Academic Journal
Evaluating unsupervised and rule-based phenotyping methods versus administrative code counts for systemic sclerosis identification.
| Τίτλος: | Evaluating unsupervised and rule-based phenotyping methods versus administrative code counts for systemic sclerosis identification. |
|---|---|
| Συγγραφείς: | Luo Y; Division of Rheumatology and Clinical Immunology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA., Zhang G; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA., Agarwal A; Division of Rheumatology and Clinical Immunology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA., Weng C; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA., Bernstein EJ; Division of Rheumatology and Clinical Immunology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA. Electronic address: ejb2153@cumc.columbia.edu. |
| Πηγή: | Seminars in arthritis and rheumatism [Semin Arthritis Rheum] 2026 Oct; Vol. 80, pp. 153033. Date of Electronic Publication: 2026 Jun 29. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: W.B. Saunders Country of Publication: United States NLM ID: 1306053 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-866X (Electronic) Linking ISSN: 00490172 NLM ISO Abbreviation: Semin Arthritis Rheum Subsets: MEDLINE |
| Imprint Name(s): | Publication: Philadelphia Pa : W.B. Saunders Original Publication: New York, Stratton. |
| Ιατρικοί όροι (MeSH): | Scleroderma, Systemic*/diagnosis , Scleroderma, Systemic*/mortality , Unsupervised Machine Learning*, Humans ; Female ; Phenotype ; Middle Aged ; Male ; Retrospective Studies ; Electronic Health Records ; International Classification of Diseases ; Aged ; Principal Component Analysis |
| Περίληψη: | Objective: Our study aims to evaluate the performance of an unsupervised machine learning approach, linear combinations of principal components (LPC); a rule-based approach, phenotype risk score (PheRS); and M34 International Classification of Disease (ICD) code counts for identifying systemic sclerosis (SSc) and predicting mortality from electronic health record (EHR). Methods: We conducted a single-center retrospective study. We developed SSc-specific PheRS and LPC score using 17 clinical features. We then evaluated these methods for identifying rheumatologist-diagnosed SSc and predict mortality. Results: Of 653 patients with at least one M34 code, 547 (84%) had rheumatologist-diagnosed SSc. When compared with randomly sampled matched controls, LPC outperformed PheRS in identifying rheumatologist-diagnosed SSc (AUROC 0.90 vs 0.87). However, in a three-method comparison among individuals with at least one M34 code, LPC was only marginally superior to PheRS and M34 code counts in distinguishing rheumatologist-diagnosed SSc from M34 non-SSc controls (AUROC 0.72 for LPC, 0.70 for PheRS and 0.70 for ICD code counts). Using at least two M34 codes had a sensitivity of 87.8% and positive predictive value of 87.0% in identifying SSc. Despite the limited performance gains for SSc identification, both LPC and PheRS had substantially higher performance in predicting mortality compared to M34 code counts in patients with SSc (AUROC 0.74 vs 0.72 vs 0.63). Conclusion: M34 code counts achieved reasonable performance for SSc identification and comparable to those of LPC and PheRS. However, both LPC and PheRS demonstrated better performance for predicting mortality compared with M34 code counts in patients with SSc. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Grant Information: | K23 AR075112 United States AR NIAMS NIH HHS; K23 AR084061 United States AR NIAMS NIH HHS; R01 HL164758 United States HL NHLBI NIH HHS |
| Contributed Indexing: | Keywords: Electronic health record; Phenotype identification; Systemic sclerosis |
| Entry Date(s): | Date Created: 20260701 Date Completed: 20260907 Latest Revision: 20260907 |
| Update Code: | 20260908 |
| PubMed Central ID: | PMC13336295 |
| DOI: | 10.1016/j.semarthrit.2026.153033 |
| PMID: | 42385561 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1532-866X |
|---|---|
| DOI: | 10.1016/j.semarthrit.2026.153033 |