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Predicting venous thrombosis in patients undergoing elective splenectomy

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ARTICLE DOWNLOAD

Predicting venous thrombosis in patients undergoing elective splenectomy

5$

Peter Szasz, Ali Ardestani, Brent T. Shoji, David C. Brooks & Ali Tavakkoli 

Abstract

Background

Venous thrombosis (VT) is an ongoing problem for patients undergoing elective splenectomy. There is limited data evaluating risk factors for VTs. An increase in platelet counts is commonly seen after splenectomy; however, there is a paucity of literature evaluating post-operative platelet counts as a risk factor for VTs in this patient cohort. The objective of this study was to determine the incidence of VT events and to use the platelet count as a predictor for VT development.

Methods

A retrospective review was undertaken at Brigham Women’s Hospital, evaluating elective splenectomy patients between 1997 and 2018. Descriptive statistics were utilized to determine the incidence of VTs. Receiver operator characteristic (ROC) curves were utilized to identify platelet counts that could predict VTs.

Results

Five hundred and twenty splenectomies were included in the study of which 344 were completed in an open manner and 176 were done laparoscopically. The overall incidence of VT events was 6.7% (35/520), 6.1% (21/344) for open, and 8.0% (14/176) for laparoscopic approaches (p = 0.43). ROC curves demonstrated platelet counts to be a good predictor for the development of VTs with an area under the curve (AUC) of 0.77 (95% CI 0.69–0.86; p < 0.001) for all splenectomy patients, 0.70 (95% CI 0.59–0.81; p < 0.001) for those completed in an open manner, and 0.88 (95% CI 0.77–0.99; p < 0.001) for those done laparoscopically. The optimal platelet cutoff was found to be 545 for the overall splenectomy cohort, 457 for the open, and 659 for the laparoscopic cohorts. These platelet counts had a diagnostic accuracy that ranged from 61 to 86% and a negative predictive value (NPV) that ranged from 97 to 99%.

Conclusion

These results suggest platelet cutoffs that predict VTs. This information can be used to individualize prophylactic strategies.

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Year 2020
Language English
Format PDF
DOI 10.1007/s00464-019-07007-2