Quality initiative in first-line multiple myeloma assessing the use of anti-CD38 agents and quadruplet therapy
Bibliographic record
Abstract
Abstract Background: The University of Pittsburgh Medical Center (UPMC) partnered with IntegraConnect to conduct a quality initiative (QI) studying the utilization of anti-CD38 agents and quadruplet (quad) therapy (lenalidomide, CD38, proteasome inhibitor and dexamethasone) in newly diagnosed multiple myeloma (NDMM), and how treatment selection has evolved throughout the QI project to date. Anti-CD38 monoclonal antibodies have revolutionized the frontline treatment of MM, producing higher rates of MRD negativity, deeper and more durable responses, and improvements in progression free survival and overall survival. Anti-CD38 therapies induce myeloma cell apoptosis, activate immune mediated MM cytotoxicity, and can lead to deeper and more sustained responses in MM patients (pts). When used as part of a quadruplet regimen, pts have an increased overall response rate with improved PFS, OS, and rates of MRD negativity are seen. According to the most recent NCCN guidelines V1.2026, quad therapies are preferred for both transplant eligible pts and/or pts for whom transplant is deferred and contraindicated. The intent of this QI was to increase guideline adherence across all UPMC care-sites with the goal of improving patient outcomes. Methods: This analysis used data from the IntegraConnect PrecisionQ real-world de-identified database. The study included MM pts, age ≥18, initiating first line of therapy (LOT 1) between January 1, 2024, and June 30, 2025. LOT 1 is defined as drugs given within 60 days of the initial induction start date. Pts with a second primary cancer were excluded unless chart curation confirmed the diagnosis of MM as the primary malignancy.Pts whose diagnosis date was greater than one year from the start of LOT 1 were also excluded, as were those who did not receive an Imid, PI, or CD38 as part of their LOT 1 therapy. In the overall analysis, all patients were considered, regardless of “quad eligibility,” which is defined as age <80 and ECOG of ≤2. A sub analysis was also completed, including only those patients that are confirmed to be quad eligible. Patients with unknown ECOG were excluded from this quad-eligible sub-analysis. The quality initiative was segmented into two study periods: a baseline period (January 1, 2024 through the end of August 2024) and post-baseline period (September 2024 through June 2025). At the end of August, the program officially initiated with a physician roundtable discussion reviewing latest guidelines and clinical trials, and as such September 1, 2024 serves as the post-baseline start date. Information regarding latest trials, current guidelines, and UPMC’s baseline use of CD38 and quadruplet therapies was shared at a physician staff meeting in November 2024. An additional physician roundtable discussion was held in April 2025 to discuss LOT 1 treatment and to review current treatment practices among UPMC oncologists. Treatment utilization in LOT 1 was monitored on a quarterly basis with practice leadership since September 2024. Results: The QI demonstrated among LOT 1 pts the use of CD38 therapy increased by 7.5% from 79% (124/156) at baseline to 85% (146/172) at post baseline. Overall, quad utilization among LOT 1 pts increased by 20% from 44% (69/156) at baseline to 53% (91/172) at post baseline. Among quad eligible pts, quad utilization in LOT 1increased 14% from 64% (47/74) at baseline to 73% post-baseline (59/81). Visual trend analysis showed these increases coincided with educational interventions and structured performance reviews. Conclusion: The QI implemented at UPMC for LOT 1 MM treatment led to an improvement in utilization of CD38 therapy, overall quad based therapy, and quad-based therapy among quad-eligible patients. Incorporation of physician focus group educational sessions further increased adherence to best practices. Additional follow-up is needed to evaluate sustainability and patient outcome. Future analyses will need to be performed to see how treatment choices are made based on risk and other demographic factors. Integration of artificial intelligence and advanced analytics could unlock predictive insights into treatment patterns, automate real-time clinical decision support, and drive, data-informed care delivery. These findings demonstrate that ongoing education and structured feedback are essential to sustaining evidence-based utilization of best frontline NDMM therapy in the quad-era, providing promise to further impact patient outcome.
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 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".