Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives.
World J Gastroenterol
; 30(10): 1329-1345, 2024 Mar 14.
Article
in En
| MEDLINE
| ID: mdl-38596504
ABSTRACT
Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Pancreatectomy
/
Pancreatic Fistula
Limits:
Humans
Language:
En
Journal:
World J Gastroenterol
Journal subject:
GASTROENTEROLOGIA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
United States