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Several months after COVID-19 infection, a high proportion of patients complains of persistent symptoms (long COVID). Regardless of acute disease severity, dyspnea and fatigue are part of major symptoms. Spontaneous recovery is unlikely without any intervention. Thus, our aim was to assess the feasibility and efficacy of a pulmonary rehabilitation (PR) program to relieve these symptoms. 39 patients with a documented COVID-19 infection and suffering from persistent symptoms were included. They took part in PR program between Feb.-Dec. 2021. The program comprised, at the very least, 18 endurance and 10 resistance training sessions and 6 educational sessions (dyspnea management, psychological support and counselling). Exercise tolerance (ET), dyspnea (D-12), fatigue (MFI-20) and quality of life (QoL) were assessed by questionnaires before (T1) and after (T2) PR. Main delay between first symptoms and PR enrolment was 297+/-131 days. Main symptoms were dyspnea (n=34), fatigue (n=29), concentration disorders (n=16) and musculoskeletal pain (n=15). After PR, D-12 and MFI-20 decreased significantly (T1:15.5+/-8;T2: 8.3+/-5.6;p<.05 and T1: 65.6+/-14.3;T2:53.2+/-12.8;p<.05, respectively). In addition, QoL, through physical (PCS) and mental (MCS) dimensions, and ET significantly improved (PCS (T1): 34.8+/-9.7;PCS (T2): 42.7+/-8.3;p<.01;MCS (T1): 40.3+/-9.3;MCS (T2): 48.8+/-8.5;p<.01 and T1: 516.6+/-131.6 m;T2: 602.6+/-118.8 m;p<.05, respectively). PR appears to be a safe, feasible and efficient therapy to relieve dyspnea and fatigue in patients with residual symptoms of COVID-19 several months after initial infection. In addition, PR allows a significant improvement in ET and QoL for these patients.
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We describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
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Objective: The effects of the covid-19 pandemic on mental health have been profound. There is a complex bi-directional relationship between mental health and diabetes self-care. We examined whether covid-19 anxiety syndrome, depression and health anxiety predicted domains of diabetes self-management and investigated whether greater covid-19 anxiety syndrome would independently contribute to poorer diabetes self-care. Methods: We sent surveys to patients attending a London hospital diabetes clinic. Participants (n = 369, mean age 50.5 years, 52.9% female) completed the diabetes self-management questionnaire (DSMQ), the covid-19 anxiety syndrome scale (C-19ASS), which measures perseveration and avoidant maladaptive coping behaviour, assessed with measures of co-existent depression and health anxiety, controlling for age, gender, and social deprivation. Clinical data including pre-and post-lockdown HbA1c measures were obtained from hospital records. Results: Depression scores were much higher than pre-pandemic reports, although the prevalence of covid-19 anxiety syndrome was not higher than measured elsewhere. Both pre-existing health anxiety and depression independently predicted poorer measures of diabetes care, as did lower socioeconomic rank. However, avoidant covid-19 anxiety responses were independently associated with higher diabetes self-care scores. HbA1c levels improved significantly over the UK lockdown in our cohort (p < 0.001). Conclusion: During the height of lockdown our research suggests that avoidant coping behaviours characteristic of the covid-19 anxiety syndrome could work to improve diabetes self-care, at least in the short term. We recommend screening for depression, and we should be aware of the significant minority of patients with covid-19 anxiety syndrome who may now find it difficult to re-engage with face-to- face clinic opportunities.
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Aims: Regional variations in adoption of real-time continuous glucose monitoring (RT-CGM) may be reflected in population-level metrics of glycaemic control. In this observational study, we characterised the impact of two different RT-CGM systems in three European countries. Methods: Anonymised data from users in Germany, Sweden, and the UK who transitioned from Dexcom's G5 to its G6 RT-CGM System in 2018 and uploaded data from both systems were analysed. The G6 (but not G5) feature set includes a predictive alert designed to mitigate hypoglycaemia. Endpoints were time in range (TIR, 3.9-10.0mmol/ L), retention rates, and intraday/interday device utilisation. Metrics were computed for three month intervals in the two year study window following G6 launches. Results: Utilisation among G5-to- G6 transitioners improved across all countries, and the user retention rate at the end of the study was 85.5%. Overall mean TIR increased from 60.1% (final three months of G5) to 62.8% (two years after switching to G6), and the proportion achieving >70% TIR increased from 28.3% to 37.9%.Regional TIR differences were observed in 2020 and may have been influenced by covid-19 lockdown approaches. Pandemic-related increases in TIR were evident in the UK and Germany, where stringent lockdown measures were introduced;TIR changes in Sweden, where lockdowns were less restrictive, were negligible. Conclusions: Population-level analysis of RT-CGM data can reveal nationwide trends and disparities in the adequacy of glycaemic control. These may be impacted by factors including features and performance attributes of the RT-CGM system itself, and by public health measures such as lockdowns.
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Background. Monoclonal antibody (Mab) infusions have reduced hospitalization and mortality among higher risk patients with mild to moderate COVID-19 symptoms. Using an interdisciplinary team approach, we created a clinical team to proactively screen and outreach patients with COVID-19 to equitably offer Mab. Methods. From December 28, 2020 - May 3, 2021, a clinical team consisting of an Infectious disease pharmacist and physician, reviewed each outpatient with a positive SARS-CoV-2 PCR test at the Atlanta VA Healthcare System (AVAHCS) daily. The clinical team used the published Emergency Use Authorization criteria to determine eligibility. Eligible patients were prioritized using the Veterans Health Administration (VACO) Index for COVID-19 Mortality, which estimates the risk of 30-day mortality after COVID-19 infection using pre-COVID-19 health status (Figure 1). Eligible patients were contacted via telephone to confirm eligibility and obtain verbal consent. We performed SARS-CoV-2 IgG antibody tests when possible prior to Mab infusion, but results did not preclude Mab receipt. Telehealth follow-up occurred at 1- and 7-days post infusion. Overview of the elements of the VACO index, part 2 of 2. Results. In total, 1,346 COVID-19 patients were identified;86 (6%) patients were eligible, and 48/86 (55%) received Mab infusions (Figure 2). The median time from symptom-onset to positive COVID-19 PCR test result was 6 days (0-9) and the median time from positive COVID-19 PCR test result to Mab infusion was 2 days (0-8). SARS-CoV-2 IgG antibodies were detected in 4 of 24 (17%) patients tested. The most common comorbidities were hypertension (73%) and diabetes, (42%) (Table). Five (10%) patients required hospitalization for worsening COVID-19 symptoms post infusion. No deaths occurred. Conclusion. This approach of combining laboratory surveillance and active screening minimized delay in symptoms onset to Mab infusion, thereby optimizing outpatient treatment of COVID-19 disease. Our approach successfully treated a more diverse patient population compared to clinical trials. Mab infusions overall was well tolerated with few hospitalizations and no deaths in this cohort.
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Background. Dalbavancin and Oritavancin are semisynthetic lipoglycopeptides (LGP) that are FDA-approved for treatment of skin and soft tissue infections, but emerging data supports LGP use for other serious gram positive (GP) infections. We describe our experience with LGP during the COVID-19 pandemic. Methods. We initiated a quality improvement project to assess the use of LGP for label and off-label indications at the Atlanta Veterans Affairs Health Care System. We define serious GP infections as infective endocarditis, osteomyelitis, joint infections, or bacteremia. Patients with serious GP infections that receivedLGP were selected at the treating physician's discretion. We reviewed medical records of all patients receiving at least one dose of long-acting LGP from March 1, 2020 - May 31, 2021. We described patient demographics, clinical information,and outcomes (90-day readmission). Results. Nineteen patients with GP infections received LGP (table). Overall, the most common infection was cellulitis 7 (35%);14 patients received LGPs for serious GP infections. All patients received at least one other non-LGP antibiotic for at least 2 days, majority vancomycin (60%) and cefazolin (30%). Overall, the median hospital stay among patients who received LGP was 8.5 days (range: 2-45 days), for those with serious GP infections the median hospital stay was 15 days (range: 4-45). 90% of patientswho received LGP were discharged home. Number of LGP doses ranged from 1 to 6 doses total, based on type of infection. Sixteen veterans (80%) followed up in outpatient clinicfollowing discharge within 2 weeks, two patients were discharged to home hospice due to complications of underlying malignancies and two patients were lost to follow up. Noadverse drug events were reported, and none with serious GP infections required rehospitalization at 90 days. Conclusion. Our experience suggests that long-acting LGP may be valuable tools to treat serious gram-positive infections by optimizing theduration of hospitalization and preventing unnecessary admissions to acute care and nursing facilities for daily antibiotic infusions. These aspects of LGP use are especially important during the COVID-19 pandemic where nosocomial transmission has been documented.
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BACKGROUND: With the development of the COVID-19 pandemic, elective foot and ankle surgeries were delayed throughout the United States to divert health care resources and limit exposure. Little is known about the impact of COVID-19 on patient's willingness to proceed with elective procedures once restrictions are lifted and factors contributing to such decision. METHODS: Patients across 6 US orthopedic institutions who had their elective foot and ankle surgeries cancelled secondary to the pandemic were given a questionnaire. Specifically, patients were asked about their willingness to move forward with surgery once restrictions were lifted and if not why. Pain-level and pain medication use were also assessed. Univariate analysis was used to identify factors that contribute to patient's decisions. RESULTS: A total of 150 patients participated in this study. Twenty-one (14%) opted not to proceed with surgery once restrictions were lifted. Forty-three percent (n = 9) listed concern for COVID infection as the reason; however, 14% of them would proceed if procedures were performed in surgery center. Twenty-nine (19% of the total cohort) patients had increased pain and 11% of patients were taking more pain meds because of the delay to their procedure. Patients who decided not to proceed with surgery reported pain reduction (3% vs 14%) and lower increase in pain medication used (5% vs 12%). CONCLUSION: COVID-19 has made a significant impact on the health care system. Delay of elective foot and ankle procedures impact patient quality of life and outcomes. Access to surgery centers may provide a partial solution during the pandemic. LEVEL OF EVIDENCE: Level III.
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In this paper, we describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge, a four-month global competition organized by the XPRIZE Foundation. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, the winning models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. We believe that this experience contributes to the necessary transition to more evidence-driven policy-making, particularly during a pandemic. © 2021, Springer Nature Switzerland AG.
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For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial "estimand", a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic.
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COVID-19 , Pandemics , Humans , SARS-CoV-2ABSTRACT
Background: The contributions of non-AIDS comorbidities and HIV-related factors to coronavirus disease 2019 (COVID-19) outcomes among persons with HIV (PWH) remain unclear. We aimed to identify risk factors for COVID-19 hospitalization among PWH. Methods: We evaluated all adult (≥18 years) PWH with a positive SARS-CoV-2 PCR evaluated in a public safety-net hospital system, a Ryan White-funded HIV clinic and a Veterans Affairs medical center in Atlanta, GA between March 1, 2020 and November 15, 2020. Demographic and clinical characteristics and COVID-19 disease outcomes were ascertained by medical record abstraction. We performed multivariable logistic regression to determine associations with COVID-19 hospitalization. Results: 180 patients (mean age 49 years, 78% male, 78% Black, 14% Latinx) were included. 97% were on antiretroviral therapy (ART), 91% had HIV-1 RNA <200 copies/ml, and mean CD4 count was 527 cells/mm3. 60 patients (33%) were hospitalized, 28 (47%) required supplemental oxygen. Overall mortality rate among PWH was 1.63%;mortality among hospitalized PWH was 5%. 130 patients (72%) had at least 1 non-AIDS comorbidity;22% had >4 comorbidities (hypertension, dyslipidemia, obesity and diabetes were most prevalent). In univariable models, age, hypertension, dyslipidemia, diabetes, heart disease, and chronic kidney disease were associated with hospitalization. HIV characteristics including CD4 count, viral load, and ART use were not associated with hospitalization. After adjusting for those baseline characteristicsassociated with hospitalization, only age [aOR(95%CI) 1.073 (1.036-1.110), p<0.0001] and diabetes mellitus [aOR(95%CI) 2.653 (1.027-6.853), p=0.0439] were associated with hospitalization. In a multivariable model adjusting only for age, comorbidity count was associated with a 25% increased risk for hospitalization [aOR(95% CI) 1.245 (1.013-1.531), p=0.0375];and having ≥4 comorbidities was associated with a 2.8-fold increased risk of hospitalization compared with 0-1 comorbidities [aOR(95% CI) 2.848 (1.174-6.910), p=0.0240] (Figure). In age-adjusted analyses restricted to CD4 <200 cells/mm3 or HIV-1 RNA >200 copies/mL, HIV-related factors were not associated with hospitalization. Conclusion: In a cohort of PWH with well-controlled HIV and COVID-19, age and non-AIDS comorbidities, but not HIV-related factors, were associated with hospitalization for COVID-19. Further research into causes of severe COVID-19 among PWH is warranted. (Figure Presented).
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We developed a 3D-Enhanced Facility Management System for Indoors Navigation (3D-EFMS-IN) to assist visually impaired users (VIU). Additionally, the system aims to facilitate the management of estate property and provide support for future scenarios related to emergencies, security, and robotics devices. The system combines four main subsystems: Mapping, Navigation Paths, Indoor Localisation and Navigation, and a Visualisation. An Integration of the subsystems has been done and a pretest with one VIU was performed to obtain feedback and tune the critical characteristics of our development. We observed that the system offers an acceptable preliminary user experience for VIU and future tests require to improve the latency of the system and usability. Shortly, we aim to obtain qualitative and quantitative measurements in a significant pool of users once the COVID lockdown ends. © 2020 ACM.
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The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion;and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. © 2020, University of Skovde. All rights reserved.