Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data

• Published in: Arthritis care & research (2010) Vol. 73; no. 3; pp. 442 - 448
• Main Authors: Tedeschi, Sara K., Cai, Tianrun, He, Zeling, Ahuja, Yuri, Hong, Chuan, Yates, Katherine A., Dahal, Kumar, Xu, Chang, Lyu, Houchen, Yoshida, Kazuki, Solomon, Daniel H., Cai, Tianxi, Liao, Katherine P.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2021
• Subjects: Aged; Aged, 80 and over; Algorithms; Calcium; Chondrocalcinosis; Chondrocalcinosis - classification; Chondrocalcinosis - diagnosis; Chondrocalcinosis - drug therapy; Codes; Crystals; Data Mining; Electronic Health Records; Electronic medical records; Female; Humans; Learning algorithms; Machine Learning; Male; Middle Aged; Natural Language Processing; Synovial fluid; Synovitis
• ISSN: 2151-464X; 2151-4658; 2151-4658
• DOI: 10.1002/acr.24132

Objective Identifying pseudogout in large data sets is difficult due to its episodic nature and a lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) deposition disease. The objective of this study was to evaluate a novel machine learning approach for classifying pseudogout using electronic health record (EHR) data. Methods We created an EHR data mart of patients with ≥1 relevant billing code or ≥2 natural language processing (NLP) mentions of pseudogout or chondrocalcinosis, 1991–2017. We selected 900 subjects for gold standard chart review for definite pseudogout (synovitis + synovial fluid CPP crystals), probable pseudogout (synovitis + chondrocalcinosis), or not pseudogout. We applied a topic modeling approach to identify definite/probable pseudogout. A combined algorithm included topic modeling plus manually reviewed CPP crystal results. We compared algorithm performance and cohorts identified by billing codes, the presence of CPP crystals, topic modeling, and a combined algorithm. Results Among 900 subjects, 123 (13.7%) had pseudogout by chart review (68 definite, 55 probable). Billing codes had a sensitivity of 65% and a positive predictive value (PPV) of 22% for pseudogout. The presence of CPP crystals had a sensitivity of 29% and a PPV of 92%. Without using CPP crystal results, topic modeling had a sensitivity of 29% and a PPV of 79%. The combined algorithm yielded a sensitivity of 42% and a PPV of 81%. The combined algorithm identified 50% more patients than the presence of CPP crystals; the latter captured a portion of definite pseudogout and missed probable pseudogout. Conclusion For pseudogout, an episodic disease with no specific billing code, combining NLP, machine learning methods, and synovial fluid laboratory results yielded an algorithm that significantly boosted the PPV compared to billing codes.