Prevalence of Postoperative Nausea and Vomiting and Pain in Patients Undergoing Elective Orthopaedic Surgery in Iran.

PURPOSE: The purpose of this study is to determine the prevalence of postoperative nausea, vomiting, and pain and the severity of postoperative pain in adult patients undergoing elective orthopaedic surgery in Iran. DESIGN: A descriptive, cross-sectional study design was used. METHODS: One hundred twenty-eight patients undergoing elective orthopaedic surgery participated in the study. Demographic and surgical characteristics, severity of pain, frequency of postoperative nausea and vomiting, amount of analgesics and antiemetics administered were measured. FINDINGS: The mean time of surgery was 123.67 min. Of all patients, 59.3% experienced nausea and 39% had postoperative vomiting; 98.4% of participants experienced pain. The mean pain intensity in the first 24 hours after surgery was 6.3 based on the Visual Analogue Scale. CONCLUSION: High prevalence rates of postoperative nausea (59.3%) and vomiting (39%) were recorded. Among 98.4% of participants, pain intensity was rated as moderate during the first 24 hours after consciousness.

The Effect of Reducing the Fasting Time on Postoperative Pain in Orthopaedic Patients: A Randomized Controlled Trial.

BACKGROUND: Patients undergoing orthopedics surgery experience the most severe postoperative pain. The fasting time is a factor that affects this complication. The aim of this study was to investigate the impact of fasting time reduction by using oral carbohydrate on postoperative pain and analgesic consumption in orthopedic patients. MATERIALS AND METHODS: This randomized control trial was conducted between November 2017 and December 2018. Sixty-four patients were randomly assigned into the intervention (which consumed 200 mL of the 12.50% carbohydrate, 2 h before the surgery) and the control group (which was fasted from midnight). Postoperative pain was measured by visual analog scale; the amount of the consumed analgesics was also recorded. The data were analyzed by using Chi-square and t-test. RESULTS: The mean (SD) of the pain scores in the control group immediately and 2, 4, 6, 12 and 24 h after consciousness were 7.19 (2.64), 6.69 (2.17), 6.31 (2.05), 6.16 (2.08), 6.06 (2.24), and 5.38 (1.86), respectively. These scores for the intervention group were 7.44 (1.48), 6.31 (1.25), 5.72 (1.17), 5.59 (1.43), 5.25 (1.13), and 4.97 (1.57). The mean of the pain scores between two groups was not different (p > 0.05). The amount of the consumed morphine (t 61= -2.10, p = 0.039), pethidine (t 62= -2.25, p = 0.028), and diclofenac (t 62= -2.51, p = 0.015) were significantly different between the two groups. CONCLUSIONS: The pain intensity in the patients with shortened fasting time was lower, but it was not statistically significant. Moreover, reducing fasting time by using carbohydrate significantly reduced the use of analgesics.

EEG-based detection of the locus of auditory attention with convolutional neural networks.

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

Quiet sleep detection in preterm infants using deep convolutional neural networks.

OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. APPROACH: In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. MAIN RESULTS: The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. SIGNIFICANCE: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data.

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.