Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study.
Purpose: To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing materna...
| Publicado en: | Abdominal Radiology Vol. 49; no. 7; pp. 2325 - 2340 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678148&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678148 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jul2024 vid: 49 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678148 177959327 10.1007/s00261-024-04419-0 178678148 ppf: 2325 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study. aug: au: Zheng, Changye Yue, Peiyan Cao, Kangyang Wang, Ya Zhang, Chang Zhong, Jian Xu, Xiaoyang Lin, Chuxuan Liu, Qinghua Zou, Yujian Huang, Bingsheng affil: https://ror.org/022s5gm85 Department of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China sug: ab: Purpose: To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing maternal morbidity. Methods: In this retrospective consecutive series study, a total of 346 patients who underwent magnetic resonance imaging (156 for training and 68 for internal test, center 1; 122 for external test, center 2) were included. IBL+ was defined as more than 1000 mL estimated blood loss during cesarean sections. The prediction models of IBL were developed based on machine-learning algorithms using CFI, radiomics features, and clinical factors. ROC analysis was performed to evaluate the performance for IBL diagnosis. Results: The support vector machine model incorporating all three modalities achieved an AUC of 0.873 (95% CI 0.769–0.941) and a sensitivity of 1.000 (95% CI 0.846–1.000) in the internal test set, with an AUC of 0.806 (95% CI 0.725–0.872) and a sensitivity of 0.873 (95% CI 0.799–0.922) in the external test set. It was also scored significantly higher than the CFI model (P = 0.035) on the internal test set, and both the CFI (P = 0.002) and radiomics-CFI models (P = 0.007) on the external test set. Additionally, the nomogram constructed based on three modalities achieved an internal testing set AUC of 0.960 (95% CI 0.806–0.999) and an external testing set AUC of 0.869 (95% CI 0.684–0.967) in the pregnant population without a pernicious placenta previa. It is noteworthy that the AUC of the proposed model did not show a statistically significant improvement compared to the Clinical-CFI model in both internal (P = 0.115) and external test sets (P = 0.533). Conclusion: The proposed model demonstrated good performance in predicting intraoperative blood loss (IBL), exhibiting high sensitivity and robust generalizability, with potential applicability to other surgeries such as vaginal delivery and postpartum hysterectomy. However, the performance of the proposed model was not statistically significantly better than that of the Clinical-CFI model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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