Translating daily COVID-19 screening into a simple glucose test: a proof of concept study.
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ABSTRACT:
SUBMITTER: Liu R
PROVIDER: S-EPMC8261732 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91-0.99), a specificity of 0.39 (95% CI 0.34-0.44) and an AUC of 0.87 (95% CI 0.83-0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.
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Project description:It is estimated that 20%-67% of those with COVID-19 develop olfactory disorders, depending on the SARS-CoV-2 variant. However, there is an absence of quick, population-wide olfactory tests to screen for olfactory disorders. The purpose of this study was to provide a proof-of-concept that SCENTinel 1.1, a rapid, inexpensive, population-wide olfactory test, can discriminate between anosmia (total smell loss), hyposmia (reduced sense of smell), parosmia (distorted odor perception), and phantosmia (odor sensation without a source). Participants were mailed a SCENTinel 1.1 test, which measures odor detection, intensity, identification, and pleasantness, using one of 4 possible odors. Those who completed the test (N = 287) were divided into groups based on their self-reported olfactory function: quantitative olfactory disorder only (anosmia or hyposmia, N = 135), qualitative olfactory disorder only (parosmia and/or phantosmia; N = 86), and normosmia (normal sense of smell; N = 66). SCENTinel 1.1 accurately discriminates quantitative olfactory disorders, qualitative olfactory disorders, and normosmia groups. When olfactory disorders were assessed individually, SCENTinel 1.1 discriminates between hyposmia, parosmia, and anosmia. Participants with parosmia rated common odors less pleasant than those without parosmia. We provide proof-of-concept that SCENTinel 1.1, a rapid smell test, can discriminate quantitative and qualitative olfactory disorders, and is the only direct test to rapidly discriminate parosmia.
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Project description:BackgroundReliable detection of slowed vertical saccades may help discriminate progressive supranuclear palsy (PSP) from the subset of Parkinson's disease patients who lack tremor (akinetic-rigid or PD-postural instability and gait disorder PIGD subtype), and from age-related oculomotor changes. We investigated the feasibility of a camera-less computerized behavioral saccade latency paradigm previously validated in PD to discriminate probable PSP-Richardson syndrome (PSP-RS) from PD-PIGD and age-matched controls.MethodsIn this proof-of-concept case-control study, reflexive saccade latencies were measured in 5 subjects with probable PSP-RS, 5 subjects with PD-PIGD subtype, and 5 age-matched controls using the behavioral paradigm. The battery was repeated approximately one month later. All subjects were examined off levodopa by a movement disorders neurologist and by an ophthalmologist, who also performed a dilated eye exam.ResultsVertical prosaccade latencies were longer in the PSP group (median = 903 ms) relative to PD (median = 268 ms) and control groups (median = 235 ms), with no overlap between groups (100% accuracy). PSP subjects also had larger vertical-horizontal discrepancies than comparison groups. Test-retest reliability for the behavioral saccade measures was good (interclass correlation coefficient = 0.948; 95% confidence interval [0.856, 0.982]), and the measures strongly correlated with clinical ratings.ConclusionsComputerized behavioral measurement of reflexive saccade latency is feasible in PSP, and potentially discriminates probable PSP-RS from the PD-PIGD subtype. Findings from this proof-of-concept study support utility of the approach for obtaining objective saccade metrics in clinical evaluations and for tracking change in future, larger trials of moderately advanced PSP. Future studies should also examine the behavioral paradigm in earlier presentations of PSP and other subtypes of PSP.
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.
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Project description:ObjectiveThe use of remote real-time continuous glucose monitoring (CGM) in the hospital has rapidly emerged to preserve personal protective equipment and reduce potential exposures during coronavirus disease 2019 (COVID-19).Research design and methodsWe linked a hybrid CGM and point-of-care (POC) glucose testing protocol to a computerized decision support system for continuous insulin infusion and integrated a validation system for sensor glucose values into the electronic health record. We report our proof-of-concept experience in a COVID-19 intensive care unit.ResultsAll nine patients required mechanical ventilation and corticosteroids. During the protocol, 75.7% of sensor values were within 20% of the reference POC glucose with an associated average reduction in POC of 63%. Mean time in range (70-180 mg/dL) was 71.4 ± 13.9%. Sensor accuracy was impacted by mechanical interferences in four patients.ConclusionsA hybrid protocol integrating real-time CGM and POC is helpful for managing critically ill patients with COVID-19 requiring insulin infusion.
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Project description:The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model's performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
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Project description:Abstract Background Post-viral myocarditis has been associated with sudden cardiac death in athletes. Since the beginning of the COVID-19 pandemic, the concern of post-viral myocarditis impacting the professional athletic community has been present. Case summary An elite-level basketball player presented after a positive COVID-19 test with findings consistent with ventricular tachycardia related to myocardial fibrosis/scar from a COVID-19-related myocarditis. Although rare, COVID-19 myocarditis can occur. This case illustrates how the consensus guidelines for return-to-play correctly identified the player as high risk with appropriate downstream evaluation by cardiac magnetic resonance (CMR) imaging. The stepwise approach is illustrated in this case and highlights the utility and success of the algorithm when approaching athletes with COVID-19-related myocarditis risk and determining a return to exercise. Discussion Diligence is required to identify competitive athletes with features suggestive of myocarditis at the initial presentation and with the return to exercise. Cardiopulmonary symptoms in the setting of recent COVID-19 infection should prompt additional testing in a stepwise fashion and often benefit from CMR in addition to the triad testing with electrocardiography, echocardiography, and cardiac troponin measurement to further investigate clinical presentations of COVID-19-related myocarditis. Graphical Abstract Graphical abstract Stepwise approach to the evaluation of myocarditis in athletes.
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