scholarly journals Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people

PLoS Medicine ◽  
2021 ◽  
Vol 18 (9) ◽  
pp. e1003777
Author(s):  
Joshua Elliott ◽  
Matthew Whitaker ◽  
Barbara Bodinier ◽  
Oliver Eales ◽  
Steven Riley ◽  
...  
Keyword(s):  
Univariate Analysis ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Contact Tracing ◽  
N Gene ◽  
Wild Type ◽  
Logistic Regression Models ◽  
Sense Of Smell ◽  
Participation Bias ◽  
Persistent Cough

Background Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. Methods and findings We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%–27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2–7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2–7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. Conclusions Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

scholarly journals Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrik Bachtiger ◽  
Alexander Adamson ◽  
Jennifer K. Quint ◽  
Nicholas S. Peters
Keyword(s):  
Public Health ◽  
Information Exchange ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Health Benefit ◽  
Healthcare Utilisation ◽  
Contact Tracing ◽  
Willingness To Participate ◽  
Logistic Regression Models ◽  
Government Rules

Abstract Contact tracing and lockdown are health policies being used worldwide to combat the coronavirus (COVID-19). The UK National Health Service (NHS) Track and Trace Service has plans for a nationwide app that notifies the need for self-isolation to those in contact with a person testing positive for COVID-19. To be successful, such an app will require high uptake, the determinants and willingness for which are unclear but essential to understand for effective public health benefit. The objective of this study was to measure the determinants of willingness to participate in an NHS app-based contact-tracing programme using a questionnaire within the Care Information Exchange (CIE)—the largest patient-facing electronic health record in the NHS. Among 47,708 registered NHS users of the CIE, 27% completed a questionnaire asking about willingness to participate in app-based contact tracing, understanding of government advice, mental and physical wellbeing and their healthcare utilisation—related or not to COVID-19. Descriptive statistics are reported alongside univariate and multivariable logistic regression models, with positive or negative responses to a question on app-based contact tracing as the dependent variable. 26.1% of all CIE participants were included in the analysis (N = 12,434, 43.0% male, mean age 55.2). 60.3% of respondents were willing to participate in app-based contact tracing. Out of those who responded ‘no’, 67.2% stated that this was due to privacy concerns. In univariate analysis, worsening mood, fear and anxiety in relation to changes in government rules around lockdown were associated with lower willingness to participate. Multivariable analysis showed that difficulty understanding government rules was associated with a decreased inclination to download the app, with those scoring 1–2 and 3–4 in their understanding of the new government rules being 45% and 27% less inclined to download the contact-tracing app, respectively; when compared to those who rated their understanding as 5–6/10 (OR for 1–2/10 = 0.57 [CI 0.48–0.67]; OR for 3–4/10 = 0.744 [CI 0.64–0.87]), whereas scores of 7–8 and 9–10 showed a 43% and 31% respective increase. Those reporting an unconfirmed belief of having previously had and recovered from COVID-19 were 27% less likely to be willing to download the app; belief of previous recovery from COVID-19 infection OR 0.727 [0.585–0.908]). In this large UK-wide questionnaire of wellbeing in lockdown, a willingness for app-based contact tracing over an appropriate age range is 60%—close to the estimated 56% population uptake, and substantially less than the smartphone-user uptake considered necessary for an app-based contact tracing to be an effective intervention to help suppress an epidemic. Difficulty comprehending government advice and uncertainty of diagnosis, based on a public health policy of not testing to confirm self-reported COVID-19 infection during lockdown, therefore reduce willingness to adopt a government contact-tracing app to a level below the threshold for effectiveness as a tool to suppress an epidemic.

scholarly journals Identification of prognostic alternative splicing events in sarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hongshuai Li ◽  
Jie Yang ◽  
Guohui Yang ◽  
Jia Ren ◽  
Yu Meng ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Functional Roles ◽  
Cancer Pathogenesis ◽  
Rare Malignancy

AbstractSarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

scholarly journals Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

2021 ◽  
pp. injuryprev-2020-044092
Author(s):  
Éric Tellier ◽  
Bruno Simonnet ◽  
Cédric Gil-Jardiné ◽  
Marion Lerouge-Bailhache ◽  
Bruno Castelle ◽  
...  
Keyword(s):  
Regression Models ◽  
Area Under The Curve ◽  
Temperature Wave ◽  
Medical Emergency ◽  
Emergency Center ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Step Model ◽  
Daily Risk ◽  
Development And Validation

ObjectiveTo predict the coast-wide risk of drowning along the surf beaches of Gironde, southwestern France.MethodsData on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather and metocean conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011 to 2013 and used to predict 2015–2017 events employing weather and ocean forecasts.ResultsAir temperature, wave parameters, seasonality and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 (95% CI 0.79 to 0.86). The AUC of the 3-hour step model was 0.85 (95% CI 0.81 to 0.88).ConclusionsDrowning events along the Gironde surf coast can be anticipated up to 3 days in advance. Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low.

scholarly journals The use of plasma donor-derived, cell-free DNA to monitor acute rejection after kidney transplantation

2019 ◽  
Vol 35 (4) ◽  
pp. 714-721 ◽  
Author(s):  
Els M Gielis ◽  
Kristien J Ledeganck ◽  
Amélie Dendooven ◽  
Pieter Meysman ◽  
Charlie Beirnaert ◽  
...  
Keyword(s):  
Acute Rejection ◽  
Serum Creatinine ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Nucleotide Polymorphisms ◽  
Kidney Transplant Recipients ◽  
Cell Free Dna ◽  
Free Dna ◽  
Set Up

Abstract Background After transplantation, cell-free deoxyribonucleic acid (DNA) derived from the donor organ (ddcfDNA) can be detected in the recipient’s circulation. We aimed to investigate the role of plasma ddcfDNA as biomarker for acute kidney rejection. Methods From 107 kidney transplant recipients, plasma samples were collected longitudinally after transplantation (Day 1 to 3 months) within a multicentre set-up. Cell-free DNA from the donor was quantified in plasma as a fraction of the total cell-free DNA by next generation sequencing using a targeted, multiplex polymerase chain reaction-based method for the analysis of single nucleotide polymorphisms. Results Increases of the ddcfDNA% above a threshold value of 0.88% were significantly associated with the occurrence of episodes of acute rejection (P = 0.017), acute tubular necrosis (P = 0.011) and acute pyelonephritis (P = 0.032). A receiver operating characteristic curve analysis revealed an equal area under the curve of the ddcfDNA% and serum creatinine of 0.64 for the diagnosis of acute rejection. Conclusions Although increases in plasma ddcfDNA% are associated with graft injury, plasma ddcfDNA does not outperform the diagnostic capacity of the serum creatinine in the diagnosis of acute rejection.

scholarly journals Molecular detection and phylogenetic relationship of wild-type strains of canine distemper virus in symptomatic dogs from Uberlândia, Minas Gerais

2015 ◽  
Vol 67 (6) ◽  
pp. 1510-1518
Author(s):  
S.A. Headley ◽  
T.R. Santos ◽  
L. Bodnar ◽  
J.P.E. Saut ◽  
A.P. Silva ◽  
...  
Keyword(s):  
Canine Distemper Virus ◽  
Phylogenetic Analyses ◽  
Clinical Manifestations ◽  
N Gene ◽  
Clinical Samples ◽  
Canine Distemper ◽  
Wild Type ◽  
Age Related ◽  
Relationship Of ◽  
Type Strains

This study investigated the occurrence of canine distemper virus (CDV) by evaluating the presence of viral RNA within urine samples of dogs from Uberlândia, MG, with clinical manifestations suggestive of infection by CDV by targeting the CDV N gene. Of the clinical samples collected ( n =33), CDV viruria was detected in 45.5%. Five dogs died spontaneously; all had characteristic CDV-associated histopathological alterations and demonstrated CDV viruria. Statistical analyses revealed that the age, gender, breed, or the organ system of the dog affected had no influence on the occurrence of canine distemper. Myoclonus and motor incoordination were the most significant neurological manifestations observed. A direct association was observed between keratoconjunctivitis and dogs with CDV viruria. These findings suggest that CDV viruria in symptomatic dogs might not be age related, and that symptomatic dogs can demonstrate clinical manifestations attributed to CDV without viruria identified by RT-PCR. Additionally, the results of the sequence identities analysed have suggested that all Brazilian wild-type strains of CDV currently identified are closely related and probably originated from the same lineage of CDV. Nevertheless, phylogenetic analyses suggest that there are different clusters of wild-type strains of CDV circulating within urban canine populations in Brazil.

scholarly journals Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

2017 ◽  
Vol 79 (02) ◽  
pp. 123-130 ◽  
Author(s):  
Whitney Muhlestein ◽  
Dallin Akagi ◽  
Justiss Kallos ◽  
Peter Morone ◽  
Kyle Weaver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Patient Outcomes ◽  
Predictive Power ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Motor Deficit ◽  
Ensemble Model ◽  
Algorithm Selection ◽  
Discharge Disposition

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

scholarly journals Prognostic model and immune-infiltrating cell landscape based on differentially expressed autophagy-related genes in TP53-mutated multiple myeloma

2021 ◽  
Author(s):  
Yan-Hua Zheng ◽  
Hong-Yuan Shen ◽  
Xiang Chen ◽  
Juan Feng ◽  
Guang-Xun Gao
Keyword(s):  
Multiple Myeloma ◽  
Prognostic Model ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Differentially Expressed ◽  
Term Survival ◽  
Tp53 Mutations ◽  
Infiltrating Cells

IntroductionAutophagy functions as a prosurvival mechanism in multiple myeloma (MM).The objective of this research was to establish an autophagy-related gene (ARG) signature for predicting the survival outcomes of MM patients with TP53 mutations.Material and methodsInformation about MM patients with TP53 mutations was downloaded from Gene Expression Omnibus (GEO) database. Cox proportional hazard regression was employed to determine the independent prognostic ARG and construct a risk signature. Time-dependent receiver-operating characteristic (t ROC) curve was used to explore the predictive accuracy of the prognostic model. A nomogram was constructed to give a more precise prediction of the probability of 5-year, 8-year and 10-year overall survival (OS). In addition, we utilized the CIBERSORT algorithm to explore the distribution difference of 22 immune-infiltrating cells.ResultsThree differentially expressed ARGs (CASP8, MAPK8, RB1CC1) were finally incorporated to construct the risk model. Area under the curve (AUC) of corresponding tROC curve for 5-year,8-year and 10-year OS were 0.735, 0.686 and 0.662, respectively. MM patients were categorized into high and low-risk group in accordance with the median threshold value (-1.724549). ARG-based risk score model was an independent prognostic element correlated with OS, giving an hazard ratio (HR) of 3.29 (95%CI 2.35-4.60, P<0.001). 13 immune infiltrating cells were found to have distribution differences between the two groups.ConclusionsWe established a three-ARGs risk signature which manifested an independent prognostic factor. The nomogram was testified to perform well in forecasting the long-term survival of TP53-mutated MM patients.

scholarly journals The Prognostic Value of NANO Scale Assessment in IDH-Wild-Type Glioblastoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Johannes Kasper ◽  
Tim Wende ◽  
Michael Karl Fehrenbach ◽  
Florian Wilhelmy ◽  
Katja Jähne ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Univariate Analysis ◽  
Extent Of Resection ◽  
Survival Prediction ◽  
Wild Type ◽  
Prognostic Information ◽  
Nano Scale ◽  
Scale Assessment ◽  
General Physical

BackgroundIDH-wild-type glioblastoma (GBM) is the most frequent brain-derived malignancy. Despite intense research efforts, it is still associated with a very poor prognosis. Several parameters were identified as prognostic, including general physical performance. In neuro-oncology (NO), special emphasis is put on focal deficits and cognitive (dys-)function. The Neurologic Assessment in Neuro-Oncology (NANO) scale was proposed in order to standardize the assessment of neurological performance in NO. This study evaluated whether NANO scale assessment provides prognostic information in a standardized collective of GBM patients.MethodsThe records of all GBM patients treated between 2014 and 2019 at our facility were retrospectively screened. Inclusion criteria were age over 18 years, at least 3 months postoperative follow-up, and preoperative and postoperative cranial magnetic resonance imaging. The NANO scale was assessed pre- and postoperatively as well as at 3 months follow-up. Univariate and multivariate survival analyses were carried to investigate the prognostic value.ResultsOne hundred and thirty-one patients were included. In univariate analysis, poor postoperative neurological performance (HR 1.13, p = 0.004), poor neurological performance at 3 months postsurgery (HR 1.37, p &lt; 0.001), and neurological deterioration during follow-up (HR 1.38, p &lt; 0.001), all assessed via the NANO scale, were associated with shorter survival. In multivariate analysis including other prognostic factors such as the extent of resection, adjuvant treatment regimen, or age, NANO scale assessment at 3 months postoperative follow-up was independently associated with survival prediction (HR 1.36, p &lt; 0.001). The optimal NANO scale cutoff for patient stratification was 3.5 points.ConclusionNeurological performance assessment employing the NANO scale might provide prognostic information in patients suffering from GBM.

scholarly journals Galectin-3 Reflects Mitral Annular Plane Systolic Excursion Being Assessed by Cardiovascular Magnetic Resonance Imaging

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Seung-Hyun Kim ◽  
Michael Behnes ◽  
Michele Natale ◽  
Julia Hoffmann ◽  
Nadine Reckord ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Clinical Care ◽  
Area Under The Curve ◽  
Right Ventricular Dysfunction ◽  
Serum Levels ◽  
Resonance Imaging ◽  
Logistic Regression Models ◽  
Galectin 3 ◽  
I 11

Background. This study investigates whether serum levels of galectin-3 may reflect impaired mitral annular plane systolic excursion (MAPSE) in patients undergoing cardiac magnetic resonance imaging (cMRI).Methods. Patients undergoing cMRI during routine clinical care were included prospectively within an all-comers design. Blood samples for biomarker measurements were collected within 24 hours following cMRI. Statistical analyses were performed in all patients and in three subgroups according to MAPSE (MAPSE I: ≥11 mm, MAPSE II: ≥8 mm–<11 mm, and MAPSE III: <8 mm). Patients with right ventricular dysfunction (<50%) were excluded.Results. 84 patients were included in the study. Median LVEF was 59% (IQR 51–64%). Galectin-3 correlated significantly with NT-proBNP (r=0.42,p=0.0001). Galectin-3 increased significantly according to the different stages of impaired MAPSE (p=0.006) and was able to discriminate both patients with impaired MAPSE <11 mm (area under the curve (AUC) = 0.645,p=0.024) and <8 mm (AUC = 0.733,p=0.003). Combining galectin-3 with NT-proBNP improved discrimination of MAPSE <8 mm (AUC 0.803,p=0.0001). In multivariable logistic regression models galectin-3 was still associated with impaired MAPSE (MAPSE < 11 mm: odds ratio (OR) = 3.53,p=0.018; MAPSE < 8 mm: OR = 3.18,p=0.06).Conclusions. Galectin-3 reflects MAPSE being assessed by cardiac MRI.

Abstract TP124: 90-Day Disability Outcomes and Stroke Etiology: An ALIAS 2 Secondary Analysis

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Eric D Goldstein ◽  
Vivek K Reddy ◽  
Stephanie Lyden ◽  
Jennifer J Majersik ◽  
Adam de Havenon
Keyword(s):  
Univariate Analysis ◽  
Secondary Analysis ◽  
Large Artery ◽  
Logistic Regression Models ◽  
Stroke Etiology ◽  
Disability Outcome ◽  
Toast Classification ◽  
Iv Tpa ◽  
Toast Criteria

Background: Acute ischemic stroke (AIS) treatment remains a leading cause of global morbidity and mortality despite advancements in therapeutic options. Cardioembolic AIS had previously been associated with the greatest long-term disability and mortality. Our aim is to provide an updated perspective of 90-day disability outcomes with regard to stroke etiology. Methods: This is a secondary analysis of the ALIAS 2 trial. The primary outcome was the 90-day mRS. Stroke etiology was defined based on TOAST criteria. Spearman’s Rho is used to determine correlation between etiology and mRS. Univariate and multivariate logistic regression models are fit to a binary stratification of our outcome (mRS 0-1 vs 2-6). Results: A total of 776 patients were enrolled between 2009 and 2012 with a mean (SD) age of 64.7 (12.7) years. The median (IQR) NIHSS was 11 (8, 17) with 55.3% being male, 76.7% white, and 89.7% having received IV TPA. Large artery atherosclerosis (LAA) (201/776, 25.9%), cardioembolism (271/776, 34.9%) and cryptogenic (196/776, 25.3%) were the most common AIS etiologies. The 90-day mRS had significant differences by TOAST category (rho = 0.013, p<0.001). Individuals with LAA had the highest mean 90-day mRS (Figure 1). LAA was associated with lower odds of good outcome in both univariate analysis (OR 0.68, 95% CI 0.48-0.96) and in a multivariate model (OR 0.66, 95% CI 0.45-0.97) adjusted for age, NIHSS, diabetes, hypertension, hyperlipidemia, sex, white race and administration of IV TPA. Conclusion: Our secondary analysis revealed that AIS with a NIHSS greater than 8 of LAA origin purported a worse 90-day disability outcome. This data may serve to remind clinicians that AIS from LAA may yield comparable or greater disability than cardioembolic AIS. Figure 1: Mean 90-day disability outcome based on TOAST classification. LAA purported worse mean disability outcomes compared to other grouped etiologies.

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