Endoscopic management offers acceptable oncologic control in select patients with upper tract urothelial carcinoma (UTUC) while preserving renal function. Adjuvant intracavitary treatment with chemotherapy or Bacillus Calmette-Guérin (BCG) has been proposed to reduce recurrence risk. We aimed to evaluate the impact of adjuvant intracavitary treatment on ipsilateral UTUC recurrence following endoscopic management. We queried a multi-institutional cohort of patients who underwent endoscopic management for UTUC. Treatment groups were defined as no instillation, single post-operative instillation, or multiple instillations. Ipsilateral UTUC recurrence-free survival (RFS) was estimated using Kaplan-Meier curves and Cox proportional hazards models evaluated factors associated with recurrence. A total of 599 renal units, of which 43 received single instillation and 86 multiple instillations, in 334 patients treated endoscopically for UTUC were analyzed. The median follow-up time for patients without recurrence was 12 months (IQR 4–33). Multiple adjuvant instillations of any intracavitary treatment were associated with a significantly improved RFS (HR 0.52, 95
Purpose To investigate the impact of an overnight critical care pharmacist shift on pharmacy services in a quality improvement initiative.Methods The department of pharmacy implemented an overnight critical care pharmacist shift in October 2023 to better align with recommendations for critical care pharmacy services from the Society of Critical Care Medicine, American College of Clinical Pharmacy, and American Society of Health-System Pharmacy. This initiative focused on patient care services and aimed to increase compliance from 6.2% to 75% during overnight hours by May 2024. Characterization of the impact included time to order verification, time to administration, and number and type of pharmacist interventions. Plan-Do-Study-Act (PDSA) cycles were utilized to measure changes over time.Results Shift initiation (PDSA cycle 1) resulted in a mean time to order verification of 9.4 minutes (SD, 24.7 minutes) compared to 7.4 minutes (SD, 13.9 minutes) before initiation. There was no difference in the mean time to order verification of stat medications. Pharmacists documented an average of 214 interventions per month compared to 84 before initiation. For PDSA cycle 2, patient profile reviews of newly admitted patients occurred during 60 of 60 (100%) shifts, with an average of 9 patients reviewed per shift. Prospective profile review led to interventions for 93.3% of shifts.Conclusion This initiative increased institutional compliance with foundational patient care recommendations from 6.2% to 67.2%. Implementation of overnight critical care pharmacists led to an increase in the number of interventions documented over time without a significant corresponding increase in the time to order verification and administration. Opportunities exist for further optimization of clinical activities during the overnight hours.
Ritlecitinib, an oral JAK3/TEC family kinase inhibitor, demonstrated efficacy over 48 weeks in patients aged ≥ 12 years with alopecia areata (AA) in the ALLEGRO phase 2b/3 study and initial extension up to month 24 in the ALLEGRO-LT study. We aimed to evaluate the efficacy of ritlecitinib up to 3 years in patients with AA from the ALLEGRO phase 2b/3 and ongoing phase 3, open-label ALLEGRO-LT studies. Patients aged ≥ 12 years with AA and ≥ 50
BACKGROUND:The Advanced Training in Laparoscopic Suturing is a proficiency-based curriculum of 6 structured tasks. In the needle handling task, participants maneuver a needle through 6 standardized holes on a circular platform. Performance (completion time and errors) is currently evaluated in person or through manual video review. This study explored the potential of artificial intelligence models to automate the assessment of this task by predicting task duration and detecting needle drop errors. METHODS:A retrospective review was conducted of Advanced Training in Laparoscopic Suturing needle handling task videos collected from 2 tertiary centers. Two complementary artificial intelligence models were developed. First, videos were annotated across 10 distinct phases. A deep expandable three-dimensional convolutional network combined with hybrid adaptive k-nearest neighbors and smoothed moving average and exponential moving average was trained for phase segmentation and duration prediction. Second, a vision transformer model was trained to detect needle drop errors by classifying frame segments based on needle visibility. RESULTS:Phase segmentation accuracy improved from 82.06% ± 0.84% to 89.67% ± 1.27%, with the highest accuracy reaching 90.56% and an F1-score of 86.90% using the hybrid k-nearest neighbors and smoothed moving average model. The predicted task duration error had a mean error of 0.84%. The vision transformer model achieved a 95.16% classification accuracy on validation frames and detected 66.6% of needle drops >2 seconds and 63.6% of needle drops >5 seconds in test videos. CONCLUSION:Artificial intelligence-based models exhibited high and moderate accuracy for task duration prediction and needle drop error, respectively, offering scalable solutions for objective surgical assessments.
