Impact of newly diagnosed diabetes on coronavirus disease 2019 severity and hyperglycemia is a research paper published in Journal of Diabetes Investigation (2022). On theSindex it has a DataRank of 0.670. It has been cited 12 times, with 8 citing works in its 1-hop citation network.
ABSTRACTAims/IntroductionDiabetes is associated with poor clinical outcomes of coronavirus disease 2019 (COVID‐19). However, the impact of newly diagnosed diabetes on prognosis has not been clarified. The objective of this study was to show the features and outcome of COVID‐19 patients with newly diagnosed diabetes in Japan.Materials and MethodsWe retrospectively analyzed 62 patients with diabetes hospitalized for COVID‐19 between 1 April and 18 August 2021 at the National Center for Global Health and Medicine in Tokyo, Japan. We evaluated the worst severity of COVID‐19 and plasma blood glucose levels in patients with newly diagnosed diabetes or pre‐existing diabetes.ResultsThis study included 62 confirmed COVID‐19 patients with diabetes, including 19 (30.6%) patients with newly diagnosed diabetes and 43 (69.4%) patients with pre‐existing diabetes. Patients with newly diagnosed diabetes significantly progressed to a critical condition more frequently during hospitalization than patients with pre‐existing diabetes (52.6% vs 20.9%, P = 0.018). In addition, patients with newly diagnosed diabetes had significantly higher average plasma blood glucose levels for the first 3 days after admission than those with pre‐existing diabetes.ConclusionsOur study suggests that the proportion of COVID‐19 patients who are newly diagnosed with diabetes is high, and they have an increased risk of developing severe disease than those with pre‐existing diabetes. It might be advisable that at the point of COVID‐19 diagnosis, blood glucose and glycated hemoglobin levels be assessed in all patients.
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Base Score Contribution
0.385
From this paper's citation signal
Citation Network Contribution
0.285
From 7 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 57% comes from its base citations and 43% from the citation network (7 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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