Return to sport and beyond following intramuscular tendon hamstring injury: A case report of an English Premier League football player

BackgroundHamstring strain injuries are the most common type of injury in elite football and are associated with a high risk of reinjury, particularly those involving the intramuscular tendon (IMT). Limited information is available regarding the rehabilitation and return to sport (RTS) processes following such injuries. This case study describes the clinical presentation of an elite football player following IMT hamstring injury, their on- and off-pitch rehabilitation alongside performance monitoring throughout RTS and beyond.Case scenarioAn elite football player suffered a grade 2c hamstring injury during an English Premier League (EPL) match. The player underwent early post-injury management, alongside progressive off-pitch physical preparation. The ‘control-chaos continuum’ was used as a framework for on-pitch rehabilitation to prepare the player for a return to full team training and competition. Objective and subjective markers of the player's response to progressive on- and off-pitch loading were monitored throughout RTS and beyond.OutcomesThe player returned to on-pitch rehabilitation after 11 days, to full team training having achieved weekly pre-injury chronic running load outputs after 35 days and played in the EPL 40 days post-injury. The player did not suffer reinjury for the rest of the EPL season.ConclusionAn understanding the unique structural and mechanical properties of the IMT, alongside expected RTS timeframes are important to inform rehabilitation and decision-making processes post-injury. Performance and frequent load-response monitoring throughout RTS and beyond, in conjunction with practitioner experience and effective communication are critical in facilitating effective RTS and reduce risk of reinjury following IMT injury.     

Buscando o motivo da instabilidade hemodinâmica: relato de caso sobre o papel do ultrassom intraoperatório

Background and objectives

A prompt and effective management of trauma patient is necessary. The aim of this case report is to highlight the importance of intraoperative echocardiography as a useful tool in patients suffering from refractory hemodynamic instability no otherwise explained.

Predictive monitoring in neonates

Premature infants are physiologically immature and are particularly vulnerable to systemic illnesses. Instability of vital signs related to immaturity of organ systems are common but may also represent early manifestations of serious illness. While vital signs are frequently or continuously monitored in critically unwell neonates, changes in these parameters are subtle in the early phase of illness such as sepsis and difficult to interpret using traditional neonatal monitoring tools. Recent advances in identifying trends and patterns of vital signs in the pre-clinical phase of the illness, particularly in the field of heart rate characteristics monitoring has opened up the potential to improve outcomes through rapid and timely investigations and early intervention. This article reviews the current evidence in predictive monitoring of neonates and discusses potential clinical implications.     

Measuring the appropriateness of carbamazepine and valproic acid prescribing and utilization using a newly implemented online system in the Tabuk Region of Saudi Arabia

Epilepsy is a common neurologic disorder, which is efficiently treated with carbamazepine and valproic acid. Moreover, Saudi Ministry of Health implemented a new E-system for Poison Control Centers called Awtar to enhance technology utilization in ensuring patients’ satisfaction and to improve treatment outcomes. Therefore, we conducted this study to assess appropriateness of indication of requests and therapeutic levels of carbamazepine and valproic acid in Tabuk area, North West Saudi Arabia. This is a retrospective observational study conducted in Poison Control & Forensic Chemistry Center, Tabuk, Saudi Arabia. Patients’ data were obtained for years 2018 and 2019. The blood levels of carbamazepine and valproic acid were measured by Therapeutic Drug Monitoring (TDM) Unit. We selected patients treated with either valproic acid or carbamazepine alone without any history of drug allergy. Data of 264 patients were extracted from Awtar E-system. Serum carbamazepine levels were within therapeutic range in 114 patients (75.50%), above-therapeutic range in 13 patients (8.61%) and sub-therapeutic levels in 24 patients (15.89%). Regarding serum valproic acid, it is within therapeutic range in 62 patients (54.87%), above-therapeutic range in 11 patients (9.73%) and sub-therapeutic levels in 40 patients (35.40%). In conclusion, this study gives information about partial appropriateness of usage of carbamazepine and low level of appropriateness of valproic acid. However, more efforts are needed to improve results of appropriateness of indication of antiepileptic drugs.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).