Steven Y Huang1, Bruno C Odisio2, Sharjeel H Sabir2, Joe E Ensor3, Andrew S Niekamp4, Tam T Huynh5, Michael Kroll6, Sanjay Gupta2. 1. Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX, 77030, USA. syhuang@mdanderson.org. 2. Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX, 77030, USA. 3. The Methodist Hospital Cancer Center, Methodist Hospital Research Institute, 6670 Bertner Avenue, Houston, TX, 77030, USA. 4. The University of Texas Health Science Center at Houston, 7000 Fannin Suite 1200, Houston, TX, 77030, USA. 5. Department of Vascular Surgery, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX, 77030, USA. 6. Department of Benign Hematology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX, 77030, USA.
Abstract
PURPOSE: Our purpose was to develop a predictive model for short-term survival (i.e. <6 months) following inferior vena cava filter placement in patients with venous thromboembolism (VTE) and solid malignancy. METHODS: Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. RESULTS: 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P < 0.0001], (2) recent or planned surgery (<30 days) (HR 0.409, P < 0.0001), (3) TNM staging (stage 1 or 2 vs. stage 4, HR 0.177, P = 0.0001; stage 3 vs. stage 4, HR 0.367, P = 0.0002). These variables were used to develop a predictive model to estimate 6 month survival with an area under the receiver operating characteristic curve of 0.815, sensitivity of 0.782, and specificity of 0.715. CONCLUSIONS: Six month survival in patients with VTE and solid malignancy requiring filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.
PURPOSE: Our purpose was to develop a predictive model for short-term survival (i.e. <6 months) following inferior vena cava filter placement in patients with venous thromboembolism (VTE) and solid malignancy. METHODS: Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. RESULTS: 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P < 0.0001], (2) recent or planned surgery (<30 days) (HR 0.409, P < 0.0001), (3) TNM staging (stage 1 or 2 vs. stage 4, HR 0.177, P = 0.0001; stage 3 vs. stage 4, HR 0.367, P = 0.0002). These variables were used to develop a predictive model to estimate 6 month survival with an area under the receiver operating characteristic curve of 0.815, sensitivity of 0.782, and specificity of 0.715. CONCLUSIONS: Six month survival in patients with VTE and solid malignancy requiring filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.
Authors: John A Kaufman; Thomas B Kinney; Michael B Streiff; Ronald F Sing; Mary C Proctor; Daniel Becker; Mark Cipolle; Anthony J Comerota; Steven F Millward; Frederick B Rogers; David Sacks; Anthony C Venbrux Journal: J Vasc Interv Radiol Date: 2006-03 Impact factor: 3.464
Authors: Charles E Ray; Erica Mitchell; Stan Zipser; Edward Y Kao; Charles F Brown; Greg L Moneta Journal: J Vasc Interv Radiol Date: 2006-10 Impact factor: 3.464
Authors: Drew M Caplin; Boris Nikolic; Sanjeeva P Kalva; Suvranu Ganguli; Wael E A Saad; Darryl A Zuckerman Journal: J Vasc Interv Radiol Date: 2011-09-03 Impact factor: 3.464
Authors: H Decousus; A Leizorovicz; F Parent; Y Page; B Tardy; P Girard; S Laporte; R Faivre; B Charbonnier; F G Barral; Y Huet; G Simonneau Journal: N Engl J Med Date: 1998-02-12 Impact factor: 91.245
Authors: So Young Park; So Yeon Lim; Sang-Won Um; Won-Jung Koh; Man Pyo Chung; Hojoong Kim; O Jung Kwon; Hye Kyeong Park; Seok Jin Kim; Young Hyuck Im; Myung-Ju Ahn; Gee Young Suh Journal: Support Care Cancer Date: 2013-01-12 Impact factor: 3.603
Authors: Stuart M Lyon; Guillermo Elizondo Riojas; Raman Uberoi; Jai Patel; Mario Enrique Baltazares Lipp; Graham R Plant; Miguel A De Gregorio; Rolf W Günther; William D Voorhees; Jennifer A McCann-Brown Journal: J Vasc Interv Radiol Date: 2009-11 Impact factor: 3.464
Authors: Paolo Prandoni; Anthonie W A Lensing; Andrea Piccioli; Enrico Bernardi; Paolo Simioni; Bruno Girolami; Antonio Marchiori; Paola Sabbion; Martin H Prins; Franco Noventa; Antonio Girolami Journal: Blood Date: 2002-07-12 Impact factor: 22.113