Knowledge, acceptance and concerns regarding COVID-19 vaccination among pregnant women on the east coast of Peninsular Malaysia: A cross-sectional study.

Introduction: This prospective cross-sectional study, conducted from 1 April 2022 to 31 October 2022, aimed to assess the knowledge, acceptance and concerns regarding COVID-19 vaccination among pregnant women visiting the Obstetrics and Gynaecology Department of Hospital Universiti Sains Malaysia. Methods: The study included all pregnant women aged >18 years. Sociodemographic data, information related to COVID-19 and vaccination and information on the knowledge, acceptance and concerns regarding COVID-19 vaccination were collected using a validated questionnaire. Results: Out of 420 eligible pregnant women, 412 participated in the study, yielding a response rate of 98.1%. Of the respondents, 97.1% had received a COVID-19 vaccine, while 2.9% had not. Approximately 85.2% demonstrated a good understanding of COVID-19 vaccination. Among those vaccinated, 76.8% based their decision on recommendations from healthcare providers or the Ministry of Health. Among those unvaccinated, 91.7% believed that COVID-19 vaccines could harm their pregnancy and baby. Although 51% of the respondents expressed concerns about vaccine safety, 202 still chose to be vaccinated, indicating a willingness to prioritise their health despite apprehensions. Conclusion: The study found no significant link between acceptance and good knowledge of COVID-19 vaccination. However, income and prior COVID-19 booster vaccination were strongly associated with acceptance. Despite safety concerns, 97.1% of the respondents had received a COVID-19 vaccine. This emphasises the importance of providing comprehensive information and addressing concerns to support informed decision-making among pregnant women. Healthcare providers play a vital role in guiding them through this crucial decision-making process.

Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN.

Objective.Left ventricular hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of LVH) severity, addressing the limitations of traditional manual grading systems.Approach.We propose the Multi-purpose Siamese Weighted Euclidean Distance Model (MSWED), which utilizes convolutional Siamese neural networks and zero-shot/few-shot learning techniques. Unlike traditional methods, our model introduces a cutoff distance-based approach for zero-shot learning, enhancing accuracy. We also incorporate a weighted Euclidean distance targeting informative regions within echocardiograms.Main results.We collected comprehensive datasets labeled by experienced echocardiographers, including Normal heart and various levels of LVH severity. Our model outperforms existing techniques, demonstrating significant precision enhancement, with improvements of up to 13% for zero-shot and few-shot learning approaches.Significance.Accurate assessment of LVH severity is crucial for clinical prognosis and treatment decisions. Our proposed MSWED model offers a more reliable and efficient solution compared to traditional grading systems, reducing subjectivity and errors while providing enhanced precision in severity classification.

New Frontiers in Wide-Awake Surgery.

LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Explain the most important benefits of wide-awake surgery to patients. 2. Tumesce large parts of the body with minimal pain local anesthesia injection technique to eliminate the need for sedation for many operations. 3. Apply tourniquet-free surgery to upper and lower limb operations to avoid the sedation required to tolerate tourniquet pain. 4. Move many procedures out of the main operating room to minor procedure rooms with no increase in infection rates to decrease unnecessary cost and solid waste in surgery. SUMMARY: Three disruptive innovations are changing the landscape of surgery: (1) minimally painful injection of large-volume, low-concentration tumescent local anesthesia eliminates the need for sedation for many procedures over the entire body; (2) epinephrine vasoconstriction in tumescent local anesthesia is a good alternative to the tourniquet and proximal nerve blocks in extremity surgery (sedation for tourniquet pain is no longer required for many procedures); and (3) evidence-based sterility and the elimination of sedation enable many larger procedures to move out of the main operating room into minor procedure rooms with no increase in infection rates. This continuing medical education article explores some of the new frontiers in which these changes affect surgery all over the body.

Research practice, satisfaction, motivation, and challenges among university academics in Kurdistan Region of Iraq.

BACKGROUND: Researchers in universities and academic institutions must be in a leading position in generating research evidence to inform and direct national policies and strategies, improve service delivery, and achieve the main objectives. This study aimed to determine the factors that promote or hinder research productivity and quality among university academics in Kurdistan Region of Iraq. METHODS: A cross-sectional study was conducted on 949 university academics from all public universities in the Kurdistan Region of Iraq. The authors developed a questionnaire that included sociodemographic data, challenges, satisfaction, and motivation for conducting research. Data were collected using a Google form. Frequencies, percentages, and the Chi-square test were used to analyze the data. RESULTS: Most university academics (94.6%) believed that research was part of their job, but only 51.6% were satisfied with their role as academic researchers. The lack of financial motivation was the main reason for dissatisfaction, while the main incentive to conduct research was the passion for science. Around 21% of the university academics had not published any research, while 53.1% published 1-5 articles. Half of the participants (49.7%) lacked training in writing research proposals, and the majority (86.1%) have not applied for international grants. Approximately half of university academics (46.9%) shared their research findings with stakeholders, and the primary method was by sharing their published papers (59.4%), followed by seminars (42.2%). One of the important challenges in conducting research was the lack of funding (62.8%). CONCLUSIONS: The academics at universities in the Kurdistan Region of Iraq are passionate about their role as researchers, but face many challenges in conducting effective research. A strategic plan is needed to provide an encouraging environment for university academics regarding infrastructure, financial, and technical support. More studies are needed to identify the root factors of academic staff needs and challenges.

Terahertz binary computing in a coupled toroidal metasurface.

The applications of terahertz metamaterials are being actively explored in recent times for applications in high-speed communication devices, miniature photonic circuits, and bio-chemical devices because of their wide advantages. The toroidal resonance, a new type of metasurface resonance, has been examined with great interest to utilize its properties in terahertz metasurface applications. This study reports a proof of concept design of a toroidal metasurface that experimentally demonstrates binary computing operations in the terahertz frequency regime. The analog computing of binary operations is achieved by the passive tuning of distance between the split ring resonators comprising the meta-molecule. The amplitude modulation is utilized as a method of determining the Boolean logic outputs of the system. The proposed metasurface could be further optimized for high amplitude modulations and active logic gate operations using tunable materials including graphene and ITO. The proposed metasurface consists of three split-ring resonators, and the near-field coupling between the adjacent resonators dictates the Boolean operations. A multipole analysis of the scattered powers of terahertz radiation determines the toroidal excitation in the metasurface. The proposed metasurfaces experimentally define AND Boolean logic operation at 0.89 terahertz, and OR Boolean logic operation at 0.97 terahertz. Numerical simulations support the experimentally obtained results. Additionally, we numerically report the excitation of NAND operation at 0.87 THz. Such toroidal analog computing metasurfaces could find applications in digitized terahertz circuits and integrated photonic devices.

Genetic-Guided Pharmacotherapy for Coronary Artery Disease: A Systematic and Critical Review of Economic Evaluations.

BACKGROUND: Genetic-guided pharmacotherapy (PGx) is not recommended in clinical guidelines for coronary artery disease (CAD). We aimed to examine the extent and quality of evidence from economic evaluations of PGx in CAD and to identify variables influential in changing conclusions on cost-effectiveness. METHODS AND RESULTS: From systematic searches across 6 databases, 2 independent reviewers screened, included, and rated the methodological quality of economic evaluations of PGx testing to guide pharmacotherapy for patients with CAD. Of 35 economic evaluations included, most were model-based cost-utility analyses alone, or alongside cost-effectiveness analyses of PGx testing to stratify patients into antiplatelets (25/35), statins (2/35), pain killers (1/35), or angiotensin-converting enzyme inhibitors (1/35) to predict CAD risk (8/35) or to determine the coumadin doses (1/35). To stratify patients into antiplatelets (96/151 comparisons with complete findings of PGx versus non-PGx), PGx was more effective and more costly than non-PGx clopidogrel (28/43) but less costly than non-PGx prasugrel (10/15) and less costly and less effective than non-PGx ticagrelor (22/25). To predict CAD risk (51/151 comparisons), PGx using genetic risk scores was more effective and less costly than clinical risk score (13/17) but more costly than no risk score (16/19) or no treatment (9/9). The remaining comparisons were too few to observe any trend. Mortality risk was the most common variable (47/294) changing conclusions. CONCLUSIONS: Economic evaluations to date found PGx to stratify patients with CAD into antiplatelets or to predict CAD risk to be cost-effective, but findings varied based on the non-PGx comparators, underscoring the importance of considering local practice in deciding whether to adopt PGx.

Description of WALANT technique in open bone and ligament wrist surgery.

The gold-standard for bone, ligament and joint surgery in the wrist is locoregional anesthesia in most countries. Wide-Awake Local Anesthesia No Tourniquet (WALANT) is commonly used for simple soft-tissue hand surgery procedures such as carpal tunnel or trigger finger release, and can now also be safely used in procedures such as proximal row carpectomy, scapholunate ligament repair or partial wrist fusion, to name but a few. This article describes the use of WALANT for complex surgery in the wrist. WALANT surgery offers many known benefits, such as enhanced patient safety and comfort, simplified perioperative process and avoidance of anesthesia-related risks, and also allows the surgeon to perform intraoperative testing of the repaired structures. Thus, the surgeon can tailor the rehabilitation program and shorten recovery time. We describe detailed guidelines for performing WALANT procedures safely and effectively, making it a favorable option for complex surgeries in the wrist.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.

Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.

Lacertus Syndrome: an Outcome Analysis After Lacertus Release.

Purpose: The lacertus fibrosus or bicipital aponeurosis is a sheet of ligamentous tissue just distal to the elbow joint and can be a compression point for the median nerve. Essentially, lacertus syndrome is a subset of pronator syndrome and an uncommon diagnosis by itself. Surgical release of the lacertus consists of a small 2-cm incision that can be performed under local anesthesia. This study aimed to evaluate the outcome of lacertus release in resolving median nerve symptoms. Methods: This retrospective study was performed at Prince Court Medical Centre, Kuala Lumpur, Malaysia, from January 2020 until June 2021. Ninety-three patients who presented with numbness of fingers, hand, or upper limb; forearm pain; and muscle weakness. They were diagnosed with lacertus syndrome on the basis of local tenderness at the lacertus fibrosus with either weakness of flexor pollicis longus and flexor digitorum profundus of the index finger or paresthesia over the thenar eminence. The patients underwent 3 months of hand therapy, and those with no symptom improvement were offered lacertus release performed by a single surgeon. The surgical technique consists of a surgical incision starting from a point 2 cm distally and 2 cm radially to the medial epicondyle. The incision projects 2 cm distally in an oblique fashion toward the radial styloid. A wide-awake local anesthesia no tourniquet (WALANT) technqiue was utilized and 20 mL of local anesthesic was injected subcutaneously around this region at least 20 minutes before the surgery. Careful dissection was made subcutaneously, and the lacertus fibrosus was identified as a thickened, shiny white structure and released. The Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) score, grip strength, and pinch strength were tested before and after surgery. At 6 months after surgery, the QuickDASH score was again assessed with a WALANT satisfactory questionnaire. Results: A total of 93 patients were included in the study. The mean age of the patients was 38.7 years, and most were women (77.4%). The mean operating time was 70 minutes. The mean preoperative QuickDASH score was 53, which significantly reduced immediately after surgery to 7.8 (P < .001) and remained low at 6 months after surgery (10.6). The mean grip strength showed a significant increase from a preoperative mean of 16 kg to a postoperative mean of 24 kg (P < .001). Pinch strength also significantly increased from a preoperative mean of 9 kg to 13 kg after surgery (P < .001). Conclusions: Lacertus syndrome remains an underdiagnosed disease that can be treated efficiently with a directed minimal surgical incision under wide-awake local anesthesia. Lacertus release appears to significantly reduce pain and numbness with markedly improved hand grip and pinch strength. The corresponding QuickDASH scores also improved significantly after surgery. This study is vital to our understanding of proximal median nerve entrapment and to accurately diagnose it. Type of study/level of evidence: Therapeutic III.

A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy.

Left ventricular hypertrophy (LVH) is a life-threatening condition in which the muscle of the left ventricle thickens and enlarges. Echocardiography is a test performed by cardiologists and echocardiographers to diagnose this condition. The manual interpretation of echocardiography tests is time-consuming and prone to errors. To address this issue, we have developed an automated LVH diagnosis technique using deep learning. However, the availability of medical data is a significant challenge due to varying industry standards, privacy laws, and legal constraints. To overcome this challenge, we have proposed a data-efficient technique for automated LVH classification using echocardiography. Firstly, we collected our own dataset of normal and LVH echocardiograms from 70 patients in collaboration with a clinical facility. Secondly, we introduced novel zero-shot and few-shot algorithms based on a modified Siamese network to classify LVH and normal images. Unlike traditional zero-shot learning approaches, our proposed method does not require text vectors, and classification is based on a cutoff distance. Our model demonstrates superior performance compared to state-of-the-art techniques, achieving up to 8% precision improvement for zero-shot learning and up to 11% precision improvement for few-shot learning approaches. Additionally, we assessed the inter-observer and intra-observer reliability scores of our proposed approach against two expert echocardiographers. The results revealed that our approach achieved better inter-observer and intra-observer reliability scores compared to the experts.

Cardiac phase detection in echocardiography using convolutional neural networks.

Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases-end-systolic (ES) and end-diastolic (ED)-which are critical for calculating heart chamber size and ejection fraction. However, non-essential frames called Non-ESED frames may appear between these phases. Currently, technicians or cardiologists manually detect these phases, which is time-consuming and prone to errors. To address this, an automated and efficient technique is needed to accurately detect cardiac phases and minimize diagnostic errors. In this paper, we propose a deep learning model called DeepPhase to assist cardiology personnel. Our convolutional neural network (CNN) learns from echocardiography images to identify the ES, ED, and Non-ESED phases without the need for left ventricle segmentation or electrocardiograms. We evaluate our model on three echocardiography image datasets, including the CAMUS dataset, the EchoNet Dynamic dataset, and a new dataset we collected from a cardiac hospital (CardiacPhase). Our model outperforms existing techniques, achieving 0.96 and 0.82 area under the curve (AUC) on the CAMUS and CardiacPhase datasets, respectively. We also propose a novel cropping technique to enhance the model's performance and ensure its relevance to real-world scenarios for ES, ED, and Non ES-ED classification.

Sunlight harvesting for heat generation inside water using biosynthesized magnetite nanoparticles.

A low-cost, simple, inexpensive, and environmentally friendly method has been employed for synthesizing magnetite nanoparticles (Fe3O4 NPs). In this study, weeping willow (Salix babylonica L.) aqueous leaf extract has been utilized as a reducing, capping, and stabilizing agent. The synthesized Fe3O4 NPs were characterized by ultraviolet-visible (UV-Vis) spectroscopy, FT-IR spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), dynamic light scattering (DLS), zeta potential analysis, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). The localized surface plasmon resonance (LSPR) performance of the Fe3O4 NPs was examined. It has been shown that the biosynthesized Fe3O4 NPs once dispersed in water can raise the temperature of water significantly when they absorb solar radiation through surface plasmon resonance (SPR). The impact of the pH value on the Fe3O4 NPs was also investigated. It has been shown that the optimum pH value among the examined pH values was pH 6. At this pH, the biosynthesized Fe3O4 NPs were able to increase the temperature of water from 25 °C to ∼36 °C. This dramatic increase in temperature was owing to the Fe3O4 NPs synthesized at pH 6 which acquired high crystallinity, monodispersity, high purity, minimum agglomeration, a small particle size, and high stability. In addition, the mechanism of converting solar energy to thermal energy has been discussed intensively. To the best of our knowledge, this study is unique and the novelty of this investigation is that Fe3O4 NPs acquire plasmonic-like properties under solar radiation. Also, they are anticipated to be an innovative photothermal adaptation material for solar-based water heating and heat absorption.

Safety of Lidocaine During Wide-Awake Local Anesthesia No Tourniquet for Distal Radius Plating.

Purpose: This study evaluated the clinical and biochemical safety profile of infiltration of lidocaine with adrenaline in wide-awake local anesthesia no tourniquet for distal radius plating. Methods: Twenty-four participants were randomly assigned to the therapeutic group (n = 19) (1% lidocaine in 1:100,000 adrenaline) and control group (n = 5) (2% lidocaine alone). Clinical parameters, including skin necrosis, duration of recovery of sensation, and lidocaine toxicity, were monitored. The serum lidocaine level was measured at different time intervals using a high-performance liquid chromatography reagent. Results: No lidocaine toxicity was recorded in any participant. The therapeutic group had a longer time for recovery of sensation. There was a significant difference in the serum lidocaine levels between both the groups at all time intervals up to 6 hours, with all participants exhibiting serum lidocaine levels below the mild toxicity level of 6.0 µg/mL. Conclusions: Lidocaine used within a safe recommended dose in wide-awake local anesthesia no tourniquet for distal radius plating is clinically and biochemically safe. Clinical relevance: Determining the clinical and biochemical safety profile of lidocaine with adrenaline in wide-awake local anesthesia no tourniquet can promote wider use of this technique.

Infant Low Birth Weight Prediction Using Graph Embedding Features.

Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.

Enhanced recovery program - Impact of ASA grade on length of hospital stay in patients undergoing hip and knee arthroplasties.

INTRODUCTION: Total hip and knee arthroplasties are two of the most quality of life enhancing orthopedic procedures performed. Enhanced recovery (ER) programs have been implemented in orthopedic surgery and have helped optimize preoperative, perioperative, and postoperative factors to reduce the physiological and psychological stress of surgery. The American Society of Anesthesiologists [ASA] classification system is now the most widely collected system for measuring physical health status by hip and knee arthroplasty registries worldwide. The aim of the study is to determine whether the ASA score is a predictive of length of hospital stay in patients undergoing hip and knee surgeries in elective setting. METHODS: Retrospective data is collected from a consecutive series of 441 charts and Irish National Orthopedics Register (INOR) for patients who underwent elective primary hip & knee replacements from January 1, 2018 to December 31, 2018 in Our Lady's Hospital, Navan. All these patients were assigned either ASA Class 2 or 3 in preoperative assessment. RESULTS: Total 441 patients were included. Patients with ASA 2 (319 patients of the total patient for the same period), average length of hospital stay was 4.8 ± 1.2 days, whereas for patients with ASA 3 (122 patients of the total), length of hospital stay was 6.5 ± 2.3 with mean difference between two groups was 1.7 days (95% confidence interval of this difference). CONCLUSION: Patients with ASA 2 stayed shorter in the hospital compared to patients with ASA 3. Therefore, we recommend that pre-operative patients' optimization to downgrade a patient from an ASA 3 to ASA 2, but prospective analysis would be beneficial to examine the resource implications of such an initiative as well as patient outcomes with longer term follow up.

Analysis of Parkinson's Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods.

Parkinson's disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903.

Application of WALANT in Diaphyseal Plating of Forearm Fractures: An Observational Study.

Purpose: Wide-Awake Local Anesthesia No Tourniquet (WALANT) is a novel anesthesia technique in distal radius and ankle fracture fixation. However, to date, there are limited studies in diaphyseal plating of forearm fractures under WALANT. This research is to study the feasibility of the use of WALANT technique in plating of diaphyseal fractures of the forearm as well as peri-operative outcomes. Methods: Sixty-one adult patients who underwent diaphyseal plating of the forearm under WALANT between the period of January 2019 and January 2021. It consists of 31 radius fractures, 15 ulna fractures and 15 ipsilateral radius and ulna fractures. Outcomes evaluated were duration of stay, peri-operative numerical pain rating scale, peri-operative blood pressure and heart rate, visual analogue scale for anxiety, blood loss, surgery duration, adverse effect, patient's satisfaction and any complications at 6 months follow up. Results: Fifty-four patients (88.5%) were male and 7 patients (11.5%) were female with a mean age of 31.7 years (SD = 13.564). Thirty-eight out of 61 patients were totally pain free throughout the surgery. Ten (13%) patients reported pain during muscle dissection, 14 (18%) patients reported pain during bone manipulation and 12 (16%) patients reported pain during bone drilling. There was no significant difference in pain score between radius and ulna bones (P > .05). There was a significant change in blood pressure after LA infiltration (P < .01). The mean estimated blood loss was 27.39 ml (SD = 11.44) and the mean duration of post-surgery hospital stay was 1 day (SD = 1.026). Fifty-six patients (92%) recommended diaphyseal plating of the forearm under WALANT. None of the patients required conversion to general anesthesia and had any adverse events or infection during 6 months follow up. Conclusions: Diaphyseal plating of the forearm under WALANT is a feasible alternative anesthesia technique and is well tolerated by patients. Type of study/level of evidence: Therapeutic III.

Technique of Wide-Awake Local Anesthesia No Tourniquet Injection for a Transradial Forearm Amputation in an Ischemic Hand.

A 48-year-old man with drug addiction presented with gangrene of the right hand following an inadvertent intra-arterial administration of crushed dihydrocodeine tartrate (DF 118) tablets (GlaxoSmithKline S.A.) dissolved in water; the solution was injected into his right antecubital fossa. After 3 weeks of pain, paresthesia, and cyanosis, his right hand became gangrenous. We performed a right forearm amputation by use of the wide-awake local anesthesia no tourniquet technique. After surgery, his wound healed well, and he was successfully fitted with a hand prosthesis.