A147 COMBINING INFLIXIMAB TROUGH LEVELS AND FECAL CALPROTECTIN LEVELS WITH CLINICAL DATA HAS THE POTENTIAL TO GUIDE CLINICAL DECISION-MAKING IN IMPROVING OUTCOMES FOR INFLAMMATORY BOWEL DISEASE PATIENTS ON MAINTENANCE INFLIXIMAB.
Bibliographic record
Abstract
Infliximab (IFX) induces and maintains remission in Crohn’s Disease (CD) and Ulcerative Colitis (UC). Secondary loss of response occurs in up to 45% of CD and 60% of UC patients on maintenance IFX. Studies are inconclusive on ideal IFX trough levels (ITLs) to prevent loss of response; recommendations range from “detectable” up to 10μg/mL. Patients with adequate ITLs still lose response, suggesting they still have gastrointestinal (GI) inflammation. Fecal Calprotectin (FCP), a marker of neutrophilic infiltration into the GI tract, when elevated predicts loss of response to maintenance IFX (sensitivity 0.80, specificity 0.82). We previously showed clinicians would alter decisions based on ITLs and FCP levels. There are no studies on using ITLs and FCP levels in conjunction with clinical presentation. To determine if 6 month outcomes in IBD outpatients on IFX maintenance could be improved if clinicians had knowledge of both ITLs and FCP levels in addition to clinical data. This was a pilot, retrospective case series of adult IBD outpatients on maintenance IFX, who prior to having levels drawn were in clinical remission. Actual clinical decisions were based on clinical presentation and standard labs. All subjects had blood ITLs drawn. A subset (Group 2) provided stools for FCP levels. An expert clinician panel made hypothetical clinical decisions with ITLs and FCP levels. Interval (between ITL and 6-months) and final 6-month outcomes were recorded. Comparisons were made between: actual clinical decisions and what ITLs should prompt (Group 1); and actual clinical decisions, what ITLs and FCP levels should prompt, and hypothetical clinical decisions (Group 2). Statistical analyses included: medians with interquartile ranges (IQRs); proportions; percentages; chi-squared and non-parametric analyses. Table 1 captures baseline demographics. There were no statistically significant differences in demographics between the two groups. Table 2 highlights patients in (a) Group 1 and (b) Group 2 in whom ITL and/or FCP levels could have impacted clinical decision making. Of note, 2 out of the 6 patients (Group 2) had “adequate” ITLs but elevated FCP. Table 2 further summarizes what could have been done to prevent the clinical outcomes that occurred without these levels. While previous studies have explored the use of ITLs and FCP levels in isolation, the results of this study demonstrate that knowledge of ITLs and FCP levels together, in addition to the patient’s clinical presentation, can aid clinicians to optimize management and improve outcomes of IBD outpatients on IFX maintenance. NoneN/A
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".