Quantitative multi-slice spiral CT perfusion parameters as predictors of collateral status and 90-day functional outcome in acute ischemic stroke

 

Predicting failure to recover IS STUPIDER THAN HELL! Deliver recovery you blithering idiots!

Send me personal hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and title(If you can't stand by your name don't bother replying anonymously) and my response in my blog. Or are you afraid to engage with my stroke-addled mind? No excuses are allowed! You're medically trained; it should be simple to precisely state EXACTLY WHERE I'M WRONG.

Exactly what in this research gets survivors recovered? 100% recovery is the only goal in stroke; NOT PREDICTIONS, BIOMARKERS, PROGNOSTICATION, OR ASSESSMENTS! I'd fire anyone doing these!

Quantitative multi-slice spiral CT perfusion parameters as predictors of collateral status and 90-day functional outcome in acute ischemic stroke

• D

  Dan Zhu

• X

  Xiaozhou Ma

• Y

  Yingzhi Jiao

• S

  Shuai Liu

• J

  Jinzhu Yan

• 

  Lixin Zhang *

• Imaging Center, Qianwei Hospital of Jilin Province, Changchun, Jilin, China

Abstract

Objective: 

To evaluate the utility of quantitative computed tomography perfusion parameters for assessing collateral circulation and predicting 90-day functional outcomes in acute ischemic stroke.

Methods: 

This retrospective study included 82 patients who underwent perfusion imaging within 24 h of symptom onset. Parameters including relative cerebral blood flow, relative cerebral blood volume, mean transit time, time to maximum, and hypoperfusion intensity ratio were analyzed. Collateral status was classified using multiphase angiography, and 90-day outcomes were assessed using the modified Rankin Scale.

Results: 

Patients with robust collateral circulation showed higher relative cerebral blood flow and volume and shorter perfusion times compared with those with poor collaterals. Hypoperfusion intensity ratio demonstrated strong diagnostic performance (area under the curve 0.925). For predicting unfavorable 90-day outcome, HIR achieved an AUC of 0.912 and showed greater discrimination than mismatch ratio in the present cohort, while the combined HIR–rCBF–Tmax model achieved an AUC of 0.938. Hypoperfusion intensity ratio correlated positively with functional disability, while relative cerebral blood flow correlated negatively with infarct volume. Favorable outcomes were more frequent in patients with robust collaterals.

Conclusion: 

Quantitative CTP parameters bridge a natomical collateral assessment and downstream tissue-level perfusion. HIR may provide a particularly informative functional marker of collateral efficiency and 90-day prognosis beyond conventional mismatch assessment.

Platelet to high-density lipoprotein cholesterol ratio predicts clinical outcomes after acute ischemic stroke: a prospective cohort study

 

Predicting failure to recover IS STUPIDER THAN HELL! Deliver recovery you blithering idiots!

Send me personal hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and title(If you can't stand by your name don't bother replying anonymously) and my response in my blog. Or are you afraid to engage with my stroke-addled mind? No excuses are allowed! You're medically trained; it should be simple to precisely state EXACTLY WHERE I'M WRONG.

Exactly what in this research gets survivors recovered? 100% recovery is the only goal in stroke; NOT PREDICTIONS, BIOMARKERS, PROGNOSTICATION, OR ASSESSMENTS! I'd fire anyone doing these!

Platelet to high-density lipoprotein cholesterol ratio predicts clinical outcomes after acute ischemic stroke: a prospective cohort study

• 

  Xuan Sun 1†

• H

  Haochen Sun 2†

• Z

  Zhijia Tang 3

• 

  Xinyang Qi 2

• X

  Xian Wang 4

• X

  Xiaoyin Wang 2*

• 1. Department of Clinical Laboratory, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China

• 2. Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China

Abstract

Background: 

The platelet/high-density lipoprotein cholesterol ratio (PHR), a marker of hypercoagulable states and disordered lipid metabolism, has been confirmed as a predictor of cardiovascular disease. However, the effects of PHR on the prognosis of acute ischemic stroke (AIS) remain unknown. We aimed to assess the associations of PHR with the risk of clinical outcomes in patients with AIS.

Methods: 

This prospective observational study included 820 patients (median age, 68 years; female, 34.6%; median NIHSS at admission, 3) with AIS. The median time from symptom onset to admission was 2 days (interquartile range [IQR], 0–4), and from admission to blood sampling was 15 h (IQR, 12–19). PHR was calculated as platelet count (PC; 109 cells/L)/HDL-C (mmol/L) at admission. PHR was analyzed both as a continuous variable and in tertile form (tertile 1-tertile 3). To analyze the associations between PHR and clinical outcomes including all-cause death, stroke recurrence and poor functional outcome at 3 months, 6 months and 1 year, we used multivariable Cox and logistic regression, Kaplan–Meier survival curves, restricted cubic splines, subgroup analysis, concordance statistic (C-statistic), net reclassification index (NRI), and integrated discrimination improvement index (IDI).

Results: 

The median PHR was 202.155 (IQR, 153.120–262.365). Kaplan–Meier survival curves identified tertile 3 as the group with the highest risk for all-cause death and stroke recurrence. After adjustment, multivariable Cox regression (tertile 1 as reference) showed that the highest PHR tertile 3 was associated with increased risk for both all-cause death and stroke recurrence across all three follow-up intervals (3 months, 6 months and 1 year). In parallel, multivariable logistic regression (tertile 1 as reference) showed that tertile 3 was associated with a greater likelihood of poor functional outcome across the same three time points. Continuous PHR showed a positive dose–response relationship with clinical outcomes. Subgroup analysis revealed significant interactions of age (p < 0.05) with PHR for all-cause death, and of BMI (p < 0.05) with PHR for mRS 3–6. A basic model’s predictive ability was strengthened by the addition of PHR (C-statistic, NRI, IDI).

Conclusion: 

A higher PHR level in patients with AIS is strongly associated with an increased risk of all-cause death, stroke recurrence and poor functional outcome. As a valuable predictive biomarker, PHR may provide a simple and effective tool for predicting clinical outcomes in patients with AIS.

Graphical Abstract

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability

 Predicting shit like this is useless, Survivors want recovery! DELIVER THAT!

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability

About the Authors

Qian Ye

Roles Data curation, Writing – original draft

‡ QY, GF also contributed equally to this work and share first authorship.

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

https://orcid.org/0009-0006-7823-5899

Guilin Fang

Roles Data curation, Formal analysis

‡ QY, GF also contributed equally to this work and share first authorship.

Affiliation Department of Nephrology, Nanjing Jinling Hospital, General Hospital of Eastern Theatre Command, Nanjing, Jiangsu, China

Liping Li

Roles Data curation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Qinggui Li

Roles Investigation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Yun Yang

Roles Investigation

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Lingling Liu

Roles Formal analysis, Methodology, Writing – review & editing

* E-mail: 1209674467@qq.com

Affiliation Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

https://orcid.org/0000-0003-4392-3676

Competing Interests

The authors have declared that no competing interests exist.

Abstract

Purpose

We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key predictors to guide rehabilitation decisions.

Materials and methods

Data of 589 stroke inpatients (2019–2024) were split into good (BI ≥ 60) and poor (BI < 60) ADL groups. Continuous variables were processed using Z-score normalization, followed by preliminary univariate regression screening (P < 0.05) and final feature selection via LASSO regression (lambda.1se = 0.0488). The screened features were used to train and validate ten machine learning algorithms; 30% of the dataset (n = 177) was allocated as an independent test set for model evaluation, and SHAP analysis was performed to interpret the optimal model.

Results

Six of 41 features were retained. Random forest achieved the best performance (AUC = 0.958; accuracy = 0.936; sensitivity = 0.934; specificity = 0.950). SHAP identified the top drivers: admission Barthel Index, standing balance, Brunnstrom stages (upper and lower limb), dressing, and grooming abilities.

Conclusion

The ADL risk prediction model constructed using machine learning, particularly the random forest model, shows excellent predictive performance and clinical interpretability, making it valuable for individualized risk assessment of daily living skills in stroke patients at discharge.

Figures

  

Citation: Ye Q, Fang G, Li L, Li Q, Yang Y, Liu L (2026) Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability. PLoS One 21(7): e0351468. https://doi.org/10.1371/journal.pone.0351468

Editor: Fan Zhang, Longhua Hospital Shanghai University of Traditional Chinese Medicine, CHINA

Received: November 28, 2025; Accepted: May 26, 2026; Published: July 2, 2026

Copyright: © 2026 Ye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data for this study are publicly available from the Zenodo repository (https://doi.org/10.5281/zenodo.20629742).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Altered temporal variability-based functional reorganization of brain networks predicts motor outcome after stroke

 Predicting recovery rather than delivering recovery IS COMPLETE INCOMPETENCE!

I take no prisoners in trying to get stroke solved and that means a lot of dead wood needs to be removed. 

Altered temporal variability-based functional reorganization of brain networks predicts motor outcome after stroke

• Guojun Xu,
• Ling Li,
• Qinyang Li,
• Hewei Wang,
• Mingxia Fan,
• Limin Sun,
• Zhiyong Zhao &
• Hongxing Wang 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Dynamic functional connectivity (FC) studies have shown that motor recovery after stroke was associated with functional reorganization of brain networks. However, most previous studies have focused on interregional variability rather than the temporal variability (TV) of specific regions or networks. TV quantifies the dynamic reconfiguration of a region’s or network’s functional connectivity profile over time and reflects neural flexibility.

Purpose

This study investigated functional reorganization in chronic subcortical stroke using TV of brain networks derived from resting-state fMRI.

Methods

Thirty-three patients with left subcortical stroke (LSS), thirty with right subcortical stroke (RSS), and fifty-six age- and sex-matched healthy controls (HCs) were enrolled. Stroke patients underwent resting-state fMRI and Upper Extremity Fugl-Meyer Assessment (UE-FMA) at two time points. TV was computed to characterize dynamic functional connectivity at regional, intra-network, and inter-network levels. Group differences were assessed using one-way ANCOVA with post hoc tests. Linear regression was used to examine associations between TV and motor outcomes. The false discovery rate was used to multiple comparisons correction.

Results

Compared with HCs, both LSS and RSS showed significantly reduced TV in the right frontal-cingulate regions, the somatomotor hand network (SSH), and the connections between SSH and higher-order cognitive networks (all p < 0.05, |Cohen’s d| > 0.49). Increased TV was observed in the left postcentral gyrus, inferior frontal gyrus, cerebellar network (CEN), and somatomotor mouth network (all p < 0.05, |Cohen’s d| > 0.48). Relative to LSS, RSS exhibited additional TV reductions in the right middle occipital gyrus, orbital middle frontal gyrus, default mode network (DMN), and interactions among higher-order cognitive networks (all p < 0.05, |Cohen’s d| > 0.65). Notably, TV in the right opercular inferior frontal gyrus (IFGoperc) (β = 102.69, adjusted p = 6.4 × 10^− 5) and CEN (β = 27.87, adjusted p = 0.011) at the first observation positively correlated with UE-FMA scores at follow-up, with effects modulated by lesion laterality.

Conclusion

TV captures multiscale functional reorganization in chronic subcortical stroke involving motor, cognitive, and sensory networks. TV of the right IFGoperc showed potential as a neuroimaging biomarker for predicting post-stroke motor recovery.

Stroke Triggers Brain Rejuvenation in Healthy Regions, Study Finds

 Will your competent? doctor and hospital ensure research is completed that will have the healthier side take over the functions of the damaged side?

Pedro Bach-y-Rita fully recovered with only a partial brain then our stroke medical 'professionals' can duplicate that! Way back in 1958 so plenty of time to analyze and create 100% recovery protocols!

No knowledge and doing nothing ARE PURE INCOMPETENCE!

Pedro Bach-y-Rita (14 posts to May 2011)

Stroke Triggers Brain Rejuvenation in Healthy Regions, Study Finds

A new study published in The Lancet Digital Health has found that the brain doesn't just adapt around damage after a stroke - it appears to actively 'rejuvenate' healthy areas to compensate. Researchers at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute analyzed brain scans from over 500 stroke survivors and found that larger strokes accelerate aging in the damaged hemisphere, while the opposite, undamaged side appears structurally 'younger.' This 'contralesional shift' suggests the brain is actively reorganizing itself to bolster healthy networks and take on lost functions.

Why it matters

This groundbreaking discovery could lead to more personalized rehabilitation strategies for stroke survivors by using brain age as a biomarker to predict recovery(WRONG, WRONG, WRONG! Survivors want recovery NOT predictions you blithering idiots!) potential and tailor treatments. The research also highlights the power of large-scale international collaboration and the role of AI in uncovering subtle patterns of neuroplasticity that were previously undetectable.

The details

The study utilized deep learning models trained on tens of thousands of MRI scans to estimate the 'brain age' of different regions in over 500 stroke survivors across eight countries. Researchers found that larger strokes accelerate aging in the damaged hemisphere, but paradoxically make the opposite, undamaged side of the brain appear structurally 'younger.' This 'youthfulness' is strongly correlated with the frontoparietal network, a crucial area for motor planning, attention, and coordination. The researchers call this phenomenon the 'contralesional shift,' suggesting the brain is actively reorganizing itself to bolster healthy networks and take on the functions lost due to injury.

• The study was published in The Lancet Digital Health on March 29, 2026.

The players

USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI)

A research institute at the University of Southern California focused on advancing neuroscience through innovative neuroimaging and informatics techniques.

Hosung Kim, PhD

Associate professor of research neurology at the Keck School of Medicine of USC and lead author of the study.

Arthur W. Toga, PhD

Director of the Stevens INI and co-author of the study.

ENIGMA Stroke Recovery Working Group

An international research collaboration focused on understanding the genetic and environmental factors influencing stroke recovery.

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What they’re saying

“We found that larger strokes accelerate aging in the damaged hemisphere but paradoxically make the opposite side of the brain appear younger.”

— Hosung Kim, PhD, Associate professor of research neurology at the Keck School of Medicine of USC

“By pooling data...and applying cutting-edge AI, we can detect subtle patterns...These findings of regionally differential brain aging...could eventually guide personalized rehabilitation strategies.”

— Arthur W. Toga, PhD, Director of the Stevens INI

What’s next

The research team is now focused on longitudinal studies, tracking patients over time to understand how brain aging patterns evolve throughout the recovery process. This could lead to the development of biomarkers that predict an individual's potential for recovery and inform tailored treatment plans.

The takeaway

This study's findings of the brain's remarkable ability to 'rejuvenate' healthy regions after a stroke could pave the way for more personalized rehabilitation strategies and dramatically improve outcomes for stroke survivors. The power of large-scale international collaboration and AI-driven analysis has unlocked new insights into the brain's remarkable neuroplasticity.