JOURNAL OF CLINICAL SURGERY ›› 2026, Vol. 34 ›› Issue (8): 870-875.doi: 10.3969/j.issn.1005-6483.20251161

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Construction and validation of anomogram prediction model for deep vein thrombosis of lower extremities in patients with traumatic rib fractures

  

  1. Department of Thoracic and Cardiovascular Surgery,Putian No.95 Hospital,Putian 351100,China
  • Received:2025-11-16 Accepted:2025-11-16 Online:2026-08-20 Published:2026-08-20

Abstract: Objective  To investigate the risk factors for deep vein thrombosis of lower extremities (LEDVT) in patients with traumatic rib fractures,construct a nomogram prediction model,and validate its performance.Methods A retrospective study was conducted on 211 patients with traumatic rib fractures admitted to Putian 95th Hospital from January 2020 to February 2025.Clinical data were collected.Variables were first screened using LASSO regression,followed by multivariate Logistic- regression analysis.A nomogram prediction model was constructed based on independent risk factors,and its predictive performance was validated.Result The postoperative incidence of lower extremity deep vein thrombosis (LEDVT) in the enrolled patients was 18.96% (40/211).The non-LEDVT group had significantly lower body mass index (BMI) than the LEDVT group[(23.10±2.55) kg/m2 vs.(24.17±2.63) kg/m2].The proportion of patients with >10 rib fractures were 13.45% and 30.00%,respectively.The proportion of patients with concomitant fractures at other sites were 42.69% and 65.00%,respectively.The operation time were (98.17±15.71) min and (105.50±14.72) min,respectively.There was a statistically significant difference between the two groups (P<0.05).The prothrombin time (PT) in the non-LEDVT group and the LEDVT group was (13.17±2.50) seconds and (12.30±2.00) seconds,respectively.There was a statistically significant difference between the two groups (P<0.05).LASSO regression analysis identified 8 key variables.Multivariate Logistic regression analysis revealed that >10 rib fractures (OR=4.070,95%CI:1.561-10.611),concomitant fractures at other sites (OR=2.871,95%CI:1.267-6.506),operation time > 110min (OR=2.986,95%CI:1.313-6.790),and D-dimer (D-D) > 2mg/L (OR=3.812,95%CI:1.655-8.778) were independent risk factors for LEDVT in patients with traumatic rib fractures (P<0.05).Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) was 0.764 (95%CI:0.665-0.864) for the training set and 0.731 (95%CI:0.634-0.828) for the test set.Calibration curve analysis demonstrated no significant differences between the predicted and actual probabilities in both the training and test sets.Decision curve analysis indicated that within the highrisk threshold range of 0.1-0.75,prophylactic intervention guided by the nomogram model yielded higher standardized net benefits compared with universal intervention or no intervention.Conclusion Postoperative LEDVT in patients with traumatic rib fractures is closely associated with the number of rib fractures,concomitant fractures at other sites,operation time,and DD levels.The nomogram prediction model constructed based on these indicators can accurately predict the occurrence of LEDVT,providing a reference for formulating early prevention and treatment strategies for clinical LEDVT.

Key words: traumatic rib fracture, deep vein thrombosis of lower extremities, nomogram, predictive model

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[2] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(10): 783 .
[3] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(11): 856 .
[4] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(11): 882 .
[5] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(11): 885 .
[6] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(12): 896 .
[7] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(12): 927 .
[8] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(12): 922 .
[9] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(12): 926 .
[10] . [J]. JOURNAL OF CLINICAL SURGERY, 2016, 24(4): 273 .