지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한외과학회 Annals of Surgical Treatment and Research Annals of Surgical Treatment and Research Vol.102 No.3
- 발행연도
- 2022.3
- 수록면
- 147 - 152 (6page)
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초록· 키워드
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Purpose: Postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). We previously developed nomogram- and artificial intelligence (AI)-based risk prediction platforms for POPF after PD. This study aims to externally validate these platforms.
Methods: Between January 2007 and December 2016, a total of 1,576 patients who underwent PD in Seoul National University Hospital, Ilsan Paik Hospital, and Boramae Medical Center were retrospectively reviewed. The individual risk scores for POPF were calculated using each platform by Samsung Medical Center. The predictive ability was evaluated using a receiver operating characteristic curve and the area under the curve (AUC). The optimal predictive value was obtained via backward elimination in accordance with the results from the AI development process.
Results: The AUC of the nomogram after external validation was 0.679 (P < 0.001). The values of AUC after backward elimination in the AI model varied from 0.585 to 0.672. A total of 13 risk factors represented the maximal AUC of 0.672 (P < 0.001).
Conclusion: We performed external validation of previously developed platforms for predicting POPF. Further research is needed to investigate other potential risk factors and thereby improve the predictability of the platform.
Methods: Between January 2007 and December 2016, a total of 1,576 patients who underwent PD in Seoul National University Hospital, Ilsan Paik Hospital, and Boramae Medical Center were retrospectively reviewed. The individual risk scores for POPF were calculated using each platform by Samsung Medical Center. The predictive ability was evaluated using a receiver operating characteristic curve and the area under the curve (AUC). The optimal predictive value was obtained via backward elimination in accordance with the results from the AI development process.
Results: The AUC of the nomogram after external validation was 0.679 (P < 0.001). The values of AUC after backward elimination in the AI model varied from 0.585 to 0.672. A total of 13 risk factors represented the maximal AUC of 0.672 (P < 0.001).
Conclusion: We performed external validation of previously developed platforms for predicting POPF. Further research is needed to investigate other potential risk factors and thereby improve the predictability of the platform.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2023-514-000221344