Citations

Purpose The time to complete oncology pharmacist tasks is needed to determine workload and productivity. The Hematology/Oncology Pharmacy Association (HOPA) and the Hematology/Oncology Practice and Research Network (PRN) of the American College of Clinical Pharmacy (ACCP) partnered with the aim of establishing consensus on the time required to complete oncology pharmacy tasks. Methods Fifteen patient care tasks and 9 non-patient care tasks, commonly completed by oncology pharmacists were each assigned an average amount of time to be completed. This list was then converted into 24 statements and the Delphi survey method was utilized with an expert panel to arrive at consensus between December 2023 and February 2024. Consensus was defined as at least 75% agreement. The complete manuscript was endorsed by HOPA and ACCP Hematology/Oncology PRN. Results Thirty-three pharmacist-experts agreed to participate in this survey with all participating in round 1, and 29 (87.9%) participating in round 2. In round 1, 9 tasks achieved consensus, with 7 of these being classified as patient care associated. Seven statements reaching 65% but less than 75% agreement were deemed to reach borderline consensus. Eight statements failed to achieve at least 65% agreement and were modified based on respondent feedback. In round 2, 15 statements were included with all achieving consensus. At the completion of round 2, all 24 statements reached consensus, and the survey was deemed complete. Conclusion This project produced the first comprehensive consensus statements for the average time for a US-based oncology pharmacist to complete common patient and non-patient care-related tasks.

Introduction Targeted immunomodulators (eg, advanced therapies) effectively achieve symptomatic remission in patients with inflammatory bowel disease (IBD). However, ~25%–50% of patients with IBD achieving symptomatic remission with an advanced therapy may have continued endoscopically/radiologically active bowel inflammation, and it is uncertain whether changing alternative advanced therapies in asymptomatic patients with IBD will reduce bowel inflammation and achieve durable deep remission. Methods and analysis The QUality Outcomes Treating IBD to Target (QUOTIENT) study is an open-label, multicentre, pragmatic, randomised, controlled trial that aims to compare the efficacy and safety of switching to an alternative advanced therapy targeting endoscopic/radiological remission (treat-to-target) versus continuing the initial, or index, advanced therapy, in asymptomatic patients with IBD with moderate-to-severe endoscopic/radiological bowel inflammation. Enrolment is planned for ~250 participants in Canada/USA, randomised 1:1 to switching to alternative advanced therapy or continuing index advanced therapy, and then followed 104 weeks within routine clinical practice. Patient-reported outcomes measure efficacy and quality of life/treatment burden/safety. Primary endpoint is the time from randomisation to treatment failure. Ethics and dissemination The study is conducted in compliance with the protocol, ICH Good Clinical Practice, applicable regulatory requirements and appropriate review boards/independent ethics committees (approval numbers: Pro00077486; Pro00061437; STUDY00002062; 22-004171; i22-01269; IRB22-0890; IRB_00154397; 2000032384; SHIRB#2022.095-2; STUDY00007146; MMC#2024-18; REB#125290; 17784; Pro00142214; 20240660-01H), with documented written informed consent. Findings will be disseminated through peer-reviewed journals, scientific presentations, and publicly available Patient-Centered Outcomes Research Institute (PCORI) websites, including lay summaries. The Crohn’s & Colitis Foundation Education, Support, and Advocacy Department, and our patient advocacy stakeholder, will develop educational and marketing resources to communicate findings to a broad audience (>250 000 patients/caregivers/healthcare professionals). Trial registration number NCT05230173 .

Purpose of review Pain during cesarean delivery (PDCD) has been overlooked and underreported and is associated with a traumatic birth experience and long-term morbidity. We review the studies reporting on PDCD and summarize available guidance providing roadmaps for anesthesiologists caring for patients during cesarean delivery. Recent findings PDCD is a patient-reported outcome and should not be defined by interventions mitigating insufficient neuraxial anesthesia, such as supplemental analgesic medication or general anesthesia. Depending on the cohorts studied and definitions used, current reported rates of PDCD range between 2 and 36%. Preoperative patient counseling, risk stratification, and testing of neuraxial block are mitigating strategies; ultimately, if pain occurs, it should be managed skillfully applying shared decision-making with a supportive follow-up to reduce psychological trauma. Statements from three international organizations offer guidance to prevent and treat PDCD. Summary This review provides strategies to mitigate the occurrence of PDCD, with targeted approaches to better prevent and manage PDCD.

Abstract Background and Aims Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. Methods Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild–moderate, and moderate MR were most likely to progress to moderate–severe or severe MR with analysis by Kaplan–Meier and Cox proportional hazards models. Results A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5–6.6)]. Conclusions An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Background Stroke remains a concern after transcatheter aortic valve replacement (TAVR). Several studies attempted to identify correlates of stroke but there remains a need for better predictors. Objective This study aims to characterize the aortic arch to identify potential correlates of stroke after TAVR using a patient cohort that includes cases with and without stroke. Methods A retrospective analysis was performed on 52 patients who underwent TAVR at Mayo Clinic and Maria Cecilia Hospital. Of these patients, 26 had a stroke after TAVR and 26 did not (control). Anatomical characterization was performed on each patient. Patient-specific 3D digital models of the ascending aorta, aortic arch, and calcium were created for each patient. Geometrical measurements, including cross-sectional areas, volumes, tortuosity, curvature, and length of the model were recorded. Results There was no significant difference in the various area measurements between the two groups, but the aortic arch cross-sectional area ratio (cerebral ostia cross-sectional areas/arch cross-sectional area) was significantly higher in the stroke group. Additionally, there was no significant difference in tortuosity, curvature, aortic volume, and length of the aorta. Aortic valve calcium score, aorta calcium, and aortic arch type (I, II, or III) were not significantly different between the groups. The prevalence of a bovine arch (BA) configuration was significantly higher in the stroke group (46% vs 8% control, p < 0.05). Conclusion Aortic arch cross-sectional area ratio and BA variant were the most significant correlates of stroke after TAVR.

Objectives/Goals: Mayo Clinic’s Clinical Trials Beyond Walls™ (CTBW) program collaborates with study teams to implement decentralized elements in clinical trials, enabling participation from home or local settings. In cancer treatment trials, traditional paper pill diaries are replaced with real-time digital tracking solutions to monitor chemotherapy adherence. Methods/Study Population: The CTBW team developed a solution to deliver electronic pill diaries to research participants using the electronic health record (EHR) system Epic and patient portal MyChart1. The solution includes a portal message to remind participants to take chemotherapy. Medication dose, date taken, and reasons for missed doses (e.g., “I forgot” or “side effects were bothersome”) are captured. An automated in-basket notification system alerts the study team when predefined conditions are met. Configurable medication schedules ensure diaries are sent according to the prescribed frequency. Reports were generated to allow study teams to monitor all participant diaries. Results/Anticipated Results: The CTBW team implemented this digital pill diary in neuro-oncology trial NCT066250472. We anticipate the pill diary in the patient portal will enhance chemotherapy adherence by capturing real-time data in a platform widely used by Mayo Clinic patients. This patient-reported data is stored in the EHR, where it is accessible to providers and study teams. This allows for continuous monitoring, which facilitates a streamlined review of potential adverse events, improved compliance visibility, and timely treatment adjustments compared to paper-based or external solutions. The system also streamlines data entry, reducing human error and eliminating manual transcription. The created language and workflow templates allow the CTBW to scale this approach to future cancer trials Discussion/Significance of Impact: Decentralized clinical trial participants may never visit Mayo Clinic, making digital recording essential. The EHR-based digital pill diary enables continuous monitoring within a familiar system for providers and patients, increasing study team visibility, and allowing for earlier intervention in cases of non-compliance or adverse events.

ImportanceCutaneous squamous cell carcinoma (CSCC) risk stratification is central to management, and physicians rely on tumor staging systems to estimate risk. The Brigham and Women’s Hospital (BWH) T staging system predicts risk based on 4 tumor risk factors (RFs). However, stage is not precisely associated with the number of RFs, as BWH stage T2b includes CSCCs with 2 and 3 RFs.ObjectiveTo determine how RF number is associated with the risk of recurrence, metastasis, and disease-related death.Design, Setting, and ParticipantsThis retrospective multination cohort study of CSCCs diagnosed between October 1, 1991, and July 19, 2023, was conducted at 12 centers in the US (10), Spain (1), and Brazil (1). Invasive CSCCs with confirmed negative margins longer than 14 days were included. Tumors were excluded if they were metastatic at presentation or received adjuvant therapy. Data were analyzed from October 2023 to August 2024.Interventions or ExposuresCSCCs were stratified by the number of the following RFs (0, 1, 2, 3, or 4): a diameter of 2 cm or larger, poorly differentiated histology, tumor extension beyond subcutaneous fat, and large caliber nerve invasion.Main Outcomes and MeasuresFive-year cumulative incidences of local recurrence, nodal metastasis, distant metastasis, and disease-specific death.ResultsA total of 16 844 CSCCs were included (5978 female individuals [35.5%]; median [IQR] age, 73.9 [65.7-81.8] years), with 0 (12 657 [75.1%]), 1 (2892 [17.2%]), 2 (1015 [6.0%]), 3 ( 225 [1.3%]) or 4 (55 [0.3%]) RFs. Median (IQ) follow up time was 33.6 (14.5-60.3) months. For local recurrence, the risk increased as the number of RF increased from 0 (1.7%; 95% CI, 1.5%-2.0%) to 1 (5.0%; 95% CI, 4.1%-5.9%) to 2 (8.8%; 95% CI, 7.0%-11.0%) to 3 (16.0%; 95% CI, 11.0%-22.0%) to 4 (33.0%; 95% CI, 19.0%-47.0%; P &amp;lt; .001 for between-group differences). This increase was also observed for nodal metastasis (0.6% [95% CI, 0.4%-0.7%] vs 3.6% [95% CI, 2.9%-4.4%] vs 11.0% [95% CI, 9.2%-13.0%] vs 20.0% [95% CI, 15.0%-26.0%] vs 28.0% [95% CI, 15.0%-42.0%], respectively; P &amp;lt; .001), distant metastasis (0.2% [95% CI, 0.1%-0.3%] vs 1.1% [95% CI, 0.7%-1.6%] vs 2.3% [95% CI, 1.4%-3.4%] vs 7.9% [95% CI, 4.6%-12.0%] vs 8.4% [95% CI, 2.6%-19.0%], respectively; P &amp;lt; .001), and disease-specific death (0.3% [95% CI, 0.2%-0.4%] vs 1.9% [95% CI, 1.4%-2.7%] vs 5.4% [95% CI, 4.0%-7.0%] vs 11.0% [95% CI, 6.7%-16.0%] vs 25% [95% CI, 12%-39%], respectively; P &amp;lt; .001). CSCCs with 3 RFs had higher cumulative incidences of local recurrence (1.6-fold), nodal metastasis (1.9-fold), distant metastasis (4.3-fold), and disease-specific death (1.9-fold) compared with those with 2 RFs.Conclusions and RelevanceThe results of this cohort study suggest that the number of RFs is an indicator of risk, and among BWH T2b tumors, those with 3 RFs represent a higher risk subset.

Objective Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) differ in many respects. Here, we address the issue of possible differences in fibrillar amyloid pathology as measured by positron emission tomography (PET), which remains unresolved due to the lack of large‐scale comparative studies. Methods Three hundred ninety‐nine cognitively impaired participants younger than 65 years of age from the multicenter Longitudinal Early‐onset Alzheimer's Disease Study (LEADS) and 450 cognitively impaired participants older than 65 years from the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent clinical assessment, brain magnetic resonance imaging (MRI), and amyloid PET and were included in this study. We compared amyloid PET outcomes (positivity rate based on visual read and quantified tracer uptake expressed as Centiloids [CLs]) between the 2 cohorts and studied their association with age, sex, APOE genotype, and cognition. Results The amyloid positivity rate was higher in LEADS (78%, 95% confidence interval [CI] = 74–82) than in ADNI (71%, 95% CI = 67–75, p = 0.02). Lower Mini‐Mental State Examination (MMSE) and APOE4 genotype increased the odds of amyloid positivity in both cohorts. Visually positive scans had higher CLs in LEADS (EOAD, mean = 95.3 ± 26.1) than in ADNI (LOAD, mean = 80.9 ± 36.8, p < 0.0001), predominantly in parietal cortex/precuneus, superior temporal, and frontal cortices. In amyloid‐positive patients, (1) CLs were higher in female patients in both cohorts; (2) APOE4 carriership was associated with lower CLs in EOAD, which was not observed in LOAD; and (3) correlations between CLs and MMSE scores were significantly stronger in EOAD than in LOAD. Interpretation Differences in the burden of amyloid pathology may contribute to differences in clinical and anatomic patterns in sporadic EOAD and LOAD, and have implications for optimizing therapeutic strategies in each group. ANN NEUROL 2025;98:236–248

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.