Peng Qiu1, Junchao Liu1, Fuzhen Wan2, Yuqian Chen3, Kaichuang Ye1, Jinbao Qin1, Qun Huang1, Xinwu Lu1. 1. Department of Vascular Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia. 3. School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China.
Abstract
BACKGROUND: Postthrombotic syndrome (PTS) is the most common long-term complication of deep vein thrombosis (DVT). Predictive models for PTS after hospitalized DVT patients, especially those with proximal DVT for whom preventative intervention decisions need to be made, are rare. We aimed to develop and externally validate a clinical predictive model for PTS in patients with proximal DVT. METHODS: This study was a retrospective, single-center, case-control study. The data used in our model were retrospectively collected from a prospective registry database in which 210 (derivation) and 90 (validation) consecutive patients were first diagnosed with proximal DVT. We developed a nomogram using the multivariate logistic regression model. External validation of our predictive model and previous predictive models in our validation set was assessed by discrimination, calibration, and clinical utility. RESULTS: Of the 30 candidate predictors, 5 were significantly associated with PTS in our final multivariable model, including the number of signs and symptoms (OR 1.33, 95% CI: 1.17 to 1.53, P<0.001), male sex (OR 1.79, 95% CI: 1.07 to 3.06, P=0.028), varicose vein history (OR 3.02, 95% CI: 1.04 to 7.60, P<0.001), BMI (OR 1.06, 95% CI: 1.00 to 1.12, P=0.052), and chronic DVT (OR 2.66, 95% CI: 1.49 to 4.79, P<0.001). The area under the curve was 0.724 in our predictive model, indicating suitable external performance. CONCLUSIONS: A simple-to-use nomogram effectively predicts the risk of PTS in patients with proximal DVT. This predictive model may be considered for use in clinical care. 2021 Annals of Translational Medicine. All rights reserved.
BACKGROUND: Postthrombotic syndrome (PTS) is the most common long-term complication of deep vein thrombosis (DVT). Predictive models for PTS after hospitalized DVT patients, especially those with proximal DVT for whom preventative intervention decisions need to be made, are rare. We aimed to develop and externally validate a clinical predictive model for PTS in patients with proximal DVT. METHODS: This study was a retrospective, single-center, case-control study. The data used in our model were retrospectively collected from a prospective registry database in which 210 (derivation) and 90 (validation) consecutive patients were first diagnosed with proximal DVT. We developed a nomogram using the multivariate logistic regression model. External validation of our predictive model and previous predictive models in our validation set was assessed by discrimination, calibration, and clinical utility. RESULTS: Of the 30 candidate predictors, 5 were significantly associated with PTS in our final multivariable model, including the number of signs and symptoms (OR 1.33, 95% CI: 1.17 to 1.53, P<0.001), male sex (OR 1.79, 95% CI: 1.07 to 3.06, P=0.028), varicose vein history (OR 3.02, 95% CI: 1.04 to 7.60, P<0.001), BMI (OR 1.06, 95% CI: 1.00 to 1.12, P=0.052), and chronic DVT (OR 2.66, 95% CI: 1.49 to 4.79, P<0.001). The area under the curve was 0.724 in our predictive model, indicating suitable external performance. CONCLUSIONS: A simple-to-use nomogram effectively predicts the risk of PTS in patients with proximal DVT. This predictive model may be considered for use in clinical care. 2021 Annals of Translational Medicine. All rights reserved.
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