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1.
JMIR Form Res ; 2022.
Article in English | PubMed | ID: covidwho-2002407

ABSTRACT

BACKGROUND: The COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic long-term, it is pivotal to understand what factors drive prevalence rates, and to predict the future trajectory of the virus. OBJECTIVE: This study has two objectives. Firstly, it tests the statistical relationship between socioeconomic status and COVID-19 prevalence. Secondly, it utilises machine learning techniques to predict cumulative COVID-19 cases in a multi-country sample of 182 countries. Taken together, these objectives will shed light upon socioeconomic status as a global risk factor of the COVID-19 pandemic. METHODS: This research utilised exploratory data analysis and supervised machine learning methods. Exploratory analysis included variable distribution, variable correlations and outlier detection. Following this, three supervised regression techniques were applied: linear regression, random forest, and adaptive boosting. Results were evaluated using k-fold cross validation and subsequently compared to analyse algorithmic suitability. The analysis involved two models. Firstly, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 reported case data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index metrics of life expectancy, mean years of schooling, expected years of schooling, and Gross National Income were used to approximate socioeconomic status. RESULTS: All variables correlated positively with 2021 COVID-19 prevalence, with R2 values ranging from 0.55-0.85. Using socioeconomic indicators, COVID-19 prevalence was predicted with a reasonable degree of accuracy. Using 2020 reported case rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R2=0.543). When the socioeconomic indicators were added alongside 2020 prevalence rates as features, average predictive performance improved considerably (R2=0.721) and all error statistics decreased. This suggested that adding socioeconomic indicators alongside 2020 reported case data optimised prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R2=0.693 on the first model and R2=0.763 on the second model, followed by random forest (0.481 and 0.722) and AdaBoost (0.454 and 0.679). Following this, the second model was retrained using a selection of additional COVID-19 risk factors (population density, median age, and vaccination uptake) instead of the HDI metrics. Average accuracy dropped to 0.649 however, which highlights the value of socioeconomic status as a predictor of COVID-19 cases in the chosen sample. CONCLUSIONS: Results show that socioeconomic status should be an important variable to consider in future epidemiological modelling, and highlights the reality of the COVID-19 pandemic as a social phenomenon as well as a healthcare phenomenon. This paper also puts forward new considerations about the application of statistical and machine learning techniques to understand and combat the COVID-19 pandemic.

2.
Preprint in English | bioRxiv | ID: ppbiorxiv-491266

ABSTRACT

Early stages of deadly respiratory diseases such as COVID-19 have been challenging to elucidate due to lack of an experimental system that recapitulates the cellular and structural complexity of the human lung, while allowing precise control over disease initiation and systematic interrogation of molecular events at cellular resolution. Here we show healthy human lung slices cultured ex vivo can be productively infected with SARS-CoV-2, and the cellular tropism of the virus and its distinct and dynamic effects on host cell gene expression can be determined by single cell RNA sequencing and reconstruction of "infection pseudotime" for individual lung cell types. This revealed the prominent SARS-CoV-2 target is a population of activated interstitial macrophages, which as infection proceeds accumulate thousands of viral RNA molecules per cell, comprising up to 60% of the cellular transcriptome and including canonical and novel subgenomic RNAs. During viral takeover, there is cell-autonomous induction of a specific host interferon program and seven chemokines (CCL2, 7, 8, 13, CXCL10) and cytokines (IL6, IL10), distinct from the response of alveolar macrophages in which neither viral takeover nor induction of a substantial inflammatory response occurs. Using a recombinant SARS-CoV-2 Spike-pseudotyped lentivirus, we show that entry into purified human lung macrophages depends on Spike but is not blocked by cytochalasin D or by an ACE2-competing monoclonal antibody, indicating a phagocytosis- and ACE2-independent route of entry. These results provide a molecular characterization of the initiation of COVID-19 in human lung tissue, identify activated interstitial macrophages as a prominent site of viral takeover and focus of inflammation, and suggest targeting of these macrophages and their signals as a new therapeutic modality for COVID-19 pneumonia and progression to ARDS. Our approach can be generalized to define the initiation program and evaluate therapeutics for any human lung infection at cellular resolution.

3.
Front Med (Lausanne) ; 8: 707895, 2021.
Article in English | MEDLINE | ID: covidwho-1690436

ABSTRACT

Treatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at the time of convalescent plasma treatment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.

4.
Int J Med Inform ; 157: 104622, 2022 01.
Article in English | MEDLINE | ID: covidwho-1507080

ABSTRACT

INTRODUCTION: Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR). MATERIALS AND METHODS: For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies. RESULTS: For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%). CONCLUSION: Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.


Subject(s)
COVID-19 , Pharmaceutical Preparations , Data Collection , Electronic Health Records , Humans , Pandemics , Reproducibility of Results , Retrospective Studies , SARS-CoV-2
5.
American Journal of Respiratory and Critical Care Medicine ; 203(9):1, 2021.
Article in English | Web of Science | ID: covidwho-1407162
6.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277190

ABSTRACT

Introduction: Electronic-cigarette or vaping-product associated lung injury (EVALI) was first identified in August 2019, when U.S. public health officials noted a clinical syndrome of acute respiratory failure and systemic inflammation associated with the use of aerosolized nicotine and cannabinoids. The presence of lipid-laden macrophages on bronchiolar lavage is a specific but not sensitive histological finding of EVALI, which is often a diagnosis of exclusion. In 2020, the first cases of COVID-19, caused by SARS-CoV2 virus, were seen in the U.S. Both COVID-19 and EVALI can affect previously healthy individuals, manifesting with severe hypoxemia and systemic inflammation, posing diagnostic challenges in distinguishing the two syndromes. Secondary spontaneous pneumothorax is a well-described complication of COVID-19 yet is only rarely associated with EVALI, with only one published case report of EVALI complicated by pneumothorax. Here, we report a case of a 34-year-old man presenting with hypoxemic respiratory failure complicated by pneumothorax, initially thought to be from COVID-19 pneumonia, found ultimately to have EVALI associated diffuse alveolar damage. Case: In April 2020, a 34-year-old man presented with one week of myalgia, shortness of breath, and a reduced exercise tolerance. Social history was notable for extensive vaping. His exam was notable for hypoxemia requiring nonrebreather. Testing showed elevated inflammatory markers and diffuse bilateral opacities on chest radiography. Nasopharyngeal PCR was negative for SARS-CoV2. CT chest revealed dense consolidation with ground grass opacities and air bronchograms. Rheumatologic and infectious workup was unremarkable. Despite six negative SARS-CoV2 tests, he was treated for COVID-19 with empiric steroids and antibiotics for community-acquired pneumonia. On hospital day 3, he developed a right-sided pneumothorax requiring chest tube. On hospital day 12, he developed a left-sided pneumothorax and a second chest tube was placed. A presumptive diagnosis of pneumonitis and diffuse alveolar damage secondary to EVALI was made. Given non-healing bilateral pneumothoraces, on hospital day 32, he underwent chemical pleurodesis with doxycycline which was complicated by ARDS. He was intubated, suffered a PEA arrest from refractory hypoxemia, and emergently cannulated to VV ECMO. A head CT demonstrated diffuse cerebral edema suggestive of anoxic brain injury. After extensive goals of care discussions, care was withdrawn and the patient passed away. Discussion: EVALI, similar to COVID-19, is syndrome of severe acute hypoxemia and systemic inflammation. Both conditions have similar radiographic findings with ground glass opacities indicative of alveolar damage, histological findings of tracheobronchitis and diffuse alveolar damage, and can lead to secondary spontaneous pneumothoraces.

8.
Open Forum Infectious Diseases ; 7(SUPPL 1):S314, 2020.
Article in English | EMBASE | ID: covidwho-1185857

ABSTRACT

Background: Most diagnostic tests for SARS-CoV-2, the causative agent of COVID-19, are RT-PCR based. This method is sensitive but cannot distinguish replicating from non-replicating virus. RT-PCR cycle threshold (Ct) values are inversely correlated with viral load, and higher Ct values have been correlated with lower in vitro viral infectivity. However, relatively few data exist on the association between Ct values and patients' duration of symptoms remains unclear. We thus evaluated Ct values and symptom duration in a cohort of patients hospitalized with COVID-19. Methods: We assessed all patients admitted to San Francisco General Hospital between April 1 and May 18, 2020 with confirmed COVID-19 infection based on RT-PCR testing (Abbott m2000 platform). We included patients having diagnostic testing for suspected COVID-19 and patients having asymptomatic testing per hospital policy. For symptomatic patients, date of symptom onset was abstracted from hospital records, and time from symptom onset to test date was calculated. RT-PCR Ct values were manually extracted. Median Ct and IQR were calculated for patients with < 10 days of symptoms, ≥10 days of symptoms, and asymptomatic disease. Betweengroup comparisons were performed using the Kruskal-Wallis test. Results: Among 61 patients with positive RT-PCR tests, 40 patients reported < 10 days of symptoms at the time of testing, 15 reported ≥10 days of symptoms, and 6 were asymptomatic. The median Ct value was 14.2 cycles (IQR, 10.2, 18.3) among patients reporting < 10 days of symptoms, 19.7 cycles (IQR, 15.3, 23.9) among patients reporting ≥10 days of symptoms, and 26.3 (IQR, 25.0, 29.1) among asymptomatic patients. Ct values were significantly lower among patients with < 10 days of symptoms compared to patients with >=10 days of symptoms (p=0.01) and when compared to asymptomatic patients (p=0.0002) [Figure]. Conclusion: SARS-CoV-2 RT-PCR cycle threshold values were higher (indicating lower viral load) in patients with longer symptom duration and were highest in asymptomatic patients. These results add to emerging data suggesting that strategies for optimal isolation of patients in both community and hospital settings could be informed by a combination of symptom duration and RT-PCR Ct values. (Table Presented).

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