Citations

Abstract Rationale Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. Objectives To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. Methods We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. Measurements and Main Results Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. Conclusions A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

Abstract INTRODUCTION Anti—amyloid‐β (Aβ) therapies are reshaping Alzheimer's disease (AD) management. Understanding changes in real‐world patterns of diagnostic testing and infusion chair usage is essential for optimizing access to care. METHODS Retrospective analysis of Mayo Clinic enterprise electronic health records (Jan 2019–Mar 2025) assessed trends in AD‐relevant brain imaging, fluid biomarkers, apolipoprotein E (APOE) testing, and lecanemab infusions. Rates of amyloid‐beta (Aβ) positivity by sex and age, APOE genotype frequencies, and lecanemab treatment initiation and discontinuation were evaluated. RESULTS Following national insurance coverage changes, lecanemab infusions grew by 110 infusions per quarter to 605 in Q1 2025. Aβ positron emission tomography scans increased (+22/quarter), cerebrospinal fluid biomarker orders declined (−25/quarter), and plasma p‐tau 217 orders rapidly increased (+238/quarter). Females were more likely to be Aβ positive ( p < 0.006). APOE ‐ε4 homozygotes were less likely to initiate lecanemab (HR = 0.11, p < 0.001). DISCUSSION The adoption of anti‐Aβ therapies coincided with a rapid shift in diagnostic workflows.

Abstract Background Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear. Questions/purposes (1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns? Methods Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results. Results The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99). Conclusion Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances. Level of Evidence Level I, therapeutic study.

AbstractIntroductionAdverse reactions to pain medication and pain can delay discharge after outpatient knee arthroplasty (TKA). Pharmacogenomics is an emerging tool that might help reduce adverse events by tailoring medication use based on known genetic variations in the CYP genes determining drug metabolism. This study was undertaken to evaluate whether pre-operative pharmacogenomic testing could optimize peri-operative pain management in patients undergoing total knee arthroplasty (TKA).MethodsThis prospective, randomized study was performed in adults undergoing primary TKA. Patients in the experimental group underwent pre-operative pharmacogenomic evaluation and medication adjustments. Medications were not optimized for control patients. The Overall Benefit of Analgesic Score (OBAS) at 24 hours post-op was the primary outcome. Postoperative pain scores (VAS scale), total opioid use, time in recovery, and time to discharge were also compared.Results76 patients enrolled. 93% of patients had a poor-intermediate phenotype for at least one of four CYP genes. OBAS did not differ significantly between the two groups (4.2 control vs. 4.7 experimental; 95% CI −1.1 to 2.1) and was below the minimum clinically important difference of 2.0 points. Furthermore, there were no differences in any OBAS subscale including pain, satisfaction, or nausea. There were no differences in time in the recovery area or to discharge from hospital, or narcotic use.ConclusionsDespite many patients having a poor-intermediate phenotype for a CYP gene, pharmacogenomic testing prior to TKA and medication adjustment did not improve anesthesia or pain management outcomes, time to discharge or mean total opioid use after surgery.

ObjectiveAdvanced practice providers (APPs) may see patients independently or assist in “collaborative” clinics in otolaryngology practices. Our goal was to redesign the collaborative physician‐APP clinic model to increase patient access, maintain financial sustainability, and optimize patient and staff experience.MethodsThe study was performed in a tertiary care academic rhinology clinic seeing adult patients. The DMAIC framework (define, measure, analyze, improve, control) was used to develop the new model. The process shift between old and new models was analyzed by utilizing a statistical process control chart. Patient and staff surveys were tracked.ResultsThe collaborative physician‐APP model was redesigned into 2 parallel and independently run (“concurrent”) physician and APP clinics. Patient access increased by 38.9% from a mean of 17.9 patients per collaborative clinic (n = 15 days, 269 patients) to 29.3 patients per concurrent clinic (n = 12 days, 352 patients). Medicare reimbursement rate modeling showed the collaborative clinic to operate at a loss of $1341.51 per day, while the concurrent clinic model operated at a $1309.88 gain (200% positive change). Patient and staff experience tracked positively.DiscussionOtolaryngology clinics can become overwhelmed by the volume of empaneled established patients. Traditional collaborative physician‐APP clinics see the same panel of patients together. However, these can be successfully redesigned to a concurrent model. Concurrent clinics improve patient access and provider satisfaction while maintaining patient satisfaction and fiscal performance.Implications for PracticeLeveraging an experienced APP to run a parallel and independent clinic alongside the physician (concurrent clinic model) may improve patient access, financial metrics, and patient/staff experience.