Risk Factors, Incidence, and Outcome of Stroke

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 660

Special Issue Editor


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Guest Editor
Miller School of Medicine, University of Miami, Miami, FL, USA
Interests: stroke care transitions; machine learning; heart failure; cardiomyopathy; clinical registry

Special Issue Information

Dear Colleagues,

This Special Issue delves into the critical medical diagnosis-related aspects of cerebrovascular events, with a particular focus on the evolving landscape of risk factor stratification, incidence trends, and diagnostic pathways that influence patient outcomes across the entire stroke care continuum. It highlights the urgent need for—and current gaps in—high-quality predictive models that span three essential phases: primary prevention (before a stroke occurs), acute management (during the event), and post-acute care (after patient stabilization and transition beyond acute settings). The Special Issue explores the integration of advanced neuroimaging, biomarker discovery, and artificial intelligence in supporting early detection, accurate subtyping, and individualized prognostic evaluation. A key emphasis is placed on the development of validated predictive tools capable of integrating modifiable variables at each stage, ultimately supporting clinical decision-making and improving functional recovery and long-term quality of life. Discussions also address diagnostic challenges in specific populations and the role of precise etiological classification in short- and long-term outcome prediction.

Dr. Emir Veledar
Guest Editor

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Keywords

  • stroke prevention
  • acute stroke management
  • post-acute stroke care
  • predictive modeling
  • risk stratification
  • neuroimaging
  • biomarkers
  • early detection
  • prognostic evaluation
  • diagnostic algorithms
  • cerebral infarction
  • intracranial hemorrhage
  • ischemic stroke subtypes

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Published Papers (1 paper)

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Research

15 pages, 283 KB  
Article
Wrist Flexor Spasticity and Hemiplegic–Contralateral Median Nerve Latency Asymmetry After Stroke: A Bilateral Nerve Conduction and Ultrasound Study
by Ki-Hyeok Ku, Seongmin Choi, Kyung Chul Noh and Eo Jin Park
Diagnostics 2026, 16(7), 1088; https://doi.org/10.3390/diagnostics16071088 - 3 Apr 2026
Viewed by 389
Abstract
Background/Objectives: The paretic wrist after stroke may exhibit median nerve conduction abnormalities, but factors underlying hemiplegic–contralateral asymmetry remain uncertain. We compared electrodiagnostic and ultrasonographic wrist measures between sides and assessed predictors of side-to-side differences in distal motor latency (ΔDML) and distal sensory latency [...] Read more.
Background/Objectives: The paretic wrist after stroke may exhibit median nerve conduction abnormalities, but factors underlying hemiplegic–contralateral asymmetry remain uncertain. We compared electrodiagnostic and ultrasonographic wrist measures between sides and assessed predictors of side-to-side differences in distal motor latency (ΔDML) and distal sensory latency (ΔDSL). Methods: We retrospectively analyzed 85 patients with stroke. Distal motor latency (DML), distal sensory latency (DSL), wrist-to-forearm ratio (WFR), and median nerve inlet cross-sectional area (CSA) were measured bilaterally. Paired t-tests evaluated hemiplegic–contralateral differences, and Wilcoxon signed-rank tests were performed as sensitivity analyses. Multivariable linear regression with robust (HC3) standard errors modeled ΔDML as the primary outcome and ΔDSL as the secondary outcome, with wrist flexor spasticity (Modified Ashworth Scale, MAS) specified a priori as the primary explanatory variable; extended models additionally included ΔWFR. Sensitivity analyses re-specified MAS as an ordered category, and complementary linear mixed-effects models using raw bilateral latency values were fitted to assess the robustness of Δ-based modeling. Results: The hemiplegic side showed higher DML (5.51 ± 0.79 vs. 4.81 ± 0.42 ms; mean difference 0.694; p < 0.001), DSL (4.51 ± 0.88 vs. 3.66 ± 0.45 ms; mean difference 0.852; p < 0.001), WFR (1.21 ± 0.30 vs. 1.07 ± 0.16; p = 0.008), and CSA (11.16 ± 3.67 vs. 9.69 ± 2.04 mm2; p = 0.032). MAS was associated with ΔDML (β = 0.336; p < 0.001) and ΔDSL (β = 0.238; p = 0.015). ΔWFR remained significant for ΔDML (β = 1.314; p < 0.001) and ΔDSL (β = 1.371; p = 0.001), improving adjusted R2 from 0.251 to 0.370 for ΔDML and from 0.142 to 0.253 for ΔDSL. Findings remained directionally consistent when MAS was modeled as an ordered category. Complementary mixed-effects models using raw bilateral latency values showed significant hemiplegic-side-by-MAS interactions for both DML (β = 0.425; 95% CI 0.275 to 0.575; p < 0.001) and DSL (β = 0.366; 95% CI 0.195 to 0.537; p < 0.001). Conclusions: In chronic stroke hemiplegia, median nerve latencies and wrist morphology may differ between sides. Wrist flexor spasticity and side-to-side increases in WFR may be independently associated with greater latency asymmetry. These interlimb latency differences should be interpreted as physiological markers of side-to-side median nerve involvement at the wrist rather than as stand-alone diagnostic criteria for carpal tunnel syndrome. Full article
(This article belongs to the Special Issue Risk Factors, Incidence, and Outcome of Stroke)
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