PRESCRIBING OF MEDICATIONS WITH PHARMACO-GENETIC GUIDANCE IN CHILDREN AND ADOLESCENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS (SLE)
Bibliographic record
Abstract
PV163 / #410 Poster Topic: AS18 - Pediatric SLE Background/Purpose Pharmacogenetics (PGx) focuses on the effect of genetic variations on metabolism and response to medications. At this moment, over 100 medications have clinical practice guidelines that provide recommendations on how genetic variations can be incorporated into clinical decision making to improve efficacy and prevent adverse drug reactions. This study aims to determine how often medications with PGx guidelines are prescribed within a cohort of children and adolescents diagnosed and followed for systemic lupus erythematosus (SLE) at the Hospital for Sick Children Lupus Clinic. Methods We completed a retrospective cohort study of patients diagnosed and followed for SLE at SickKids between January 1997 and December 2017. All patients met ACR, SLICC, or EULAR/ACR SLE classification criteria and had clinical and medication data in the dedicated lupus database. We created a list of medications with clinical PGx guidelines (eg, Clinical Pharmacogenetics Implementation Consortium [CPIC] and Dutch Pharmacogenetic Working Group [DPWG]) or clinical PGx recommendations in FDA-approved drug labels using the Pharmacogenomics Knowledgebase ( www.pharmgkb.org ). We used descriptive statistics to calculate the number of prescriptions of medications with PGx guidance, the proportion of the total prescriptions to PGx prescriptions, and the time to the first and second instance of PGx prescribing following SLE diagnosis using Kaplan-Meier analysis. Characteristics of the cohort such as age, sex, ethnicity, and SLE manifestations were also extracted and compared between people prescribed and not prescribed PGx medications (P < 0.05). Results Our cohort included 616 children and adolescents with SLE. The cohort was 80% female, with a median age at diagnosis of 14 years (IQR 11-15), with the largest reported ethnic group being Europeans (38%). The most common SLE manifestations were malar rash (79%), lymphopenia (68%), and the presence of anti-dsDNA antibodies (66%). Patients prescribed a PGx medication within the first year after diagnosis showed a statistically increased prevalence of neuropsychiatric, renal, and musculoskeletal manifestations, and hypocomplementemia (Table 1). Patients were prescribed 219 distinct medications, including 43 with PGx guidance. The most commonly prescribed PGX medications were azathioprine (42%), omeprazole (33%), and lansoprazole (15%). Of the 616 patients, 413 (67%) were prescribed at least 1 PGx medication, with most individuals receiving prescriptions within the first year of SLE diagnosis (n=290, 47%). Table 1: Cohort Demographic and Feature Characteristics (a) Sex, SLE features, and ethnicity were analyzed with a Fisher’s exact test, age was analyzed with Wilcoxon rank-sum test. (*) Denotes statistical difference between the groups (p < 0.05) Conclusions In a large cohort study of children and adolescents with SLE, we observed that 68% of patients were prescribed a medication with PGx guidance during the course of their disease, with nearly half of these patients being prescribed within the first year of SLE diagnosis. Future plans include expanding the cohort and follow-up by 5 years and completing analyses of genetic variants for the most commonly prescribed PGx medications using Stargazer, a star allele calling software. Our research aims to provide a rationale for the use of PGx in individualized care for children and adolescents with SLE.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".