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Clinical Information Patient Initials or Identifier Number: A Relevant Clinical History and Physical Exam: 47yr old man, suffered a blast injury at the workplace after an O2 tank exploded while he was transferring liquid gas into a tank for welding purposes. The impact has caused him to temporary loss of consciousness. Upon awakening, he had severe chest pain associated with shortness of breath. On examination, superficial partial thickness injury on the chest wall, and lungs: reduced breath sound bi-basally, no murmur heard. BP:106/77mmHg, HR:100/min, SPO2 100% on HFM 15L/min. [Formula presented] [Formula presented] [Formula presented] Relevant Test Results Prior to Catheterization: Serial ECGs were done and showed dynamic changes in the anterior leads Bedside echo before invasive coronary angiograms shows mild LVSD, normal valves, and no pericardial effusion [Formula presented] [Formula presented] Relevant Catheterization Findings: Right radial approach 6F system Opitorque catheter for diagnostic angiogram LMS: smooth LAD: ATO mid LAD, DG1 prox ATO LCx: smooth RCA: smooth Impression: ATO to LAD and Diagonal 1 ( Dual ATO) [Formula presented] [Formula presented] [Formula presented] Interventional Management Procedural Step: Right radial coronary angiogram via 6F system EBU 3.0 engaged with good support Sion blue wired into LAD, export catheter delivered, and aspirated red thrombus Pre-dilated with Sapphire 3 SC 2.5x15mm @ 6-10ATM Flow established in LAD, however, decided to interrogate DG1 as it shows ATO BMW wired into the DG1 and pre-dilated with Sapphire 3 SC 2.0x15mm Noted nonflow limiting dissection and decided to stent DG1 with 2.25x34mm@12ATM, dissection sealed and TIMI III flow established Stented mid LAD with 2.5x30mm @12ATM just before LAD/DG1 bifurcation, then stented proximal LAD with 2.5x 26mm@ 12ATM. Post-dilated LAD with 2.75x15mm@ 14-20ATM TIMI II-III flow IV Tirofiban has been given a loading dose due to a high thrombus burden and sluggish flow [Formula presented] [Formula presented] [Formula presented] Conclusion(s): Myocardial infarction is a rare complication of blunt chest trauma. This case demonstrates how blast shock waves result in the dissection of the coronary vessel leading to total occlusion of the two vessels. It also promotes red thrombus within the coronary vessels. Percutaneous coronary intervention is the most suitable way to treat this condition. Intravascular imaging such as IVUS or OCT would be beneficial to demonstrate the physiology behind this MI and would also be helpful in planning and optimizing the lesions. Unfortunately, intravascular imaging was not used for this patient to reduce procedural time as he was treated during the height of the COVID pandemic.Copyright © 2023
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Introduction: Covid-19 vaccinations are effective as preventive measures against the COVID-19 pandemic infection. There are different types of COVID-19 vaccines available worldwide. The impacts of different types COVID-19 vaccination on hospitalized patients remained uncertain at the early stage of the outbreak. This study examines the clinical outcomes of vaccinated and vaccine naive hospitalized COVID-19 patients. Method(s): This is a retrospective study of hospitalized COVID-19 patients admitted into University Malaya Medical Center from January 2021 until June 2021. Data on patients who were>= 18 years old and hospitalized for >= 48 hours for confirmed COVID-19 infection were captured. Clinical parameters and demographic of patients were collected from electronic medical records (EMR). Vaccine status was determined based on EMR entry and retrospective phone interviews. 2 cohorts of patients were classified based on their COVID vaccination status, and outcome analysis was done with appropriate statistical methods. Result(s): A total of 1529 patients were captured with a male-to-female ratio of 759 (49.6%) and 770 (50.3%). The median age was 55 (IQR: 36-66). 182 patients (12%) received COVID-19 vaccination before admission. The types of COVID vaccination received by patients were Pfizer (n=127;8.4%), AstraZeneca (n=27;1.8%), and Sinovac (n=28;1.8%) respectively.107 patients (7.1%) received two doses of vaccine, 72 patients had one dose (4.7%), and three were injected with three doses (0.2%). Biochemical analysis showed that the vaccination naive group has a higher median of lymphocyte counts (17 vs. 2.02;p<0.001), ferritin (363 vs 221;p=0.001), serum creatinine (67 vs 63;p=0.032). Renal outcome data revealed that unvaccinated patients had more incidence of acute kidney injury (AKI) (92.9% vs 7.1%;p=0.002) and increased level proteinuria (88.5% vs 11.5%;p= 0.011). Covid-19 vaccination group had lower incidence rate of pneumonia (8.9% vs 91.1%;p<0.001), lower ventilator use (4.8% vs 95.2%;p=0.01), and lower ICU admission (5.1% vs 94.9%;p<0.001) compared to vaccine-naive group. The mortality rate was lower 5.2% (n=7) in COVID-19 vaccination compared to 94.8% (n=127) vaccine-naive group;p=0.012. [Formula presented] Conclusion(s): In our study, COVID-19 vaccination effectively lowered the incidence of AKI, pneumonia, ICU admission, and mortality translating to better overall outcome. No conflict of interestCopyright © 2023
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Introduction: Coronavirus disease 2019 (COVID-19) related acute kidney injury (AKI) is a recognized complication of the disease and may result in high morbidity and mortality rate. The reported incidence and outcome vary worldwide. This study aimed to assess the AKI rate in hospitalized COVID-19 patients and identify risk predictors/prognosticator associated with the complication. Method(s): This is a retrospective study of hospitalized COVID-19 patients at the University Malaya Medical Center admitted from January 2021 until June 2021. Data on patients who were>= 18 years old and hospitalized for >= 48 hours for confirmed COVID-19 infection were captured. Clinical parameters and demographic of patients were collected from electronic medical records. The staging of AKI was based on criteria as per KDIGO guidelines. Result(s): A total of 1529 patients were found to have fulfilled the criteria for the study with a male-to-female ratio of 759 (49.6%) to 770 (50.3%). The median age was 55 (IQR: 36-66). 500 patients (32.7%) had diabetes, 621 (40.6%) had hypertension, and 5.6% (n=85) had pre-existing chronic kidney disease (CKD). The incidence rate of AKI was 21.1% (n=323). The proportion of different AKI stages of 1,2 and 3 were 16.3%, 2.1%, and 2.7%, respectively. A total of 15 hospitalized patients (0.98%) needed dialysis. 190 patients (58.8%) of AKI group had complete recovery of renal function. Demographic factors that were associated with an increased risk of developing AKI included: age (p< 0.001), diabetes (p<0.001), hypertension (p<0.001), CKD (p<0.001) and vaccination status (p=0.002). Analysis of biochemical parameters in AKI cohort revealed statistically significant lower lymphocytes & platelet counts, higher ferritin levels, and poorer renal function (creatinine based)) compared with the non-AKI cohort. Outcome analysis in our cohort revealed that AKI was associated with prolonged hospitalization (p<0.001) and higher mortality rates with P< 0.001). [Formula presented] Conclusion(s): AKI is a common complication among hospitalized COVID-19 patients. The increased risk was associated with underlying comorbidities and had an adverse outcome on patient morbidity and mortality. No conflict of interestCopyright © 2023
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The world immediately studied Coronavirus Disease 2019 (COVID-19) and raced towards fmding the cure and developing an effective treatment. An automated approach is needed to discover drug candidates and provide those data to facilitate clinical trials in saving time and only focusing on the candidates which potentially become the cure for COVID-19. We propose the Drug Candidates for the Prevention of COVID-19 (DCPC) Database. DCPC Database provides a list of candidates of potential drugs for the prevention of COVID-19 based on disease-drug associations which are automatically discovered from biomedical literature. DCPC database is an integrative structural database, which involves a chemical database repository, such as PubChem and DrugBank to ensure that drug compound candidates have a standard representation of compounds. The database provides keyword-chosen categories and a determination of minimum supported articles for search, a list of drug candidates in the sorted table followed by the detail for each candidate, and a download feature. The keyword category consists of three keywords, they are Chinese herbal compounds, Indian medicinal plants, and Indian medicinal plants & diabetic treatment herbs. Each candidate links to an article in the biomedical literature and to a page of the compound structure visualization. DCPC is freely available at https://dcpc.brin.go.id/dcpc/.
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Background/Purpose: MIS-C is uncommon and yet potentially life threatening disorder associated with COVID-19 infection. MIS-C Malaysia Study Group had reported 174 cases, mostly affecting children < 12 years old (93.7%). The fatality rate was 4%. Hereby, we report a case of MIS-C at our adult rheumatology centre. Method(s): Patient's admission note and electrical medical information were reviewed. Result(s): This is a 17 year-old adolescent with underlying obesity (BMI 42 kg/m2). He completed COVID-19 vaccination (Pfizer-BioNTech x 2 doses) in October 2021. In end-February 2022, he presented acutely with recurrent seizures associated with fever (40.3degreeC) and headache. The COVID-19 RTK antigen and PCR tests were positive, and COVID-19 IgM & IgG were negative. At emergency room, he developed haemodynamic instability, needing ventilatory support for respiratory failure and inotropic therapy on Day 1 of illness. The initial diagnosis was severe COVID-19 infection with encephalitis and secondary bacterial infection. Subsequent investigations showed evidence of systemic inflammation with organ dysfunction involving neurological (seizures, CNS vasculitis), cardiac (myocarditis), renal (acute kidney injury) and gastrointestinal (acute livery injury) systems. MIS-C was then diagnosed with early initiation of immunomodulatory treatment (IVIg 2 g/kg and IV methylprednisolone 1-2 mg/kg/day) according to ACR recommendation. Low dose aspirin and high intensity prophylactic SC enoxaparin were prescribed but were discontinued soon due to bleeding tendency. Antimicrobial therapy was continued until microbiological study was proven sterile. With the immunomodulatory treatment, he had rapid clinical and laboratory improvement within first week and was transferred out from ICU on Day 10 of illness. The organ dysfunction was mostly resolved with no sequelae except for high blood pressure requiring antihypertensive. Inflammatory markers were markedly reduced;Serum ferritin reduced from 22,339 to 565.8 mug/L, procalcitonin decreased from 26.7 ng/ml to 1.5 ng/ml and CRP normalised (<5 mg/L). Home discharge was made on Day 16 of illness with oral prednisolone 60 mg daily without antiplatelet. During clinic visit after D30 of illness, he remained asymptomatic with good effort tolerance and normal blood pressure readings. He subsequently completed the high school examination in April 2022 and even enrolled at college later. Oral prednisolone was eventually tapered off at 3rd month of illness with appointments for MRI cardiac and brain scheduled for further assessment. Conclusion(s): MIS-C is a hyperinflammatory syndrome which requires high clinical suspicion as many patients response well to early immunodulatory treatment without sequelae. Long term follow up maybe needed for those with cardiac involvement. (Table Presented).
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The world immediately studied Coronavirus Disease 2019 (COVID-19) and raced towards finding the cure and developing an effective treatment. An automated approach is needed to discover drug candidates and provide those data to facilitate clinical trials in saving time and only focusing on the candidates which potentially become the cure for COVID-19. We propose the Drug Candidates for the Prevention of COVID-19 (DCPC) Database. DCPC Database provides a list of candidates of potential drugs for the prevention of COVID-19 based on disease-drug associations which are automatically discovered from biomedical literature. DCPC database is an integrative structural database, which involves a chemical database repository, such as PubChem and DrugBank to ensure that drug compound candidates have a standard representation of compounds. The database provides keyword-chosen categories and a determination of minimum supported articles for search, a list of drug candidates in the sorted table followed by the detail for each candidate, and a download feature. The keyword category consists of three keywords, they are Chinese herbal compounds, Indian medicinal plants/and Indian medicinal plants & diabetic treatment herbs. Each candidate links to an article in the biomedical literature and to a page of the compound structure visualization. DCPC is freely available at https://dcpc.brin.go.id/dcpc/. © 2022 IEEE.
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Aims: To understand consumer and clinician experiences in utilizing telehealth in cancer care since the introduction of newMedicare Benefits Schedule (MBS) item numbers for telehealth in response to the COVID-19 pandemic. Method(s): A mixed-methods approach was used to collect qualitative and quantitative data on telehealth use during the pandemic: MBS services for cancer-related professional attendances were examined by delivery type, provider type and population group. Health service staff (n = 59) and consumers (n = 1162) from cancer services acrossAustralia were invited to complete online surveys and semi-structured interviews about the use of video and telephone telehealth, perceived effectiveness of video and telephone compared to in-person consultations, and the key barriers and enablers. A virtual roundtable was held with more than 40 key cancer control stakeholders regarding strategies to support the cancer community to offer and utilize best practice approaches to telehealth. Result(s): Telehealth was used across all stages of the cancer care pathway, and was most frequently used during the 'treatment' and 'care after initial treatment and recovery' stages. Although video consults were perceived to be more effective than telephone consults, telephone use was more frequent. Patients and their carers often felt less engagedwith their clinician during telephone consults and perceived that they were rarely given a choice between the consultation modes. Key enablers included medical leadership and administrative support, remuneration (MBS telehealth items), reduced risk of infection, reduction in travel time and costs and existing relationship between patient and clinician. Key barriers included inadequate infrastructure, lack of training, access issues (e.g., internet connectivity) and not being offered the choice of a video consultation. Conclusion(s): Telehealth is appropriate for the delivery of cancer care. A hybrid model of care (telehealth and in-person options) and the ability to give consumers choice is integral to supporting best practice telehealth in cancer care.
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Objective: To improve compliance of post-stroke patients attending outpatient rehabilitation from 65% to 80% after discharged from an acute inpatient rehabilitation unit within a 1-year period. Design(s): Quality improvement project Setting: Acute inpatient rehabilitation unit Participants: Rehabilitation team members and poststroke patients Interventions: Root causes were identified by fish bone analysis. The most important root causes were subsequently determined by Pareto vote. Using a tree diagram and prioritization matrix, 2 specific interventions were developed. Firstly, on a specific day of the week, assigned rehabilitation nurses would check if outpatient referral to local rehabilitation center was completed and submitted by the team. This intervention was presented at the department meeting to promote awareness among staff about the importance of timely referral. Secondly, when a patient with stroke was identified to be suitable for outpatient rehabilitation, he/she would be briefed by the team about the importance for outpatient rehabilitation and given an education pamphlet. Main Outcome Measure(s): Compliance, measured by percentage of patients with an arranged appointment for outpatient rehabilitation services within a week of discharge Time, measured as time taken for outpatient referral creation to submission of referral. Result(s): Compliance improved from 65% at baseline to 75%. Time taken for creation of referral to submission was reduced from 7.8 days to 1.4 days. Conclusion(s): We did not achieve our goal of 80% compliance rate likely due to small sample size and COVID-19 pandemic with concerns of safety for elderly to attend outpatient rehabilitation. The improvement was 10% which could have been partially accounted by the suspension of outpatient rehabilitation services during the circuit breaker period which was a nationwide partial lockdown, instituted by the government, to contain the spread of COVID-19 in Singapore. Nonetheless, the improvement is still of significance, as studies had showed functional improvements gained while in inpatient rehabilitation were reinforced and maintained in outpatient rehabilitation.
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Machine Learning methods have been used to combat COVID-19 since the pandemic has started in year 2020. In this regard, most studies have focused on detecting and identifying the characteristics of SARS-CoV-2, especially via image processing. Some studies have applied machine learning for contact tracing to minimise the transmission of COVID-19 cases. Limited work has, however, reported on how geospatial features have an influence on the transmission of COVID-19 and formation of clusters at local scale. Therefore, this paper has aimed to study the importance of geospatial features that had resorted to COVID-19 cluster formation in Kuala Lumpur, Malaysia in year 2021. Several datasets were used in this work, which have included the address details of confirmed positive COVID-19 cases and the details of nearby residential areas and Points of Interest (POI) located within the federal territory of Kuala Lumpur. The datasets were pre-processed and transformed into an analytical dataset for conducting empirical investigations. Various feature selection methods were applied, including the Boruta Algorithm, Chi-square (Chi2) Test, Extra Trees Classifier (ETC), Recursive Feature Elimination (RFE) method, and Deep Learning Autoencoder (DLA). Detailed investigations on the top-n features were performed to elicit a set of optimal features. Subsequently, several machine learning models were trained using the optimal features, including Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC), and Extreme Gradient Boosting (XGBoost). It was revealed that Boruta produced the optimal number of features with n = 96, whereas RFC achieved the best prediction results compared to other classifiers, with around 95% accuracy. Consequently, the findings in this paper help to recognize the geospatial features that have impacts on the formation of COVID-19 and other infectious disease clusters at local scale. © 2022, Success Culture Press. All rights reserved.
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The purpose of this study is to investigate and describe an example of COVID-19 pandemic information visualization in human-computer interface to serve as a reference for future pandemic information visualization performance improvements. The relevant local and international literature was investigated and studied, as well as the visualization projects of pandemic information from each stage released by major media outlets in the mid-term outbreak of COVID-19. Thereby, the assessment model of information charts, color evaluation, and interaction model for pandemic information visualization of human-computer interface design was obtained. To make the assessment model more effective, an evaluation system was introduced which contributes to the popularization of information, narrows the gap between truth and public cognition, and arouses public attention and vigilance. In the future, pandemic prevention and control will be supported by the assessment model for the design of information visualization of human-computer interface in different scenarios by systematization, standardization and real-time. © 2022, Success Culture Press. All rights reserved.
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Introduction: In-class engagement enhances learning and can be measured using observational tools. As the COVID-19 pandemic shifted teaching online, we modified a tool to measure the engagement of instructors and students, comparing in-person with online teaching and different class types. Methods: Video recordings of in-person and online teachings of six identical topics each were evaluated using our ‘In-class Engagement Measure’ (IEM). There were three topics each of case-based learning (CBL) and lecture-based instruction (LLC). Student IEM scores were: (1) no response, (2) answers when directly questioned, (3) answers spontaneously, (4) questions spontaneously, (5) initiates group discussions. Instructor IEM scores were: (1) addressing passive listeners, (2) asking ≥1 students, (3) initiates discussions, (4) monitors small group discussion, (5) monitoring whole class discussions. Results: Twelve video recorded sessions were analysed. For instructors, there were no significant differences in percentage time of no engagement or IEM scores when comparing in-person with online teaching. For students, there was a significantly higher percentage time of no engagement for the online teaching of two topics. For class type, there was overall less percentage time of no engagement and higher IEM scores for CBL than LLC. Conclusion: Our modified IEM tool demonstrated that instructors’ engagement remained similar, but students’ engagement reduced with online teaching. Additionally, more in-class engagement was observed in CBL. “Presenteeism”, where learners were online but disengaged was common. More effort is needed to engage students during online teaching. © 2022 TAPS. All rights reserved.
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Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation. © 2023, Canadian Society for Civil Engineering.
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Sound and effective solid waste management practices are important to mitigate health risks and protect the surrounding environment. Proper Municipal Solid Waste (MSW) management practices are especially important during the COVID pandemic. Since the beginning of the COVID pandemic different waste disposal and recycling behaviors are observed in Regina, the capital city of Saskatchewan. It is believed the changes of waste disposal rate at Regina landfill is related to the new hygienic guidelines, more opportunities for work from home, distinct consumer behaviors, and COVID related regulations and recommendations. Waste generation and recycling behaviors are complex and multi-dimensional. The objective of this study is to model waste disposal rate at the City of Regina landfill using a System Dynamics (SD) model. The proposed SD model will help us better manage the City of Regina’s human resources during the pandemic and estimate the demand of additional personal protective equipment required for the waste management workers. In the present work, a total of 7.5 years of waste disposal data is collected, consolidated, and verified. Socio-economical parameters such as GDPs and population at Regina were also collected during the study period from 2013 to 2020. The model is built using stock-flow diagram to illustrate the effects of various inter-related variables on the waste generation behaviors. Construction and demolition (C&D), Grit, Asphalt Shingles Only, Asphalt Shingles Mixed and Treated Biomedical wastes data are used in the SD model. Results show that waste generation has affected by the pandemic and there is a general increase in amount of waste generation. The modeling results are important because it helps us predict the amount of MSW during and after the pandemic. © 2023, Canadian Society for Civil Engineering.
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The study of architectural design is similar to the execution in architecture firms, and the chief designer must keep an eye on all the work progress. Before the outbreak of Covid-19, tutors can check the progress of design assignments as long as they walk into students' studios;as all design drawings or models are on the table. It is possible to save time by leaving notes for improvement. However, in distance teaching, especially in the case of being unable to meet for a long time, what communication tools can effectively maintain the connection between tutors and students is the focus of this study. After trying and experiencing various tools, our teachers and students have found that LINE, a social software, has played a great role. The second is the communication products of Google and Microsoft;the functions of these two are comparable. After several semesters of online communication, the teacher team has also accumulated a set of methods suitable for architectural and/or design learning, at least currently effective. The acceptance of digital tools by a teacher's age group also affects online quality. In addition to reflecting on various suboptimal experiences, this paper also reports improvements at this stage, especially in the interaction between teachers. © 2022 IEEE.
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The COVID-19 pandemic has become a critical threat to global health and the economy since its first outbreak in 2019. The standard diagnosis for COVID-19, Reverse Transcription Polymerase Chain Reaction (RT-PCR) is time consuming, and has lower sensitivity compared to CT-scans. Therefore, CT-scans can be used as a complementary method, alongside RT-PCR tests for COVID-19 infection prediction. However, manually reviewing CT scans is time consuming. In this paper, we propose DECOVID-CT, a deep learning model based on 3D convolutional neural network (CNN) for the detection of COVID-19 infection with CT images. The model is trained and tested on the RICORD dataset, a multinational dataset, for higher robustness. Our model achieved an accuracy of 100%, for predicting COVID-19 positive images. © 2022 IEEE.
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Purpose: To date, we know little about the impact of responsive shifts from conventional to remote learning during the Covid-19 pandemic on student outcomes. If learning suffered, this may potentially have significant negative effects on students’ knowledge and skills acquisition hence eventually impacting the overall quality of our doctors and the care they provide. To address this gap in knowledge, we investigated the impact of switching from classroom team-based learning (cTBL) to online TBL (oTBL) on medical student performance in class tests and end-of-year examinations. Methods: Our participants were 137 second-year medical students who had cTBL experience prior to the shift to oTBL. We held the structure, activities and organisation of TBL constant. The only difference was that oTBL students engaged virtually while cTBL students met in person. We examined if there were differences between cTBL and oTBL in terms of individual (iRA) and team performance (tRA) in class and end-of-year exam scores. Our educational focus was the female reproductive system. We also examined the mean iRA and tRA scores for all modules. Analysis was via repeatedmeasures ANOVA. Results: There was a statistically significant difference between cTBL and oTBL groups’ iRA, tRA and specific exam items for female reproductive knowledge. Similarly, when we looked at year 2 teaching more generally, students scored significantly higher on the iRAs and exam items that were taught by means of oTBL compared to cTBL. Discussion: During a time of educational disruption, shifting a highly structured instructional design from the classroom to online, while keeping all other factors constant, maintained learning outcomes. This reassurance of the effectiveness in respect of student learning opens the door for further research to explore the educational, social and interactional processes of both face-to-face and online TBL © 2022 Association of Medical Education in the Eastern Mediterranean Region (AMEEMR). This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Sponsored by King Saud bin Abdulaziz University for Health Sciences