Evidence map›Paper›PMID 33585058›Full record

ArticleJournal of pediatric intensive care2021

Implementation of an Intravenous Fluid Titration Algorithm to Treat Pediatric Diabetic Ketoacidosis.

Christopher Babbitt, Marc Dadios, Ariya Chau, Graham Tse, Lisa Brown, Tracy Ladbury, Tricia Morphew, Mario Brakin

Abstract read
In one paragraph

Article in Journal of pediatric intensive care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Christopher BabbittDivision of Pediatric Critical Care, Miller Children's and Women's Hospital Long Beach, Long Beach, California, United States.ORCID 0000-0002-5345-0465
Marc DadiosDivision of Pediatric Critical Care, Children's Hospital of Orange County, Orange, California, United States.ORCID 0000-0001-6147-2632
Ariya ChauDivision of Pediatric Critical Care, Children's Hospital of Los Angeles, Los Angeles, California, United States.
Graham TseDivision of Pediatric Critical Care, Miller Children's and Women's Hospital Long Beach, Long Beach, California, United States.
Lisa BrownDivision of Pediatric Critical Care, Miller Children's and Women's Hospital Long Beach, Long Beach, California, United States.
Tracy LadburyDivision of Pediatric Critical Care, Miller Children's and Women's Hospital Long Beach, Long Beach, California, United States.
Tricia MorphewMorphew Consulting LLC, Bothell, Washington, United States.
Mario BrakinDivision of Pediatric Endocrinology, Miller Children's and Women's Hospital Long Beach, Long Beach California, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic ketoacidosis (DKA) is a common cause of admission to the pediatric intensive care unit and many centers utilize the "two-bag system" to treat DKA. We developed an intravenous fluid (IVF) titration algorithm to standardize adjustments of the two bags. A retrospective cohort study was performed comparing 155 patients treated before and 175 patients treated after implementation of the IVF titration algorithm. Postimplementation patients reached the blood glucose target zone faster and had a higher probability of remaining at goal while on insulin infusion. There was no significant difference in incidence of cerebral edema or hypoglycemia between study groups. Overall IVF titration algorithm compliance was 95%. Implementation of an IVF titration algorithm is safe and effective when treating DKA in children.

Indexed as

diabetesinsulinketoacidosis

Identifiers

PMID33585058
PMCPMC7870333

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.