Search arXivSearch

arXiv · 2510.10763

Influence of coronary plaque morphology on local mechanical states and associated in-stent restenosis

Abstract

In-stent restenosis (ISR) after percutaneous coronary intervention is a multi-factorial process. Specific morphological lesion characteristics were observed to contribute to the occurrence of ISR. Local mechanical factors, such as stresses and strains, are known to influence tissue adaptation after stent implantation. However, the influence of morphological features on those local mechanical states and, hence, on the occurrence of ISR remains understudied. This work explores how local mechanical quantities relate to ISR by evaluating the stress distributions in the artery wall during and after stent implantation for morphology-informed lesion examples. We perform computational simulations of the stenting procedure with physics-based patient-specific coronary artery models. Different morphologies are assessed using the spatial plaque composition information from high-resolution coronary computed tomography angiography data. In the sample cases, elevated local tensile stresses were consistently found at sites corresponding to ISR. We found that specific morphological characteristics like circumferential or asymmetric block calcifications result in higher stresses in the surrounding tissue. These findings show that for the observed connection between plaque morphology and ISR, the local mechanical state may represent a relevant link. This study provides a mechanistic, illustrative insight for the examined cases. Future work with larger cohorts and systematic follow-up can establish statistically robust associations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Janina C. Datz, Ivo Steinbrecher, Johannes Krefting, Leif-Christopher Engel, Alexander Popp, Martin R. Pfaller, Heribert Schunkert, Wolfgang A. Wall. 2026-02-05. Influence of coronary plaque morphology on local mechanical states and associated in-stent restenosis. https://arxiv.org/abs/2510.10763

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Cosm: Collective switched motion for sparse Ising optimization

We introduce Collective Switched Motion (Cosm), a heuristic optimization framework based on switched collective dynamics. Cosm compiles the objective into subobjectives that are activated sequentially, temporally separating competing local influences. The interplay of switching and local interactions among variables gives rise to collective search behavior, while a new correlated perturbation mechanism encourages coordinated cluster motion. Tests on tuned-hardness spin-glass benchmarks suggest more favorable algorithmic scaling than that reported for leading dynamical solvers. Cosm heuristically attains the certified optima of three of the largest Gset instances (G72, G77, G81), exceeding the previously reported heuristic solutions. On the large random-graph instances G61 and G70, a CPU implementation reliably attains the best-known solutions, reaching cuts of 5799 and 9595 with 99%-confidence times-to-target of 15 s and 2.4 s, respectively. On the heterogeneous-degree G64 instance, Cosm establishes a new best-known cut of 8753. Broadly, the results suggest an alternative approach to heuristic design in which local dynamics and orchestration mechanisms are carefully designed so that effective search emerges.

cs.CE

Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with nested cross-validation) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID beta = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction in 13 of 30 alternative specifications. Unsupervised clustering produced three trajectory groups: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Pre-crisis financial fundamentals showed only modest predictive power for resilience (nested CV AUC = 0.635, 95% CI: 0.558-0.711; permutation p = 0.016), consistent with the limited informativeness of publicly available signals for anticipating crisis outcomes. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.

cs.CE

An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale

Small-business owners need expert guidance on their own terms, across schedules, languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.

cs.CE