Contemporary studies of risks in emerging technology, Part A

With the rapid development of technologies, it becomes increasingly important for us to remain up-to-date on new and emerging technologies. This series, therefore, aims to deliver content on current and future technologies and how the young generation benefits from this. The global financial crisis has highlighted major weaknesses in financial records, information, and data. These weaknesses have led to inadequacies in the access to financial records and information, higher operational risks, flawed bankruptcies, and foreclosure proceedings. The Lockdown due to the ongoing pandemic COVID-19 has increased the scope for criminals to exploit vulnerabilities and commit financial crimes. The increased online presence and homeworking have significantly expanded the attack surface for cybercriminals. Criminals are exploiting vulnerabilities, increasing the risks of cyber-attacks, money laundering and terrorist financing. Research is therefore needed to identify trends, tools and applications that will provide the needed records, information, and data to support more effective financial analysis and risk management. Financial Technology (FinTech) has become one of the most pioneering and cost-effective disruptive technologies. Initial adaptation of FinTech solutions has permitted several start-ups, financial service providers, and other assorted sectors to accomplish an augmented pace of growth. Contemporary Studies of Risks in Emerging Technology: Part A also highlights how emerging technologies are altering the subtleties of doing business for financial services benefactors, possibility of emerging technologies, advantages and disadvantages, technology linked issues/challenges in financial services, and also highlights drivers of this revolution. © 2023 Simon Grima, Kiran Sood, and Ercan Özen.

The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach

Fintech allows investors to explore previously unavailable investment opportunities;it provides new return opportunities while also introducing new risks. The aim of this study is to investigate the relationship between risk and return in the fintech industry in the Indian stock market. This article is based on market-based research that focuses on demonstrating the volatility in the fintech market's prices and demystifying the opportunities. Secondary data were collected from the Bombay Stock Exchange's official fintech industry website from January 2017 to July 2022 to determine whether there is any dynamic link between risk and return in the Indian fintech market. The variance-based Mean-GARCH (GARCH-M) model was used to determine whether there is a dynamic link between risk and return in the Indian fintech market. The findings emphasize the importance of taking the risk of investing in India's fintech industry. The implications for stock investors' and fund managers' portfolio composition and holding periods of equities or market exposure are significant. Finally, depending on their investment horizons, the Indian fintech industry may yield significant profits for risk-taking individuals.

Remdesivir and Tocilizumab for the Treatment of Severe COVID-19 in a Community Hospital: A Retrospective Cohort Study

Background. Growing evidence supports the use of remdesivir and tocilizumab for the treatment of hospitalized patients with severe COVID-19. The purpose of this study was to evaluate the use of remdesivir and tocilizumab for the treatment of severe COVID-19 in a community hospital setting. Methods. We used a de-identified dataset of hospitalized adults with severe COVID-19 according to the National Institutes of Health definition (SpO2 < 94% on room air, a PaO2/FiO2 < 300 mm Hg, respiratory frequency > 30/min, or lung infiltrates > 50%) admitted to our community hospital located in Evanston Illinois, between March 1, 2020, and March 1, 2021. We performed a Cox proportional hazards regression model to examine the relationship between the use of remdesivir and tocilizumab and inpatient mortality. To minimize confounders, we adjusted for age, qSOFA score, noninvasive positive-pressure ventilation, invasive mechanical ventilation, and steroids, forcing these variables into the model. We implemented a sensitivity analysis calculating the E-value (with the lower confidence limit) for the obtained point estimates to assess the potential effect of unmeasured confounding. Figure 1. Kaplan-Meier survival curves for in-hospital death among patients treated with and without steroids The hazard ratio was derived from a bivariable Cox regression model. The survival curves were compared with a log-rank test, where a two-sided P value of less than 0.05 was considered statistically significant. Figure 2. Kaplan-Meier survival curves for in-hospital death among patients treated with and without remdesivir The hazard ratio was derived from a bivariable Cox regression model. The survival curves were compared with a log-rank test, where a two-sided P value of less than 0.05 was considered statistically significant. Results. A total of 549 patients were included. The median age was 69 years (interquartile range, 59 - 80 years), 333 (59.6%) were male, 231 were White (41.3%), and 235 (42%) were admitted from long-term care facilities. 394 (70.5%) received steroids, 192 (34.3%) received remdesivir, and 49 (8.8%) received tocilizumab. By the cutoff date for data analysis, 389 (69.6%) patients survived, and 170 (30.4%) had died. The bivariable Cox regression models showed decreased hazard of in-hospital death associated with the administration of steroids (Figure 1), remdesivir (Figure 2), and tocilizumab (Figure 3). This association persisted in the multivariable Cox regression controlling for other predictors (Figure 4). The E value for the multivariable Cox regression point estimates and the lower confidence intervals are shown in Table 1. The hazard ratio was derived from a bivariable Cox regression model. The survival curves were compared with a log-rank test, where a two-sided P value of less than 0.05 was considered statistically significant. The hazard ratios were derived from a multivariable Cox regression model adjusting for age as a continuous variable, qSOFA score, noninvasive positive-pressure ventilation, and invasive mechanical ventilation. Table 1. Sensitivity analysis of unmeasured confounding using E-values CI, confidence interval. Point estimate from multivariable Cox regression model. The E value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to explain away a specific exposure-outcome association fully: i.e., a confounder not included in the multivariable Cox regression model associated with remdesivir or tocilizumab use and in-hospital death in patients with severe COVID-19 by a hazard ratio of 1.64-fold or 1.54-fold each, respectively, could explain away the lower confidence limit, but weaker confounding could not. Conclusion. For patients with severe COVID-19 admitted to our community hospital, the use of steroids, remdesivir, and tocilizumab were significantly associated with a slower progression to in-hospital death while controlling for other predictors included in the models.

Determining the Return Volatility of Major Stock Markets Before and During the COVID-19 Pandemic by Applying the EGARCH Model

With this study, we aim to determine the effect of the Covid-19 pandemic on the return volatility of the DJI, the DAX, the FTSE100 and the CAC40 stock indexes. We take return volatility between 1st January 2019 and 17th July 2020 and split it into two separate periods - before the Covid-19 pandemic outbreak and the first wave of the 'In-Pandemic’ period. Only the so-called first wave of the pandemic was chosen to avoid the influence of knowledge of possible vaccines and antiviral solutions. Data were analysed by using the exponential GARCH (EGARCH) model. Findings show excessive volatility in the major stock markets with short volatility persistence and the presence of leverage in returns during the first wave of the Covid-19 pandemic outbreak. Moreover, during the pandemic period, positive shocks have been observed to have a greater effect than negative socks on the stock index return volatility. © 2021. All Rights Reserved.

Clinical characteristics and outcomes of COVID-19 patients with AKI at community-based hospital

Background: The disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and later called Covid-19 has resulted in significant morbidity worldwide. The virus can cause various complications and affect many organ systems. Preliminary reports have shown that Acute Kidney Injury (AKI) is common in patients with Covid-19, however, outcomes of kidney injury in hospitalized patients, especially at the communitybased hospitals are not well described. The aim of this study was to describe the incidence, severity, and outcomes of Covid-19 patients with AKI at the community-based hospital. Methods: This was a single-center, retrospective observational cohort study. All patients (age ≥18) with positive by polymerase chain reaction testing for Covid-19 who required hospitalization were included in the study. Patients with End-Stage Kidney Disease and kidney transplants were excluded. We compared outcomes of patients with and without AKI. We used univariable and multivariable Cox regression model to evaluate the relationship between AKI and in-hospital mortality. Results: 220 patients were included in the study. 89 (40%) patients developed AKI, of whom 6 (7%) required Kidney replacement therapy (KRT) and 131 (60%) did not develop AKI. In-hospital mortality of patients with AKI was markedly higher than patients without AKI. Among the patients with AKI, 39 (43.8%) experienced in-hospital death while in patients without AKI, 23 (17.5%) died (P<0.001). Unadjusted HR was 2.01 (CI 1.23-3.14;P<0.001). The risk of in-hospital death remained significantly high following adjustment for baseline demographics and comorbidities with adjusted HR 1.8 (CI 1.50-2.74, P=0.015). The median hospital length of stay of patients who were discharged alive differed based upon AKI status. Patients with AKI-KRT had the longest median length of stay (15.5 days IQR 8.5-23.7), followed by patients with AKI non-KRT (7 days, IRQ 5-14) and patients without AKI (6 days, IQR 4-10). Conclusions: AKI is a common condition among patients hospitalized with Covid-19 and is associated with an increased risk of in-hospital mortality. It is important to consider this complication in the management of Covid-19 patients.

Teleworking and Emotional Experience and Wellbeing: The Case in the Turkish Financial Services Industry During COVID-19

Since the worldwide increase in COVID-19 cases, teleworking in the Turkish Financial Services Industry has become increasingly popular. Wellbeing of working outside traditional workplace settings, is still in its infancy and as far as we understand, has not yet been addressed in the Turkish Financial Industry. We administered a survey using the telephone, e-mail and other social media asking employees in the financial sector in Turkey currently working under these new conditions to provide us with specific responses which take as our data. 438 valid responses were received and analysed using Structural Equation Modelling on Lisrel. We tested the relationship between teleworking, Covid-19 fear, emotional experience, and affective well-being. As a result of the study we found a significant relationship (i) between Covid-19 fear and individual differences and (ii) between individual differences and affective wellbeing. The findings will allow financial institutions managers to re-evaluate working conditions during the pandemic period, while guiding legislators to produce policies. © 2021. All Rights Reserved.

THE FIRST AND SECOND WAVES OF THE COVID-19 PANDEMIC IN A COMMUNITY HOSPITAL ICU

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Several countries have seen a two-wave pattern of the COVID-19 pandemic. However, clinical characteristics and outcomes between waves vary across regions. A study in England suggested a substantial improvement in survival amongst people admitted to critical care with COVID-19, with markedly higher survival rates in people admitted in the first wave compared with those admitted in the second wave, while a study in Africa, the second wave appeared to be much more aggressive. Therefore, regional-specific analyses are needed. METHODS: We retrospectively reviewed a de-identified dataset of patients with COVID-19 admitted to our community hospital ICU, from March 1, 2020, to February 28, 2021. Only molecularly confirmed COVID-19 cases defined by a positive result on an RT-PCR assay or NAAT of a specimen collected on a nasopharyngeal swab were included. We then identified patients from the first wave as those admitted during the initial peak of admissions observed at our hospital between March 1, 2020, and September 3, 2020. The second wave was defined as those admitted during the second peak of admissions observed between October 1, 2020, and February 28, 2021. Descriptive statistics were performed to summarize data. RESULTS: Between March 1, 2020, and February 28, 2021, a total of 190 patients were admitted to our community-hospital ICU. Of those, 132 (69.5%) were identified as patients from the first wave, and 58 (30.5%) were identified as patients from the second wave. The median age was not significantly different among patients from the first and second wave (69 years [IQR 59 – 78 years] vs. 69 years [IQR 61 – 77.25 years;p=.841]. Sex distribution was also not significantly different between the two waves (85/132 males [64.4%] vs. 40/58 males [69%];p=.541). A significantly higher rate of patients was admitted from long-term care facilities during the first wave compared to the second wave (77/132 [58.3%] vs. 7/58 [12.1%];p<.001). The distribution of comorbidities was similar between groups, except for neurocognitive disorders, which were mostly observed in the first wave (46/132 [34.8% vs. 7/58 [12.1%];p=.001). While the rates of invasive mechanical ventilation were similar between groups (75/132 [56.8%] vs. 36-58 [62.1%];p=.499, significant higher rates of patients received humidified high-flow nasal cannula (19/132 [14.4%] vs. 29/58 [50%];p<.001) and noninvasive ventilation (9/132 [6.8%] vs. 23/58 [39.7%];p<.001) during the second wave. Following the release of some pivotal clinical trials, more patients during the second wave received corticosteroids (87/132 [65.9%] vs. 56/58 [96.6%];p<.001) and remdesivir (19/132 [14.4%] vs. 48/58 [82.8%];p<.001). However, the in-hospital case-fatality rate was not significantly different between groups (68/132 [51.5%] vs. 32/58 [55.2%];p=.642). CONCLUSIONS: While epidemiological characteristics of patients with COVID-19 admitted to our ICU between the two waves were grossly similar, a significantly higher rate of patients was admitted from long-term care facilities during the first wave, and non-invasive ventilation and targeted therapies were used more during the second wave. The in-hospital case-fatality rate was not significantly different. CLINICAL IMPLICATIONS: In our community hospital in the Chicago North Shore area, the ICU case-fatality rate was not significantly different between two different waves of the COVID-19 pandemic. DISCLOSURES: No relevant relationships by Chul Won Chung, source=Web Response No relevant relationships by Goar Egoryan, source=Web Response No relevant relationships by Harvey Friedman, source=Web Response No relevant relationships by Emre Ozcekirdek, source=Web Response No relevant relationships by Ece Ozen, source=Web Response No relevant relationships by Bidhya Poudel, source=Web Response No relevant relationships by Guillermo Rodriguez-Nava, source=Web Response No relevant relationships by Daniela Trelles Garcia, source=Web Response No relevant relationships by Valer a Trelles Garcia, source=Web Response No relevant relationships by Maria Yanez-Bello, source=Web Response No relevant relationships by Qishuo Zhang, source=Web Response

EXPERIENCE WITH REMDESIVIR FOR THE TREATMENT OF CRITICAL COVID-19 IN A COMMUNITY-BASED HOSPITAL IN EVANSTON, ILLINOIS

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: In late December 2019, a novel coronavirus named SARS-CoV-2 was discovered in Wuhan, China using deep unbiased sequencing in samples from patients with pneumonia. From its discovery, SARS-CoV-2 has caused global public health emergencies, economic crises, and innumerable deaths. To date, only corticosteroids have been proven to be effective in reducing mortality from COVID-19. From antiviral agents, remdesivir has been recently recognized as a promising therapy against COVID-19, but its mortality benefit is still a matter of controversy. In this study, we analyzed the effect of remdesivir on in-hospital death in our community hospital in the Chicago North Shore. METHODS: We retrospectively reviewed a de-identified dataset of 190 patients with COVID-19 admitted to a community hospital Intensive Care Unit (ICU) in Evanston, Illinois, from March 2020 to December 2020. Only molecularly confirmed COVID-19 cases defined by a positive result on a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay or nucleic acid amplification test (NAAT) of a specimen collected on a nasopharyngeal swab were included. We performed a Cox proportional hazards model to analyze the effect of remdesivir on the hazard of in-hospital death in our patient population. To minimize confounders, age, qSOFA score, invasive mechanical ventilation, and other targeted COVID-19 therapies used at any given time (including corticosteroids, tocilizumab, hydroxychloroquine, colchicine, azithromycin, and atorvastatin) were forced as covariables into the model. For sensitivity analysis, we calculated the E value (with the lower confidence limit) for the obtained point estimate. The E value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to explain away a specific exposure-outcome association fully. RESULTS: Between 190 patients admitted to the ICU, the median age was 69 years (IQR, 59 – 78 years), 125 (65.8%) were male, 62 (23.6 %) were White, and 84 (44.2%) were admitted from a long-term care facility. Of those patients, 143 (75.3) received corticosteroids, 67 (35.3%) received remdesivir, and 66 (34.7%) received both. Among survivors, 34/90 (37.8%) received remdesivir compared to 33/100 (33%) nonsurvivors. The Cox regression model showed decreased hazard of in-hospital death associated with the administration of remdesivir (Hazard Ratio [HR] 0.55;95% CI 0.29 – 0.94, p=.028). The E value for the point estimate was 3.04 and the E value for the lower confidence interval was 1.32, meaning that a confounder not included in the multivariable Cox regression model associated with remdesivir use and in-hospital mortality in patients with critical COVID-19 by a hazard ratio of 1.32-fold each could explain away the lower confidence limit, but weaker confounding could not. CONCLUSIONS: According to the data presented above, we concluded that in our patient population, the patients who did not receive remdesivir had a 65% chance of dying sooner compared to the ones who did receive remdesivir (when probability = HR/HR + 1). This could indicate a potential mortality benefit of remdesivir in critically ill patients. CLINICAL IMPLICATIONS: In our patient population, the use of remdesivir was associated with a slower progression to death in critically ill patients with COVID-19. DISCLOSURES: No relevant relationships by Chul Won Chung, source=Web Response No relevant relationships by Goar Egoryan, source=Web Response No relevant relationships by Harvey Friedman, source=Web Response No relevant relationships by Emre Ozcekirdek, source=Web Response No relevant relationships by Ece Ozen, source=Web Response No relevant relationships by Bidhya Poudel, source=Web Response No relevant relationships by Guillermo Rodriguez-Nava, source=Web Response No relevant relationships by Daniela Trelles Garcia, source=Web Response No relevant relationships by Maria Y nez-Bello, source=Web Response No relevant relationships by Qishuo Zhang, source=Web Response