Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study

Table 1 Clinical baseline data and surgical information, n (%)

Variables	Total (n = 381)
Age, median (IQR), years	65 (57, 70)
Sex: Female	138 (36.2)
BMI, mean ± SD, kg/m2	22.8 ± 3.3
Diabetes mellitus	91 (23.9)
Hypertension	105 (27.6)
CAD	28 (7.4)
Alcohol consumption	66 (17.3)
Smoking	74 (19.4)
Jaundice	241 (63.3)
COPD	13 (3.4)
ASA
I/II	243 (63.8)
III/IV	138 (36.2)
Pathology: PDAC/CP	154 (40.4)
MPDD, median (IQR), mm	2.9 (1.7, 4.7)
Surgical modality
PD	229 (60.1)
PPPD	152 (39.9)
Venous resection	83 (21.8)
Pancreatic texture
Firm	194 (50.9)
Soft	187 (49.1)
Pancreatic duct stent
No	6 (1.6)
Internal stent	344 (90.3)
External stent	31 (8.1)
surgery time, median (IQR), minute	370 (306, 507)
EBL, median (IQR), mL	300 (200, 400)

IQR: Interquartile range; BMI: Body mass index; CAD: Coronary artery disease; COPD: Chronic obstructive pulmonary disease; ASA: American Society of Anesthesiologists; PDAC: Pancreatic ductal adenocarcinoma; CP: Chronic pancreatitis; MPDD: Main pancreatic duct diameter; PD: Pancreaticoduodenectomy; PPPD: Pylorus-preserving pancreatoduodenectomy; EBL: Estimated blood loss.