Evidence map›Paper›PMID 42232681›Full record

ArticleJCEM case reports2026

The use of automated insulin delivery system in people with type 1 diabetes and end stage kidney disease on hemodialysis.

Ramita Anakepeerasak, Jutipond Jitchana, Nitchakarn Laichuthai

Abstract readCase Reports
In one paragraph

Article in JCEM case reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Ramita AnakepeerasakDepartment of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.ORCID https://orcid.org/0009-0004-3912-0664
Jutipond JitchanaDivision of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.ORCID https://orcid.org/0009-0003-9120-3179
Nitchakarn LaichuthaiDivision of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.ORCID https://orcid.org/0000-0002-4801-9697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Managing patients with type 1 diabetes (T1D) with end stage kidney disease (ESKD) on hemodialysis presents unique challenges due to altered insulin kinetics and rapid glucose fluxes. We report the case of a 48-year-old female with long-standing T1D and ESKD who suffered from severe glycemic variability and recurrent hypoglycemia despite intensive insulin management. The patient was transitioned to the Medtronic MiniMed™ 780G automated insulin delivery (AID) system. Although glycemic control generally improved, she experienced frequent intradialytic hypoglycemia and hypotension. A specialized protocol was implemented to mitigate these risks: setting a temporary glucose target of 150 mg/dL (SI: 8.3 mmol/L) (reference range, 70-180 mg/dL [SI: 3.9-10 mmol/L]) two hours prior to dialysis, suspending the pump upon dialysis initiation, and resuming delivery only when sensor glucose exceeded 200 mg/dL (SI: 11.1 mmol/L). Following this adjustment, intradialytic adverse events were resolved, and the patient achieved a time in range of 69% with a glucose management indicator (GMI) of 7.0%. This case demonstrates that while standard AID algorithms are not calibrated for dialysis, they can be safely employed in ESKD patients through tailored modifications, specifically strategic pump suspension and proactive target elevation.

Indexed as

automated insulin deliveryend stage kidney diseaseglycemic variabilityhemodialysisintradialytic hypoglycemiatype 1 daysiabetes

Identifiers

PMID42232681
PMCPMC13223060

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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.