Development of a prediction model for deep vein thrombosis in a retrospective cohort of patients with suspected deep vein thrombosis in primary care

Shekarchian, Soroosh; Notten, Pascale; Barbati, Mohammad E.; Laanen, Jorinde van; Piao, Linhua; Nieman, Fred; Razavi, Mahmood K.; Lao, Mildred; Mees, Barend; Jalaie, Houman

doi:10.1016/j.jvsv.2022.04.009

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Although previous studies have demonstrated that many risk factors are associated with DVT after lower limb fractures, they failed to translate the findings into accurate predictive model to identify DVT in elderly patients with lower limb fractures (7,(11)(12)(13). Indeed, these identified factors either relied too heavily on the subjective judgment of the assessors, or lacked well-validated biomarkers (14)(15)(16). Therefore, it is almost impossible to use these "isolated" risk factors to determine the "intuitive" and "real" possibility that one patient develops a DVT.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients

Han,

Bai,

Jiao

et al. 2023

Front. Surg.

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BackgroundPrevention of deep vein thrombosis (DVT) is indispensable in the treatment of lower limb fractures during the perioperative period. This study aimed to develop and validate a novel model for predicting the risk of DVT in elderly patients after orthopedic surgeries for lower limb fractures.MethodsThis observational study included 576 elderly patients with lower limb fractures who were surgically treated from January 2016 to December 2018. Eleven items affecting DVT were optimized by least absolute shrinkage and selection operator regression analysis. Multivariable logistic regression analysis was performed to construct a predictive model incorporating the selected features. C-index was applied to evaluate the discrimination. Decision curve analysis was employed to determine the clinical effectiveness of this model and calibration plot was applied to evaluate the calibration of this nomogram. The internal validation of this model was assessed by bootstrapping validation.ResultsPredictive factors that affected the rate of DVT in this model included smoking, time from injury to surgery, operation time, blood transfusion, hip replacement arthroplasty, and D-dimer level after operation. The nomogram showed significant discrimination with a C-index of 0.919 (95% confidence interval: 0.893–0.946) and good calibration. Acceptable C-index value could still be reached in the interval validation. Decision curve analysis indicated that the DVT risk nomogram was useful within all possibility threshold.ConclusionThis newly developed nomogram could be used to predict the risk of DVT in elderly patients with lower limb fractures during the perioperative period.

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Section: Introductionmentioning

confidence: 99%

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients

Han,

Bai,

Jiao

et al. 2023

Front. Surg.

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Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning

Zhang

2024

Am J Cancer Res

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This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.

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Development of a prediction model for deep vein thrombosis in a retrospective cohort of patients with suspected deep vein thrombosis in primary care

Cited by 2 publications

References 30 publications

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients

Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning

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