Acupuncture as a Complementary Treatment Modality in the Post-Anesthesia Care Setting: A Feasibility Study.

Introduction: Postoperative pain management and postoperative nausea and vomiting are a persistent challenge for both health care providers and patients. Acupuncture is an effective and safe modality for the management of pain and nausea, and has the potential to play a key role in postoperative pain management. This study explores the utility and feasibility of acupuncture in the immediate postoperative setting. Methods: In a retrospective case-control study, 22 patients who underwent elective surgeries and received acupuncture in the post-anesthesia care unit (PACU) were compared with 88 case controls. Indications for acupuncture therapy included persistent pain, nausea, or anxiety. Patient satisfaction and symptom improvement after acupuncture were assessed. PACU nurses and patients were queried on their perspectives on using this therapy. Demographic data, perioperative opioid consumption, pain score in the PACU, incidence of postoperative nausea, PACU length of stay, and unintended hospital admission were assessed. The groups with/without acupuncture were compared using Wilcoxon rank sum test or Fisher's exact test as appropriate. Results: A total of 78.9% of patients receiving acupuncture felt improvement in their symptoms. 94.7% of recovery nurses who cared for patients who received acupuncture felt that it was helpful and 78.9% did not believe it was disruptive. Patients who opted for acupuncture had a statistically significant higher overall median (interquartile range) pain score in the PACU (7.0 [5.2, 9.5] vs. 5.0 [3.0, 7.0], p = 0.009) and higher postoperative opioid consumption (22.5 [9.8, 44.8] vs. 15.0 [0.0, 30.0], p = 0. 03). There was no difference between total perioperative opioid consumption between groups (p = 0.94). Conclusions: Most patients who received acupuncture therapy in the PACU were satisfied with their therapy and would recommend it to future patients undergoing surgery. Most recovery nurses felt it was helpful, was not disruptive, and would like to see it utilized in the PACU.

Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.

Purpose: The inherent characteristics of transthoracic echocardiography (TTE) images such as low signal-to-noise ratio and acquisition variations can limit the direct use of TTE images in the development and generalization of deep learning models. As such, we propose an innovative automated framework to address the common challenges in the process of echocardiography deep learning model generalization on the challenging task of constrictive pericarditis (CP) and cardiac amyloidosis (CA) differentiation. Approach: Patients with a confirmed diagnosis of CP or CA and normal cases from Mayo Clinic Rochester and Arizona were identified to extract baseline demographics and the apical 4 chamber view from TTE studies. We proposed an innovative preprocessing and image generalization framework to process the images for training the ResNet50, ResNeXt101, and EfficientNetB2 models. Ablation studies were conducted to justify the effect of each proposed processing step in the final classification performance. Results: The models were initially trained and validated on 720 unique TTE studies from Mayo Rochester and further validated on 225 studies from Mayo Arizona. With our proposed generalization framework, EfficientNetB2 generalized the best with an average area under the curve (AUC) of 0.96 (±0.01) and 0.83 (±0.03) on the Rochester and Arizona test sets, respectively. Conclusions: Leveraging the proposed generalization techniques, we successfully developed an echocardiography-based deep learning model that can accurately differentiate CP from CA and normal cases and applied the model to images from two sites. The proposed framework can be further extended for the development of echocardiography-based deep learning models.

Women Authorship Trends in the Highest-Impact Anesthesiology Journals from 2005 to 2021.

Background: Although women and men have matriculated into medical schools in similar proportions since the 1980s, recent data indicate that anesthesiology is lagging in gender equity, especially in academic leadership roles.1,2 As promotion in academic medicine is strongly influenced by publications, understanding whether a lack of women authorship is contributing to this gender gap is crucial.3,4 This article aims to assess how woman authorship trends have changed in the last 16 years, including during the COVID-19 pandemic. Materials and Methods: The five highest impact journals in anesthesia were identified as Journal of Clinical Anesthesia, British Journal of Anaesthesia, Anesthesiology, PAIN, and Regional Anesthesia & Pain Medicine. Number of total authors, including women, men, and unknown gender authors as well as incidence of woman first and/or last author, was documented from articles published in 2005, 2010, 2015, 2020, and 2021. Results: This analysis shows that women are gaining representation in anesthesia publications. Overall, there was a statistically significant increase in the total number of women authors and women first and last authorship. However, as of 2021, women still only represented ∼40% of total and first authors and ∼24% of last authors. In addition, increase in first/last woman authorship was not present in all journals when stratified. Conclusion: These journal differences may suggest the editorial evaluation process as a potential source of gender bias. There was a statistically significant relationship between women senior authors and articles with 50% or more women authors, indicating that woman mentorship is contributing to closing equity gap. These data present a starting point for further investigations into gender disparities within anesthesia to continue the forward progression for women in academic medicine.

Hybrid Closed-Loop Insulin Pump Technology Can Be Safely Used in the Inpatient Setting.

BACKGROUND: Hybrid closed-loop (HCL) systems, also known as automated insulin delivery systems, are a rapidly growing technology in diabetes management. Because more patients are using these systems in the outpatient setting, it is important to also assess inpatient safety to determine whether HCL use can be continued when those patients become hospitalized. METHODS: The records of patients using HCL technology on admission to our hospital between June 1, 2020, and June 30, 2021, were analyzed. RESULTS: The final analysis included 71 patients divided into 3 categories based on their pump use as an inpatient: (1) HCL users; (2) manual pump users; and (3) pump removed. All cohorts were similar in age, sex, race, hemoglobin A1C at admission, and in Medicare Severity Diagnosis Related Group. Pairwise comparisons indicated that patient-stay mean glucose levels, frequency of patient-specific hyperglycemic measurements, and frequency of hypoglycemic events were similar between all groups. No adverse events, particularly occurrences of diabetic ketoacidosis, pump site complications or infection, or equipment malfunction, were reported. CONCLUSION: This preliminary case series review indicates that continued use of HCL technology in the hospital is safe. Moreover, glycemic control in HCL users was comparable with that in those using insulin pump with manual settings and those converted to basal-bolus insulin therapy.