Effects of CD19 CAR T Cell Therapy on Quality of Life and Direct Healthcare Costs in Systemic Lupus Erythematosus: A Preliminary Analysis
Bibliographic record
Abstract
OBJECTIVE: Patients with systemic lupus erythematosus (SLE) require long-term treatment and experience reduced quality of life (QOL). CD19 chimeric antigen receptor (CAR) T cell therapy can achieve sustained drug-free remission in patients with SLE. The impact of CAR T cell therapy on QOL and direct healthcare costs has not been evaluated. Here, we analyzed longitudinal QOL before and after CAR T cell therapy and performed an assessment of direct healthcare costs. METHODS: Physical and mental health were assessed using the standardized 36-item Short Form Health Survey before and 1 year after treatment. Annual direct healthcare costs were analyzed based on inpatient admissions, emergency department visits, outpatient visits, and prescription drug costs in the German healthcare service. RESULTS: A preliminary analysis was conducted on 8 patients with SLE (7 female, 1 male; age range 19-38 years) who received CAR T cell therapy and who were followed for > 2 years. CAR T cell therapy resulted in improvement in the QOL in all patients. The most notable improvement was observed in physical health (from 22.4% to 75.5%), although mental health also improved (from 24.7% to 63%). QOL values rose to the level of a healthy comparison cohort. Additionally, CAR T cell therapy led to a substantial decrease in annual direct healthcare costs from €29,672/year (US $34,353/year) to €3094/year ($3582/year) after treatment. CONCLUSION: In addition to clinical efficacy, in this preliminary cohort, CD19 CAR T cell treatment improves QOL in patients with SLE and may substantially reduce the direct socioeconomic burden associated with active disease by > 90%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".