Cluster analysis of longitudinal treatment patterns in patients newly diagnosed with systemic lupus erythematosus in the United States
Bibliographic record
Abstract
Treatments for systemic lupus erythematosus (SLE) include corticosteroids (CS), antimalarials, nonsteroidal anti-inflammatory drugs, cytotoxic agents, and immunosuppressive/immunomodulatory agents. We examined treatment patterns in newly diagnosed SLE patients from a multipayer US claims database. This study (GSK HO-13-13054) retrospectively followed incident SLE patients' treatment for 4 years in the MarketScan commercial claims database. The earliest medical claim date with SLE diagnosis (ICD-9 code 710.0x; 1 January 2002 to 31 March 2008) was the index date. Patients were ≥18 years at index, had continuous medical and pharmacy benefits for 12 months pre index without SLE diagnosis and 48 months post index, with ≥1 SLE-related inpatient claim or ≥2 office or emergency room visits with SLE diagnosis ≥30 days apart within 12 months post index. A specialist must have made ≥1 SLE diagnosis at index or within 12 months post index. Results were stratified by provider type (primary care physician (PCP)/specialist). A disjoint k-means cluster analysis identified treatment pathways using annual prescription numbers for CS, hydroxychloroquine (HCQ), mycophenolate mofetil, azathioprine, and methotrexate as input variables. The study identified 2,086 newly diagnosed SLE patients (mean age: 47.2 years; female: 91%). In the 4 years post index, 1,031 (49.4%) patients were not actively treated (<0.05 prescriptions/year). Of the 219 (10.5%) patients who primarily received CS, 42 had persistently high numbers of prescriptions (~1/month), and 177 received 4.9 (mean) prescriptions in Year 1, decreasing in Years 2 to 4. Three subgroups emerged within the 606 (29.1%) patients who primarily received HCQ: persistent high number of prescriptions (~1/month), persistent moderate number of prescriptions (3.2 to 4.1/year), and poor adherence (Year 1, 8.7 prescriptions; Years 2 to 4, decreasing prescriptions). Both CS and HCQ were received by 138 (6.6%) patients; 56 had high numbers of prescriptions (Years 1 to 4); 82 showed progressively decreasing prescriptions. Fifty-four (2.6%) and 38 (1.8%) patients had moderate numbers of prescriptions for methotrexate (5.4 to 8.4/year) and azathioprine (5.7 to 7.5/year), respectively, with some CS and HCQ prescriptions. Treatment patterns differed in patients seen by specialists versus PCPs ( P < 0.0001). Specialist-treated patients had a lower no-treatment rate than PCP-treated patients, and higher rates in active treatment clusters (Table 1 ). Treatment patterns were observed among SLE patients using medical resources. In the 4 years post diagnosis: ~50% of patients were not actively treated; 50% received CS, HCQ, and immunosuppressants with differing combinations, intensities, and adherence levels. Specialists provided more intensive treatment than PCPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".