| Literatürler Hematoloji Uzmanlık Derneği
Literatür Detay Bilgisi
Validation of a Venous Thromboembolism Risk Assessment Model in Gynecologic Oncology.

Yazarlar : Stroud W, Whitworth JM, Miklic M

Yayın : Gynecol Oncol

Yayın Yılı : 2014

Pubmed Linki : http://www.ncbi.nlm.nih.gov/pubmed/24796634

Konu : Tromboz

Literatür İçeriği :  

BACKGROUND:

Gynecologic oncology patients undergoing surgery are at an increased risk for venous thromboembolism (VTE). We attempted to validate a VTE risk assessment model in gynecologic oncology patients.

METHODS:

All gynecologic oncology patients who underwent a laparotomy for the diagnosis or suspicion of gynecologic malignancy from 2004 to 2010 were included. Demographic, surgicopathologic, and complication data were collected. VTE was based on the symptomatic diagnosis. Data for the Caprini risk assessment model (RAM) was used to score and stratify patients on their risk for VTE.

RESULTS:

1,123 gynecologic oncology patients were included within this study. Ovarian cancer was the most common diagnosis (39%) with a median age of 56.1. All patients received SCDs with 40% receiving double prophylaxis. The overall incidence of VTE was 3.3%, with lower extremity deep venous thrombosis (DVT) n=17 and pulmonary embolism (PE) n=20. Complication rates were similar in each group. Based on the Caprini scoring model 92% of patients scored in the "Highest Risk" category. The Caprini RAM accurately predicted all 37 VTEs, all of which scored in the "Highest Risk" category. The percentage of patients that received double prophylaxis increased with time from 12% in 2004 to 63% in 2010. Importantly, 25 of the 37 VTEs (68%) did not receive double prophylaxis.

CONCLUSIONS:

The use of the Caprini RAM accurately predicted patients at the highest risk of experiencing VTE. Considering accurate identification of patients allows proper administration of double prophylaxis, we recommend the use of this scoring model preoperatively in patients undergoing surgery for gynecologic malignancies.

Copyright © 2014. Published by Elsevier Inc.


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