Global trends of hand and wrist trauma: a systematic analysis of fracture and digit amputation using the Global Burden of Disease 2017 Study

Global prevalence of traumatic non-fatal limb amputation

BACKGROUND

Reliable information on both global need for prosthetic services and the current prosthetist workforce is limited. Global burden of disease estimates can provide valuable insight into amputation prevalence due to traumatic causes and global prosthetists needed to treat traumatic amputations.

OBJECTIVES

This study was conducted to quantify and interpret patterns in global distribution and prevalence of traumatic limb amputation by cause, region, and age within the context of prosthetic rehabilitation, prosthetist need, and prosthetist education.

STUDY DESIGN

A secondary database descriptive study.

METHODS

Amputation prevalence and prevalence rate per 100,000 due to trauma were estimated using the 2017 global burden of disease results. Global burden of disease estimation utilizes a Bayesian metaregression and best available data to estimate the prevalence of diseases and injuries, such as amputation.

RESULTS

In 2017, 57.7 million people were living with limb amputation due to traumatic causes worldwide. Leading traumatic causes of limb amputation were falls (36.2%), road injuries (15.7%), other transportation injuries (11.2%), and mechanical forces (10.4%). The highest number of prevalent traumatic amputations was in East Asia and South Asia followed by Western Europe, North Africa, and the Middle East, high-income North America and Eastern Europe. Based on these prevalence estimates, approximately 75,850 prosthetists are needed globally to treat people with traumatic amputations.

CONCLUSION

Amputation prevalence estimates and patterns can inform prosthetic service provision, education and planning.

Epidemiology of hand traumas during the COVID-19 confinement period

INTRODUCTION

hand injuries are a common emergency mainly caused by domestic accidents or sport injuries. During the COVID-19 pandemic confinement period, with a cut off in transportation as well as in occupational and physical activities, we observed a decrease in medical and elective surgical activities but emergency cases of upper limb and hand surgery increased.

MATERIALS AND METHODS

we conducted a retrospective epidemiological study to analyze two periods between the same dates in 2019 and 2020, for all the duration of the confinement period. We compared the numbers of consultations in the emergency department, elective surgeries, hand and upper limb emergency cases in our center and urgent limb surgeries in the nearby hospital. Then we compared the mechanisms and severity of injuries and the type of surgery.

RESULTS

between 2019 and 2020 there was a decrease of consultations in the emergency department in our institution of 52%, a decrease of total elective surgeries of 75%, a decrease in surgeries for urgent peripheral limb injuries of 50%, whereas the hand and upper limb emergency remained stable or even increased by 4% regard to occupational and domestic accidents. There was a significant difference in the mechanism of injury with an increase of domestic accident and a decrease of occupational, road traffic and sport accidents. Severity of the injuries increased, with augmentation of the number of tissues involved and longer expected time of recovery.

CONCLUSION

during the confinement period of the COVID-19 pandemic, despite an important reduction of medical activities, the amount and severity of hand emergency cases increased. A specific plan regarding duty shift organization for hand trauma should be maintained regardless of the sanitary situation.

Critical evaluation of deep neural networks for wrist fracture detection

Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection-DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set-average precision of 0.99 (0.99-0.99) versus 0.64 (0.46-0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98-0.99) versus 0.84 (0.72-0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.