Evidence map›Paper›PMID 36119784›Full record

ReviewGlobal health & medicine2022

The patient-centered diabetes management during the COVID-19 pandemic.

Noriko Kodani, Mitsuru Ohsugi

Open access · bronzeAbstract readReview
In one paragraph

Review in Global health & medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 50% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Trial
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Noriko KodaniDepartment of Diabetes, Endocrinology and Metabolism, National Center for Global Health and Medicine, Tokyo, Japan.
Mitsuru OhsugiDepartment of Diabetes, Endocrinology and Metabolism, National Center for Global Health and Medicine, Tokyo, Japan.
National Center for Global Health and Medicine · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since December 2019, in the fight against the coronavirus disease 2019 (COVID-19) pandemic, we observed that glycemic control in people with diabetes is easily affected by lifestyle changes. To maintain a good health condition, a patient-centered approach with mental support and close monitoring is required. For these, telemedicine and online continuous glucose monitoring (CGM), are effective systems. Therefore, based on our experience during the two-year period, we reviewed the literature for appropriate actions required for the management of diabetes to prevent COVID-19 infection and avoid unfavorable outcomes in COVID-19 cases. Once infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is a high risk of a poor prognosis in patients with diabetes. Glucocorticoid therapy in severe COVID-19 cases leads to further hyperglycemia. Since good glycemic control has been shown to improve outcomes, strict glycemic control using CGM is recommended. Using CGM data, insulin can be adequately titrated without causing hypoglycemia, and remote data monitoring can reduce the risk of infection for health care professionals, by reducing the frequency of patient contact. Among patients with COVID-19, some are found to have newly-diagnosed diabetes at admission. Those newly diagnosed patients present with a higher risk of poor prognosis compared to those with pre-existing diabetes. Therefore, glycemic status should be evaluated in all patients with COVID-19 admitted to hospitals.

Indexed as

continuous glucose monitoring (CGM)glycemic controlinsulinlifestyle changeprognosis

Identifiers

PMID36119784
PMCPMC9420329
OpenAlexW4285150620

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.