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1.
NPJ Digit Med ; 4(1): 117, 2021 Jul 27.
Article in English | MEDLINE | ID: covidwho-1328860

ABSTRACT

Understanding the relationships between pre-existing conditions and complications of COVID-19 infection is critical to identifying which patients will develop severe disease. Here, we leverage ~1.1 million clinical notes from 1803 hospitalized COVID-19 patients and deep neural network models to characterize associations between 21 pre-existing conditions and the development of 20 complications (e.g. respiratory, cardiovascular, renal, and hematologic) of COVID-19 infection throughout the course of infection (i.e. 0-30 days, 31-60 days, and 61-90 days). Pleural effusion was the most frequent complication of early COVID-19 infection (89/1803 patients, 4.9%) followed by cardiac arrhythmia (45/1803 patients, 2.5%). Notably, hypertension was the most significant risk factor associated with 10 different complications including acute respiratory distress syndrome, cardiac arrhythmia, and anemia. The onset of new complications after 30 days is rare and most commonly involves pleural effusion (31-60 days: 11 patients, 61-90 days: 9 patients). Lastly, comparing the rates of complications with a propensity-matched COVID-negative hospitalized population confirmed the importance of hypertension as a risk factor for early-onset complications. Overall, the associations between pre-COVID conditions and COVID-associated complications presented here may form the basis for the development of risk assessment scores to guide clinical care pathways.

2.
Sci Rep ; 11(1): 8363, 2021 04 16.
Article in English | MEDLINE | ID: covidwho-1189289

ABSTRACT

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.


Subject(s)
COVID-19/pathology , Environmental Pollutants/analysis , Air Pollutants/analysis , COVID-19/epidemiology , COVID-19/virology , Humans , Models, Theoretical , Nitric Oxide/analysis , Ozone/analysis , Pandemics , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Sulfur Dioxide/analysis
3.
EClinicalMedicine ; 34: 100793, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1144586

ABSTRACT

BACKGROUND: Consecutive negative SARS-CoV-2 PCR test results are being considered to estimate viral clearance in COVID-19 patients. However, there are anecdotal reports of hospitalization from protracted COVID-19 complications despite such confirmed viral clearance, presenting a clinical conundrum. METHODS: We conducted a retrospective analysis of 222 hospitalized COVID-19 patients to compare those that were readmitted post-viral clearance (hospitalized post-clearance cohort, n = 49) with those that were not re-admitted post-viral clearance (non-hospitalized post-clearance cohort, n = 173) between February and October 2020. In order to differentiate these two cohorts, we used neural network models for the 'augmented curation' of comorbidities and complications with positive sentiment in the Electronic Hosptial Records physician notes. FINDINGS: In the year preceding COVID-19 onset, anemia (n = 13 [26.5%], p-value: 0.007), cardiac arrhythmias (n = 14 [28.6%], p-value: 0.015), and acute kidney injury (n = 7 [14.3%], p-value: 0.030) were significantly enriched in the physician notes of the hospitalized post-clearance cohort. INTERPRETATION: Overall, this retrospective study highlights specific pre-existing conditions that are associated with higher hospitalization rates in COVID-19 patients despite viral clearance and motivates follow-up prospective research into the associated risk factors. FUNDING: This work was supported by Nference, inc.

4.
Elife ; 92020 07 07.
Article in English | MEDLINE | ID: covidwho-635065

ABSTRACT

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n = 2,317) versus COVID-19-negative (COVIDneg; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.


Subject(s)
Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Adult , Betacoronavirus/isolation & purification , COVID-19 , COVID-19 Testing , Chills/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/physiopathology , Coronavirus Infections/virology , Diarrhea/virology , Dysgeusia/virology , Female , Fever/virology , Humans , Male , Middle Aged , Myalgia/virology , Olfaction Disorders/virology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/physiopathology , Pneumonia, Viral/virology , Polymerase Chain Reaction , SARS-CoV-2
5.
Elife ; 92020 05 28.
Article in English | MEDLINE | ID: covidwho-401507

ABSTRACT

The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.


Subject(s)
Coronavirus Infections/virology , Libraries, Medical , Pneumonia, Viral/virology , Receptors, Virus/metabolism , Animals , Betacoronavirus/genetics , Betacoronavirus/metabolism , COVID-19 , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Gene Expression Profiling , Humans , Knowledge Discovery , Mice , Pandemics , Pneumonia, Viral/metabolism , Pneumonia, Viral/pathology , Receptors, Coronavirus , Receptors, Virus/chemistry , Receptors, Virus/genetics , SARS-CoV-2
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