Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis.

BACKGROUND: MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification. OBJECTIVES: We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations. METHODS: We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, 'manual 1' guided by consensus reading of CIS-specific lesions and 'manual 2' by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods. RESULTS: WM lesion volumes were comparable at 1.5T between 'manual 2' and FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R2 = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R2 = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations. CONCLUSIONS: FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.

Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Bullseye's representation of cerebral white matter hyperintensities.

BACKGROUND AND PURPOSE: Visual rating scales have limited capacities to depict the regional distribution of cerebral white matter hyperintensities (WMH). We present a regional-zonal volumetric analysis alongside a visualization tool to compare and deconstruct visual rating scales. MATERIALS AND METHODS: 3D T1-weighted, T2-weighted spin-echo and FLAIR images were acquired on a 3T system, from 82 elderly participants in a population-based study. Images were automatically segmented for WMH. Lobar boundaries and distance to ventricular surface were used to define white matter regions. Regional-zonal WMH loads were displayed using bullseye plots. Four raters assessed all images applying three scales. Correlations between visual scales and regional WMH as well as inter and intra-rater variability were assessed. A multinomial ordinal regression model was used to predict scores based on regional volumes and global WMH burdens. RESULTS: On average, the bullseye plot depicted a right-left symmetry in the distribution and concentration of damage in the periventricular zone, especially in frontal regions. WMH loads correlated well with the average visual rating scores (e.g. Kendall's tau [Volume, Scheltens]=0.59 CI=[0.53 0.62]). Local correlations allowed comparison of loading patterns between scales and between raters. Regional measurements had more predictive power than global WMH burden (e.g. frontal caps prediction with local features: ICC=0.67 CI=[0.53 0.77], global volume=0.50 CI=[0.32 0.65], intra-rater=0.44 CI=[0.23 0.60]). CONCLUSION: Regional-zonal representation of WMH burden highlights similarities and differences between visual rating scales and raters. The bullseye infographic tool provides a simple visual representation of regional lesion load that can be used for rater calibration and training.