In British journal of anaesthesia ; h5-index 72.0

BACKGROUND : Rebound pain is a common, yet under-recognised acute increase in pain severity after a peripheral nerve block (PNB) has receded, typically manifesting within 24 h after the block was performed. This retrospective cohort study investigated the incidence and factors associated with rebound pain in patients who received a PNB for ambulatory surgery.

METHODS : Ambulatory surgery patients who received a preoperative PNB between March 2017 and February 2019 were included. Rebound pain was defined as the transition from well-controlled pain (numerical rating scale [NRS] ≤3) while the block is working to severe pain (NRS ≥7) within 24 h of block performance. Patient, surgical, and anaesthetic factors were analysed for association with rebound pain by univariate, multivariable, and machine learning methods.

RESULTS : Four hundred and eighty-two (49.6%) of 972 included patients experienced rebound pain as per the definition. Multivariable analysis showed that the factors independently associated with rebound pain were younger age (odds ratio [OR] 0.98; 95% confidence interval [CI] 0.97-0.99), female gender (OR 1.52 [1.15-2.02]), surgery involving bone (OR 1.82 [1.38-2.40]), and absence of perioperative i.v. dexamethasone (OR 1.78 [1.12-2.83]). Despite a high incidence of rebound pain, there were high rates of patient satisfaction (83.2%) and return to daily activities (96.5%).

CONCLUSIONS : Rebound pain occurred in half of the patients and showed independent associations with age, female gender, bone surgery, and absence of intraoperative use of i.v. dexamethasone. Until further research is available, clinicians should continue to use preventative strategies, especially for patients at higher risk of experiencing rebound pain.

Barry Garrett S, Bailey Jonathan G, Sardinha Joel, Brousseau Paul, Uppal Vishal

2020-Dec-31

ambulatory surgical procedures, dexamethasone, pain management, peripheral nerve block, rebound pain, regional anaesthesia

In Frontiers in neurology

Planchuelo-Gómez Álvaro, Trigo Javier, de Luis-García Rodrigo, Guerrero Ángel L, Porta-Etessam Jesús, García-Azorín David

2020

COVID-19, headache disorders, machine learning, migraine, tension-type headache

In International journal of legal medicine

Zhang Ji, Zhou Yuanyuan, Vieira Duarte Nuno, Cao Yongjie, Deng Kaifei, Cheng Qi, Zhu Yongzheng, Zhang Jianhua, Qin Zhiqiang, Ma Kaijun, Chen Yijiu, Huang Ping

2021-Jan-03

Convolutional neutral network, Deep learning, Diatom, Digital pathology, Drowning, Site of drowning

In Journal of the American College of Emergency Physicians open

Introduction : Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine.

Methods : The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation.

Results : Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out-of-hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome.

Conclusion : AI-related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction-based diagnoses. Higher quality evidence is needed to further assess both short- and long-term clinical outcomes.

Kirubarajan Abirami, Taher Ahmed, Khan Shawn, Masood Sameer

2020-Dec

algorithm, artificial intelligence, artificial neural networks, emergency department, emergency medicine, machine learning, technology

In Journal of the American College of Emergency Physicians open

Objective : The objective of this study was to develop a US-representative prediction model identifying factors with a greater likelihood of patients leaving without being seen.

Methods : We conducted a retrospective cohort analysis using a 2016 nationwide emergency department (ED) sample. Patient factors considered for analysis were the following: age, sex, acuity, chronic diseases, weekend visit, quarter of presentation, median household income quartile for patient's zip code, primary/secondary insurance, total charges for the visit, and urban/rural household. Hospital factors considered were urban/rural location, trauma center/teaching hospital, and annual ED volume. Multivariable logistic regression was used to find significant predictors and their interactions. A random forest algorithm was used to determine the order of importance of factors.

Results : A total of 32,680,232 hospital-based ED visits with 466,047 incidences of leaving without being seen were included. The cohort comprised 55.5% females, with a median (IQR) age of 37 (21-58) years. Positively associating factors were male sex (odds ratio [OR], 1.22; 99% confidence interval [CI], 1.17-1.26), lower acuity (P < 0.001), and annual ED visits ≥60,000 (OR, 1.44; 99% CI, 1.21-1.7) versus <20,000. Negatively associating factors were primary insurance being Medicare/Tricare or private insurance (P < 0.001); weekend presentations (OR, 0.87; 99% CI, 0.85-0.89); age >64 or <18 years (P < 0.001); and higher median household income for patient's zip code second (OR, 0.86; 99% CI, 0.77-0.97), third (OR, 0.8; 99% CI, 0.7-0.91), and fourth (OR, 0.7; 99% CI, 0.6-0.8) quartiles versus the first quartile. Significant interactions existed between age, acuity, primary insurance, and chronic conditions. Primary insurance was the most predictive.

Conclusion : Our derivation model reiterated several modifiable and non-modifiable risk factors for leaving without being seen established previously while rejecting the importance of others.

Sheraton Mack, Gooch Christopher, Kashyap Rahul

2020-Dec

ED wait times, LWBS, NEDS Database, emergency department, health services research, machine learning, prediction model

In Journal of the American College of Emergency Physicians open

Objective : Accurate triage in the emergency department (ED) is critical for medical safety and operational efficiency. We aimed to predict the number of future required ED resources, as defined by the Emergency Severity Index (ESI) triage protocol, using natural language processing of nursing triage notes.

Methods : We constructed a retrospective cohort of all 265,572 consecutive ED encounters from 2015 to 2016 from 3 separate clinically heterogeneous academically affiliated EDs. We excluded encounters missing relevant information, leaving 226,317 encounters. We calculated the number of resources used by patients in the ED retrospectively and based outcome categories on criteria defined in the ESI algorithm: 0 (30,604 encounters), 1 (49,315 encounters), and 2 or more (146,398 encounters). A neural network model was trained on a training subset to predict the number of resources using triage notes and clinical variables at triage. Model performance was evaluated using the test subset and was compared with human ratings.

Results : Overall model accuracy and macro F1 score for number of resources were 66.5% and 0.601, respectively. The model had similar macro F1 (0.589 vs 0.592) and overall accuracy (65.9% vs 69.0%) compared to human raters. Model predictions had slightly higher F1 scores and accuracy for 0 resources and were less accurate for 2 or more resources.

Conclusions : Machine learning of nursing triage notes, combined with clinical data available at ED presentation, can be used to predict the number of required future ED resources. These findings suggest that machine learning may be a valuable adjunct tool in the initial triage of ED patients.

Sterling Nicholas W, Brann Felix, Patzer Rachel E, Di Mengyu, Koebbe Megan, Burke Madalyn, Schrager Justin D

2020-Dec

emergency department, machine learning, natural language processing, resources, triage