Accuracy of two scores in the diagnosis of stroke subtype in a multicenter cohort study

The Validity of Clinical Scoring in the Diagnosis of Stroke Subtype: Validation Study

BACKGROUND

The gold standard for distinguishing stroke subtype is non-contrast CT. However, it's still prohibitively expensive and out of reach for the majority of recourse-constrained settings. Clinically, not all patients will have a definite diagnosis of hemorrhagic/ischemic stroke. To overcome these challenges and improve clinical bedside diagnosis, clinical stroke scores for stroke subtypes have been developed and recommended to be used in the absence of appropriate imaging modality.

METHODS

We conducted a prospective cross-sectional study among stroke patients to compare the accuracy of level of clinical stroke score methods in differentiating stroke type with CT. it was conducted on 140 people at MTU teaching hospital in Bench-Sheko Zone, South-west Ethiopia. Data were collected using check list. Analysis of the data was done using SPSS version 24.

RESULTS

Our result revealed an incidence of hemorrhagic stroke were 50%, ischemic stroke were 48.6% by CT evaluation. Specificity, sensitivity, positive predictive value, negative predictive value and the overall accuracy of Siriraj stroke score for differentiation of hemorrhage from ischemic stroke were 68.6%, 83.9%, 74.6%, 79.5%, and 82% respectively, the Guys score were 89.7%, 47.8%, 73.3%, 74.5% and 74.5% respectively and while the Bensson score were 88.6%, 35.3%, 75%, 58.5%, and 62.3% respectively.

CONCLUSION

We conclude that Siriraj stroke score showed good sensitivity and fair overall accuracy for hemorrhagic stroke even if it had poor specificity.

Machine learning based reanalysis of clinical scores for distinguishing between ischemic and hemorrhagic stroke in low resource setting

BACKGROUND

Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification.

METHODS

We systematically searched PubMed, ERIC, ScienceDirect, and IEEE-Xplore from 2001 to 2021 for studies that validated the Siriraj, Guys Hospital/Allen, Greek, and Besson scores for stroke classification. From included studies we extracted the reported cross-tabulation to identify and correct the above listed issues for an accurate comparative analysis of the performance of clinical scores.

RESULTS

A total of 21 studies were included. Comparative analysis demonstrates Siriraj Score outperforms others. For Siriraj Score the reported sensitivity range (Ischemic Stroke-diagnosis) 43-97% (Median = 78% [IQR 65-88%]) is significantly higher than our calculated range 40-90% (Median = 70% [IQR 57-73%]), also the reported sensitivity range (Hemorrhagic Stroke-diagnosis) 50-95% (Median = 71% [IQR 64-82%]) is higher than our calculated range 34-86% (Median = 59% [IQR 50-79%]) which indicates overestimation of performance by the included studies. Guys Hospital/Allen and Greek Scores show similar trends. Recommended weighted-accuracy metric provides better estimate of the performance.

CONCLUSION

We demonstrate that clinical attributes have a potential for stroke classification, however the performance of all scores varies across demographics, indicating the need to fine-tune scores for different demographics. To improve this variability, we suggest creating global data pool with statistically significant attributes. Machine Learning classifiers trained over such dataset may perform better and generalise at scale.

Use of a Diagnostic Score to Prioritize Computed Tomographic (CT) Imaging for Patients Suspected of Ischemic Stroke Who May Benefit from Thrombolytic Therapy

Background

A shortage of computed tomographic (CT) machines in low and middle income countries often results in delayed CT imaging for patients suspected of a stroke. Yet, time constraint is one of the most important aspects for patients with an ischemic stroke to benefit from thrombolytic therapy. We set out to assess whether application of the Siriraj Stroke Score is able to assist physicians in prioritizing patients with a high probability of having an ischemic stroke for urgent CT imaging.

Methods

From the Malaysian National Neurology Registry, we selected patients aged 18 years and over with clinical features suggesting of a stroke, who arrived in the hospital 4.5 hours or less from ictus. The prioritization of receiving CT imaging was left to the discretion of the treating physician. We applied the Siriraj Stroke Score to all patients, refitted the score and defined a cut-off value to best distinguish an ischemic stroke from a hemorrhagic stroke.

Results

Of the 2176 patients included, 73% had an ischemic stroke. Only 33% of the ischemic stroke patients had CT imaging within 4.5 hours. The median door-to-scan time for these patients was 4 hours (IQR: 1;16). With the recalibrated score, it would have been possible to prioritize 95% (95% CI: 94%–96%) of patients with an ischemic stroke for urgent CT imaging.

Conclusions

In settings where CT imaging capacity is limited, we propose the use of the Siriraj Stroke Score to prioritize patients with a probable ischemic stroke for urgent CT imaging.

Accuracy of clinical stroke scores for distinguishing stroke subtypes in resource poor settings: A systematic review of diagnostic test accuracy

Background:

Stroke is the second leading cause of death globally. Computerized tomography is used to distinguish between ischemic and hemorrhagic subtypes, but it is expensive and unavailable in low and middle income countries. Clinical stroke scores are proposed to differentiate between stroke subtypes but their reliability is unknown.

Materials and Methods:

We searched online databases for studies written in English and identified articles using predefined criteria. We considered studies in which the Siriraj, Guy's Hospital, Besson and Greek stroke scores were compared to computerized tomography as the reference standard. We calculated the pooled sensitivity and specificity of the clinical stroke scores using a bivariate mixed effects binomial regression model.

Results:

In meta-analysis, sensitivity and specificity for the Siriraj stroke score, were 0.69 (95% CI 0.62-0.75) and 0.83 (95% CI 0.75-0.88) for ischemic stroke and 0.65 (95% CI 0.56-0.73) and 0.88 (95% CI 0.83-0.91) for hemorrhagic stroke. For the Guy's hospital stroke score overall sensitivity and specificity were 0.70 (95% CI 0.53-0.83) and 0.79 (95% CI 0.68-0.87) for ischemic stroke and 0.54 (95% CI 0.42-0.66) and 0.89 (95% CI 0.83-0.94) for hemorrhagic stroke.

Conclusions:

Clinical stroke scores are not accurate enough for use in clinical or epidemiological settings. Computerized tomography is recommended for differentiating stroke subtypes. Larger studies using different patient populations are required for validation of clinical stroke scores.

The burden of inpatient neurologic disease in a tropical African hospital

BACKGROUND

Neurologic disorders represent a major burden of disease globally and the spectrum ranges from noncommunicable disorders like stroke and neurodegenerative disorders to central nervous system infections.

OBJECTIVE

The purpose of the study is to assess the burden of neurological diseases in a tropical environment.

METHODS

A one year retrospective survey of neurological diseases seen at the University of Calabar Teaching Hospital, Nigeria, was evaluated using patients' medical record.

RESULTS

Neurological diseases constituted 24.2% of all medical conditions seen over a one year period. Stroke was found to be the commonest cause of admissions accounting for 42.1% of the cases followed by peripheral neuropathy (13.8%) and meningoencephalitis (7.2%). The immediate case fatality rate was 33.6%. Fifty two percent were discharged home with various levels of recovery while 12.5% left against medical advice. About 2% were referred to other tertiary health institutions.

CONCLUSION

The pattern of neurologic diseases in the local medical wards was not remarkably different from those observed in Nigeria and elsewhere. Stroke remains the most frequent cause of neurologic admissions and mortality in this region is same as observed elsewhere.