31 December 2023: Clinical Research
Generation of a Predictive Clinical Model for Isolated Distal Deep Vein Thrombosis (ICMVT) Detection
Yan Xu 1ABCDE , Mingmin Xu 1CD , Xiaofang Zheng 1CD , Fengxia Jin 1BCD , Bin Meng 1ABCDFG*DOI: 10.12659/MSM.942840
Med Sci Monit 2023; 29:e942840
Table 1 Baseline characteristics of the study population.
| Characteristics | All patients | Training sample | Validation sample | P value |
|---|---|---|---|---|
| Number | N=462 | N=328 | N=134 | |
| Sex, N (%) | 0.941 | |||
| Male | 247 (53.5%) | 175 (53.4%) | 72 (53.7%) | |
| Female | 215 (46.5%) | 153 (46.6%) | 62 (46.3%) | |
| Active cancer, N (%) | 0.133 | |||
| Yes | 59 (12.8%) | 37 (11.3%) | 22 (16.4%) | |
| No | 403 (87.2%) | 291 (88.7%) | 112 (83.6%) | |
| Paralysis, paresis, or recent plaster immobilization of the lower extremities, N (%) | 0.97 | |||
| Yes | 189 (40.9%) | 134 (40.9%) | 55 (41%) | |
| No | 273 (59.1%) | 194 (59.1%) | 79 (59%) | |
| Recent immobilization >3 days or major surgery within the past 4 weeks, N (%) | 0.985 | |||
| Yes | 210 (45.5%) | 149 (45.4%) | 61 (45.5%) | |
| No | 252 (54.5%) | 179 (54.6%) | 73 (54.5%) | |
| Localized tenderness of deep venous system, N (%) | 0.052 | |||
| Yes | 94 (20.3%) | 76 (23.2%) | 18 (13.4%) | |
| No | 368 (79.7%) | 252 (76.8%) | 116 (86.6%) | |
| Entire leg swollen, N (%) | 0.854 | |||
| Yes | 77 (16.7%) | 54 (16.5%) | 23 (17.2%) | |
| No | 385 (83.3%) | 274 (83.5%) | 111 (82.8%) | |
| Calf swelling >3 cm compared with asymptomatic side, N (%) | 0.043 | |||
| Yes | 81 (17.5%) | 65 (19.8%) | 16 (11.9%) | |
| No | 381 (82.5%) | 263 (80.2%) | 118 (88.1%) | |
| Unilateral pitting edema, N (%) | 0.840 | |||
| Yes | 53 (11.5%) | 37 (11.3%) | 16 (11.9%) | |
| No | 409 (88.5%) | 291 (88.7%) | 118 (88.1%) | |
| Age, N (%) | 0.948 | |||
| 79 (17.1%) | 56 (17.1%) | 23 (17.2%) | ||
| 50–60 | 87 (18.8%) | 63 (19.2%) | 24 (17.9%) | |
| >60 | 296 (64.1%) | 209 (63.7%) | 87 (64.9%) | |
| WBC(10/L), N (%) | 0.526 | |||
| 21 (4.5%) | 16 (4.9%) | 5 (3.7%) | ||
| 3.5–9.5 | 358 (77.5%) | 257 (78.4%) | 101 (75.4%) | |
| >9.5 | 83 (18%) | 55 (16.7%) | 28 (20.9%) | |
| NC (10/L), N (%) | 0.632 | |||
| 12 (2.6%) | 9 (2.7%) | 3 (2.2%) | ||
| 1.8–6.36 | 326 (70.6%) | 235 (71.6%) | 91 (67.9%) | |
| >6.6 | 124 (26.8%) | 84 (25.7%) | 40 (29.9%) | |
| LC (10/L), N (%) | 0.646 | |||
| 171 (37%) | 125 (38.1%) | 46 (34.3%) | ||
| 1.1–3.2 | 282 (61%) | 196 (59.8%) | 86 (64.2%) | |
| >3.2 | 9 (2.0%) | 7 (2.1%) | 2 (1.5%) | |
| PLT (10/L), N (%) | 0.783 | |||
| 72 (15.6%) | 53 (16.2%) | 19 (14.2%) | ||
| 125–3506 | 370 (80.1%) | 260 (79.3%) | 110 (82.1%) | |
| >350 | 20 (4.3%) | 15 (4.5%) | 5 (3.7%) | |
| PLTV (fl), N (%) | 0.428 | |||
| 0 | 0 | 0 | ||
| 6.5–13 | 437 (94.6%) | 312 (95.1%) | 125 (93.3%) | |
| >13 | 25 (5.4%) | 16 (4.9%) | 9 (6.7%) | |
| FIB (g/l), N (%) | 0.976 | |||
| 26 (5.6%) | 18 (5.5%) | 8 (6.0%) | ||
| 1.8–3.5 | 272 (58.9%) | 193 (58.8%) | 79 (59.0%) | |
| >3.5 | 164 (35.5%) | 117 (35.7%) | 47 (35.0%) | |
| D-dimer (ng/ml), N (%) | 0.482 | |||
| 156 (33.8%) | 114 (34.8%) | 42 (31.3%) | ||
| 500–1000 | 69 (14.9%) | 45 (13.7%) | 24 (17.9%) | |
| >1000 | 237 (51.3%) | 169 (51.5%) | 68 (50.7%) | |
| CRP (mg/L), N (%)* | 0.453 | |||
| 305 (66%) | 220 (67.1%) | 85 (63.4%) | ||
| ≥17.7 | 157 (34%) | 108 (32.9%) | 49 (36.6%) | |
| NP, N (%)* | 0.287 | |||
| 286 (61.9%) | 198 (60.4%) | 88 (65.7%) | ||
| ≥0.73 | 176 (38.1%) | 130 (39.6%) | 46 (34.3%) | |
| WBC – white blood cell count; PLT – platelet count; PLTV – platelet volume; NC – neutrophil count; LC – lymphocyte count; FIB – fibrinogen; CRP – C-reactive protein; NP – neutrophil percentage. * Queue segmentation based on cut-off values. | ||||