Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study.

Background: Venous thromboembolism (VTE) is a preventable, common vascular disease that has been estimated to affect up to 900,000 people per year. It has been associated with risk factors such as recent surgery, cancer, and hospitalization. VTE surveillance for patient management and safety can be improved via natural language processing (NLP). NLP tools have the ability to access electronic medical records, identify patients that meet the VTE case definition, and subsequently enter the relevant information into a database for hospital review. Objective: We aimed to evaluate the performance of a VTE identification model of IDEAL-X (Information and Data Extraction Using Adaptive Learning; Emory University)-an NLP tool-in automatically classifying cases of VTE by "reading" unstructured text from diagnostic imaging records collected from 2012 to 2014. Methods: After accessing imaging records from pilot surveillance systems for VTE from Duke University and the University of Oklahoma Health Sciences Center (OUHSC), we used a VTE identification model of IDEAL-X to classify cases of VTE that had previously been manually classified. Experts reviewed the technicians' comments in each record to determine if a VTE event occurred. The performance measures calculated (with 95% CIs) were accuracy, sensitivity, specificity, and positive and negative predictive values. Chi-square tests of homogeneity were conducted to evaluate differences in performance measures by site, using a significance level of .05. Results: The VTE model of IDEAL-X "read" 1591 records from Duke University and 1487 records from the OUHSC, for a total of 3078 records. The combined performance measures were 93.7% accuracy (95% CI 93.7%-93.8%), 96.3% sensitivity (95% CI 96.2%-96.4%), 92% specificity (95% CI 91.9%-92%), an 89.1% positive predictive value (95% CI 89%-89.2%), and a 97.3% negative predictive value (95% CI 97.3%-97.4%). The sensitivity was higher at Duke University (97.9%, 95% CI 97.8%-98%) than at the OUHSC (93.3%, 95% CI 93.1%-93.4%; P<.001), but the specificity was higher at the OUHSC (95.9%, 95% CI 95.8%-96%) than at Duke University (86.5%, 95% CI 86.4%-86.7%; P<.001). Conclusions: The VTE model of IDEAL-X accurately classified cases of VTE from the pilot surveillance systems of two separate health systems in Durham, North Carolina, and Oklahoma City, Oklahoma. NLP is a promising tool for the design and implementation of an automated, cost-effective national surveillance system for VTE. Conducting public health surveillance at a national scale is important for measuring disease burden and the impact of prevention measures. We recommend additional studies to identify how integrating IDEAL-X in a medical record system could further automate the surveillance process.

Validation of apixaban anti-factor Xa assay and impact of body weight.

BACKGROUND: Direct oral anticoagulants (DOACs) are widely used as therapies for venous thromboembolism and other cardiovascular diseases. However, routine coagulation monitoring is not required, but may be clinically indicated in high risk populations including obese patients. OBJECTIVES: The aims of this study were two fold; to validate a chromogenic assay for anti-factor Xa measurement in patients taking apixaban, and correlate it with PT/INR and PTT, and to measure anti-factor Xa levels in patients who weighed >120 kg. PATIENTS/METHODS: Patients who were taking apixaban had 3 blood samples drawn over a 4 h period. Apixaban levels were determined using an anti-factor Xa activity assay (STA-Liquid Anti-Xa) using STA-Apixaban Calibrator and STA-Apixaban Controls. The PT/INR was determined using standard methodology. Apix MS, using manufacturer provided apixaban standard, was performed on plasma. RESULTS AND CONCLUSIONS: 18 normal weight patients, 39 obese patients and 14 controls were enrolled. There was a strong correlation between apixaban anti-factor Xa activity compared to plasma Apix MS (r = 0.95). In patients >120 kg, there was a statistically significant decreased rate of change in anti-factor Xa levels after ingestion. Further, the area under the curve for apixaban anti-factor Xa levels was significantly lower in patients over 120 kg. While INR correlated with apixaban MS and apixaban anti-factor Xa activity in both normal weight and obese patients, the association was not sufficiently strong to clinically manage patients, normal weight or obese. Given these findings, research is necessary to investigate the clinical utility of apixaban anti-factor Xa activity measurement in selected populations.