Pharmacological agents for procedural sedation and analgesia in the emergency department and intensive care unit: a systematic review and network meta-analysis of randomised trials.

BACKGROUND: We aimed to evaluate the comparative effectiveness and safety of various i.v. pharmacologic agents used for procedural sedation and analgesia (PSA) in the emergency department (ED) and ICU. We performed a systematic review and network meta-analysis to enable direct and indirect comparisons between available medications. METHODS: We searched Medline, EMBASE, Cochrane, and PubMed from inception to 2 March 2023 for RCTs comparing two or more procedural sedation and analgesia medications in all patients (adults and children >30 days of age) requiring emergent procedures in the ED or ICU. We focused on the outcomes of sedation recovery time, patient satisfaction, and adverse events (AEs). We performed frequentist random-effects model network meta-analysis and used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach to rate certainty in estimates. RESULTS: We included 82 RCTs (8105 patients, 78 conducted in the ED and four in the ICU) of which 52 studies included adults, 23 included children, and seven included both. Compared with midazolam-opioids, recovery time was shorter with propofol (mean difference 16.3 min, 95% confidence interval [CI] 8.4-24.3 fewer minutes; high certainty), and patient satisfaction was better with ketamine-propofol (mean difference 1.5 points, 95% CI 0.3-2.6 points, high certainty). Regarding AEs, compared with midazolam-opioids, respiratory AEs were less frequent with ketamine (relative risk [RR] 0.55, 95% CI 0.32-0.96; high certainty), gastrointestinal AEs were more common with ketamine-midazolam (RR 3.08, 95% CI 1.15-8.27; high certainty), and neurological AEs were more common with ketamine-propofol (RR 3.68, 95% CI 1.08-12.53; high certainty). CONCLUSION: When considering procedural sedation and analgesia in the ED and ICU, compared with midazolam-opioids, sedation recovery time is shorter with propofol, patient satisfaction is better with ketamine-propofol, and respiratory adverse events are less common with ketamine.

Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning.

The recent advances in artificial intelligence have led to the rapid development of computer-aided skin cancer diagnosis applications that perform on par with dermatologists. However, the black-box nature of such applications makes it difficult for physicians to trust the predicted decisions, subsequently preventing the proliferation of such applications in the clinical workflow. In this work, we aim to address this challenge by developing an interpretable skin cancer diagnosis approach using clinical images. Accordingly, a skin cancer diagnosis model consolidated with two interpretability methods is developed. The first interpretability method integrates skin cancer diagnosis domain knowledge, characterized by a skin lesion taxonomy, into model development, whereas the other method focuses on visualizing the decision-making process by highlighting the dominant of interest regions of skin lesion images. The proposed model is trained and validated on clinical images since the latter are easily obtainable by non-specialist healthcare providers. The results demonstrate the effectiveness of incorporating lesion taxonomy in improving model classification accuracy, where our model can predict the skin lesion origin as melanocytic or non-melanocytic with an accuracy of 87%, predict lesion malignancy with 77% accuracy, and provide disease diagnosis with an accuracy of 71%. In addition, the implemented interpretability methods assist understand the model's decision-making process and detecting misdiagnoses. This work is a step toward achieving interpretability in skin cancer diagnosis using clinical images. The developed approach can assist general practitioners to make an early diagnosis, thus reducing the redundant referrals that expert dermatologists receive for further investigations.

Intravenous ketorolac versus morphine in children presenting with suspected appendicitis: a pilot single-centre non-inferiority randomised controlled trial.

OBJECTIVES: Despite a lack of evidence demonstrating superiority to non-steroidal anti-inflammatory drugs, like ketorolac, that are associated with lower risk of harms, opioids remain the most prescribed analgesic for acute abdominal pain. In this pilot trial, we will assess the feasibility of a definitive trial comparing ketorolac with morphine in children with suspected appendicitis. We hypothesise that our study will be feasible based on a 40% consent rate. METHODS AND ANALYSIS: A single-centre, non-inferiority, blinded (participant, clinician, investigators and outcome assessors), double-dummy randomised controlled trial of children aged 6-17 years presenting to a paediatric emergency department with ≤5 days of moderate to severe abdominal pain (≥5 on a Verbal Numerical Rating Scale) and are investigated for appendicitis. We will use variable randomised blocks of 4-6 and allocate participants in 1:1 ratio to receive either intravenous (IV) ketorolac 0.5 mg/kg+IV morphine placebo or IV morphine 0.1 mg/kg+IV ketorolac placebo. Analgesic co-intervention will be limited to acetaminophen (commonly used as first-line therapy). Participants in both groups will be allowed rescue therapy (morphine 0.5 mg/kg) within 60 min of our intervention. Our primary feasibility outcome is the proportion of eligible patients approached who provide informed consent and are enrolled in our trial. Our threshold for feasibility will be to achieve a ≥40% consent rate, and we will enrol 100 participants into our pilot trial. ETHICS AND DISSEMINATION: Our study has received full approval by the Hamilton integrated Research Ethics Board. We will disseminate our study findings at national and international paediatric research conferences to garner interest and engage sites for a future multicentre definitive trial. TRIAL REGISTRATION: NCT04528563, Pre-results.

Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study.

BACKGROUND: The paucity of dark skin images in dermatological textbooks and atlases is a reflection of racial injustice in medicine. The underrepresentation of dark skin images makes diagnosing skin pathology in people of color challenging. For conditions such as skin cancer, in which early diagnosis makes a difference between life and death, people of color have worse prognoses and lower survival rates than people with lighter skin tones as a result of delayed or incorrect diagnoses. Recent advances in artificial intelligence, such as deep learning, offer a potential solution that can be achieved by diversifying the mostly light-skin image repositories through generating images for darker skin tones. Thus, facilitating the development of inclusive cancer early diagnosis systems that are trained and tested on diverse images that truly represent human skin tones. OBJECTIVE: We aim to develop and evaluate an artificial intelligence-based skin cancer early detection system for all skin tones using clinical images. METHODS: This study consists of four phases: (1) Publicly available skin image repositories will be analyzed to quantify the underrepresentation of darker skin tones, (2) Images will be generated for the underrepresented skin tones, (3) Generated images will be extensively evaluated for realism and disease presentation with quantitative image quality assessment as well as qualitative human expert and nonexpert ratings, and (4) The images will be utilized with available light-skin images to develop a robust skin cancer early detection model. RESULTS: This study started in September 2020. The first phase of quantifying the underrepresentation of darker skin tones was completed in March 2021. The second phase of generating the images is in progress and will be completed by March 2022. The third phase is expected to be completed by May 2022, and the final phase is expected to be completed by September 2022. CONCLUSIONS: This work is the first step toward expanding skin tone diversity in existing image databases to address the current gap in the underrepresentation of darker skin tones. Once validated, the image bank will be a valuable resource that can potentially be utilized in physician education and in research applications. Furthermore, generated images are expected to improve the generalizability of skin cancer detection. When completed, the model will assist family physicians and general practitioners in evaluating skin lesion severity and in efficient triaging for referral to expert dermatologists. In addition, the model can assist dermatologists in diagnosing skin lesions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34896.