Evaluation of the Economic Benefit of Earlier Systemic Lupus Erythematosus (SLE) Diagnosis Using a Multivariate Assay Panel (MAP)
Bibliographic record
Abstract
OBJECTIVE: Diagnosis of systemic lupus erythematosus (SLE) made by standard diagnostic laboratory tests (SDLTs) has sensitivity and specificity of 83% and 76%, respectively. A multivariate assay panel (MAP) combining complement C4d activation products on erythrocytes and B cells with SDLTs yields a sensitivity and specificity of 80% and 86%, respectively, presumably enabling earlier SLE diagnosis at lower severity, with associated lower health care costs compared with SDLT diagnoses. We compared the payer budget impact of diagnosing SLE using MAP (incremental cost of $108) versus SDLTs. METHODS: We modeled a health plan of 1 million enrollees. SLE diagnosis among suspected patients was 9.2%. The MAP arm assumed 80%/20% of patients were tested with MAP/SDLTs, versus 100% tested with SDLTs in the SDLT arm. Prediagnosis direct costs were estimated from claims data, and postdiagnosis costs were obtained from the literature. Based on improved MAP performance, the assumed hazard ratio for diagnosis rate compared with SDLTs was 1.74 (71%, 87%, 90%, and 91% of patients who develop SLE are diagnosed in years 1 to 4 compared with 53%, 75%, 84%, and 88% of patients diagnosed with SDLTs). RESULTS: Total 4-year pre- and postdiagnosis direct costs for patients with suspected SLE tested with MAP were $59 183 666 compared with $61 174 818 tested by SDLTs, with lower costs in the MAP arm due primarily to prediagnosis savings related to reduced hospital admissions. CONCLUSION: Incorporating MAP into SLE diagnosis results in estimated 4-year direct cost savings of $1 991 152 ($0.04 per member per month). By facilitating earlier diagnosis of SLE, MAP may enhance patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".