Ankle and Toe Brachial Index Extraction from Clinical Reports For Peripheral Artery Disease Identification: Unlocking Clinical Data through Novel Methods

ABSTRACT Importance: Despite its high prevalence and poor outcomes, research on peripheral artery disease (PAD) remains limited due to the poor accuracy of billing codes for identifying PAD in health systems. Objective: Design a natural language processing (NLP) system that can extract ankle brachial index (ABI) and toe brachial index (TBI) values and evaluate the performance of extracted ABI/TBI values to identify patients with PAD in the Veterans Health Administration (VHA). Design, Setting, Participants: From a corpus of 392,244 ABI test reports at 94 VHA facilities during 2015-2017, we selected a random sample of 800 documents for NLP development. Using machine learning, we designed the NLP system to extract ABI and TBI values and laterality (right or left). Performance was optimized through sequential iterations of 10-fold cross validation and error analysis on 3 sets of 200 documents each, and tested on a final, independent set of 200 documents. Performance of NLP-extracted ABI and TBI values to identify PAD in a random sample of Veterans undergoing ABI testing was compared to structured chart review. Exposure: ABI <0.9, or TBI <0.7 in either right or left limb used to define PAD at the patient-level Main Outcome: Precision (or positive predictive value), recall (or sensitivity), F-1 measure (overall measure of accuracy, defined as harmonic mean of precision and recall) Results: The NLP system had an overall precision of 0.85, recall of 0.93 and F1-measure of 0.89. The F-1 measure was similar for both ABI and TBI (0.88 to 0.91). Recall was higher for ABI (0.95 to 0.97) while precision was higher for TBI (0.94 to 0.95). Among 261 patients with ABI testing (49% with PAD), the NLP system was able to extract ABI and TBI values in 238 (91.2%) patients. The NLP system had a positive predictive value of 92.3%, sensitivity of 89.3% and specificity of 92.3% to identify PAD. Conclusion: We have successfully developed and validated an NLP system to extract ABI and TBI values which can be used to accurately identify PAD within the VHA. Our findings have broad implications for PAD research and quality improvement efforts in large health systems.

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Validation of Diagnosis and Procedure Codes for Revascularization for Peripheral Artery Disease in Ontario Administrative Databases

Clinical & Investigative Medicine ◽

10.25011/cim.v44i2.36354 ◽

2021 ◽

Vol 44 (2) ◽

pp. E36-43

Author(s):

Jean Jacob-Brassard ◽

Mohammed Al-Omran ◽

Thérèse A. Stukel ◽

Muhammad Mamdani ◽

Douglas S. Lee ◽

...

Keyword(s):

Positive Predictive Value ◽

Peripheral Artery Disease ◽

Predictive Value ◽

Population Based ◽

Administrative Databases ◽

Peripheral Artery ◽

Predictive Values ◽

Diagnosis Codes ◽

Artery Disease ◽

Procedure Codes

Purpose: To estimate the positive predictive value of diagnosis and procedure codes for open and endovascular revascularization for peripheral artery disease (PAD) in Ontario administrative databases. Methods: We conducted a retrospective validation study using population-based Ontario administrative databases (2005-2019) to identify a random sample of 600 patients who underwent revascularization for PAD at two academic centres, based on ICD-10 diagnosis codes and Canada Classification of Health Intervention procedure codes. Administrative data coding was compared to the gold standard diagnosis (PAD vs. non-PAD) and revascularization approach (open vs. endovascular) extracted through blinded hospital chart re-abstraction. Positive predictive values and 95% confidence intervals were calculated. Combinations of procedure codes with or without supplemental physician claims codes were evaluated to optimize the positive predictive value. Results: The overall positive predictive value of PAD diagnosis codes was 87.5% (84.6%-90.0%). The overall positive predictive value of revascularization procedure codes was 94.3% (92.2%-96.0%), which improved through supplementation with physician fee claim codes to 98.1% (96.6%-99.0%). Algorithms to identify individuals revascularized for PAD had combined positive predictive values ranging from 82.8% (79.6%-85.8%) to 95.7% (93.5%-97.3%). Conclusion: Diagnosis and procedure codes with or without physician claims codes allow for accurate identifi-cation of individuals revascularized for PAD in Ontario administrative databases.

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Invasive screening for lower extremity peripheral artery disease: Killing two birds with one stone?

Vascular ◽

10.1177/1708538119881592 ◽

2019 ◽

Vol 28 (2) ◽

pp. 196-202 ◽

Cited By ~ 1

Author(s):

Savas Celebi ◽

Ozlem Ozcan Celebi ◽

Serkan Çetin ◽

Elif Hande Ozcan Cetin ◽

Erdem Diker ◽

...

Keyword(s):

Coronary Angiography ◽

Positive Predictive Value ◽

Lower Extremity ◽

Likelihood Ratio ◽

Peripheral Artery Disease ◽

Predictive Value ◽

Routine Screening ◽

Smoking History ◽

Peripheral Artery ◽

Artery Disease

Objectives There is substantial evidence that the majority of cases of lower extremity peripheral artery disease are undetected. As a result, there is great interest in the detection of lower extremity peripheral artery disease through routine screening. However, routine screening of lower extremity peripheral artery disease is still debated. Methods In our cross-sectional study, we included 200 consecutive patients with symptoms suggestive of angina who were undergoing coronary angiography. Irrespective of intermittent claudication, we subsequently performed peripheral angiography to detect lower extremity peripheral artery disease. The predictors of lower extremity peripheral artery disease were analyzed, and the diagnostic utility of these predictors and their combinations were determined. Additionally, the determinants of the amount of radio-opaque material used and peripheral fluoroscopy time were investigated. Results The overall prevalence of lower extremity peripheral disease was 16%. Being older than 65 years, having coronary artery disease and smoking history remained significant predictors after adjusting for other well-known parameters. Having the combination of age ≥65 and smoking was associated with a positive predictive value of 50% (likelihood ratio 5.06), and having all of the predictors was associated with a positive predictive value of 100% (likelihood ratio >1000). Conclusions Routine screening for lower extremity peripheral disease patients undergoing coronary angiography may be useful in selected patients.

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The reliability of the ankle-brachial index and toe-brachial index in the daily practice of peripheral artery disease monitoring in vascular medicine ward

Atherosclerosis ◽

10.1016/j.atherosclerosis.2018.06.778 ◽

2018 ◽

Vol 275 ◽

pp. e245

Author(s):

J. Krezel ◽

P. Klapacz ◽

M. Maga ◽

A. Wachsmann ◽

M. Plachno ◽

...

Keyword(s):

Peripheral Artery Disease ◽

Ankle Brachial Index ◽

Daily Practice ◽

Disease Monitoring ◽

Peripheral Artery ◽

Vascular Medicine ◽

Toe Brachial Index ◽

Artery Disease

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Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Open Heart ◽

10.1136/openhrt-2018-000955 ◽

2019 ◽

Vol 6 (1) ◽

pp. e000955 ◽

Cited By ~ 3

Author(s):

Cian P McCarthy ◽

Shreya Shrestha ◽

Nasrien Ibrahim ◽

Roland R J van Kimmenade ◽

Hanna K Gaggin ◽

...

Keyword(s):

Diabetes Mellitus ◽

Peripheral Artery Disease ◽

Predictive Value ◽

Characteristic Curve ◽

Kidney Injury ◽

Clinical Variable ◽

Peripheral Artery ◽

Peripheral Vessel ◽

Artery Disease ◽

Kidney Injury Molecule 1

BackgroundPatients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.Methods and resultsThe HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.ConclusionA clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.Trial registration numberNCT00842868.

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