Development and validation of a simple clinical prediction model for post-stroke seizures in critically ill patients: a multicenter retrospective cohort study

 

Predicting a problem with NO solution is a fireable offense in any business!

Let's check how long you have NOT BEEN SOLVING THE PROBLEM!

• 10% seizures post stroke (19 posts to April 2017)
• 5% epileptic seizures after stroke (10 posts to April 2021)

• epileptic seizures (6 posts to December 2015)

• post-stroke epilepsy (24 posts to December 2016) 

Development and validation of a simple clinical prediction model for post-stroke seizures in critically ill patients: a multicenter retrospective cohort study

• Y

  Yue Zhang ^1,2

• X

  Xiaocui Yang ^1,2

• Y

  Yu Kang ^1,2

• 

  Wenyao Cui ^1,2*

• 1. Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China

• 2. West School of Nursing, Sichuan University, Chengdu, China

Abstract

Background: 

Post-stroke seizures (PSS) are common in critically ill stroke patients, but ICU-specific risk prediction tools are lacking, and the independent impact of PSS on mortality remains controversial.

Methods: 

This retrospective multicenter cohort included 6,586 adult stroke patients from 208 US ICUs in the Collaborative Research Database (eICU-CRD). Patients were randomly divided 7:3 into training and internal validation sets. The primary endpoint was coded in-hospital post-stroke seizures identified via diagnosis codes during the index hospitalization. Candidate predictors were screened using logistic regression and least absolute shrinkage and selection operator (LASSO) regression. Model discrimination, calibration, and clinical utility were, respectively, evaluated using the area under the receiver operating characteristic curve (AUROC), calibration slopes and calibration plots, and decision curve analysis. Propensity score matching (PSM) was used to evaluate the association between PSS and in-hospital mortality and length of stay.

Results: 

The overall incidence of coded in-hospital PSS was 7.5%. Head-to-head comparison identified SOFA as the best-performing routine ICU severity score for coded PSS prediction, outperforming GCS, OASIS and APACHE II. Multivariate logistic regression analysis identified five independent clinical predictors for coded in-hospital PSS: younger age, elevated admission body temperature, pre-existing hypertension, lower mean corpuscular hemoglobin concentration, and lower Glasgow Coma Scale (GCS) score. The model achieved AUCs of 0.702 in the training set and 0.713 in the validation set, with stable performance across age, gender, and stroke subtype subgroups. A three-tier bedside risk stratification was derived, with observed coded PSS rates of 3.4, 6.8, and 16.2% for low-risk, intermediate-risk, and high-risk groups. After PSM, coded PSS was not associated with increased in-hospital mortality (adjusted OR = 0.84, p = 0.295) or prolonged hospital stay (mean difference = −0.30 days, p = 0.167).

Conclusion: 

We developed and internally validated a simple prediction model for coded in-hospital PSS in critically ill stroke patients. Coded PSS itself was not independently associated with increased mortality. This model remains a research tool that has not undergone external validation and cannot yet guide real-world clinical monitoring or pharmacological intervention. These findings are applicable to clinically evident, coded convulsive seizures and may not generalize to non-convulsive seizure phenotypes.

A clinical and biomarker model for predicting post-stroke cognitive impairment: a prospective cohort study

 

 Describing a problem with NO solution is a fireable offense in any business!

Let's check how long you have NOT BEEN SOLVING THE PROBLEM!

• 24 to 70% post-stroke cognitive impairment (4 posts to November 2019)

A clinical and biomarker model for predicting post-stroke cognitive impairment: a prospective cohort study

• X

  Xinyu Zhang ^1†

• X

  Xiangwen Hao ^2†

• Y

  Yanyang Li ²

• J

  Jingqi Chen ²

• X

  Xiaoyan Zheng ²

• A

  Anren Zhang ^3*

• 

  Qiuhong Man ^2*

• 1. School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China

• 2. Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China

Abstract

Background and objectives: 

Post-stroke cognitive impairment (PSCI) is highly prevalent and substantially compromises functional recovery and quality of life; however, early identification remains challenging. This study aimed to develop and validate an early prediction model for PSCI by integrating routinely available blood biomarkers with clinical characteristics.

Methods: 

Patients with acute ischemic stroke were prospectively enrolled at Shanghai Fourth People's Hospital between March and November 2024. Cognitive function was assessed 3 months after stroke using the Telephone Montreal Cognitive Assessment (T-MoCA), a telephone-based cognitive screening instrument, with PSCI defined as a corrected T-MoCA score <19. Univariable logistic regression, Spearman correlation analysis, and variance inflation factor analysis were performed to characterize associations and predictor redundancy. Bootstrap least absolute shrinkage and selection operator (LASSO) logistic regression with 1,000 resampling iterations was used to assess variable-selection stability, followed by multivariable logistic regression and nomogram construction. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration analysis, decision curve analysis (DCA), and clinical impact curves (CIC), with 1,000 bootstrap iterations used for internal validation.

Results: 

A total of 198 patients were included, of whom 138 developed PSCI and 60 were classified as post-stroke cognitively normal (PSCN, non-PSCI group). The final prediction model incorporated age, prior stroke history, diabetes mellitus, small dense low-density lipoprotein (sdLDL), apolipoprotein A-I (ApoA-I), homocysteine, cystatin C, and D-dimer. The model achieved an apparent AUC of 0.798. Following 1,000 bootstrap resampling iterations, the mean AUC was 0.777 (95% CI, 0.733–0.802), and the optimism-corrected AUC was 0.762. Calibration analysis suggested reasonable agreement between predicted and observed risks, while DCA and CIC analyses indicated potential utility for PSCI risk stratification.

Conclusions: 

This study developed and internally validated a multivariable model combining clinical characteristics and circulating biomarkers to estimate PSCI risk after acute ischemic stroke. The model showed acceptable predictive performance and may support early risk stratification.

Gut microbiota-derived tryptophan metabolites and inflammatory factors enhance prediction of early neurological deterioration after intravenous thrombolysis in stroke patients: a comparative model study

 

Describing a problem with NO solution is a fireable offense in any business!

Gut microbiota-derived tryptophan metabolites and inflammatory factors enhance prediction of early neurological deterioration after intravenous thrombolysis in stroke patients: a comparative model study

• W

  Wei Li ^1†

• Z

  Zhiyan Feng ^2†

• C

  Chunrong Zeng ²

• Z

  Zhimei Cao ²

• Y

  Yunfei Bai ²

• H

  Honglei Wang ²

• 

  Jiagui Huang ^2*

• 1. Medical Imaging Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, Yibin, China

• 2. Department of Neurology, Second People’s Hospital of Yibin City-West China Yibin Hospital, Sichuan University, Yibin, China

Abstract

Background and purpose: 

Early neurological deterioration (END) after stroke thrombolysis predicts poor outcomes. This study developed predictive nomograms that incorporate gut-derived tryptophan metabolites and inflammatory markers to predict END.

Methods: 

We evaluated demographic, clinical, and laboratory variables, including gut-derived tryptophan metabolites and inflammatory markers. A multivariable logistic regression model was developed to predict END and presented as a nomogram. Model performance was assessed using variance inflation factors, ROC analysis, calibration curves, and decision curve analysis. Net Reclassification Improvement and Integrated Discrimination Improvement were used to compare models.

Results: 

In this study, END occurred in 11.26% of stroke patients and was independently associated with systolic blood pressure (SBP), fasting blood glucose, and serum levels of indole-3-propionic acid (IPA), indole-3-lactic acid (ILA), and interferon γ (IFN γ). Two nomogram models were developed to predict END: one based on conventional risk factors (SBP and glucose) and another incorporating emerging biomarkers (SBP, glucose, ILA, IPA, and IFN γ). The models achieved AUC scores of 0.719 (95% CI 0.658–0.780) and 0.909 (95% CI 0.879–0.939), respectively. Comparing the models showed a categorical Net Reclassification Improvement of 0.601 (p = 0.000) and an Integrated Discrimination Improvement of 0.197 (p = 0.000).

Conclusion: 

Incorporating gut microbiota-derived tryptophan metabolites and inflammatory factors improves prediction of END among thrombolyzed stroke patients.

Multiparametric MRI radiomics and machine learning for outcome prediction in acute ischemic stroke: a retrospective study of 353 patients

 Predictions DON'T GET SURVIVORS RECOVERED! Are you that blitheringly stupid?

Multiparametric MRI radiomics and machine learning for outcome prediction in acute ischemic stroke: a retrospective study of 353 patients

• X

  Xiaoxing Peng ¹

• J

  Jiannan Guo ²

• Q

  Qiuyan Zhou ³

• S

  Shunyu Wang ⁴

• 

  Tingting Li ^5*

• 1. Department of Medical Imaging, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China

• 2. Department of Electro-diagnosis, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China

Abstract

Background: 

Acute ischemic stroke (AIS) remains a leading cause of death and disability worldwide. Early prediction for clinical outcomes is essential for guiding therapy.(Wrong! An EXACT DAMAGE DIAGNOSIS is required for mapping to EXACT REHAB POROTOCOLS! All this still needs to be created.)  Multiparametric Magnetic Resonance Imaging (MRI) offers detailed characterization of ischemic tissue and may predict prognosis in AIS patients. This study assesses the prognostic value of multiparametric MRI parameters in predicting functional outcome, recurrence risk and mortality in AIS patients.

Methods: 

We conducted a retrospective analysis of 353 AIS patients who underwent multiparametric MRI within 24 h of onset. MRI metrics including diffusion-weighted imaging (DWI) lesion volume, ADC minimum, perfusion parameters and Susceptibility-Weighted Imaging (SWI) microbleeds were correlated with National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores at discharge and 90 days. Predictive performance for clinical outcomes was assessed by receiver-operating characteristic (ROC) analysis.

Results: 

Among 353 AIS patients with a mean door-to-imaging time of 27 ± 10 min, higher NIHSS, larger DWI volume, prolonged mean transit time (MTT) and Tmax, and increased microbleeds significantly correlated with mortality and disability, while lower ADC, CBF, and CBV correlated inversely with clinical outcomes. NIHSS ≥10.5 and DWI lesion volume ≥52.5 mL yielded AUCs of 0.963 and 0.975 and accuracies of 91.35% and 92.79%, respectively; CBF ≤ 20.5 yielded an AUC of 0.975. NIHSS ≥8.5 and MTT ≥8.5 s showed AUCs of 0.948 and 0.931, respectively, for predicting hemorrhagic transformation. Perfusion thresholds modestly predicted 90-day recurrence (AUC = 0.7). Hypertension, diabetes, and prior stroke were significant clinical predictors of poor outcome. These predictive metrics reflect internal, within-cohort validation only; independent external validation is required before clinical application.

Conclusion: 

Multiparametric MRI provides robust prognostic information in AIS. Quantitative thresholds for perfusion and diffusion parameters help in risk stratification for hemorrhage and mortality, enhancing individualized management and outcome prediction.

Perfusion Mismatch Ratio Predictive in Medium Vessel Occlusion Strokes

 Since NOTHING WILL BE DONE with this to get survivors recovered; TOTALLY FUCKING USELESS! With NO leadership or strategy in stroke, better plan on not having one! Prognostication alone has never gotten anyone recovered; prove me wrong, you blithering idiots!

Perfusion Mismatch Ratio Predictive in Medium Vessel Occlusion Strokes

Small trial suggests potential for CT imaging measure in prognostication, treatment selection

by Crystal Phend, Contributing Editor, MedPage Today

October 15, 2026 • 3 min read

For medium vessel occlusion strokes, perfusion mismatch ratio predicted follow-up infarct volume, a multinational trial showed, supporting a role similar to that in large vessel occlusion.

In the 94 isolated M2-segment occlusion patients studied, higher mismatch ratio had a beta score of -0.26 (95% CI -0.37 to -0.14, P<0.001) for smaller follow-up infarct volume. The relationship remained significant on multivariate analysis (beta -0.14, 95% CI -0.27 to -0.02, P=0.026).

However, absolute mismatch volume -- hypoperfused volume minus the ischemic core volume -- wasn't predictive (P=0.84), Hamza Adel Salim, MD, of the University of Texas MD Anderson Cancer Center in Houston, reported at the at the American Society of Functional Neuroradiology meeting in Newport Beach, California.

Mismatch ratio -- a CT-based calculation of the volume of hypoperfused tissue that is potentially salvageable to the volume of the irrecoverable ischemic core -- has largely been studied in large vessel occlusion stroke. Clinical trials have shown strong benefit of both thrombolytics and endovascular thrombectomy among patients with higher mismatch, although low mismatch hasn't necessarily proven a good rule out for thrombectomy selection.

"The performance of mismatch ratio in this population was consistent with its performance in large vessel occlusion," Salim told MedPage Today. "Predictive value was consistent with what we expected."

But unlike in large vessel ischemic strokes, trials have had mixed results on benefit of mechanical thrombectomy in distal medium vessel occlusion, he noted: "So the question that needs to be answered here is which distal medium vessel occlusion patient could benefit from thrombectomy? In this study, we've shown that mismatch ratio does represent fall of infarct volume in these patients. So this study should pave the way for future studies that could assess whether the treatment of distal medium vessel occlusion patients who had low mismatch ratio is not that beneficial compared to patients who have high mismatch ratio."

The study included 94 patients in an international registry with prospectively collected data who had been treated from September 2017 through July 2021 for acute ischemic stroke with isolated M2-segment occlusion. Participants had to have baseline perfusion imaging and follow-up CT or MRI for infarct volume assessment.

Other predictors of greater follow-up infarct volume were higher baseline NIH Stroke Scale score, lower Alberta Stroke Program Early CT Score (ASPECTS), and right-sided occlusion (all P<0.05).

Limitations centered on the retrospective observational design, which made selection bias and residual confounding possible. The small cohort was predominantly treated with thrombectomy, limiting treatment comparisons, and the local perfusion processing and CT/MRI-based infarct measurements varied across centers.

Also, high mismatch ratios are sensitive to small estimated core volumes, the researchers cautioned. They called for prospective external validation to confirm mismatch ratio as a biomarker to triage patients for mechanical thrombectomy.

Salim emphasized that the study did not establish that patient's treatment should be selected based on mismatch ratios in medium vessel occlusion stroke.

It might be useful in the clinical framework as a complement to imaging-based prognostication, he said. However, pending trials demonstrating utility in treatment selection, "its use should be exclusive to the prognosis to tell patients or their families or the clinicians or the neurologists that in these patients we expect to have higher follow-up infarct volume compared to patients who had high mismatch ratio," Salim said.

Crystal Phend is a contributing editor at MedPage Today. Connect:

Disclosures

Salim disclosed no relevant conflicts of interest.

Primary Source

American Society of Functional Neuroradiology

Source Reference: Salim HA "Mismatch ratio is associated with follow-up infarct volume in M2 segment medium vessel occlusions: A multicenter multinational study" ASFNR 2026.