Introduction Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD. Methods We used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2-regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set. Results We identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total-tau (t-tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration ( P < .001). On the test set, the predictive model achieved an area under the receiver operating characteristic curve of 0.85. Discussion Progression to AD, in terms of biomarker trajectories, can be predicted based on participant-specific factors. Such disease staging tools could help in targeting high-risk patients for therapeutic intervention trials. As longitudinal data with richer features are collected, such models will help increase our understanding of the factors that drive the different trajectories of AD.
Common presentation of primary gallbladder carcinoma is with abdominal pain, or it may be detected incidentally in postcholecystectomy specimen for cholelithiasis. Primary gallbladder carcinoma spreads to adjacent hepatic parenchyma and locoregional nodes. Lung is a common extra-abdominal site, with other sites being relatively rare. We report a case of primary gallbladder carcinoma, which presented with elbow swelling in the absence of locoregional nodal spread detected on whole-body 18F-FDG PET/contrast-enhanced CT at initial evaluation.
Shaman, 2020) and 70% of CCBs in each county were assumed to be occupied by non-COVID-19 patients. For each county, three potential constraints on increasing capacity were estimated: the number of nurses, the number of physicians (including APPs), and the number of CCBs. One or more constraints could be active at any time. Results: Prior to optimization, 91% of counties were able to meet the demand for projected case counts. In contrast, 8.4% were limited by nursing resources, 0.09% by physicians, and 0.8% by the number of CCBs. After optimization, 16.9% of counties sent nurses to a different county(s) (median 6 nurses sent, IQR 13.75) compared with 5.5% counties receiving them (median 23, IQR 43.5). Fewer physicians were relocated (0.09% sent, median 1, IQR 1; 0.06% received, median 2.5, IQR 1.5) (Figure). Using baseline staffing ratios and availability, these redistributions led to a reduction in total unmet demand from 24,155 to 19,976. In order to fully meet demand across the US under these conditions, an additional 1,225 physicians, 41,939 nurses and 13,905 CCBs would have been needed. Conclusion: This work shows that with the redeployment of resources even within state boundaries may provide relief to areas of need without causing strain in other locations. While validation with actual redeployment during the pandemic can improve estimates, these models can provide decision support to stakeholders by suggesting optimal reallocation or the ability of existing resources to support additional capacity.
Objective: COVID-19 is a new infectious disease with an unclear incidence and an unknown rate of progression to severe disease. The Gibraltar COVID-19 Cohort utilises two distinct cohorts -a clinical cohort and a random population based cohort -, to provide an accurate assessment of case severity rate. Design: Retrospective analysis of a SARS-CoV2 RT-PCR point prevalence study and a RT-PCR confirmed positive clinical case cohort to calculate case severity rates. Settings and Participants: Over a three day period nasopharyngeal swabs were sampled from a randomly selected 1.2% of the population of Gibraltar and then analysed via RT-PCR to determine the background incidence of COVID-19 infection. The results were then analysed and compared to the clinical case cohort. The rate of progression to severe COVID-19 disease in those with COVID-19 infection was then calculated. Results: Gibraltar tested 1500 suspected COVID-19 cases over a 35 day period. Of these, 125 cases were confirmed positive for COVID-19 via RT-PCR analysis. The rate of progression to severe disease in this clinical cohort was 7.2% (95% CI 3.3 -13.2%). 25 days into the initial surge of cases, 400 members of the public were randomly selected from the electoral register and over a subsequent three days were tested for COVID-19 by nasopharyngeal swab RT-PCR analysis. 2.5% of this total population sample were confirmed as positive for the infection (95% CI, 1.2 to 4.6%). Combining both clinical case detection with random case detection, at first using an ultraconservative model of infectivity, readjusted rate of progression to severe COVID-19 disease to 0.93% (95% CI, 0.43-1.8%). A secondary analysis adjusting for projected number of cases over 35 days and correcting for the sensitivity of the RT-PCR analysis via nasopharyngeal swab led to a readjusted rate of progression to severe COVID-19 disease of 0.18%. Conclusion: From the Gibraltar COVID-19 Cohort, at a population level, the rate of progression to severe COVID-19 disease in those with COVID-19 infection is estimated at between 0.1% and 1%.
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