Impact on the Service Volume of a Single Hand Surgery Center during the COVID-19 Pandemic Period in Daegu

Purpose: We investigated what changes occurred at single hand surgery center during the coronavirus disease 2019 (COVID-19) pandemic in Daegu, Korea using patient data of 4 years (2018–2021).Methods: This is a single-center retrospective study of patients visiting our center during the COVID-19 pandemic for 4 years (January 22 to May 6). Service volumes (SVs) including the number of in/outpatient, emergency room, elective, and emergency surgery were analyzed. During the peak period of the COVID-19 (February 24 to March 9, 2020), patient’s demographics, injury mechanism, and place of injury of hand trauma were analyzed.Results: SVs were significantly reduced in 2020 as compared with other years. The SVs except for the number of emergency surgeries have recovered after 2 months from the first confirmed case of COVID-19 in Daegu, Korea. At the peak period, the effect of COVID-19 was weak on emergency room-related SVs. In addition, a daily number of in/outpatients and elective surgeries had a statistically significant negative correlation with the number of COVID-19 confirmed (p<0.05). During the peak period, superficial laceration increased and finger and wrist fractures decreased. The number of cases occurring in workplace increased, however, occurring outside during daily life decreased.Conclusion: COVID-19 pandemic greatly reduces service volume in our center. Thorough protective strategy from COVID-19 such as personal protective equipment was essential for early recovery of hospital functions during the pandemic. In addition, manpower for the emergency room must be preserved during the pandemic. The results of our study, which reported SVs through the pandemic will help maintain the function of hand surgery centers.

QSAR STUDIES OF SOME SUBSTITUTED IMIDAZOLE DERIVATIVES AS POTENT ANTIFUNGAL AGENTS

Fracture of distal radius is the commonest fracture present in the upper limb. In fact, it is most commonly treated by the doctor. An outstretched hand is the most common cause of distal radius or wrist fractures. The fracture of distal radius can also lead to nerve injury mostly median nerve. Physical Therapy plays important role which provides positive effect in treating post fracture cases. A case of 45 years female is presented in this report who had a fall over right wrist joint and diagnosed with distal radius fracture and operated conservatively results into pain over wrist joint, decrease in physical activities. Rehabilitation protocol is explained below in the report. We report that there were improvement in patient outcomes level increases in muscles strength, provide pain relief and improvement in patient functional Independence.

Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

Background Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. Methods and design Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. Discussion The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.