Evidence map›Paper›PMID 42908986›Full record

ArticleFrontiers in cardiovascular medicine2026

CTA-based morphological parameters predict adverse prognosis in stanford type B aortic dissection: a single-center retrospective cohort study.

Xiaolong Pan, Mengli Wang, Yang Sun, Tingting Zhang

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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Xiaolong PanDepartment of Radiology, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China.
Mengli WangDepartment of Radiology, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China.
Yang SunDepartment of Radiology, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China.
Tingting ZhangDepartment of Radiology, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the prognostic value of computed tomography angiography (CTA)-based morphological parameters for adverse aortic events in patients with acute Stanford type B aortic dissection (TBAD) and to develop an internally validated prediction model. Methods: This single-center retrospective cohort study included 292 consecutive patients with CTA-confirmed acute TBAD. Baseline clinical and CTA-derived morphological variables were collected, and the primary endpoint was a composite of adverse aortic events. Candidate predictors were evaluated using least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross-validation, followed by multivariable logistic regression. Model performance was assessed using discrimination, calibration, precision-recall analysis, and decision curve analysis, with internal validation based on 1,000 bootstrap resamples. Results: During a median follow-up of 13.20 months (interquartile range, 8.15-19.52 months), 78 patients (26.7%) experienced adverse aortic events. The final LASSO-selected model included age, symptom onset-to-admission time, white blood cell count, D-dimer, maximum aortic diameter, TL/FL ratio, and false lumen status. In multivariable analysis, older age (adjusted OR = 1.043), higher white blood cell count (OR = 1.164), higher D-dimer (OR = 1.056), and larger maximum aortic diameter (OR = 1.110) were associated with higher odds of adverse events, whereas a higher TL/FL ratio was inversely associated with the outcome (OR = 0.060). Compared with partial false-lumen thrombosis, patent false lumen was associated with higher odds (OR = 3.718), whereas complete thrombosis was associated with lower odds (OR = 0.253). The apparent AUC was 0.849 (95% CI: 0.800-0.898), and the optimism-corrected AUC was 0.828 after 1,000 bootstrap resamples. The optimism-corrected calibration slope and Brier score were 0.893 and 0.143, respectively. The AUPRC was 0.708 vs. a no-skill baseline of 0.267, and decision curve analysis showed positive net benefit across a broad range of threshold probabilities. Conclusion: A model integrating baseline clinical and CTA-derived morphological characteristics demonstrated good internally validated discrimination and reasonable calibration for short- to medium-term adverse aortic events in acute TBAD. Independent external validation is required before broader clinical application.

Indexed as

computed tomography angiographyfalse lumenlogistic regressionmorphological parametersprognosis predictionrisk stratificationStanford type B aortic dissection

Identifiers

PMID42908986
PMCPMC13683654

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.