Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events

Anna M. Sailer, Sander M.J. Van Kuijk, Patricia J. Nelemans, Anne S. Chin, Aya Kino, Mark Huininga, Johanna Schmidt, Gabriel Mistelbauer, Kathrin Bäumler, Peter Chiu, Michael P. Fischbein, Michael D. Dake, D. Craig Miller, Geert Willem H. Schurink, Dominik Fleischmann

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Background - Medical treatment of initially uncomplicated acute Stanford type-B aortic dissection is associated with a high rate of late adverse events. Identification of individuals who potentially benefit from preventive endografting is highly desirable. Methods and Results - The association of computed tomography imaging features with late adverse events was retrospectively assessed in 83 patients with acute uncomplicated Stanford type-B aortic dissection, followed over a median of 850 (interquartile range 247-1824) days. Adverse events were defined as fatal or nonfatal aortic rupture, rapid aortic growth (>10 mm/y), aneurysm formation (≥6 cm), organ or limb ischemia, or new uncontrollable hypertension or pain. Five significant predictors were identified using multivariable Cox regression analysis: connective tissue disease (hazard ratio [HR] 2.94, 95% confidence interval [CI]: 1.29-6.72; P=0.01), circumferential extent of false lumen in angular degrees (HR 1.03 per degree, 95% CI: 1.01-1.04, P=0.003), maximum aortic diameter (HR 1.10 per mm, 95% CI: 1.02-1.18, P=0.015), false lumen outflow (HR 0.999 per mL/min, 95% CI: 0.998-1.000; P=0.055), and number of intercostal arteries (HR 0.89 per n, 95% CI: 0.80-0.98; P=0.024). A prediction model was constructed to calculate patient specific risk at 1, 2, and 5 years and to stratify patients into high-, intermediate-, and low-risk groups. The model was internally validated by bootstrapping and showed good discriminatory ability with an optimism-corrected C statistic of 70.1%. Conclusions - Computed tomography imaging-based morphological features combined into a prediction model may be able to identify patients at high risk for late adverse events after an initially uncomplicated type-B aortic dissection.

Original languageEnglish (US)
Article numbere005709
JournalCirculation: Cardiovascular Imaging
Volume10
Issue number4
DOIs
StatePublished - Apr 1 2017
Externally publishedYes

Fingerprint

Dissection
Tomography
Confidence Intervals
Aortic Rupture
Aptitude
Connective Tissue Diseases
Aneurysm
Ischemia
Extremities
Arteries
Regression Analysis
Hypertension
Pain
Growth
Therapeutics

Keywords

  • aneurysm
  • angiography
  • aorta
  • aortic rupture
  • computed
  • hypertension
  • regression analysis
  • tomography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

Cite this

Sailer, A. M., Van Kuijk, S. M. J., Nelemans, P. J., Chin, A. S., Kino, A., Huininga, M., ... Fleischmann, D. (2017). Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events. Circulation: Cardiovascular Imaging, 10(4), [e005709]. https://doi.org/10.1161/CIRCIMAGING.116.005709

Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events. / Sailer, Anna M.; Van Kuijk, Sander M.J.; Nelemans, Patricia J.; Chin, Anne S.; Kino, Aya; Huininga, Mark; Schmidt, Johanna; Mistelbauer, Gabriel; Bäumler, Kathrin; Chiu, Peter; Fischbein, Michael P.; Dake, Michael D.; Miller, D. Craig; Schurink, Geert Willem H.; Fleischmann, Dominik.

In: Circulation: Cardiovascular Imaging, Vol. 10, No. 4, e005709, 01.04.2017.

Research output: Contribution to journalArticle

Sailer, AM, Van Kuijk, SMJ, Nelemans, PJ, Chin, AS, Kino, A, Huininga, M, Schmidt, J, Mistelbauer, G, Bäumler, K, Chiu, P, Fischbein, MP, Dake, MD, Miller, DC, Schurink, GWH & Fleischmann, D 2017, 'Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events', Circulation: Cardiovascular Imaging, vol. 10, no. 4, e005709. https://doi.org/10.1161/CIRCIMAGING.116.005709
Sailer, Anna M. ; Van Kuijk, Sander M.J. ; Nelemans, Patricia J. ; Chin, Anne S. ; Kino, Aya ; Huininga, Mark ; Schmidt, Johanna ; Mistelbauer, Gabriel ; Bäumler, Kathrin ; Chiu, Peter ; Fischbein, Michael P. ; Dake, Michael D. ; Miller, D. Craig ; Schurink, Geert Willem H. ; Fleischmann, Dominik. / Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events. In: Circulation: Cardiovascular Imaging. 2017 ; Vol. 10, No. 4.
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AU - Chin, Anne S.

AU - Kino, Aya

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AU - Schmidt, Johanna

AU - Mistelbauer, Gabriel

AU - Bäumler, Kathrin

AU - Chiu, Peter

AU - Fischbein, Michael P.

AU - Dake, Michael D.

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N2 - Background - Medical treatment of initially uncomplicated acute Stanford type-B aortic dissection is associated with a high rate of late adverse events. Identification of individuals who potentially benefit from preventive endografting is highly desirable. Methods and Results - The association of computed tomography imaging features with late adverse events was retrospectively assessed in 83 patients with acute uncomplicated Stanford type-B aortic dissection, followed over a median of 850 (interquartile range 247-1824) days. Adverse events were defined as fatal or nonfatal aortic rupture, rapid aortic growth (>10 mm/y), aneurysm formation (≥6 cm), organ or limb ischemia, or new uncontrollable hypertension or pain. Five significant predictors were identified using multivariable Cox regression analysis: connective tissue disease (hazard ratio [HR] 2.94, 95% confidence interval [CI]: 1.29-6.72; P=0.01), circumferential extent of false lumen in angular degrees (HR 1.03 per degree, 95% CI: 1.01-1.04, P=0.003), maximum aortic diameter (HR 1.10 per mm, 95% CI: 1.02-1.18, P=0.015), false lumen outflow (HR 0.999 per mL/min, 95% CI: 0.998-1.000; P=0.055), and number of intercostal arteries (HR 0.89 per n, 95% CI: 0.80-0.98; P=0.024). A prediction model was constructed to calculate patient specific risk at 1, 2, and 5 years and to stratify patients into high-, intermediate-, and low-risk groups. The model was internally validated by bootstrapping and showed good discriminatory ability with an optimism-corrected C statistic of 70.1%. Conclusions - Computed tomography imaging-based morphological features combined into a prediction model may be able to identify patients at high risk for late adverse events after an initially uncomplicated type-B aortic dissection.

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