A60 IMPROVING PROCESS IN THE EDMONTON PEDIATRIC INFLAMMATORY BOWEL DISEASE CLINIC: AN INFLIXIMAB INFUSION QUALITY IMPROVEMENT PROJECT
Bibliographic record
Abstract
There is a growing body of literature demonstrating that sustained clinical remission using infliximab is related to adequate trough serum drug levels and inversely to the development of antibodies against the drug. Antibody formation typically occurs when serum drug levels fall too low; this might happen when scheduled infusion treatments are delayed and not given as planned. Although there is little literature specifically reviewing the outcomes of patients with respect to adherence to scheduled dosing intervals, it stands to reason that patients who repeatedly receive their infusion outside of the intended treatment window may be at greater risk of low drug levels & loss of response to therapy. We sought to audit the use of infliximab amongst patients of the Edmonton Pediatric IBD Clinic (EPIC) by retrospective analysis of the duration between infusions to determine how many patients fall outside of their intended treatment window. PDSA Cycles: Data Inadequacy Our initial audit revealed significant deficiencies in our established data collection and patient tracking processes. These were in part due to:a mix of medical day unit infusions, community infusion center infusions and out of province infusions leading to inconsistent data; unreliable Infliximab Infusion Patient Report forms (clinical report forms); unscheduled visits: ER visits and hospitalizations inconsistently recordedImplementation of Revised Data Collection Processes Revision of process to ensure report forms consistently returned New EMR flow sheets to capture ER visits, surgeries and hospitalizations.Prospective Data Collection Utilizing Revised Processes - 95 patients tracked over 12 months 90% adherence to prescribed dosing interval Dose changes & frequency changes mostly related to symptoms and/or low infliximab serum levels/development of anti-infliximab antibodies Most changes were in fact a reduction in scheduled interval This informs us that out of window infusions are not the driving cause of antibody development and therapeutic loss of response. As a result of these findings and an increased awareness of the shortfalls in our data collection and patient tracking, EPIC has now developed and implemented a comprehensive patient registry which collates and tracks multiple facets of daily patient care, patient oriented outcomes (such as quality of life scores and patient satisfaction) and clinically focused outcomes (such as steroid free remission, hospitalizations and surgical interventions, growth and development). In doing this, we have been able to improve the way in which we personalize patient care, track patient outcomes and recognize new areas for continuing quality improvement. AHS Integrated Quality Management Unit and the provincial Quality Health Improvement Team.
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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 | 0.053 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".