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©The Author(s) 2022.
World J Gastrointest Surg. Sep 27, 2022; 14(9): 963-975
Published online Sep 27, 2022. doi: 10.4240/wjgs.v14.i9.963
Published online Sep 27, 2022. doi: 10.4240/wjgs.v14.i9.963
Table 1 Baseline demographic and clinicopathological characteristics of patients
Variables | Training set | Testing set | ||||||
Overall (n = 432) | Non-POPF (n = 354) | POPF (n = 78) | P value | Overall (n = 186) | Non-POPF (n = 166) | POPF (n = 20) | P value | |
Age, median (IQR) | 55.0 (49.0–61.0) | 55.0 (49.0–61.0) | 53.0 (47.25–61.0) | 0.147 | 55.0 (50.0–60.0) | 55.0 (50.0–60.0) | 51.50 (45.75–59.50) | 0.182 |
BMI, median (IQR) | 23.10 (21.80–24.60) | 22.80 (21.50–24.20) | 25.0 (23.33–26.92) | < 0.001 | 22.85 (21.72–24.30) | 22.70 (21.52–23.98) | 24.35 (22.88–26.13) | < 0.001 |
Gender (%) | ||||||||
Male | 283 (65.5) | 227 (64.1) | 56 (71.8) | 0.247 | 127 (68.3) | 110 (66.3) | 17 (85.0) | 0.148 |
Female | 149 (34.5) | 127 (35.9) | 22 (28.2) | 59 (31.7) | 56 (33.7) | 3 (15.0) | ||
Smoking (%) | ||||||||
Yes | 198 (45.8) | 143 (40.4) | 55 (70.5) | < 0.001 | 89 (47.8) | 76 (45.8) | 13 (65.0) | 0.165 |
No | 234 (54.2) | 211 (59.6) | 23 (29.5) | 97 (52.2) | 90 (54.2) | 7 (35.0) | ||
Drinking history (%) | ||||||||
Yes | 129 (29.9) | 78 (22.0) | 51 (65.4) | < 0.001 | 54 (29.0) | 40 (24.1) | 14 (70.0) | < 0.001 |
No | 303 (70.1) | 276 (78.0) | 27 (34.6) | 132 (71.0) | 126 (75.9) | 6 (30.0) | ||
Diabetes (%) | ||||||||
Yes | 110 (25.5) | 49 (13.8) | 61 (78.2) | < 0.001 | 44 (23.7) | 30 (18.1) | 14 (70.0) | < 0.001 |
No | 322 (74.5) | 305 (86.2) | 17 (21.8) | 142 (76.3) | 136 (81.9) | 6 (30.0) | ||
Hypertension (%) | ||||||||
Yes | 164 (38.0) | 129 (36.4) | 35 (44.9) | 0.208 | 59 (31.7) | 49 (29.5) | 10 (50.0) | 0.108 |
No | 268 (62.0) | 225 (63.6) | 43 (55.1) | 127 (68.3) | 117 (70.5) | 10 (50.0) | ||
Abdominal operation (%) | ||||||||
Yes | 130 (30.1) | 103 (29.1) | 27 (34.6) | 0.409 | 53 (28.5) | 47 (28.3) | 6 (30.0) | 1 |
No | 302 (69.9) | 251 (70.9) | 51 (65.4) | 133 (71.5) | 119 (71.7) | 14 (70.0) | ||
Remnant texture (%) | ||||||||
Soft | 121 (28.0) | 62 (17.5) | 59 (75.6) | < 0.001 | 44 (23.7) | 27 (16.3) | 17 (85.0) | < 0.001 |
Hard | 311 (72.0) | 292 (82.5) | 19 (24.4) | 142 (76.3) | 139 (83.7) | 3 (15.0) | ||
Blood transfusion (%) | ||||||||
Yes | 232 (53.7) | 188 (53.1) | 44 (56.4) | 0.686 | 96 (51.6) | 84 (50.6) | 12 (60.0) | 0.577 |
No | 200 (46.3) | 166 (46.9) | 34 (43.6) | 90 (48.4) | 82 (49.4) | 8 (40.0) | ||
Anemia (%) | ||||||||
Yes | 218 (50.5) | 179 (50.6) | 39 (50.0) | 1 | 84 (45.2) | 69 (41.6) | 15 (75.0) | 0.009 |
No | 214 (49.5) | 175 (49.4) | 39 (50.0) | 102 (54.8) | 97 (58.4) | 5 (25.0) | ||
Lesion size (%), cm | ||||||||
> 3 | 182 (42.1) | 125 (35.3) | 57 (73.1) | < 0.001 | 67 (36.0) | 54 (32.5) | 13 (65.0) | 0.009 |
≤ 3 | 250 (57.9) | 229 (64.7) | 21 (26.9) | 119 (64.0) | 112 (67.5) | 7 (35.0) | ||
Pancreatic duct diameter (%), mm | ||||||||
< 3 | 154 (35.6) | 93 (26.3) | 61 (78.2) | < 0.001 | 63 (33.9) | 49 (29.5) | 14 (70.0) | 0.001 |
≥ 3 | 278 (64.4) | 261 (73.7) | 17 (21.8) | 123 (66.1) | 117 (70.5) | 6 (30.0) | ||
ASA classification (%) | ||||||||
I + II | 231 (53.5) | 188 (53.1) | 43 (55.1) | 0.843 | 85 (45.7) | 78 (47.0) | 7 (35.0) | 0.436 |
III + IV | 201 (46.5) | 166 (46.9) | 35 (44.9) | 101 (54.3) | 88 (53.0) | 13 (65.0) | ||
CRP, median (IQR), mg/L | 32.0 (22.0–44.0) | 29.0 (21.0–38.0) | 88.50 (56.0–120.0) | < 0.001 | 30.0 (22.0–40.0) | 29.0 (21.0–38.0) | 84.50 (42.25–109.25) | < 0.001 |
WBC, median (IQR), 109 | 5.70 (5.30–6.30) | 5.70 (5.20–6.20) | 6.0 (5.60–6.60) | < 0.001 | 5.70 (5.20–6.30) | 5.60 (5.20–6.20) | 6.40 (5.52–6.82) | 0.002 |
PCT, median (IQR), μg/L | 0.54 (0.37–0.68) | 0.49 (0.34–0.61) | 1.06 (0.78–1.21) | < 0.001 | 0.52 (0.37–0.67) | 0.49 (0.35–0.63) | 0.84 (0.68–1.09) | < 0.001 |
AGR, median (IQR) | 1.50 (1.30–1.60) | 1.50 (1.40–1.60) | 1.35 (1.20–1.40) | < 0.001 | 1.50 (1.30–1.60) | 1.50 (1.40–1.60) | 1.35 (1.17–1.52) | 0.003 |
PNI, median (IQR) | 49.60 (48.10–51.23) | 49.90 (48.32–51.60) | 48.60 (47.35–49.60) | < 0.001 | 50.10 (48.40–51.48) | 50.30 (48.42–51.60) | 49.30 (46.85–50.37) | 0.02 |
Neutrophil count, median (IQR), 109 | 4.02 (3.49–4.59) | 4.18 (3.70–4.68) | 3.36 (3.03–3.74) | < 0.001 | 3.94 (3.51–4.54) | 4.03 (3.57–4.57) | 3.46 (3.11–3.76) | < 0.001 |
Lymphocyte count, median (IQR), 109 | 1.64 (1.51–1.78) | 1.63 (1.50–1.76) | 1.79 (1.60–1.94) | < 0.001 | 1.64 (1.53–1.76) | 1.63 (1.52–1.73) | 1.83 (1.69–1.98) | < 0.001 |
Platelet count, median (IQR), 109 | 230.0 (208.0–252.0) | 236.0 (213.0–255.0) | 206.0 (185.25–229.75) | < 0.001 | 229.0 (206.0–253.75) | 232.0 (208.25–257.75) | 200.0 (182.50–225.0) | < 0.001 |
Monocyte count, median (IQR), 109 | 0.52 (0.45–0.60) | 0.55 (0.47–0.62) | 0.44 (0.39–0.49) | < 0.001 | 0.53 (0.46–0.61) | 0.54 (0.47–0.62) | 0.48 (0.42–0.52) | 0.003 |
Hemoglobin, median (IQR), g/L | 132.0 (124.0–139.0) | 130.0 (121.25–138.0) | 138.0 (133.0–142.75) | < 0.001 | 132.0 (126.0–140.0) | 132.0 (126.0–139.75) | 134.50 (130.0–141.0) | 0.026 |
NLR, median (IQR) | 2.0 (1.70–2.30) | 1.90 (1.70–2.20) | 2.70 (2.22–3.10) | < 0.001 | 2.0 (1.70–2.30) | 1.90 (1.60–2.20) | 2.80 (2.42–3.05) | < 0.001 |
NAR, median (IQR) | 0.08 (0.07–0.09) | 0.08 (0.07–0.09) | 0.60 (0.30–0.88) | < 0.001 | 0.08 (0.07–0.09) | 0.08 (0.07–0.09) | 0.65 (0.38–0.80) | < 0.001 |
PLR, median (IQR) | 136.20 (116.68–157.43) | 143.85 (123.23–161.70) | 113.15 (102.58–128.0) | < 0.001 | 136.45 (120.62–155.80) | 141.0 (121.22–159.78) | 120.15 (104.78–128.57) | < 0.001 |
LMR, median (IQR) | 3.40 (2.90–3.80) | 3.30 (2.80–3.70) | 3.90 (3.52–4.70) | < 0.001 | 3.50 (3.0–3.80) | 3.40 (2.90–3.70) | 4.15 (3.75–4.48) | < 0.001 |
HALP, median (IQR) | 53.95 (51.08–56.50) | 52.90 (50.50–55.20) | 72.75 (69.32–75.25) | < 0.001 | 52.45 (50.40–55.18) | 51.95 (50.10–54.30) | 70.10 (68.18–72.62] | < 0.001 |
Table 2 The operating characteristic curve analyses for each machine learning-based model
Model | AUC | No. of candidate variables | |
Mean | 95%CI | ||
RFC | 0.897 | 0.370–1.424 | 7 |
SVM | 0.726 | 0.191–1.261 | 8 |
DT | 0.807 | 0.250–1.364 | 8 |
ANN | 0.882 | 0.321–1.443 | 7 |
XGboost | 0.793 | 0.270–1.316 | 9 |
- Citation: Long ZD, Lu C, Xia XG, Chen B, Xing ZX, Bie L, Zhou P, Ma ZL, Wang R. Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy. World J Gastrointest Surg 2022; 14(9): 963-975
- URL: https://www.wjgnet.com/1948-9366/full/v14/i9/963.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v14.i9.963