FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3110677219> ?p ?o ?g. }

• W3110677219 endingPage "e87" @default.
• W3110677219 startingPage "e78" @default.
• W3110677219 abstract "The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (laboratory tests, blood gas measurements, and vital signs). These data are typically available within the first hour of presentation to hospitals in high-income and middle-income countries, within the existing laboratory infrastructure.We trained linear and non-linear machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic controls, using electronic health record data for patients presenting to the emergency department and admitted across a group of four teaching hospitals in Oxfordshire, UK (Oxford University Hospitals). Data extracted included presentation blood tests, blood gas testing, vital signs, and results of PCR testing for respiratory viruses. Adult patients (>18 years) presenting to hospital before Dec 1, 2019 (before the first COVID-19 outbreak), were included in the COVID-19-negative cohort; those presenting to hospital between Dec 1, 2019, and April 19, 2020, with PCR-confirmed severe acute respiratory syndrome coronavirus 2 infection were included in the COVID-19-positive cohort. Patients who were subsequently admitted to hospital were included in their respective COVID-19-negative or COVID-19-positive admissions cohorts. Models were calibrated to sensitivities of 70%, 80%, and 90% during training, and performance was initially assessed on a held-out test set generated by an 80:20 split stratified by patients with COVID-19 and balanced equally with pre-pandemic controls. To simulate real-world performance at different stages of an epidemic, we generated test sets with varying prevalences of COVID-19 and assessed predictive values for our models. We prospectively validated our 80% sensitivity models for all patients presenting or admitted to the Oxford University Hospitals between April 20 and May 6, 2020, comparing model predictions with PCR test results.We assessed 155 689 adult patients presenting to hospital between Dec 1, 2017, and April 19, 2020. 114 957 patients were included in the COVID-negative cohort and 437 in the COVID-positive cohort, for a full study population of 115 394 patients, with 72 310 admitted to hospital. With a sensitive configuration of 80%, our emergency department (ED) model achieved 77·4% sensitivity and 95·7% specificity (area under the receiver operating characteristic curve [AUROC] 0·939) for COVID-19 among all patients attending hospital, and the admissions model achieved 77·4% sensitivity and 94·8% specificity (AUROC 0·940) for the subset of patients admitted to hospital. Both models achieved high negative predictive values (NPV; >98·5%) across a range of prevalences (≤5%). We prospectively validated our models for all patients presenting and admitted to Oxford University Hospitals in a 2-week test period. The ED model (3326 patients) achieved 92·3% accuracy (NPV 97·6%, AUROC 0·881), and the admissions model (1715 patients) achieved 92·5% accuracy (97·7%, 0·871) in comparison with PCR results. Sensitivity analyses to account for uncertainty in negative PCR results improved apparent accuracy (ED model 95·1%, admissions model 94·1%) and NPV (ED model 99·0%, admissions model 98·5%).Our models performed effectively as a screening test for COVID-19, excluding the illness with high-confidence by use of clinical data routinely available within 1 h of presentation to hospital. Our approach is rapidly scalable, fitting within the existing laboratory testing infrastructure and standard of care of hospitals in high-income and middle-income countries.Wellcome Trust, University of Oxford, Engineering and Physical Sciences Research Council, National Institute for Health Research Oxford Biomedical Research Centre." @default.
• W3110677219 created "2020-12-21" @default.
• W3110677219 creator A5008431949 @default.
• W3110677219 creator A5013914724 @default.
• W3110677219 creator A5017409051 @default.
• W3110677219 creator A5021412054 @default.
• W3110677219 creator A5023184985 @default.
• W3110677219 creator A5026480684 @default.
• W3110677219 creator A5040302008 @default.
• W3110677219 creator A5055850985 @default.
• W3110677219 creator A5064800233 @default.
• W3110677219 creator A5066859021 @default.
• W3110677219 date "2021-02-01" @default.
• W3110677219 modified "2023-10-06" @default.
• W3110677219 title "Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test" @default.
• W3110677219 cites W2078271269 @default.
• W3110677219 cites W2167728254 @default.
• W3110677219 cites W2244501064 @default.
• W3110677219 cites W2770037695 @default.
• W3110677219 cites W3008827533 @default.
• W3110677219 cites W3011802905 @default.
• W3110677219 cites W3012751338 @default.
• W3110677219 cites W3012755403 @default.
• W3110677219 cites W3012991084 @default.
• W3110677219 cites W3014281460 @default.
• W3110677219 cites W3014524604 @default.
• W3110677219 cites W3023519397 @default.
• W3110677219 cites W3025069580 @default.
• W3110677219 cites W3025948831 @default.
• W3110677219 cites W3026764413 @default.
• W3110677219 cites W3027764902 @default.
• W3110677219 cites W3033572074 @default.
• W3110677219 cites W3038925693 @default.
• W3110677219 cites W3047978089 @default.
• W3110677219 cites W3091866546 @default.
• W3110677219 cites W3165656738 @default.
• W3110677219 doi "https://doi.org/10.1016/s2589-7500(20)30274-0" @default.
• W3110677219 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7831998" @default.
• W3110677219 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33509388" @default.
• W3110677219 hasPublicationYear "2021" @default.
• W3110677219 type Work @default.
• W3110677219 sameAs 3110677219 @default.
• W3110677219 citedByCount "78" @default.
• W3110677219 countsByYear W31106772192021 @default.
• W3110677219 countsByYear W31106772192022 @default.
• W3110677219 countsByYear W31106772192023 @default.
• W3110677219 crossrefType "journal-article" @default.
• W3110677219 hasAuthorship W3110677219A5008431949 @default.
• W3110677219 hasAuthorship W3110677219A5013914724 @default.
• W3110677219 hasAuthorship W3110677219A5017409051 @default.
• W3110677219 hasAuthorship W3110677219A5021412054 @default.
• W3110677219 hasAuthorship W3110677219A5023184985 @default.
• W3110677219 hasAuthorship W3110677219A5026480684 @default.
• W3110677219 hasAuthorship W3110677219A5040302008 @default.
• W3110677219 hasAuthorship W3110677219A5055850985 @default.
• W3110677219 hasAuthorship W3110677219A5064800233 @default.
• W3110677219 hasAuthorship W3110677219A5066859021 @default.
• W3110677219 hasBestOaLocation W31106772191 @default.
• W3110677219 hasConcept C116675565 @default.
• W3110677219 hasConcept C118552586 @default.
• W3110677219 hasConcept C126322002 @default.
• W3110677219 hasConcept C141071460 @default.
• W3110677219 hasConcept C142724271 @default.
• W3110677219 hasConcept C187212893 @default.
• W3110677219 hasConcept C188816634 @default.
• W3110677219 hasConcept C194828623 @default.
• W3110677219 hasConcept C201903717 @default.
• W3110677219 hasConcept C2776890885 @default.
• W3110677219 hasConcept C2777120189 @default.
• W3110677219 hasConcept C2779134260 @default.
• W3110677219 hasConcept C2779599708 @default.
• W3110677219 hasConcept C2780724011 @default.
• W3110677219 hasConcept C3008058167 @default.
• W3110677219 hasConcept C524204448 @default.
• W3110677219 hasConcept C545542383 @default.
• W3110677219 hasConcept C71924100 @default.
• W3110677219 hasConcept C72563966 @default.
• W3110677219 hasConcept C89623803 @default.
• W3110677219 hasConceptScore W3110677219C116675565 @default.
• W3110677219 hasConceptScore W3110677219C118552586 @default.
• W3110677219 hasConceptScore W3110677219C126322002 @default.
• W3110677219 hasConceptScore W3110677219C141071460 @default.
• W3110677219 hasConceptScore W3110677219C142724271 @default.
• W3110677219 hasConceptScore W3110677219C187212893 @default.
• W3110677219 hasConceptScore W3110677219C188816634 @default.
• W3110677219 hasConceptScore W3110677219C194828623 @default.
• W3110677219 hasConceptScore W3110677219C201903717 @default.
• W3110677219 hasConceptScore W3110677219C2776890885 @default.
• W3110677219 hasConceptScore W3110677219C2777120189 @default.
• W3110677219 hasConceptScore W3110677219C2779134260 @default.
• W3110677219 hasConceptScore W3110677219C2779599708 @default.
• W3110677219 hasConceptScore W3110677219C2780724011 @default.
• W3110677219 hasConceptScore W3110677219C3008058167 @default.
• W3110677219 hasConceptScore W3110677219C524204448 @default.
• W3110677219 hasConceptScore W3110677219C545542383 @default.
• W3110677219 hasConceptScore W3110677219C71924100 @default.
• W3110677219 hasConceptScore W3110677219C72563966 @default.
• W3110677219 hasConceptScore W3110677219C89623803 @default.

• next