An Evidence-Based Guideline Improves Outcomes for Patients With Hemophagocytic Lymphohistiocytosis and Macrophage Activation Syndrome
Bibliographic record
Abstract
Objective To compare clinical outcomes in children with hemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS) who were managed before and after implementation of an evidence-based guideline (EBG). Methods A management algorithm for MAS-HLH was developed at our institution based on literature review, expert opinion, and consensus building across multiple pediatric subspecialties. An electronic medical record search retrospectively identified hospitalized patients with MAS-HLH in the pre-EBG (October 15, 2015, to December 4, 2017) and post-EBG (January 1, 2018, to January 21, 2020) time periods. Predetermined outcome metrics were evaluated in the 2 cohorts. Results After the EBG launch, 57 children were identified by house staff as potential patients with MAS-HLH, and rheumatology was consulted for management. Ultimately, 17 patients were diagnosed with MAS-HLH by the treating team. Of these, 59% met HLH 2004 criteria, and 94% met 2016 classification criteria for MAS complicating systemic juvenile idiopathic arthritis. There was a statistically significant reduction in mortality from 50% before implementation of the EBG to 6% in the post-EBG cohort (P = 0.02). There was a significant improvement in time to 50% reduction in C-reactive protein level in the post-EBG vs pre-EBG cohorts (log-rank P < 0.01). There were trends toward faster time to MAS-HLH diagnosis, faster initiation of immunosuppressive therapy, shorter length of hospital stay, and more rapid normalization of MAS-HLH–related biomarkers in the patients post-EBG. Conclusion While the observed improvements may be partially attributed to advances in treatment of MAS-HLH that have accumulated over time, this analysis also suggests that a multidisciplinary treatment pathway for MAS-HLH contributed meaningfully to favorable patient outcomes.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".