The Effect of 2 Different Distraction Methods on Pain, Fear, and Anxiety Levels During Venous Blood Draw in Children in a Pediatric Emergency Unit: A Randomized Controlled Study.

BACKGROUND: Blood draw procedures can cause pain, fear, and anxiety in the pediatric population. PURPOSE: To compare the effects of watching cartoons either with virtual reality (VR) or via a tablet on pain, fear, and anxiety during venous blood draw procedures in children. METHODS: A randomized controlled study was conducted with 159 children aged 5 to 12 years in the pediatric emergency unit. The 3 groups included cartoons with VR (n = 53) or a tablet (n = 53), and a control group (n = 53). RESULTS: Children in the 2 intervention groups had lower perceptions of pain, fear, and anxiety, with those watching cartoons via VR having the lowest perceptions. CONCLUSIONS: Findings from this study showed a reduction in the perception of pain, fear, and anxiety in children who watched cartoons with VR or tablets during blood draw procedures. Nurses should consider using these nonpharmacological methods to reduce pain, fear, and anxiety, among pediatric patients.

Adherence to the European Society of Cardiology/European Society of Anaesthesiology recommendations on preoperative cardiac testing and association with positive results and cardiac events: a cohort study.

BACKGROUND: European Society of Cardiology/European Society of Anaesthesiology (ESC/ESA) guidelines inform cardiac workup before noncardiac surgery based on an algorithm. Our primary hypotheses were that there would be associations between (i) the groups stratified according to the algorithms and major adverse cardiac events (MACE), and (ii) over- and underuse of cardiac testing and MACE. METHODS: This is a secondary analysis of a multicentre prospective cohort. Major adverse cardiac events were a composite of cardiac death, myocardial infarction, acute heart failure, and life-threatening arrhythmia at 30 days. For each cardiac test, pathological findings were defined a priori. We used multivariable logistic regression to measure associations. RESULTS: We registered 359 MACE at 30 days amongst 6976 patients; classification in a higher-risk group using the ESC/ESA algorithm was associated with 30-day MACE; however, discrimination of the ESC/ESA algorithms for 30-day MACE was modest; area under the curve 0.64 (95% confidence interval: 0.61-0.67). After adjustment for sex, age, and ASA physical status, discrimination was 0.72 (0.70-0.75). Overuse or underuse of cardiac tests were not consistently associated with MACE. There was no independent association between test recommendation class and pathological findings (P=0.14 for stress imaging; P=0.35 for transthoracic echocardiography; P=0.52 for coronary angiography). CONCLUSIONS: Discrimination for MACE using the ESC/ESA guidelines algorithms was limited. Overuse or underuse of cardiac tests was not consistently associated with cardiovascular events. The recommendation class of preoperative cardiac tests did not influence their yield. CLINICAL TRIAL REGISTRATION: NCT02573532.

Using operative features to identify surgical complexity: a case in breast surgery practice.

Increasing workload is one of the main problems that surgical practices face. This increase is not only due to the increasing demand volume but also due to increasing case complexity. This raises the question on how to measure and predict the complexity to address this issue. Predicting surgical duration is critical to parametrize surgical complexity, improve surgeon satisfaction by avoiding unexpected overtime, and improve operation room utilization. Our objective is to utilize the historical data on surgical operations to obtain complexity groups and use this groups to improve practice.Our study first leverages expert opinion on the surgical complexity to identify surgical groups. Then, we use a tree-based method on a large retrospective dataset to identify similar complexity groups by utilizing the surgical features and using surgical duration as a response variable. After obtaining the surgical groups by using two methods, we statistically compare expert-based grouping with the data-based grouping. This comparison shows that a tree-based method can provide complexity groups similar to the ones generated by an expert by using features that are available at the time of surgical listing. These results suggest that one can take advantage of available data to provide surgical duration predictions that are data-driven, evidence-based, and practically relevant.