Background: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most. Methods: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country. Results: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61–0.86), 0.58 (0.31–0.84), and 0.54 (0.44–0.64), respectively. An online time–event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents. Conclusion: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

Background: To overcome the drawback of individual item-by-item box plots of disclosure for patient views on healthcare service quality, we propose to inspect interrelationships among items that measure a common entity. A visual diagram on the Internet is developed to provide thorough information for hospitals.

Aims/IntroductionHyperglycemic crises without a history of diabetes have not been well studied. We compared the clinical characteristics of patients with and without a history of diabetes, and evaluated the glycated hemoglobin levels.Materials and MethodsConsecutive adult patients (aged >18 years) visiting the emergency department (ED) between January 2004 and December 2010 were enrolled if they met the criteria for a hyperglycemic crisis. Patients were separated into those without and those with a history of diabetes. The 30-day mortality was the primary end-point.ResultsWe enrolled 295 patients who made 330 visits to the ED. Patients without a history of diabetes made up 24.5% (81/330) of the hyperglycemic crises. Patients without a history of diabetes were more prone than patients with a history of diabetes to be younger and male, and to have better consciousness and renal function, more significant diabetic signs and symptoms (e.g., thirst, polydipsia, polyuria and bodyweight loss), higher blood sugar, and less opportunity of infection and mortality. Most of the patients (93.8%, 76/81) had glycated hemoglobin of ≥6.5%.ConclusionsThe present study delineates the clinical characteristics of patients with hyperglycemic crises, but without a history of diabetes. Most patients had glycated hemoglobin ≥6.5%, which raises the argument of using this biomarker for routine screening of diabetes.

The loss of vitamin D-binding protein, haptoglobin and alpha-2-microglobulin may be due to a change in the permeability of the peritoneal membrane to middle-sized proteins or leakage from peritoneal inflammation. Lower levels of complement C4-A in dialysate may shed light on the beginning of peritoneal membrane scleroses.