Anat Rabinovich1, Chu-Shu Gu2, Suresh Vedantham3, Clive Kearon4, Samuel Z Goldhaber5, Heather L Gornik6, Susan R Kahn7. 1. Thrombosis and Hemostasis Unit, Hematology Institute, Soroka University Medical Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel. 2. Department of Oncology, McMaster University, Hamilton, ON, Canada. 3. Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA. 4. Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, ON, Canada. 5. Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 6. University Hospitals, Case Western Reserve University, Cleveland, OH, USA. 7. Jewish General Hospital, Center for Clinical Epidemiology, Lady Davis Institute, Montreal, QC, Canada.
Abstract
BACKGROUND: Using data from the SOX Trial, we recently developed a clinical prediction model for occurrence of the postthrombotic syndrome (PTS) after proximal deep vein thrombosis (DVT), termed the SOX-PTS score. The score includes anatomical extent of DVT; body mass index; and baseline Villalta score. OBJECTIVE: To externally validate the SOX-PTS score. METHODS: Logistic regression analysis of data from the ATTRACT Trial that evaluated pharmacomechanical catheter directed thrombolysis in patients with proximal DVT. The primary outcome was the occurrence of PTS (defined as Villalta score ≥ 5) from 6 to 24 months after DVT. Secondary outcomes included moderate-severe PTS (Villalta scale ≥ 10) and severe PTS (Villalta scale ≥ 14). Predictive performance was assessed by discrimination and calibration. An updated score was evaluated in an exploratory analysis. RESULTS: Six hundred and ninety-one ATTRACT patients were included, of whom 328 (47%) developed PTS. The c-statistic was 0.63; 95% confidence interval (CI) 0.59-0.67 for PTS. The model's performance appeared to be better for the outcomes moderate to severe PTS and severe PTS (c-statistic 0.67; 95% CI 0.62-0.72 for moderate-severe PTS and 0.70; 0.64-0.77 for severe PTS). An updated model with age as an additional variable performed similarly to the original model. CONCLUSION: We externally validated the SOX-PTS score for estimating the risk of developing PTS, moderate to severe PTS, and severe PTS, in patients with proximal DVT. The score may be useful to predict PTS at the time of DVT diagnosis. Further external validation in different patient cohorts is required.
BACKGROUND: Using data from the SOX Trial, we recently developed a clinical prediction model for occurrence of the postthrombotic syndrome (PTS) after proximal deep vein thrombosis (DVT), termed the SOX-PTS score. The score includes anatomical extent of DVT; body mass index; and baseline Villalta score. OBJECTIVE: To externally validate the SOX-PTS score. METHODS: Logistic regression analysis of data from the ATTRACT Trial that evaluated pharmacomechanical catheter directed thrombolysis in patients with proximal DVT. The primary outcome was the occurrence of PTS (defined as Villalta score ≥ 5) from 6 to 24 months after DVT. Secondary outcomes included moderate-severe PTS (Villalta scale ≥ 10) and severe PTS (Villalta scale ≥ 14). Predictive performance was assessed by discrimination and calibration. An updated score was evaluated in an exploratory analysis. RESULTS: Six hundred and ninety-one ATTRACT patients were included, of whom 328 (47%) developed PTS. The c-statistic was 0.63; 95% confidence interval (CI) 0.59-0.67 for PTS. The model's performance appeared to be better for the outcomes moderate to severe PTS and severe PTS (c-statistic 0.67; 95% CI 0.62-0.72 for moderate-severe PTS and 0.70; 0.64-0.77 for severe PTS). An updated model with age as an additional variable performed similarly to the original model. CONCLUSION: We externally validated the SOX-PTS score for estimating the risk of developing PTS, moderate to severe PTS, and severe PTS, in patients with proximal DVT. The score may be useful to predict PTS at the time of DVT diagnosis. Further external validation in different patient cohorts is required.
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Authors: Suresh Vedantham; Samuel Z Goldhaber; Jim A Julian; Susan R Kahn; Michael R Jaff; David J Cohen; Elizabeth Magnuson; Mahmood K Razavi; Anthony J Comerota; Heather L Gornik; Timothy P Murphy; Lawrence Lewis; James R Duncan; Patricia Nieters; Mary C Derfler; Marc Filion; Chu-Shu Gu; Stephen Kee; Joseph Schneider; Nael Saad; Morey Blinder; Stephan Moll; David Sacks; Judith Lin; John Rundback; Mark Garcia; Rahul Razdan; Eric VanderWoude; Vasco Marques; Clive Kearon Journal: N Engl J Med Date: 2017-12-07 Impact factor: 176.079
Authors: Benilde Cosmi; Agata Stanek; Matja Kozak; Paul W Wennberg; Raghu Kolluri; Marc Righini; Pavel Poredos; Michael Lichtenberg; Mariella Catalano; Sergio De Marchi; Katalin Farkas; Paolo Gresele; Peter Klein-Wegel; Gianfranco Lessiani; Peter Marschang; Zsolt Pecsvarady; Manlio Prior; Attila Puskas; Andrzej Szuba Journal: Front Cardiovasc Med Date: 2022-02-24