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BMC Medical Informatics and Decision Making

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Open Access 01-12-2020 | Stroke | Research article

A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics

Authors: Alia Stanciu, Mihai Banciu, Alireza Sadighi, Kyle A. Marshall, Neil R. Holland, Vida Abedi, Ramin Zand

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

Abstract

Background

Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke.

Methods

We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling.

Results

The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as “TIA mimic” and 83% of the “TIA” discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%.

Conclusion

The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.

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Title: A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics
Authors: Alia Stanciu
Mihai Banciu
Alireza Sadighi
Kyle A. Marshall
Neil R. Holland
Vida Abedi
Ramin Zand
Publication date: 01-12-2020
Publisher: BioMed Central
Keyword: Stroke
Published in: BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-020-01154-6

At a glance: The STEP trials

Springer Medicine

A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics

Abstract

Background

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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