ArticleArthritis care & research2021
Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data.
Article in Arthritis care & research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Pooled it
- Coronary artery calcium and atherosclerotic cardiovascular disease risk scores in patients with calcium pyrophosphate deposition disease.Rheumatology (Oxford, England) · 2025Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- Fractures in Patients With Acute Calcium Pyrophosphate Crystal Arthritis Versus Matched Comparators in a Large Cohort Study.Arthritis & rheumatology (Hoboken, N.J.) · 2024Article
- Mapping two decades of research in rheumatology-specific journals: a topic modeling analysis with BERTopic.Therapeutic advances in musculoskeletal disease · 2024Article
- Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.Pharmacoepidemiology and drug safety · 2024Article
- Calcium pyrophosphate deposition disease: historical overview and potential gaps.Frontiers in medicine · 2024Review
- Patient-Reported Outcomes in Calcium Pyrophosphate Deposition Disease Compared to Gout and Osteoarthritis.The Journal of rheumatology · 2023Article
- Researchers in rheumatology should avoid categorization of continuous predictor variables.BMC medical research methodology · 2023Article
- Confirming Prior and Identifying Novel Correlates of Acute Calcium Pyrophosphate Crystal Arthritis.Arthritis care & research · 2023Article
- Artificial Intelligence and Deep Learning for Rheumatologists.Arthritis & rheumatology (Hoboken, N.J.) · 2022Review
- Risk of cardiovascular events in patients having had acute calcium pyrophosphate crystal arthritis.Annals of the rheumatic diseases · 2022Article
- Review: Outcome measures in calcium pyrophosphate deposition.Best practice & research. Clinical rheumatology · 2021Review
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveIdentifying 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.
methodsWe 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.
resultsAmong 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.
conclusionFor 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.