Simulation to Educate Practicing Urologists in Rural Locations: Identifying Strategies to Increase Procedural Adoption

Introduction: The COVID-19 pandemic necessitated proliferation of telesimulation. This pedagogy may be useful in rural areas to increase procedural adoption and reduce healthcare disparities. Our aim was to determine the current status of surgical simulation education to retool rural practicing Urologists. Method(s): Literature search was performed with a trained librarian for PubMed, EMBASE and Web of Science. Title/ screening were performed to include all studies of surgical simulation involving rural surgical learners to identify simulation education opportunities for practicing rural Urologists. Data was then extracted: simulation event, skills focus, MERSQI score, type/number of learners, learner assessment and event evaluation. Result(s): Seven manuscripts met inclusion criteria. Most were published 2019-2020 and were cross sectional (5/7, 71%). Mean adjusted MERSQI score was 13 (range 6-15.5). A wide range of surgical skills were taught (incl. laparoscopy, cricothyroidotomy, chest tube insertion, damage control laparotomy), but no Urological surgical skills. Two articles described mobile simulation units for rural areas. A total of 232 learners were identified including 69 medical students. One fifth of rural learners were non-medical or non-physicians. Only one study involved faculty, who were general surgeons. Conclusion(s): Telesimulation education for practicing Urologists in rural areas is lacking. Current in-operating room telementoring for rural Urologists requires surgeons to travel and perform their first cases utilizing this new technique on patients. Telesimulation to teach Urological skills in rural areas of the US may increase dissemination of techniques with no patient risk and has significant potential to redress current healthcare disparities.

Patient Safety Culture in the Emergency Center at the University Clinical Center of Kosovo and the COVID-19 Pandemic

BACKGROUND: Despite the developments in Kosovo's healthcare, there are still many challenges that hamper the delivery of proper health-care service. This was especially highlighted during the coronavirus disease 2019 (COVID-19) pandemic. AIM: This study aims to elucidate the factors that impede proper health service as well as reduce preventable medical errors by focusing on safety as a fundamental principle in patient care and a key component health services quality management. The main goal is to improve the overall approach to the patient by improving the workers performance and redesigning systems, with the goal of reducing patient risk not only in normal working environment but also in new and unusual situations such as COVID-19 pandemic. METHOD(S): In this cross-sectional study, data were collected and analyzed. Two questionnaires were compiled for this research: one was compiled to address patients who sought health services at the Emergency Center;the second questionnaire was designed for the Emergency Center personnel to identify the relationships between the workers, managerial staff, the problems of reporting errors, and similar. Moreover, relevant publications on the impact of the pandemic on the provision of health services were compared. Statistical analysis was done by IBM SPSS version 25. CONCLUSION(S): There is a need for improving Patient Safety Culture in The Emergency Center at the University Clinical Center of Kosovo. By reorganizing working hours for the workers of the Emergency Center, preventable medical errors would be reduced. Raising the capacities of the primary care level would reduce the load of the Emergency Center from interventions, which can be handled without a problem at the lower levels. Continuous professional trainings, as well as trainings focused on stress management, working under time pressure, and relationships between health service providers would significantly improve the level of patient safety in the Emergency Center.Copyright © 2023, Scientific Foundation SPIROSKI. All rights reserved.

Fungal pneumonia in COVID-19 patients: Risk factors and early prediction

Objective To determine the risk factors for developing secondary fungal pneumonia in moderate to severe coronavirus disease 2019 (COVID-19) cases. Using predictors of fungal infection helps to guide the diagnosis and treatment in these cases and save their lives. Patients and methods A total of 257 patients with moderate to severe COVID-19 pneumonia were examined in this retrospective study at Al Qassimi Hospital of EHS. An assessment of clinical, laboratory, and radiologic findings was performed upon admission. The data were collected and analyzed. Results Overall, 32% of critically ill COVID cases had fungal infection;47% of them were candida, whereas aspergillosis and yeast were positive in 26.5% each. At the time of hospitalization, computed tomography chest findings had a strong correlation with fungal culture results in COVID-19 cases. Fungal infection in COVID-19 cases correlated strongly with metabolic acidosis, high erythrocyte sedimentation rate, high blood sugar, need for mechanical ventilation at admission, vasopressor use, renal replacement, long duration of steroid treatment, long stay in ICU, and long duration on mechanical ventilation. The longer the duration of PCR positivity, the higher the incidence of positive sputum fungal culture result. Conclusion COVID-19-infected patients with other risk factors for fungal infections should always be considered to have fungal infections if pathogenic organisms are isolated from respiratory secretions or other microbiological or immunological markers appear positive. Computed tomography chest finding in COVID-19 cases is an important predictor for fungal infection.Copyright © 2023 The Egyptian Journal of Chest Diseases and Tuberculosis.

Covid-19 Technology Assessment Service

Purpose: As the COVID-19 emergency evolved, a wide range of 'new' technology based solutions were offered to meet clinical and occupational health needs in Europe. This technology extended beyond the standard medical devices usually deployed in clinical settings, and therefore required rapid assessment of suitability for use in hospitals. Here we describe a hospital-based COVID-19 technology assessment service (www.misa.ie/researchdevelopment/ bioengineering-lab/technology-assessment) that was developed and share our experience of its implementation. Material(s) and Method(s): A scientifically grounded assessment service was established to evaluate specific technological solutions. This service was led by a team of 2 Senior Medical Physicists and 1 Senior Clinical Engineer, with each assessment drawing on pan-hospital expertise and a specialist technology evaluation infrastructure. Each solution was evaluated using a standardized agile process: 1) user centric needs assessment;2) applicable literature and international standards review;3) balanced risk-benefit assessment;4) initial device functionality and usability assessment;5) in-depth device technical testing and safety assessment;6) rapid communications and detailed reporting;7) support for local clinical implementation/ installation with on-going evaluation. Evaluations were described in the form of short Bulletins with a webpage developed to share these findings internationally. Result(s): To date, a diverse range of technological systems and innovative solutions were evaluated, including thermal cameras for mass temperature screening, baby monitor devices for isolation room communications, augmented reality systems, a varied range of thermometers, and connected health technologies for remote working and clinical testing. Substantial variability in quality and standard of systems on offer was identified, with potential patient risks highlighted and mitigated. Critical success factors of the assessment service identified include: a central focus on the impact of solutions on both patients and staff, accessible local scientific and technical expertise supporting real-world testing and user feedback, an agile process which was responsive to high levels of uncertainty and a rapid communications process that was adaptive, responsive and connected both locally and nationally. Conclusion(s): Emergency situations, while challenging, are a huge stimulus for healthcare system-wide changes where barriers to technological innovation are significantly reduced, providing significant opportunities for adoption of new and innovative solutions. While there is a need for timely and practical technology assessments during an acute emergency, these should still be grounded in well-established scientific and safety principles that prioritize the health and safety of patients, staff and the public. A hospital-based COVID-19 technology assessment service has provided a practical and successful solution to this challenge.Copyright © 2023 Southern Society for Clinical Investigation.

The role of cardiac troponin-I as a prognostic tool for mortality in patients hospitalized with COVID-19

Background: Coronavirus disease 2019 (COVID-19) has been associated with significant morbidity and mortality, with cardiovascular involvement being usual. Elevations in cardiac Troponin-I level has proposed as an independent biomarker for mortality among patients with COVID-19. Aim(s): To evaluate the role of high sensivity Troponin-I (hs-TnI) level at hospital admission in predicting 30 day in-hospital mortality and 6-month mortality in patients hospitalized with a COVID-19 diagnosis. Method(s): We performed a retrospective single-center cohort study including consecutive patients aged 18 years and older who were admitted for COVID-19, during a 1-year period (n=818). We excluded patients with acute coronary syndrome (n=23), patients with acute heart failure (n=42), and patients in which hs-TnI level was not dosed at admission (n=163). Patients were divided into two groups according to hs-TnI levels: Hs-TnI <19.8 vs hs-TnI >=19.8 pg/mL. Primary outcomes were 30-day in-hospital mortality and 6-months mortality. According to the data distribution, appropriate statistical tests were conducted to compare independent samples. Multivariable logistic regression was used to analyze mortality risk. Receiver operator characteristics (ROC) curve and area under the curve (AUC) were obtained to determine the discriminative power of hs-TnI as a predictor of mortality. (Figure 1). Result(s): This cohort included 590 patients. Mean age was 71 >=+/-15 years and 52.4% were men. Overall, 209 patients (35.4%) had elevated hs- TnI levels and 381 patients had normal hs-TnI levels. Individuals in the hs-TnI >=19.8 pg/mL group were older (80+/-11 vs 66+/-14 years, p<0.001) and presented higher prevalence of chronic heart failure (24.9% vs 7.1%, p<0.001), hypertension (77.0% vs 57.5%, p<0.001), atrial fibrillation/flutter (19.1% vs 5.5%, p<0.001), prior stroke (12.4% vs 5.2%, p=0.001) and ischemic heart disease (12.4% vs 3.7%, p<0.001). There was no difference in length of hospital stay between the groups (8.0 [IQR 9.6] in hs-TnI 19.8 pg/mL group vs 9.0 [IQR 8.0] normal hs-TnI group, p=0.669). Troponin-I was the only independent predictor of in-hospital mortality (OR 3.80, CI 95%: 2.44-5.93, p<0.001), see Table 1. The troponin levels had the highest area under the receiver operating characteristic curv (AUC) with an AUC of 0.705 (95% CI: 0.667-0.742, p<0.001) for association with the inhospital mortality (figure 1). There was no difference in 6-months mortality between the two groups. Conclusion(s): Acute myocardial injury is common in patients hospitalized with COVID-19. In the present study a TnI level >=19.8 pg/mL was predictor of 30 days in-hospital mortality, suggesting that raised levels of this biomarker is associated with adverse prognosis. This tool might be useful for COVID-19 patient risk stratification. Further studies are needed to provide robust data and reliable recommendations on this theme.

Pneumothorax in ARDS hospitalized Covid-19 patients: risk factors, therapeutic strategies and outcome

Introduction: Pneumothorax was described as complication in COVID-19 patients although clinical risk predictors for its presentation and the potential role in patient's outcome is still unclear. Aim of the study: To assess risk predictors, therapeutic strategies and outcome of ARDS hospitalized COVID-19 patients with pneumothorax. Method(s): We performed a retrospective case-control analysis of 184 patients admitted for severe respiratory failure to our COVID-19 semi-intensive care respiratory unit (SARS-CoV-2 infection confirmed by molecular testing) from october 2020 to march 2021 reporting clinical and radiological features, comorbidities, treatments and outcomes. Result(s): The 8% of sample experienced spontaneous PNX (of which 75% right PNX and 8% bilateral PNX). The mean age of whole sample was 76 years, 53% males, 43% were obese, 50.5% current or former smokers, 52.7% had hypertension, 80% had a history of cognitive impairment, 80% had received non-invasive ventilation before pneumothorax. The mean P/F of pneumothorax group at our unit admission was 168. The 100% of them underwent chest dreinage. Their mortality was 83.1% (p<0.001). Conclusion(s): PNX may be a complication of severe COVID-19 infection associated with a worse prognosis in terms of mortality, consistently with the possible mechanism of hyperinflammatory form associated with critical illness. In our experience high-flow oxygen therapy may be a safer alternative to avoid the potential fatal occurrence of pneumothorax in COVID-19.

COVID-19 severity prediction based on patient risk factors and number of vaccines received

Background: Vaccines are highly effective in preventing severe disease and death from COVID-19, and new medications that can reduce disease severity have been approved. However, many countries are facing limited supply of vaccine doses and medications. Research question: A model estimating the probabilities for hospitalisation and mortality according to individual risk factors and vaccine doses received could help prioritise vaccination and yet scarce medications to maximise lives saved and reduce the burden on hospitalisation facilities. Method(s): Electronic health records from 101,034 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, were extracted from a national healthcare organization in Israel. Logistic regression models were built to estimate the risk for subsequent hospitalization and death based on the number of BNT162b2 mRNA vaccine doses received and few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and presence of hypertension, pulmonary disease or malignancy). Result(s): The models built predicts the outcome of newly infected individuals with remarkable accuracy: area under the curve was 0.889 for predicting hospitalisation, and 0.967 for predicting mortality. Even when a breakthrough infection occurs, receiving three vaccination doses significantly reduces the risk of hospitalization by 66% (OR = 0.336) and death by 78% (OR = 0.220). Conclusion(s): The models enable rapid identification of individuals at high risk for hospitalisation and death when infected. These patients can be prioritised to receive booster vaccination and the yet scarce medications. A calculator based on these models is made public: http://covidest.web.app.

Nasal high flow oxygen therapy compared to conventional oxygen therapy in hospitalised patients with respiratory illness: a systematic review and meta-analysis

Background: Nasal high flow oxygen (NHFO) therapy has been increasingly adopted to treat respiratory illnesses, especially since the COVID-19 pandemic. Aim(s): This systematic review aimed to compare health outcomes of nasal high flow oxygen (NHFO) therapy versus conventional oxygen therapy (COT) in hospitalised adults with acute and chronic respiratory illnesses. Method(s): A comprehensive search was performed across Medline, Embase, and CENTRAL databases in June 2021 for randomised controlled trials (RCTs), interventional, and observational studies that reported on at least one predefined outcome. Result(s): 54 studies (20 RCTs) of acute respiratory illnesses (ARI) and 14 studies (8 RCTs) of chronic respiratory illnesses (CRI) were included. In patients with ARI, compared to COT, NHFO did not reduce the overall need for invasive mechanical ventilation (IMV) escalation, risk ratio (RR)=0.85 [95%CI 0.72-1.01;p=0.06]. In patients with CRI, NHFO significantly reduced IMV escalation need, RR=0.66 [95%CI 0.46-0.93;p=0.02]. In COVID-19 patients, NHFO was associated with mortality benefits in the short-term, RR= 0.62 [95% CI 0.48-0.79;p=0.0001] and longterm, RR= 0.67 [95% CI 0.48-0.92;p=0.01]. Moreover, NHFO significantly reduced the need for IMV escalation in COVID-19 patients, RR= 0.72 [95% CI 0.63-0.82;p<0.00001] and in patients with acute respiratory failure (ARF) from all-causes, RR= 0.82 [95% CI 0.71-0.96;p=0.01]. Conclusion(s): Compared to COT, NHFO significantly reduced the overall need for IMV escalation in patients with CRI, COVID-19, and ARF.

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2.

BACKGROUND: This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data. METHODS: This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models. RESULTS: We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation. CONCLUSIONS: The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.

CLUSTER Trial An 82 U.S. Hospital Cluster Randomized Trial (CRT) to Assess the Impact of an Automated Statistical Outbreak Detection Tool and Response Protocol to Limit Hospital Transmission

Background. The CLUSTER trial assessed the impact of prospective identification of clusters coupled with a response protocol on the containment of hospital clusters. Methods. This 82-hospital CRT in 16 states compared clusters of bacterial and fungal healthcare pathogens using a statistical outbreak detection tool (WHONET-SaTScan) coupled with a standardized response protocol (automated cluster detection arm) compared to routine surveillance with the response protocol (control arm). Trial periods: 24 mo Baseline (2/17-1/19);5 mo Phase-in (2/19-6/ 19);30 mo Intervention (7/19-1/22). The primary outcome was the number of additional cases occurring after initial cluster detection. Analyses used generalized linear mixed models to assess differences in additional cases between the intervention vs baseline periods across arms, clustering by hospital. Results were assessed overall and, to account for the effect of COVID-19 on hospital operations, stratified into pre-COVID-19 (7/19-6/20) and during COVID-19 (7/20-1/22) intervention periods. We also assessed the probability that a patient was in a cluster. Results. In the baseline period, the automated cluster detection and control arms had 0.09 and 0.07 additional cluster cases/1000 admissions, respectively. The automated cluster detection arm had a 22% greater relative reduction in additional cluster cases in the intervention vs baseline period compared to control (P=0.5). Within the intervention period, the automated cluster detection arm had a significant 64% relative reduction pre-COVID-19 (P< 0.05) and a non-significant 6% relative reduction during COVID-19 (P=0.9) compared to control (Figure). When evaluating patient risk of being part of a cluster across the entire intervention period, the automated cluster detection arm had a significant 35% relative reduction vs control (P< 0.01). Conclusion. A statistical automated tool coupled with a response protocol improved cluster containment by 64% pre-COVID-19 but not during COVID-19;there were no significant differences between the arms when using the entire intervention period. Automated cluster detection may substantially improve outbreak containment in non-pandemic periods when infection prevention programs are able to optimize containment protocols. (Figure Presented).

Transplant Patients Risk for SARS-CoV-2 Infection Post-2nd Vaccine Dose, We Are Not Home and Dry Yet

Purpose: SARS-CoV-2 is associated with high mortality among transplant recipients. Data of transplant patients' infections post-2nd vaccine dose is not available. The aim of the study was to establish the extend to which vaccinated patients were protected from severe infection. Method(s): We recruited 920 kidney transplant patients receiving at least one dose of SARS-CoV-2 vaccine (Astra-Zeneca-AZ or Pfizer) excluding patients with known virus pre-exposure. Serological status was determined using the COVIDSeroKlir enzyme-linked-immunosorbent-assay (ELISA) (Kantaro-EKF). Patients with corrected antibody level less than 0.7AU/mL were considered seronegative. All SARS-CoV-2 infections post-2nd and up to 2-weeks post the third dose were recorded. We considered severe the infections requiring admission and moderate the infections lasting over 10 days or requiring A&E (ER) attendance without admission. Result(s): 593 patients had their samples analysed post-second dose. 42.8% of AZ patients seroconverted (148/346) compared to 52.6% of Pfizer (130/247, p=0.02, HR 1.07-2.06). There were 53 PCR-confirmed infections between 1/7/21 and 20/11/21, 33 in AZ and 18 in Pfizer patients. Two patients had received no vaccine and 3 patients who received AZ had no specimen for analysis. 10 patients' infection was over 6 months post-2nd dose.41/315 (13%) of seronegative patients got infected compared to 7/278 (2.5%) of seropositive patients (p=0.00001, OR 5.9 CI 2.554- 13.139) during this period.There were 15 mild, 5 moderate, and 13 severe infections post AZ and 11 mild, 3 moderate, 4 severe post Pfizer respectively. 16/17 patients admitted and 7/8 with moderate disease had no demonstrable antibody response at their latest sample post-2nd vaccine dose. There were 2 deaths. We observed at least 3 seropositive patients who became seronegative and got infection. Conclusion(s): 5.5% of vaccinated and 13% of seronegative transplant patients got SARS-CoV-2 infection following the 2nd vaccine dose. 92% of patients with moderate/severe disease were seronegative. A significant proportion of transplant patients remains at risk of serious illness due to SARS-CoV-2 because they do not demonstrate an antibody response to vaccination.

RARE CASE OF A NONTRAUMATIC LEFT MAIN BRONCHIAL TEAR

SESSION TITLE: Bad bugs and Mediastinal Madness SESSION TYPE: Case Reports PRESENTED ON: 10/19/2022 09:15 am - 10:15 am INTRODUCTION: Non-traumatic bronchial injury (NTBI) incidence is not well described but traumatic Tracheobronchial injury (TBI) incidence is 3% with a 70 -100% mortality3. Causes identified for NTBI are associated with vascular supply compromise2. TBI presents with dyspnea, subcutaneous emphysema, pneumothorax, and/or pneumomediastinum4. It can be missed up to 68% of the cases. Bronchoscopy is the study of choice and management is based on studies from traumatic TBI2, 3. This report describes a unique case of NTBI in a patient with recent COVID-19 infection, uncontrolled diabetes, and invasive pseudomembranous Aspergillosis presenting with a left bronchial tear (LBT). CASE PRESENTATION: A 41-year-old with uncontrolled diabetes and prior admission for COVID-19 infection and diabetic ketoacidosis (DKA) managed with steroids and antibiotics. Presenting cough, fever, intermittent chest pain, and palpitations. He was afebrile, tachycardic, and hypoxemic requiring supplemental oxygen. Chest examination revealed crackles and decreased breath sounds at the lung bases. Laboratory studies showed leukocytosis, hyperglycemia, and anion gap metabolic acidosis. SARS-CoV-2 PCR was negative. CT chest revealed an anterior wall defect of the left bronchus with a pneumomediastinum. Bronchoscopy showed pseudomembranous necrotic debris of the tracheobronchial tree and left main bronchus tear with visible rhythm-beating pericardium surrounding the heart. Cytopathological findings of the bronchoalveolar fluid were consistent with Aspergillus species (AS). DISCUSSION: NTBI are rare with a high mortality3. NTBI due to AS has been described in post-lung transplant patients. AS produces endotoxins and proteases that damage the epithelium, leading to erosion of surrounding structures2,3. Since COVID-19, invasive fungal infections (IFI) have risen due to lung damage and immunologic deficits associated with the virus or immunomodulatory therapy6. Our patient risk factors for IFI included recent COVID-19 infection, steroid use, and uncontrolled diabetes. This unholy trinity has coexisted during COVID-19 self-potentiating the problem of immune dysregulation leading to IFI and tissue necrosis7. This may cause NTBI as in our case presenting with LBT. Despite antimicrobial therapy, he died due to massive hemoptysis from erosion of the pericardium or angio-invasion of surrounding vessels. CONCLUSIONS: Rarity of NTBI constitutes a challenge for early diagnosis and management. Identifying predisposing risk factors, a high clinical suspicion, and appropriate diagnostic workup is of vital importance. During the COVID-19 pandemic, IFI have an increased incidence associated with high mortality rates. Despite more cases being described there are still knowledge gaps related to prevention, diagnosis, and management. Reference #1: Jones D, Nelson A, Ma OJ. Pulmonary Trauma. In: Tintinalli JE, Stapczynski JS, Ma OJ, Yealy DM, Meckler GD, Cline DM, eds. Tintinalli's Emergency Medicine: A Comprehensive Study Guide, 8e. McGraw-Hill Education;2016. accessmedicine.mhmedical.com/content.aspx?aid=1121516674 Reference #2: Aerni MR, Parambil JG, Allen MS, Utz JP. Nontraumatic Disruption of the Fibrocartilaginous Trachea: Causes and Clinical Outcomes. Chest. 2006;130(4):1143-1149. doi:https://doi.org/10.1016/S0012-3692(15)51151-3 Reference #3: AK AK, Anjum F. Tracheobronchial Tear. StatPearls Publishing;2022. Accessed March 13, 2022. https://www.ncbi.nlm.nih.gov/books/NBK560900/ DISCLOSURES: No relevant relationships by Jorge Alejandro Bernal No relevant relationships by Adriana Betancourth No relevant relationships by Reham Majzoub No relevant relationships by Juan Pablo Sarmiento Cano

ONE YEAR of DIGITAL HEALTH APPLICATIONS (DIGA) in GERMANY-RHEUMATOLOGISTS' PERSPECTIVES

Background: Based on given legislation (ŞŞ 33a and 139e SGB V, Social Code Book V) the German approach to digital health applications (Digitale Gesundheitsanwendungen, DiGA) allows reimbursed prescription of approved therapeutic software products (listed in the DIGA directory https://diga.bfarm.de/de/verzeichnis) for patients since October 6th, 2020. Objectives: To evaluate the level of knowledge on DiGA among members of the German Society for Rheumatology (DGRh) after one year of DiGA under the conditions of the COVID-19 pandemic using the DiGA Toolbox of the 'health innovation hub' (hih), a think tank and sparrings partner of the German Federal Ministry of Health. Methods: Anonymous cross-sectional online survey using LimeSurvey (https://limesurvey.org). The survey was promoted by newsletters sent out to DGRh newsletter recipients and Twitter posts. Ethical approval was obtained. Results: 75 valid participants reported that they care more than 80% of their working time for patients with rheumatic diseases. Most were working in outpatient clinics (54%) and older than 40 years of age (84%). Gender distribution was balanced (50%). 70% were aware of the possibility to prescribe DiGA. Most were informed on this for the frst time via trade press (63%), and only 8% via the professional society. 46% expect information on DiGA from professional societies and the medical chambers (36%) but rarely from the manufacturer (10%) and the responsible ministry (4%). Respondents would like to be informed about DIGA via continuing education events (face-to-face 76%, online 84%), trade press (86%), and manufacturers test accounts (64%). Only 7% have already prescribed a DiGA, 46% planned to do so, and 47% did not intend DiGA prescriptions. Relevant aspects for prescription are given in Figure 1. 86% believe that using DiGA/medical apps would at least partially be feasible and understandable to their patients. 83% thought that data collected by the patients using DiGA or other digital solutions could at least partially influence health care positively. 51% appreciated to get DiGA data directly into their patient documentation system resp. clinical electronic health record (EHR) and 29% into patients' owned EHR. Conclusion: DiGA awareness was high whereas prescription rate was low. Mostly, physician-desired aspects for DiGA prescriptions were proven efficacy and efficiency for physicians and patients, risk of adverse effects and health care costs were less important. Evaluation of patients' barriers and needs are warranted. Our results will contribute to the implementation and dissemination of DIGA.

Computed Tomography and Patient Risk: Facts, Perceptions and Uncertainties

Aim: To assess the role of computed tomography for management of Covid-19. Study design: Prospective study Place and duration of study: Department of Radiology, Ghulam Muhammad Mahar Medical College Teaching Hospital Sukkur from 1st November 2020 to 31st December 2021. Methodology: One hundred cases within various ages 5-55 years for analyzing their risk for CT scanning on them by highlighting the facts related to CT scan, patient perceptions and uncertainties regarding it. A 50 radiologist and 50 emergency doctors were also asked questions regarding their knowledge about CT scan risks and their responses were also documented. However previous CT record of patients suffering from carcinoma was also analyzed for understanding the fact related with CT imaging. Results: The mean age of patients undergoing CT scan was 39.5±5.6 years. There were 55% males who underwent CT scans while 45% females. The usual dosage for various radiological procedure shows that highest dose deliverance was given to the patients of CT pulmonary angiogram and coronary angiography. Only 50% of radiologists knew that CT scan is associated with high risk of malignancies. There were only 10% emergency medical doctors who also knew CT imaging relation with malignancy risk. Only 54% patients considered abdomen pelvic scan to be associated with increasing lifetime risk of cancer while 23% of the patients considered chest scan to be associated with escalating the risk of cancer. Conclusion: Computed tomography scan is related with a high risk of radiation exposure. There is a dire need of perception development and risk understanding with medical professionals and general public for minimizing this risk.

Bias in algorithms of AI systems developed for COVID-19: A scoping review.

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.

COVID-19 vaccine responsiveness in patients with multiple myeloma and waldenström macroglobulinemia

BACKGROUND: Multiple myeloma (MM) and Waldenström macroglobulinemia (WM) are associated with significant immunoparesis. Based on the ongoing COVID-19 pandemic, there is an urgent need to understand whether patients are able to mount a sufficient response to COVID-19 vaccines. METHODS: MM and WM patients are vaccinated with mRNA-1273 (Moderna), BNT162b2 mRNA (Pfizer/BioNTech), or JNJ-78436735 (Johnson & Johnson) in a prospective clinical trial. Primary endpoint is SARS-CoV-2 spike protein (S) antibody (Ab) detection 28 days after final vaccination. Secondary endpoints include functional serologic assessments and T-cell responses at 28 days, 6 months, 9 months, and 12 months following vaccination. S Abs were detected by Elecsys assay (Roche Diagnostics), with 3 0.80 U/mL defined as positive and titers > 250 U/mL considered stronger correlates of neutralization. SARS-CoV-2 wildtype and variant S-specific Ab isotypes and FcγR binding profiles were quantified by custom Luminex assay. Antibody-dependent neutrophil and cellular phagocytosis (ADNP and ADCP) were assessed using flow cytometry. RESULTS: To date 141 patients have been enrolled, 137 (91 MM and 46 WM) of whom had an S Ab assessment. Median Ab titer was 178.0 (IQR, 16.10-1166.0) for MM and 3.92 (IQR, 0-278.9) for WM. S Ab response rate was 91% (83/91) in MM and 56% (27/46) in WM. However, responses achieving S Ab >250 U/mL were 47.3% (43/91) in MM and 26.1% (12/46) in WM. In patients 375 years, responses >250 u/mL were 13.3% (2/15;p<0.05). Vaccine-specific S Ab responses >250 u/mL following mRNA-1273, BNT162b2, and JNJ-78436735 were 67.6% (23/34;p<0.05), 38.3% (18/47;p=NS), and 20% (2/10;p=NS) in MM and 50.0% (8/16;p<0.05), 14.8% (4/27;p<0.05), and 0% (0/3;p=NS) in WM. Among MM patients with progressive disease, S Ab response >250 u/mL occurred in 30% (6/20) as opposed to 55.6% (30/54) for VGPR+ (p<0.05). MM patients having autologous stem cell transplant within 12 months demonstrated 100% (5/5;p<0.05) S Ab responses. For MM patients actively receiving an anti-CD38 monoclonal Ab or an immunomodulatory drug, S Ab response occurred in 38.9% (14/36;p=NS) and 50.9% (28/55;p<0.05). Among WM patients, S Ab responses >250 U/mL occurred in 63.6% (7/11;p<0.05) previously untreated;0% (0/9;p<0.05) who received rituximab within 12 months;10% (2/20);p<0.05) on an active Bruton Tyrosine Kinase (BTK) inhibitor;and 20% (3/15;p=NS) who received other therapies. Functional Ab studies were performed on 14 MM patients, 14 WM, patients, and 14 healthy donors (HD) (Figure 1). All patients were assessed 28 days following their final vaccination and myeloma patients had an additional assessment 28 days following initial vaccination. MM and WM patients demonstrated less IGG1 and IGG3 S Ab production than HD. MM patients showed increased IgA and IgM S Ab production as well as increased FcgR2A binding following a second vaccine in contrast to HD. Both ADNP and ADCP were reduced in MM and WM patients. MM patients demonstrated improved ADCP in SARS-CoV-2 variants B.1.351, B.1.117, and P.1 versus wildtype (p<0.05). CONCLUSIONS: We report the first known evidence of impaired functional humoral responses following COVID-19 vaccines in patients with MM and WM. Overall, WM patients showed more severe impairment of COVID-19 S Ab responses. Most previously untreated WM patients achieved S Ab responses, however the most significant reduction in S Ab responses were seen in WM patients who received rituximab within 12 months or active BTK inhibitors. For MM patients, being in disease remission associated with improved S Ab response. Among MM and WM patients, age 375 years associated with significantly lower rates and vaccination with MRNA-1273 (Moderna) elicited significantly higher S Ab response rates than other vaccines. A defect in ADNP and more profound defect in ADCP suggests overall compromised opsinophagocytic activity among MM and WM patients. Data comparing first and second vaccine responses in MM patients, suggest less efficient class switching to IGG as well as incomple e maturation of their FcgR2A binding profiles but normal maturation of FcgR3A. Interestingly, ADCP was improved in several emerging SARS-CoV-2 variants. T-cell studies are pending and will be updated. Further understanding of the immunological response to COVID19 vaccination is needed to clarify patients risks, and necessity for booster or alternative protective measures against COVID-19. (Figure Presented).

Identification, risk, and diagnosis: Do you believe in tests?

We are in the midst of an osteoporotic pandemic for decades, and global fragility fracture numbers continue to rise exponentially. Causal factors include the aging of the world's population, while many people live with multiple competing or contributing comorbidities, often in countries where skeletal health and the resources to manage it are not a priority. Fragility fracture associated morbidity, mortality, and economic toll remain stubbornly high. Although the illness burden for major fractures are similar to stroke, MI or cancer, governments, health professionals, and the public do not attach the same importance. Many tools can identify those at risk for fracture, or with osteoporosis today, perhaps too many. Sadly studies continue to show most people are neither diagnosed or managed for their underlying osteoporosis before or after they fracture, and effective assessment and management in practice remains poor. Novel strategies are required to address this pandemic, and 'flatten the curve' of fragility fractures. Many lessons have been learned from the COVID-19 pandemic over the past year which could help. Historically fracture risk and prevention has been thought of and taught to be a multifactorial process linked to individual patient risk factors such as low BMD, aging and others. A more prudent approach for this pandemic considers these in aggregate, broken down into categories like innate patient factors such as age, gender and genetics, medical disorders, and their treatments such as rheumatoid arthritis and corticosteroids, and societal issues such as government policy, priorities, and resources including staffing, equipment, and treatment. A strategic rearrangement such as this may have a more emphatic 'flattening of the curve' by communicating and implementing effective processes targeted at a patient level, a healthcare level and a government or regional level to prevent the disease for those without it, and a more sustainable way of living for those with it.

A Qualitative Study Based on a Case Series of Obstetric COVID-19 Patients to Determine Risks in Management Associated with Severity in a Government Hospital in the Philippines

Background. Enterprise Risk Management (ERM) in healthcare is a method used to identify, assess and reduce risk to patients and the hospital organization. The objective of this study is to identify clinical and organizational challenges and risks in healthcare management caused by COVID-19, and its impact on patients and healthcare workers, in a low-resource obstetric setting. Methods. From a census of patients from 1 April 2020 to 30 July 2020, four cases of COVID-19 in pregnancy representing different severity levels were selected. A patient tracer activity was done for each patient, documenting events that the patient and healthcare team experienced from admission to discharge. A case series on these patients was written. A focus group consisting of an OB-GYN resident, OB-GYN consultant, OB-GYN nurse, OB-GYN infectious disease consultant, and internal medicine resident and consultant, was formed. Each case was presented to the focus group to establish the context of risk assessment. Risks were identified using the framework of Enterprise Risk Management. Each risk was classified according to their risk domain and severity. Root cause analysis via the fishbone method was used to identify the causes of the risks. Results. Operational risks identified were delayed swab results, false negative swab results, and delayed patient transport. Clinical/Patient risks identified were COVID-19 exposure of healthcare workers and other non-COVID patients, inadvertent community exposure, risk for severe clinical manifestations of COVID-19, and lack of specific treatment for COVID-19. Risk to human capital identified were COVID-19 infection of hospital staff and decreased quantity of workforce due to quarantine. Most risks were assessed to be moderate risk or high risk in terms of severity. Root cause analysis showed that common causes of risks were due to exposure to asymptomatic patients and delayed and false-negative swab results. Conclusion. The results of this study may be used towards the final steps of ERM: risk evaluation, treatment and management, in a low resource setting.

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings.

BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections. METHODS: A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm. RESULTS: 625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively. CONCLUSION: M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19.

An Audit to investigate the incidence of face-to-face patient review by gps prior to referral to surgical assessment unit

Aims: Since COVID-19, GP's have been encouraged to do fewer face-toface consultations to prevent unnecessary patient contact1. Anecdotally, this initially resulted in many patients being referred to SAU who had not been seen by a GP, and then being discharged back to the community the same day, causing potentially increased risk of contracting COVID-19 through hospital attendance. The aim of this audit was to investigate the incidence of patients referred to SAU not seen by a GP and discharged the same day. Methods: GP referrals were identified over a 7 day period through the surgical take electronic system AramisVC . The case notes and GP documentation were reviewed to identify whether a face-to-face GP consultation occurred, and then whether the patient was admitted to SAU or discharged the same day. Results: During a 7 day period, there were 24 (n=24) GP referrals of which only 3 (12.5%) were not seen by the GP, all of whom were admitted for at least one night. However, of the patients referred and seen by GP, 7 (29%) were discharged the same day. Conclusions: This demonstrates that during this 7-day period, there was no incidence of inappropriate GP referral to SAU of patients not seen by a GP, and the majority of GP referrals warranted admission. This suggests that in most cases, GPs are avoiding unnecessary emergency surgical referrals and attempt to review patients face-to-face prior to referral, thus reducing patient risk of contact with COVID-19 in the hospital setting.