National Institutes of Health Stroke Scale Score is an Unreliable Predictor of Perfusion Deficits in Acute Stroke

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables

Purpose

The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes.

Methods

A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC.

Results

A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86.

Conclusion

Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

The relationship between the baseline geriatric nutritional risk index (GNRI) and neurological function at the convalescence stage in patients with stroke: a cross-sectional study

BACKGROUND

Malnutrition is a common complication after stroke and may worsen neurological outcomes for patients. There are still no uniform tools for screening nutritional status for the patients with stroke. We aimed to explore the relationship between the baseline geriatric nutritional risk index (GNRI) and neurological function at the convalescence stage for patients with stroke and assessed the predictive value of the GNRI for adverse neurological outcomes.

METHODS

A total of 311 patients with stroke were enrolled retrospectively. Basic information and laboratory results on admission since onset of stroke were collected. The GNRI on admission was calculated and neurological outcomes evaluated by the Barthel index at 1 month after the onset of stroke. Statistical analyses, including correlation coefficient tests, multivariate regression analyses, and receiver operating characteristic (ROC) analyses, were applied in this study.

RESULTS

Compared with the good outcome group, the poor outcome group showed a significantly lower GNRI on admission (P < 0.05). GNRI was associated with Barthel index (r = 0.702, P < 0.01). The GNRI was independently correlated with the Barthel index (Standardization β = 0.721, P < 0.01) and poor outcome 0.885 (95% CIs, 0.855-0.917, P < 0.01) after adjusting for covariates. Compared with no nutritional risk grades (Q4), the OR of GNRI to poor neurological outcome increased across increasing nutritional risk grades of GNRI (OR = 2.803, 95% CIs = 1.330-5.909 in Q3, 7.992, 95% CIs = 3.294-19.387 in Q2 and 14.011, 95% CIs = 3.972-49.426 in Q1, respectively, P for trend < 0.001). The area under ROC curves (AUC) of the GNRI was 0.804, which was larger than that of the NIHSS, BMI, or Albumin (P < 0.01), with an optimal cut-off value of 97.69, sensitivity of 69.51% and specificity of 77.27%. Combined GNRI with NIHSS gained the largest AUC among all the variables (all P < 0.05), with an AUC of 0.855, sensitivity of 84.75 and specificity of 72.73%.

CONCLUSIONS

For patients with stroke, higher nutritional risk grades at baseline indicated worse neurological function at the convalescence stage. Compared with NIHSS, BMI, and Albumin, GNRI was a competitive indicator for the risk of poor neurological outcome. The predictive property of GNRI for adverse neurological outcomes might be more powerful when combined with NIHSS.

FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke

At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) images can predict clinical outcome of patients with AIS. Patients with AIS were divided into a training (n = 110) and an external validation (n = 80) sets. A total of 753 radiomics features were extracted from each FLAIR and ADC image of the 190 patients. Interquartile range (IQR), Wilcoxon rank sum test, and least absolute shrinkage and selection operator (LASSO) were used to reduce the feature dimension. The six strongest radiomics features were related to an unfavorable outcome of AIS. A logistic regression analysis was employed for selection of potential predominating clinical and conventional magnetic resonance imaging (MRI) factors. Subsequently, we developed several models based on clinical and conventional MRI factors and radiomics features to predict the outcome of AIS patients. For predicting unfavorable outcome [modified Rankin scale (mRS) > 2] in the training set, the area under the receiver operating characteristic curve (AUC) of ADC radiomics model was 0.772, FLAIR radiomics model 0.731, ADC and FLAIR radiomics model 0.815, clinical model 0.791, and clinical and conventional MRI model 0.782. In the external validation set, the AUCs for the prediction with ADC radiomics model was 0.792, FLAIR radiomics model 0.707, ADC and FLAIR radiomics model 0.825, clinical model 0.763, and clinical and conventional MRI model 0.751. When adding radiomics features to the combined model, the AUCs for predicting unfavorable outcome in the training and external validation sets were 0.926 and 0.864, respectively. Our results indicate that the radiomics features extracted from FLAIR and ADC can be instrumental biomarkers to predict unfavorable clinical outcome of AIS and would additionally improve predictive performance when adding to combined model.

CT perfusion in hyper-acute ischemic stroke: the acid test for COVID-19 fear

PURPOSE

The fear of COVID-19 infection may discourage patients from going to the hospital even in case of sudden onset of disabling symptoms. There is growing evidence of the reduction of stroke admissions and higher prevalence of severe clinical presentation. Yet, no studies have investigated the perfusion pattern of acute strokes admitted during the lockdown. We aimed to evaluate the effects of the COVID-19 pandemic on hyper-acute stroke CT perfusion (CTP) pattern during the first months of the pandemic in Italy.

METHODS

In this retrospective observational study, we analyzed CTP images and clinical data of ischemic stroke patients admitted between 9 March and 2 June 2020 that underwent CTP (n = 30), to compare ischemic volumes and clinical features with stroke patients admitted during the same period in 2019 (n = 51). In particular, CTP images were processed to calculate total hypoperfused volumes, core volumes, and mismatch. The final infarct volumes were calculated on follow-up CT.

RESULTS

Significantly higher total CTP hypoperfused volume (83.3 vs 18.5 ml, p = 0.003), core volume (27.8 vs 1.0 ml, p < 0.001), and unfavorable mismatch (0.51 vs 0.91, p < 0.001) were found during the COVID-19 period compared to no-COVID-19 one. The more unfavorable perfusion pattern at admission resulted in higher infarct volume on follow-up CT during COVID-19 (35.5 vs 3.0 ml, p < 0.001). During lockdown, a reduction of stroke admissions (- 37%) and a higher prevalence of severe clinical presentation (NIHSS ≥ 10; 53% vs 36%, p = 0.029) were observed.

CONCLUSION

The results of CTP analysis provided a better insight in the higher prevalence of major severity stroke patients during the COVID-19 period.

Ischemic Volume and Neurological Deficit: Correlation of Computed Tomography Perfusion with the National Institutes of Health Stroke Scale Score in Acute Ischemic Stroke

BACKGROUND

The National Institutes of Health Stroke Scale (NIHSS) is the most adopted stroke patients' evaluation tool in emergency settings to assess the severity of stroke and to determine the patients' eligibility for specific treatments. Computed tomography perfusion (CTP) is crucial to identify salvageable tissue that can benefit from the reperfusion treatment. The aim of this study is to identify the relation between the NIHSS scores and the hypoperfused volumes evaluated by CTP in patients with hyperacute ischemic stroke.

METHODS

This retrospective study was conducted on 105 patients with ischemic stroke who underwent NIHSS assessment and CTP in the hyperacute phase. Hypoperfused volume was evaluated by CTP maps processed with semi-automatic algorithm. An analysis was conducted to determine the degree of correlation between the NIHSS scores and the ischemic lesion volumes and to investigate the relation between the anterior and the posterior circulation strokes, as well as between the right and the left hemispheric strokes.

RESULTS

A significant correlation was found between ischemic volume and NIHSS score at baseline (r = .82; P < .0001) in the entire cohort. A high NIHSS-volume correlation was identified in the anterior circulation stroke (r = .76; P < .0001); whereas, it was nonsignificant in the posterior circulation stroke. NIHSS score and volume correlated for the left and the right hemispheric strokes (r = .83 and .81; P < .0001), showing a slightly higher slope in the left.

CONCLUSION

This study showed a strong correlation between the baseline NIHSS score and the ischemic volume estimated by CTP. We confirmed that NIHSS is a reliable predictor of perfusion deficits in acute ischemic stroke. CTP allows fast imaging assessment in the hyperacute phase. The results highlight the importance of these diagnostic tools in the assessment of stroke severity and in acute decision-making.