Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives

Table 5 Machine learning prediction models of postoperative pancreatic fistula after pancreaticoduodenectomy

Ref.	Year	Country	Center	Study period	Design cohort	CR-POPF (%)	C-index/AUC (95%CI)	Validation
Mu et al[54]	2020	China	Multi	2006-2019	359	15.6	0.85 (0.80-0.90)	Internal-external
Han et al[55]	2020	Korea	Single	2007-2016	1769	12.5	0.74
Skawran et al[48]	2021	Switzerland	Single	2008-2018	62	27.4	0.82 (0.74-0.89), 0.74 (0.63-0.89), 0.90 (0.84-0.95)	Internal
Giovinazzo et al[56]	2021	Multinational	Multi		1638	27	0.962 (0.940-0.984)
Shen et al[57]	2022	China	Single	2010-2021	2421	17.5	0.79-0.81	Internal
Long et al[58]	2022	China	Multi	2012-2021	618	18.1	0.897 (0.370-1.424)	Internal
Capretti et al[59]	2022	Italy	Single1	2011-2019	100	20	0.807, 0.749	Internal
Chen et al[60]	2022	United States	Nationwide	2014-2019	13940	14.4	0.746 (0.733-0.760)	Internal-external
Zheng et al[61]	2023	China	Single	2013-2021	2572	21.8	0.977	Internal
Ingwersen et al[22]	2023	Netherlands	Nationwide	2014-2020	4912	16.3	0.74 (0.73-0.74)
Verma et al[62]	2023	United States	Nationwide	2014-2018	8597	11	0.74 (0.72-0.76)	Internal-external
Ashraf Ganjouei et al[63]	2023	United States	Nationwide	2014-2019	8666	13	0.67-0.72	Internal
Ingwersen et al[64]	2023	Multinational	Multi	2013-2018	118	42.4	0.9 (0.71-0.99), 0.86, 0.81, 0.8	Internal-external

¹Indicates prospective studies, others are retrospective studies.

²Surgical procedures included open and laparoscopic pancreaticoduodenectomy.

AUC: Area under the curve; CI: Confidence interval; CR-POPF: Clinically relevant postoperative pancreatic fistula.