CS-07 Economic evaluation of damage accrual in an international SLE inception cohort
Bibliographic record
Abstract
Background Little is known about the association of healthcare costs with damage accrual in SLE. We describe the costs associated with damage progression using multi-state modeling. Methods Patients fulfilling the revised ACR Classification Criteria for SLE from 32 centres in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Annual data on demographics, SLE disease activity (SLEDAI-2K), damage (SLICC/ACR Damage Index [SDI] if ≥6 months from diagnosis), hospitalizations, medications, dialysis, and utilization of selected medical/surgical procedures were collected. Annual health resource utilization was costed using 2017 Canadian prices. Annual costs associated with SDI states were obtained from multiple regressions adjusting for age, sex, race/ethnicity, and disease duration. As there were relatively few transitions to SDI states 5–11, these were merged into a single SDI state. Five and 10 year cumulative costs were estimated by multiplying annual costs associated with each SDI state by the expected duration in each state, which was forecasted using a multi-state model and longitudinal SDI data from the SLICC Inception Cohort (Bruce IN et al. Ann Rheum Dis 2015;74:1706–13). Future costs were discounted at a yearly rate of 3%. Results 1676 patients participated, 88.7% female, 49.2% Caucasian, mean age at diagnosis 34.6 years (SD 13.4), mean disease duration at enrollment 0.5 years (range 0–1.3 years), and mean follow up 7.8 years (range 0.6–16.9 years). Health resource utilization and annual costs (after adjustment using regression) were markedly higher in those with higher SDIs (SDI=0, annual costs $1847, 95% CI $1120 to $2574; SDI≥5, annual costs $26 772, 95% CI $19 631 to $33 813). At SDI≤2, hospitalizations and medications accounted for 97.1% of direct costs, whereas at SDI≥3, dialysis was responsible for 55.0%. Five and 10 year cumulative costs stratified by baseline SDI were calculated by multiplying the annual costs associated with each SDI by the expected duration in that state. Five and 10 year costs were greater in those with the highest SDIs at baseline (table 1). Conclusions Patients with the highest baseline SDIs incur annual costs and 10 year cumulative costs that are at least 10-fold higher than those with the lowest baseline SDI. By estimating the expected duration in each SDI state and incorporating annual costs, disease severity at presentation can be used to predict future healthcare costs, critical knowledge for cost-effectiveness evaluations of novel therapies. Acknowledgements The Systemic Lupus International Collaborating Clinics (SLICC) research network received partial funding for this study from UCB Pharmaceuticals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".