Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 9 de 9
Filter
1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309799

ABSTRACT

Diagnosing COVID-19 early in domestic settings is possible through smart home devices that can classify audio input of coughs, and determine whether they are COVID-19. Research is currently sparse in this area and data is difficult to obtain. However, a few small data collection projects have enabled audio classification research into the application of different machine learning classification algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), and Convolution Neural Networks (CNN). We show here that a CNN using audio converted to Mel-frequency cepstral coefficient spectrogram images as input can achieve high accuracy results;with classification of validation data scoring an accuracy of 97.5% correct classification of covid and not covid labelled audio. The work here provides a proof of concept that high accuracy can be achieved with a small dataset, which can have a significant impact in this area. The results are highly encouraging and provide further opportunities for research by the academic community on this important topic.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308873

ABSTRACT

Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. Methods: : We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a ‘pandemic-free world’ and ‘world including a pandemic’ are of interest. Results: : In any trial, investigators should;(1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic;(2) Establish what data are missing for the chosen estimand;(3) Perform primary analysis under the most plausible missing data assumptions followed by;(4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a ‘pandemic-free world’, participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the ‘world including a pandemic’, all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption – potentially incorporating a pandemic time-period indicator and participant infection status – or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. Conclusions: : Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296847

ABSTRACT

ABSTRACT Background Non-random selection into analytic subsamples could introduce selection bias in observational studies of SARS-CoV-2 infection and COVID-19 severity (e.g. including only those have had a COVID-19 PCR test). We explored the potential presence and impact of selection in such studies using data from self-report questionnaires and national registries. Methods Using pre-pandemic data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (mean age=27.6 (standard deviation [SD]=0.5);49% female) and UK Biobank (UKB) (mean age=56 (SD=8.1);55% female) with data on SARS-CoV-2 infection and death-with-COVID-19 (UKB only), we investigated predictors of selection into COVID-19 analytic subsamples. We then conducted empirical analyses and simulations to explore the potential presence, direction, and magnitude of bias due to selection when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. Results In both ALSPAC and UKB a broad range of characteristics related to selection, sometimes in opposite directions. For example, more educated participants were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB. We found bias in many simulated scenarios. For example, in one scenario based on UKB, we observed an expected odds ratio of 2.56 compared to a simulated true odds ratio of 3, per standard deviation higher BMI. Conclusion Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depends on the outcome definition, the true effect of the risk factor, and the assumed selection mechanism. Key messages Observational studies assessing the association of risk factors with SARS-CoV-2 infection and COVID-19 severity may be biased due to non-random selection into the analytic sample. Researchers should carefully consider the extent that their results may be biased due to selection, and conduct sensitivity analyses and simulations to explore the robustness of their results. We provide code for these analyses that is applicable beyond COVID-19 research.

5.
Ann Glob Health ; 87(1): 1, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1043783

ABSTRACT

Background: UC San Diego Health System (UCSDHS) is an academic medical center and integrated care network in the US-Mexico border area of California contiguous to the Mexican Northern Baja region. The COVID-19 pandemic deeply influenced UCSDHS activities as new public health challenges increasingly related to high population density, cross-border traffic, economic disparities, and interconnectedness between cross-border communities, which accelerated development of clinical collaborations between UCSDHS and several border community hospitals - one in the US, two in Mexico - as high volumes of severely ill patients overwhelmed hospitals. Objective: We describe the development, implementation, feasibility, and acceptance of a novel critical care support program in three community hospitals along the US-Mexico border. Methods: We created and instituted a hybrid critical care program involving: 1) in-person activities to perform needs assessments of equipment and supplies and hands-on training and education, and 2) creation of a telemedicine-based (Tele-ICU) service for direct patient management and/or consultative, education-based experiences. We collected performance metrics surrounding adherence to evidence-based practices and staff perceptions of critical care delivery. Findings: In-person intervention phase identified and filled gaps in equipment and supplies, and Tele-ICU program promoted adherence to evidence-based practices and improved staff confidence in caring for critically ill COVID-19 patients at each hospital. Conclusion: A collaborative, hybrid critical care program across academic and community centers is feasible and effective to address cross-cultural public health emergencies.


Subject(s)
Academic Medical Centers , COVID-19/therapy , Critical Care/methods , Hospitals, Community , Interdisciplinary Communication , Telemedicine , Algorithms , COVID-19/prevention & control , California , Critical Care/organization & administration , Equipment and Supplies, Hospital , Evidence-Based Medicine , Health Personnel/education , Humans , Infection Control/methods , Intensive Care Units , International Cooperation , Mexico , Nursing/methods , SARS-CoV-2 , Self Efficacy
7.
Nat Commun ; 11(1): 5749, 2020 11 12.
Article in English | MEDLINE | ID: covidwho-922259

ABSTRACT

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Betacoronavirus , Bias , COVID-19 , Humans , Observational Studies as Topic , Pandemics , Risk Factors , SARS-CoV-2 , Treatment Outcome
8.
BMC Med ; 18(1): 286, 2020 09 09.
Article in English | MEDLINE | ID: covidwho-751217

ABSTRACT

When designing a clinical trial, explicitly defining the treatment estimands of interest (that which is to be estimated) can help to clarify trial objectives and ensure the questions being addressed by the trial are clinically meaningful. There are several challenges when defining estimands. Here, we discuss a number of these in the context of trials of treatments for patients hospitalised with COVID-19 and make suggestions for how estimands should be defined for key outcomes. We suggest that treatment effects should usually be measured as differences in proportions (or risk or odds ratios) for outcomes such as death and requirement for ventilation, and differences in means for outcomes such as the number of days ventilated. We further recommend that truncation due to death should be handled differently depending on whether a patient- or resource-focused perspective is taken; for the former, a composite approach should be used, while for the latter, a while-alive approach is preferred. Finally, we suggest that discontinuation of randomised treatment should be handled from a treatment policy perspective, where non-adherence is ignored in the analysis (i.e. intention to treat).


Subject(s)
Betacoronavirus , Coronavirus Infections/therapy , Pneumonia, Viral/therapy , COVID-19 , Clinical Trials as Topic , Coronavirus Infections/drug therapy , Hospitalization , Humans , Odds Ratio , Pandemics , Research Design , SARS-CoV-2
9.
BMC Med Res Methodol ; 20(1): 208, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-713161

ABSTRACT

BACKGROUND: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. METHODS: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemic-free world' and 'world including a pandemic' are of interest. RESULTS: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. CONCLUSIONS: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.


Subject(s)
Outcome Assessment, Health Care/statistics & numerical data , Practice Guidelines as Topic , Randomized Controlled Trials as Topic/statistics & numerical data , Research Design/statistics & numerical data , Betacoronavirus/physiology , COVID-19 , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Coronavirus Infections/virology , Humans , Outcome Assessment, Health Care/methods , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Randomized Controlled Trials as Topic/methods , Reproducibility of Results , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL