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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Aim

Diagnosis of pulmonary thromboembolism (PTE) can be delayed if the signs and symptoms of patients are nonspecific.

Introduction

To assess the clinical value of deep vein thrombosis (DVT) density on computed tomography (CT) venography for predicting PTE.

Methods

From 2016 to 2021, patients with DVT diagnosed on lower-extremity CT venography were included. Of these patients, those without PTE were classified into ‘DVT-only group’ and those with PTE were classified into the ‘DVT with PTE group’. The DVT Hounsfield unit (HU) density was measured by drawing free-hand region-of-interests within the thrombus at the most proximal filling defect level. The risk factors associated with PTE were identified by using multivariate logistic regression analysis. A receiver operating characteristic (ROC) analysis was used to evaluate the value of DVT density for predicting the risk of PTE.

Results and Discussion

This study included 177 patients with a mean age of 41.7 ± 10.3 years (DVT-only group: 105 patients; DVT with PTE group: 72 patients). DVT density was significantly higher in DVT with the PTE group than DVT-only group (66.8HU ± 8.7 57.9HU ± 11.1, < 0.001). The ROC analysis revealed that the area under the curve (AUC), sensitivity, and specificity for predicting the risk of PTE were 0.737, 72.2%, and 66.7%, respectively, at a DVT density cutoff of 63.0 HU. On univariate and multivariate analysis, DVT density was the only significant risk factor associated with PTE.

Conclusion

Higher DVT density was a significant risk factor for PTE. In addition, DVT density could be a predictive factor for PTE.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
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References

  1. JeongM.J. KwonH. NohM. KoG.Y. GwonD.I. LeeJ.S. KimM.J. ChoiJ.Y. HanY. KwonT.W. ChoY.P. Relationship of lower-extremity deep venous thrombosis density at CT venography to acute pulmonary embolism and the risk of postthrombotic syndrome.Radiology2019293368769410.1148/radiol.201919035831592733
    [Google Scholar]
  2. FowkesF.J.I. PriceJ.F. FowkesF.G.R. Incidence of diagnosed deep vein thrombosis in the general population: Systematic review.Eur. J. Vasc. Endovasc. Surg.20032511510.1053/ejvs.2002.177812525804
    [Google Scholar]
  3. GalanaudJ.P. HolcroftC.A. RodgerM.A. KovacsM.J. BetancourtM.T. WellsP.S. AndersonD.R. ChagnonI. Le GalG. SolymossS. CrowtherM.A. PerrierA. WhiteR.H. VickarsL.M. RamsayT. KahnS.R. Predictors of post-thrombotic syndrome in a population with a first deep vein thrombosis and no primary venous insufficiency.J. Thromb. Haemost.201311347448010.1111/jth.1210623279046
    [Google Scholar]
  4. EberhardtR.T. RaffettoJ.D. Chronic venous insufficiency.Circulation2005111182398240910.1161/01.CIR.0000164199.72440.0815883226
    [Google Scholar]
  5. HuismanM.V. KlokF.A. Current challenges in diagnostic imaging of venous thromboembolism.Blood2015126212376238210.1182/blood‑2015‑05‑64097926585807
    [Google Scholar]
  6. LiuY. XieM. GaoX. LiuR. Predictive value of circulating microRNA-134 levels for early diagnosis of acute pulmonary embolism: meta-analysis.J. Cardiovasc. Transl. Res.202114474475310.1007/s12265‑020‑10087‑433409960
    [Google Scholar]
  7. GiordanoN.J. JanssonP.S. YoungM.N. HaganK.A. KabrhelC. Epidemiology, pathophysiology, stratification, and natural history of pulmonary embolism.Tech. Vasc. Interv. Radiol.201720313514010.1053/j.tvir.2017.07.00229029707
    [Google Scholar]
  8. LiY. HeY. MengY. FuB. XueS. KangM. DuanZ. ChenY. WangY. TianH. Development and validation of a prediction model to estimate risk of acute pulmonary embolism in deep vein thrombosis patients.Sci. Rep.202212164910.1038/s41598‑021‑04657‑y35027609
    [Google Scholar]
  9. VenkateshA.K. KlineJ.A. CourtneyD.M. CamargoC.A. PlewaM.C. NordenholzK.E. MooreC.L. RichmanP.B. SmithlineH.A. BeamD.M. KabrhelC. Evaluation of pulmonary embolism in the emergency department and consistency with a national quality measure: Quantifying the opportunity for improvement.Arch. Intern. Med.2012172131028103210.1001/archinternmed.2012.180422664742
    [Google Scholar]
  10. AndersonD. RodgerM. GinsbergJ. KearonC. GentM. TurpieA. BormanisJ. WeitzJ. ChamberlainM. BowieD. BarnesD. HirshJ. WellsP. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: Increasing the models utility with the SimpliRED D-dimer.Thromb. Haemost.200083341642010.1055/s‑0037‑161383010744147
    [Google Scholar]
  11. PenalozaA. SouliéC. MoumnehT. DelmezQ. GhuysenA. El KouriD. BriceC. MarjanovicN.S. BougetJ. MoustafaF. Trinh-DucA. Le GallC. ImsaadL. ChrétienJ.M. GableB. GirardP. SanchezO. SchmidtJ. Le GalG. MeyerG. DelvauN. RoyP.M. Pulmonary embolism rule-out criteria (PERC) rule in European patients with low implicit clinical probability (PERCEPIC): A multicentre, prospective, observational study.Lancet Haematol.2017412e615e62110.1016/S2352‑3026(17)30210‑729150390
    [Google Scholar]
  12. Le GalG. RighiniM. RoyP.M. SanchezO. AujeskyD. BounameauxH. PerrierA. Prediction of pulmonary embolism in the emergency department: The revised Geneva score.Ann. Intern. Med.2006144316517110.7326/0003‑4819‑144‑3‑200602070‑0000416461960
    [Google Scholar]
  13. HendriksenJ.M.T. GeersingG.J. LucassenW.A.M. ErkensP.M.G. StoffersH.E.J.H. van WeertH.C.P.M. BüllerH.R. HoesA.W. MoonsK.G.M. Diagnostic prediction models for suspected pulmonary embolism: Systematic review and independent external validation in primary care.BMJ2015351h443810.1136/bmj.h443826349907
    [Google Scholar]
  14. ChamM.D. YankelevitzD.F. ShahamD. ShahA.A. ShermanL. LewisA. RademakerJ. PearsonG. ChoiJ. WolffW. PrabhuP.M. GalanskiM. ClarkR.A. SostmanH.D. HenschkeC.I. Deep venous thrombosis: Detection by using indirect CT venography.Radiology2000216374475110.1148/radiology.216.3.r00se4474410966705
    [Google Scholar]
  15. KirchhofK. WelzelT. MeckeC. ZoubaaS. SartorK. Differentiation of white, mixed, and red thrombi: Value of CT in estimation of the prognosis of thrombolysis phantom study.Radiology2003228112613010.1148/radiol.227302053012728185
    [Google Scholar]
  16. Martin BlandJ. AltmanD. Statistical methods for assessing agreement between two methods of clinical measurement.Lancet1986327847630731010.1016/S0140‑6736(86)90837‑82868172
    [Google Scholar]
  17. MokinM. MorrS. NatarajanS.K. LinN. SnyderK.V. HopkinsL.N. SiddiquiA.H. LevyE.I. Thrombus density predicts successful recanalization with Solitaire stent retriever thrombectomy in acute ischemic stroke: Table 1.J. Neurointerv. Surg.20157210410710.1136/neurintsurg‑2013‑01101724510378
    [Google Scholar]
  18. PuigJ. PedrazaS. DemchukA. Daunis-i-EstadellaJ. TermesH. BlascoG. SoriaG. BoadaI. RemolloS. BañosJ. SerenaJ. CastellanosM. Quantification of thrombus hounsfield units on noncontrast CT predicts stroke subtype and early recanalization after intravenous recombinant tissue plasminogen activator.AJNR Am. J. Neuroradiol.2012331909610.3174/ajnr.A287822158924
    [Google Scholar]
  19. ChoE.S. ChungJ.J. KimS. KimJ.H. YuJ.S. YoonC.S. CT venography for deep vein thrombosis using a low tube voltage (100 kVp) setting could increase venous enhancement and reduce the amount of administered iodine.Korean J. Radiol.201314218319310.3348/kjr.2013.14.2.18323482914
    [Google Scholar]
  20. SasakiT. FujimotoY. IshitoyaS. NabaaB. WatanabeN. YamakiT. TakahashiK. Improved detectability of thromboses of the lower limb using low kilovoltage computed tomography.Medicine2018976e977510.1097/MD.000000000000977529419670
    [Google Scholar]
  21. OdaS. UtsunomiyaD. FunamaY. ShimonoboT. NamimotoT. ItataniR. HiraiT. YamashitaY. Evaluation of deep vein thrombosis with reduced radiation and contrast material dose at computed tomography venography: Clinical application of a combined iterative reconstruction and low-tube-voltage technique.Circ. J.201276112614262210.1253/circj.CJ‑12‑003222784997
    [Google Scholar]
  22. ParkC.K. ChooK.S. JeonU.B. BaikS.K. KimY.W. KimT.U. KimC.W. JeongY.J. JeongD.W. LimS.J. Image quality and radiation dose of 128-slice dual-source CT venography using low kilovoltage combined with high-pitch scanning and automatic tube current modulation.Int. J. Cardiovasc. Imaging201329S1475110.1007/s10554‑013‑0252‑423748369
    [Google Scholar]
  23. SchinderaS.T. WinklehnerA. AlkadhiH. GoettiR. FischerM. GnanntR. Szucs-FarkasZ. Effect of automatic tube voltage selection on image quality and radiation dose in abdominal CT angiography of various body sizes: A phantom study.Clin. Radiol.2013682e79e8610.1016/j.crad.2012.10.00723219454
    [Google Scholar]
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