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

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.

Risk of Subsequent Stroke Among Patients Receiving Outpatient vs Inpatient Care for Transient Ischemic Attack

“…As presented in the eTable 4 in the Supplement , the difference among estimated risk of subsequent stroke measured by seven estimators (DL, HE, HS, ML, REML, SJ, and EB) was negligible. The forest plots based on the REML estimator 43 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 96 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , …”

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

“…ML has been explored by researchers for stroke screening [ 42 ], detection of stroke and large vessel occlusion using CTA imaging [ 43 , 44 ], detection and subtyping of hemorrhagic stroke on CT scans [ 45 , 46 , 47 , 48 ], and to predict post-stroke mortality [ 49 , 50 ]. Researchers have also used ML to aid in the acute diagnosis of TIAs and differentiate them from their mimics [ 51 ].…”

Age-Dependent Differences in the Rate and Symptoms of TIA Mimics in Patients Presenting With a Suspected TIA to a Neurological Emergency Room

“…In the near future, computational models might be a promising approach in order to distinguish between TIA and mimics: a recent predictive analytics model has taken clinical and imaging elements considered for the diagnosis of TIA, a TIA mimic or a minor stroke into account and developed two different multinomial models incorporating feature selection for differentiating between these potential diagnoses in a cohort with a similar size. Although this approach seems promising, further improvements like noise filters are still warranted in order to improve the quality of these predictive models ( 9 ).…”

“…This becomes even more important in western countries due to the increasing percentage of aged people in the population ( 8 ). So far, TIA mimics have been studied ( 2 , 3 , 9 , 10 ) but not in an age-dependent manner. Some previous publications present infections as potential cause of TIA mimics, but they do not differentiate in which age these infections occur.…”