Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods.

Friberg JE, Qazi AH, Boyle B, Franciscus C, Vaughan-Sarrazin M, Westerman D, Patterson OV, Parr SK, Matheny ME, Arya S, Smolderen KG, Lund BC, Gobbel GT, Girotra S. Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods. Circulation. Cardiovascular interventions. 2022 Mar 1; 15(3):e011092.

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Abstract:

BACKGROUND: Despite its high prevalence and clinical impact, research on peripheral artery disease (PAD) remains limited due to poor accuracy of billing codes. Ankle-brachial index (ABI) and toe-brachial index can be used to identify PAD patients with high accuracy within electronic health records. METHODS: We developed a novel natural language processing (NLP) algorithm for extracting ABI and toe-brachial index values and laterality (right or left) from ABI reports. A random sample of 800 reports from 94 Veterans Affairs facilities during 2015 to 2017 was selected and annotated by clinical experts. We trained the NLP system using random forest models and optimized it through sequential iterations of 10-fold cross-validation and error analysis on 600 test reports and evaluated its final performance on a separate set of 200 reports. We also assessed the accuracy of NLP-extracted ABI and toe-brachial index values for identifying patients with PAD in a separate cohort undergoing ABI testing. RESULTS: The NLP system had an overall precision (positive predictive value) of 0.85, recall (sensitivity) of 0.93, and F1 measure (accuracy) of 0.89 to correctly identify ABI/toe-brachial index values and laterality. Among 261 patients with ABI testing (49% PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1%, and specificity of 93.1% to identify PAD when compared with a structured chart review. The above findings were consistent in a range of sensitivity analysis. CONCLUSIONS: We successfully developed and validated an NLP system for identifying patients with PAD within the Veterans Affairs electronic health record. Our findings have broad implications for PAD research and quality improvement.