Investigating the impact of glycated hemoglobin levels on stroke severity in patients with acute ischemic stroke

 You investigated BUT DID NOTHING! Good to know you'll be fired soon! Stroke research is supposed to solve stroke, not just tell us of the problems! Doesn't anyone in stroke have two functioning neurons to rub together for a spark of intelligence?

Investigating the impact of glycated hemoglobin levels on stroke severity in patients with acute ischemic stroke

• Naif M. Alhawiti,
• Emadeldin M. Elsokkary,
• Jehad A. Aldali &
• Badi A. Alotaibi 

Scientific Reports volume 15, Article number: 12114 (2025) Cite this article

• Metrics details

Abstract

Stroke is a sudden neurological decline caused by cerebrovascular diseases or impaired blood circulation. Research investigating the connection between glycated hemoglobin A1c (HbA1c) levels and stroke severity is limited. This study examined the connection between HbA1c levels and stroke severity in patients with acute ischemic stroke. A retrospective cross-sectional analysis of the medical records of 1103 patients with acute ischemic stroke from January 2020 to January 2024 was conducted. Patients were divided into seven groups on the basis of their HbA1c levels. Stroke severity within these groups was assessed via the National Institutes of Health Stroke Scale (NIHSS), with the aim of identifying correlations between stroke severity and glycemic status. This study examined the impact of various HbA1c levels on a range of demographic and clinical characteristics in stroke patients. The patients were grouped into seven categories on the basis of their HbA1c levels, and characteristics such as age; body mass index (BMI); LDL, HDL, and creatinine levels; and NIHSS scores at hospital admission were compared across these groups. Significant differences were observed in age, LDL levels (F = 3.999, P < 0.001), and creatinine levels (F = 1.303, P = 0.253) among the HbA1c categories. However, there were no significant differences in BMI, HDL levels, or length of hospital stay. A positive correlation was found between HbA1c levels and NIHSS scores, indicating that higher HbA1c levels are associated with greater stroke severity. This study revealed that the risk of severe stroke increases significantly when HbA1c levels exceed 6.5%. In contrast, maintaining HbA1c levels below 6.5% is linked to a reduced risk of severe stroke and lower mortality. Additionally, older adults are at greater risk and tend to experience more severe strokes.

Rehabilitation Robotics and Machine Learning for Stroke Severity Classification

I'm finding graduate level theses are better than most researchers, probably because the professors involved are more up-to-date on existing research

Rehabilitation Robotics and Machine Learning for Stroke Severity Classification

Raymond GreenfieldFollow

Date of Award

Fall 12-16-2022

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Igor Belykh

Abstract

Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. This study uses supervised learning methods to address an autonomous classification via stroke severity labeled data by a clinician. Thirty-three patients with chronic stroke performed a variety of rehabilitation activities while utilizing the Motus Nova rehabilitation technology to capture upper and lower body motion. Based on the minimum, maximum, and mean of the range of motion and pressure as well as the number of movements, force flexion, and extension for each game and session provided from the sensor data. Supervised learning methods were applied to a harmonized dataset of roughly 32,000 patient sessions based on the maximum score per session per game. With this approach using light gradient boosting methods we achieved an average of 94% accuracy with 10-fold cross-validation to prevent overfitting. This thesis shows objectively-measured rehabilitation training, enabling the identification of the stroke severity class with the hopes to have patients have a less severe class in the future.

Over the last 10 years robotic rehabilitation has been utilized in inpatient therapy. Robotic rehabilitation has been shown to be effective in improving the severity of stroke in some cases. In particular, robotic devices can be used to help stroke survivors regain movement, improve their functional abilities and improve depression (11). These devices can provide a high level of precision and repeatability, allowing patients to perform therapeutic exercises with greater accuracy and consistency (1). Additionally, because robotic devices can be programmed to provide different levels of assistance, they can be tailored to the individual needs of each patient. This allows for a more personalized and effective rehabilitation in-home program (21).

Recommended Citation

Greenfield, Raymond, "Rehabilitation Robotics and Machine Learning for Stroke Severity Classification." Thesis, Georgia State University, 2022.
https://scholarworks.gsu.edu/math_theses/176

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns

So you have described something, but done nothing to help recover from these lesions. Useless. Predicting stroke severity doesn't help any survivor.

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns

• Anna K. Bonkhoff,
• Markus D. Schirmer,
• […]
• Natalia S. Rost 

Nature Communications volume 12, Article number: 3289 (2021) Cite this article

• 1517 Accesses

• 32 Altmetric

• Metrics details

Abstract

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.

Introduction

Stroke affects >15 million people each year¹. It is known to result in a substantial overall degree of long-term impairment across men and women^2,3. However, numerous epidemiological studies indicate clinically relevant, sex-related differences in the characteristics of ischemic cerebrovascular disease^4,5. For instance, due to a longer life expectancy, more women than men experience a stroke each year⁶. Expected demographical changes, i.e., an aging population, will widen this gap further: in the US, projections suggest that ~200,000 more women will be disabled after stroke than men by 2030⁷.

Further sex differences relate to women more often presenting with non-classic stroke symptoms, such as fatigue or changes in mental status^8,9, and having a higher risk of delays in hospital arrival^10,11. Also, women feature a higher risk of cardioembolic stroke due to atrial fibrillation¹², which may contribute to the often-observed higher acute ischemic stroke (AIS) severity in female patients¹³. This excess in stroke severity in women persists even after adjusting for their greater age at onset, comorbidities, and prestroke level of independence^14,15. Importantly, women seem to experience more severe strokes despite comparable lesion sizes in men and women¹⁶. In fact, a similar observation of sex-specific lesion volume effects was noted in the case of aphasia, where women had a smaller lesion volume threshold to cause aphasia than men¹⁷.

Going beyond lesion volume, lesion-symptom mapping studies have enriched our understanding of anatomically unique lesion locations underlying specific symptoms post-stroke^18,19,20. In the case of stroke severity, these analyses have determined widespread lesions in white matter, basal ganglia, pre- and postcentral gyri, opercular, insular, and inferior frontal regions to be most relevant for a higher stroke severity, especially if affecting the left hemisphere²¹. While these lesion-symptom studies have uncovered eloquent lesion locations with high spatial resolution, they have been systematically blind to any potential sex disparities. If considered at all, sex was treated as a nuisance variable and regressed out prior to the main analysis²¹. Thus, none of the recently employed analytical approaches in clinical neuroimaging allowed for a dedicated, explicit investigation of sex-specific lesion pattern effects in relation to continuous outcome scores.

In this work, we aim to design and conduct a lesion-symptom analysis capable of capturing male- and female-specific lesion patterns, underlying stroke severity in a statistically robust and spatially precise manner to address previous methodological constraints. For this purpose, we leverage neuroimaging data originating from two large, independent hospital-based cohorts gathering data of 555 (derivation) and 503 (validation) AIS patients in total. We tailor and deploy sex-aware hierarchical Bayesian models to simulate predictions of AIS severity and to elucidate the sex-specific effects of lesion patterns affecting similar brain regions in women and men. We seek to map the lesion constellations underpinning female-specific more severe strokes, potentially indicating sex-specific maps of functional deficits on the one hand and encouraging more sex-aware acute stroke treatment decisions on the other. Such a sex-informed acute stroke care has the potential to alleviate the burden of disease on an individual patient level, as well as broader and socioeconomically relevant levels.

Results

We here present a generative analysis of acute stroke severity, putting a particular focus on sex-specific lesion pattern effects. We successively combined (1) the automated low-dimensional embedding of high-dimensional DWI-derived lesion information via non-negative matrix factorization (NMF)²², and (2) probabilistic modeling, based on the latent NMF embedding, to simulate the prediction of acute stroke severity, as measured by the National Institute of Health Stroke Scale (NIHSS)²³. We thus first determined pivotal, general lesion pattern effects across all patients and successively concentrated on similarities and differences between men and women (sex assessed by patients’ medical records). We interpreted explanatory relevances on the level of NMF-derived low-dimensional lesion representations, that we call lesion atoms, as well as the same relevances transformed back to the level of the anatomical gray matter brain regions and white matter tracts.

More at limk.

 

How active are stroke patients in physiotherapy sessions and is this associated with stroke severity?

You had to do research on this? IT IS COMPLETELY OBVIOUS!  Do you have two functioning neurons that you can rub together?

How active are stroke patients in physiotherapy sessions and is this associated with stroke severity?

Jimmy James  ¹ , Mark P McGlinchey  ¹

Affiliations

• PMID: 33794718
• DOI: 10.1080/09638288.2021.1907459

Abstract

Purpose: Exercise improves functional outcome post-stroke, but how long patients with differing severity spend undertaking active exercise within physiotherapy sessions is unknown. We aimed to investigate if stroke severity is associated with time undertaking active exercise in physiotherapy sessions, and if any differences between planned and actual physiotherapy session length existed.

Materials and methods: A prospective observational study of 107 stroke rehabilitation sessions in a UK acute stroke unit. Data recorded included patient demographics (age, gender, time post-stroke and Barthel Index score) and session attributes (planned and actual session length, time undertaking active exercise, grade of treating therapist).

Results: There was a significant negative association between increasing stroke severity and percentage of time undertaking active exercise in physiotherapy sessions (p < 0.001). No other observed factors were associated with time undertaking active exercise. Mean session length across all levels of stroke severity was 32 min (SD 9.26) which was significantly less than planned (p < 0.05). There was no difference in mean session length or between planned and actual physiotherapy session length between patients of differing severity.

Conclusions: Patients with greater stroke severity participate in less active exercise in physiotherapy sessions than those with lesser stroke severity. Reasons for this disparity warrant further investigation.Implications for rehabilitationStroke patients with higher levels of severity engage in less active exercise during rehabilitation.A discrepancy exists between patients' planned physiotherapy session lengths and actual session lengths during stroke rehabilitation.Physiotherapists should be mindful in how to adapt their sessions (particularly with severe stroke patients) to maximise the amount of activity they undertake.Physiotherapists should be flexible in their delivery of rehabilitation to ensure that the length of patient sessions reflect patients' needs.

 

 

Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization

By using the NIHSS they still are not objectively reporting on stroke severity. You need 3d scans to do that. My god, the massive amount of incompetence out there in stroke land.

Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization

Jennifer Schwartz, Yongfei Wang, Li Qin, Lee H. Schwamm, Gregg C. Fonarow, Nicole Cormier, Karen Dorsey, Robert L. McNamara, Lisa G. Suter, Harlan M. Krumholz, Susannah M. Bernheim

https://doi.org/10.1161/STROKEAHA.117.017960 Originally published September 27, 2017

• Article
• Figures & Tables
• Supplemental Materials
• Info & Metrics

This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.

---

Abstract

Background and Purpose—The Centers for Medicare & Medicaid Services publicly reports a hospital-level stroke mortality measure that lacks stroke severity risk adjustment. Our objective was to describe novel measures of stroke mortality suitable for public reporting that incorporate stroke severity into risk adjustment.

Methods—We linked data from the American Heart Association/American Stroke Association Get With The Guidelines-Stroke registry with Medicare fee-for-service claims data to develop the measures. We used logistic regression for variable selection in risk model development. We developed 3 risk-standardized mortality models for patients with acute ischemic stroke, all of which include the National Institutes of Health Stroke Scale score: one that includes other risk variables derived only from claims data (claims model); one that includes other risk variables derived from claims and clinical variables that could be obtained from electronic health record data (hybrid model); and one that includes other risk variables that could be derived only from electronic health record data (electronic health record model).

Results—The cohort used to develop and validate the risk models consisted of 188 975 hospital admissions at 1511 hospitals. The claims, hybrid, and electronic health record risk models included 20, 21, and 9 risk-adjustment variables, respectively; the C statistics were 0.81, 0.82, and 0.79, respectively (as compared with the current publicly reported model C statistic of 0.75); the risk-standardized mortality rates ranged from 10.7% to 19.0%, 10.7% to 19.1%, and 10.8% to 20.3%, respectively; the median risk-standardized mortality rate was 14.5% for all measures; and the odds of mortality for a high-mortality hospital (+1 SD) were 1.51, 1.52, and 1.52 times those for a low-mortality hospital (−1 SD), respectively.

Conclusions—We developed 3 quality measures that demonstrate better discrimination than the Centers for Medicare & Medicaid Services’ existing stroke mortality measure, adjust for stroke severity, and could be implemented in a variety of settings.

Stroke-Certified Centers Have Better Early Survival Rates

All these damned statistics and they still tell you nothing useful. No way to use this to compare which hospital to go to. What would be even more useful would be the disability rate and severity of stroke for each hospital.
http://www.medpagetoday.com/Cardiology/Strokes/59332?xid=nl_mpt_cardiodaily_2016-07-27&eun=g424561d0r

• by Kristin Jenkins
  Contributing Writer, MedPage Today

• This article is a collaboration between MedPage Today® and:
  

Action Points

• Note that this study of Medicare administrative data found improved survival when patients with ischemic stroke were treated at a Primary Stroke Center.
• Be aware that assumptions in the data (such as the use of zip code centroids as home location) could have subtly biased these results.

Sending stroke patients to a primary stroke center (PSC) for specialized treatment was associated with better early survival than at noncertified hospitals, a retrospective cohort study showed.
The study revealed that admission to PSCs -- centers certified by The Joint Commission to ensure adherence to guidelines and efficient delivery of disease-specific care -- was associated with 1.8% (95% CI −2.1% to −1.4%) lower 7-day and 1.8% (95% CI −2.3% to −1.4%) lower 30-day case fatality.
However, travelling 60 minutes to a PSC offset the 7-day survival advantage, Kimon Bekelis, MD, of Dartmouth-Hitchcock Medical Center in Lebanon, N.H., and colleagues reported online in JAMA Internal Medicine.
Similarly, if the 'drip and ship' trip took longer than 90 minutes, the travel time offset the 30-day survival advantage, according to researchers.
"These results are statistically significant and are clinically significant, implying one life saved for every 56 treated in a PSC," the researchers wrote.
"With the current distribution of PSCs, 16.4% of patients are located at least 90 minutes by ground transportation from the nearest PSC," they noted. "Further investigations are necessary to identify the best combination of approaches to improve access to centers of excellence and stroke outcomes."
Sending patients via air could get almost all patients to a PSC on time, the researchers suggested. Expanding telemedicine applications, upgrading smaller hospitals into Acute Stroke-Ready Hospitals, and creating a broader hospital network could also improve access to specialized stroke care, they said.
But improving stroke survival by getting patients to a specialized treatment center in the first 90 minutes only works for patients eligible for reperfusion therapy and those with hemorrhagic stroke requiring immediate clot evacuation or ventriculostomy, Lee H. Schwamm, MD, of the Stroke Service at Massachusetts General Hospital, Harvard Medical School, Boston, said in an accompanying editorial.
Hemorrhagic stroke carries with it less diagnostic uncertainty, a greater likelihood of transfer, but also greater mortality than ischemic stroke. Relatively few patients with hemorrhagic stroke have a dramatically altered outcome despite treatment in the "golden hour," he pointed out.
"Until we have data from randomized trials of pre-hospital triage, it is unlikely given the prevalence of stroke that we will find a more refined and pragmatic recommendation than the following: if it is a disabling stroke that started in the last 6 hours, then go to the highest-level stroke center that is within 30 to 45 extra minutes of drive time," Schwamm wrote. "However, because many hospitals with the highest levels of stroke resources are urban medical centers struggling to manage their annual increases in ED volume, this approach to sorting may increase competing risks to patient outcome."
When large-vessel occlusion is suspected and the patient re-routing mechanism kicks into high gear, "let us make sure that the destination of interest can deliver the goods," Schwamm added. "In the words of Albus Dumbledore, 'We must all face the choice between what is right, and what is easy. ...'"
For "smart triage," what's needed is a unified stroke care system that brings together centers that report performance data, Schwamm suggested. "Stroke incidence and 90-day functional outcomes should become a reportable disease so that meaningful data can be collected on all patients with stroke," he said.
Smartphone apps could determine the best possible destination for each patient, factoring in crucial data such as the stroke onset time, severity, travel times, hospital door-to-needle and door-to-puncture times, re-canalization success rates, and in-hospital mortality.
"Such a prehospital system should adhere to national standards but be customized to reflect the local resources, prevalence of stroke, best available screening tools, acceptable levels of erroneous triage, and competing costs of the additional transport and reduced EMS availability," Schwann said. "It will not be easy, but it is well worth doing."
The study looked at 865,184 Medicare beneficiaries seen with a stroke from Jan. 1, 2010, to Dec. 31, 2013. Mean age was 78.9 years and 55.5% were female.
More than half of the cohort (53.9%) was treated at one of 976 PSCs in the nation. Drive times were calculated based on zip code centroids and StreetMap North America was used to calculate the optimal travel time routes.
Although researchers had no information on where the patient was at the time of the stroke, the Framingham Study has demonstrated that most strokes happen at home, so they used population-weighted zip code points to represent patient origins.
Almost one-quarter of patients lived closer to a PSC than to a non-PSC institution. The review showed that patients admitted to a PSC were more likely to receive IV tissue plasminogen activator (6.0% versus 2.8%) or undergo mechanical thrombectomy (1.0% versus 0.2%) for ischemic stroke compared with their counterparts taken to non-PSC institutions.
The review also showed that differential travel time was a strong factor in PSC admission. A total of 87.5% of patients were admitted to a PSC when it was at least 1 hour closer than the nearest non-PSC institution. On the other hand, only 38.8% of patients were admitted to a PSC when it was 1 hour farther from the non-PSC institution.
"We did not find evidence that those who lived nearest to a PSC were sicker than those living far from a PSC: predicted mortality in the former was 15.8%, while that in the latter was 15.7%" (P=0.57), Bekelis and colleagues said.
Receiving treatment in a PSC was associated with a 30-day survival benefit for patients who travelled for:

• 20 minutes (adjusted difference 2.7%, 95% CI 1.5%-3.9%)
• 20 to 39 minutes (AD 1.8%, 95% CI 1.3%-2.2%)
• 40 to 59 minutes (AD 2.6%, 95% CI 0.7%-2.8%)
• 60 to 89 minutes (AD 1.7%, 95% CI 0.2%-2.4%)

The study had a number of limitations, the researchers acknowledged, including residual confounding caused by differences in time from stroke onset as well as the fact that stroke severity was unmeasured in the Medicare claims data. In addition, assigning populations to zip code centroids may have given falsely low travel times for some patients while overestimating travel times for others, they said.

Predicting activities after stroke: what is clinically relevant?

I'd have to say that the National Institutes of Health Stroke Scale is not objective or really useful in predicting recovery. This research is pretty useless with the Barthel scales also being subjective. Isn't anyone ever going to start using objective 3d scans of damage and location to determine stroke severity and recovery possibilities?
http://onlinelibrary.wiley.com/doi/10.1111/j.1747-4949.2012.00967.x/abstract

1. G. Kwakkel^1,2,3,* and
2. B. J. Kollen⁴

Version of Record online: 24 DEC 2012

DOI: 10.1111/j.1747-4949.2012.00967.x

© 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization

Issue

International Journal of Stroke

Volume 8, Issue 1, pages 25–32, January 2013

Additional Information(Show All)

How to CiteAuthor InformationPublication History

1. Conflict of interest: None declared.

SEARCH

Search Scope

Search String

• Advanced >
• Saved Searches >

ARTICLE TOOLS

• Get PDF (258K)
• Save to My Profile
• E-mail Link to this Article
• Export Citation for this Article
• Get Citation Alerts

Share|

• Abstract
• Article
• References
• Cited By

View Full Article (HTML) Enhanced Article (HTML) Get PDF (258K)

Keywords:

• activities;
• prognosis;
• stroke

Abstract

Knowledge about factors that determine the final outcome after stroke is important for early stroke management, rehabilitation goals, and discharge planning. This narrative review provides an overview of current knowledge about the prediction of activities after stroke. We reviewed the pattern of stroke recovery for functions and activities, the impact of spontaneous recovery on activities, and the measurement of improvement in general. We explored the activities profiles during the chronic phase and predictors for activities of daily living independence after stroke, and finally, we discussed where to from here? Mathematical regularities explain the nonlinear patterns of recovery, making the outcome of activities of daily living highly predictable. Initial severity of disability and extent of improvement observed within the first weeks poststroke are important indicators of the outcome at six-months. The sequence of progress in activities is almost fixed in time. Studies showed that most motor recovery is almost completed within 10 weeks poststroke. On average, stroke recovery plateaus three- to six-months after onset. Strong evidence was found that age and scores on scales assessing severity of neurological deficits in the early poststroke phase are strongly associated with the final basic activities of daily living outcome after three-months poststroke. The validated prediction models using simple algorithms, such as National Institutes of Health Stroke Scale or Barthel Index, need to be implemented in rehabilitation services and used for stratifying stroke patients in trials. Future studies should investigate the accuracy of dynamic models that includes time poststroke to optimize the application of prediction rules in individuals with stroke.

View Full Article (HTML) Enhanced Article (HTML) Get PDF (258K)