Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes

Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity. In this study, we built a PAD surveillance system using natural language processing (NLP) for early detection of PAD from narrative clinical notes. Our NLP algorithm had excellent positive predictive value (0.93) and identified 41% of PAD cases before the initial ankle-brachial index (ABI) test date while in 12% of cases the NLP algorithm detected PAD on the same date as the ABI (the gold standard for comparison). Hence, our system ascertains PAD patients in a timely and accurate manner. In conclusion, our PAD surveillance NLP algorithm has the potential for translation to clinical practice for use in reminding clinicians to order ABI tests in patients with suspected PAD and to reinforce the implementation of guideline recommended risk modification strategies in patients diagnosed with PAD.