hepatolenticular degeneration splenectomy portal vein system thrombosis nomogram predictive variables |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1103223/full |
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