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A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients

To analyze the incidence of PICC associated venous thrombosis. To predict the risk factors of thrombosis. To validate the best predictive model in predicting PICC associated thrombosis. Consecutive oncology cases in 341 who initially naive intended to be inserted central catheter for chemotherapy, w...

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Autores principales: Song, Xiaomin, Lu, Hong, Chen, Fang, Bao, Zuowei, Li, Shanquan, Li, Siqin, Peng, Yinghua, Liu, Qiao, Chen, Xiaohui, Li, Jingzhen, Zhang, Weimin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308336/
https://www.ncbi.nlm.nih.gov/pubmed/32572092
http://dx.doi.org/10.1038/s41598-020-67038-x
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author Song, Xiaomin
Lu, Hong
Chen, Fang
Bao, Zuowei
Li, Shanquan
Li, Siqin
Peng, Yinghua
Liu, Qiao
Chen, Xiaohui
Li, Jingzhen
Zhang, Weimin
author_facet Song, Xiaomin
Lu, Hong
Chen, Fang
Bao, Zuowei
Li, Shanquan
Li, Siqin
Peng, Yinghua
Liu, Qiao
Chen, Xiaohui
Li, Jingzhen
Zhang, Weimin
author_sort Song, Xiaomin
collection PubMed
description To analyze the incidence of PICC associated venous thrombosis. To predict the risk factors of thrombosis. To validate the best predictive model in predicting PICC associated thrombosis. Consecutive oncology cases in 341 who initially naive intended to be inserted central catheter for chemotherapy, were recruited to our dedicated intravenous lab. All patients used the same gauge catheter, Primary endpoint was thrombosis formation, the secondary endpoint was infusion termination without thrombosis. Two patients were excluded. 339 patients were divided into thrombosis group in 59 (17.4%) and non-thrombosis Group in 280 (82.6%), retrospectively. Tumor, Sex, Age, Weight, Height, BMI, BSA, PS, WBC, BPC, PT, D-dimer, APTT, FIB, Smoking history, Location, Catheter length, Ratio and Number as independent variables were analyzed by Fisher’s scoring, then Logistic risk regression, ROC analysis and nomogram was introduced. Total incidence was 17.4%. Venous mural thrombosis in 2 (3.4%), “fibrin sleeves” in 55 (93.2%), mixed thrombus in 2 (3.4%), symptomatic thrombosis in 2 (3.4%), asymptomatic thrombosis in 57 (96.6%), respectively. Height (χ² = 4.48, P = 0.03), D-dimer (χ² = 37.81, P < 0.001), Location (χ² = 7.56, P = 0.006), Number (χ² = 43.64, P < 0.001), Ratio (χ² = 4.38, P = 0.04), and PS (χ² = 58.78, P < 0.001), were statistical differences between the two groups analyzed by Fisher’s scoring. Logistic risk regression revealed that Height (β = −0.05, HR = 0.95, 95%CI: 0.911–0.997, P = 0.038), PS (β = 1.07, HR = 2.91, 95%CI: 1.98–4.27, P < 0.001), D-dimer (β0.11, HR = 1.12, 95%CI: 1.045–1.200, P < 0.001), Number (β = 0.87, HR = 2.38, 95% CI: 1.619–3.512, P < 0.001) was independently associated with PICC associated thrombosis. The best prediction model, D-dimer + Number as a novel co-variable was validated in diagnosing PICC associated thrombosis before PICC. Our research revealed that variables PS, Number, D-dimer and Height were risk factors for PICC associated thrombosis, which were slightly associated with PICC related thrombosis, in which, PS was the relatively strongest independent risk factor of PICC related thrombosis.
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spelling pubmed-73083362020-06-23 A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients Song, Xiaomin Lu, Hong Chen, Fang Bao, Zuowei Li, Shanquan Li, Siqin Peng, Yinghua Liu, Qiao Chen, Xiaohui Li, Jingzhen Zhang, Weimin Sci Rep Article To analyze the incidence of PICC associated venous thrombosis. To predict the risk factors of thrombosis. To validate the best predictive model in predicting PICC associated thrombosis. Consecutive oncology cases in 341 who initially naive intended to be inserted central catheter for chemotherapy, were recruited to our dedicated intravenous lab. All patients used the same gauge catheter, Primary endpoint was thrombosis formation, the secondary endpoint was infusion termination without thrombosis. Two patients were excluded. 339 patients were divided into thrombosis group in 59 (17.4%) and non-thrombosis Group in 280 (82.6%), retrospectively. Tumor, Sex, Age, Weight, Height, BMI, BSA, PS, WBC, BPC, PT, D-dimer, APTT, FIB, Smoking history, Location, Catheter length, Ratio and Number as independent variables were analyzed by Fisher’s scoring, then Logistic risk regression, ROC analysis and nomogram was introduced. Total incidence was 17.4%. Venous mural thrombosis in 2 (3.4%), “fibrin sleeves” in 55 (93.2%), mixed thrombus in 2 (3.4%), symptomatic thrombosis in 2 (3.4%), asymptomatic thrombosis in 57 (96.6%), respectively. Height (χ² = 4.48, P = 0.03), D-dimer (χ² = 37.81, P < 0.001), Location (χ² = 7.56, P = 0.006), Number (χ² = 43.64, P < 0.001), Ratio (χ² = 4.38, P = 0.04), and PS (χ² = 58.78, P < 0.001), were statistical differences between the two groups analyzed by Fisher’s scoring. Logistic risk regression revealed that Height (β = −0.05, HR = 0.95, 95%CI: 0.911–0.997, P = 0.038), PS (β = 1.07, HR = 2.91, 95%CI: 1.98–4.27, P < 0.001), D-dimer (β0.11, HR = 1.12, 95%CI: 1.045–1.200, P < 0.001), Number (β = 0.87, HR = 2.38, 95% CI: 1.619–3.512, P < 0.001) was independently associated with PICC associated thrombosis. The best prediction model, D-dimer + Number as a novel co-variable was validated in diagnosing PICC associated thrombosis before PICC. Our research revealed that variables PS, Number, D-dimer and Height were risk factors for PICC associated thrombosis, which were slightly associated with PICC related thrombosis, in which, PS was the relatively strongest independent risk factor of PICC related thrombosis. Nature Publishing Group UK 2020-06-22 /pmc/articles/PMC7308336/ /pubmed/32572092 http://dx.doi.org/10.1038/s41598-020-67038-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Song, Xiaomin
Lu, Hong
Chen, Fang
Bao, Zuowei
Li, Shanquan
Li, Siqin
Peng, Yinghua
Liu, Qiao
Chen, Xiaohui
Li, Jingzhen
Zhang, Weimin
A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title_full A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title_fullStr A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title_full_unstemmed A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title_short A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients
title_sort longitudinal observational retrospective study on risk factors and predictive model of picc associated thrombosis in cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308336/
https://www.ncbi.nlm.nih.gov/pubmed/32572092
http://dx.doi.org/10.1038/s41598-020-67038-x
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