Evidence map›Paper›PMID 40561190›Full record

Observational studyCritical care explorations2025

The Acidemia Index: A Near Real-Time Risk Analytics Algorithm is Associated With Measured Acidemia in Pediatric Critical Care Patients, a Multi-Center Validation Study.

Ahmed Asfari, Avihu Z Gazit, Craig Futterman, Robert B Kelly, Andrew Macfadyen, David S Copper, Joshua W Salvin, Adam Szadkowski, Santiago Borasino, Joshua Wolovits and 1 more

Abstract readMulticenter StudyObservational StudyValidation Study
In one paragraph

Observational study in Critical care explorations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

11 authors.

Ahmed AsfariDivision of Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX.
Avihu Z GazitDivisions of Critical Care and Cardiology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA.
Craig FuttermanDivision of Cardiac Critical Care Medicine, Department of Pediatrics, George Washington University, Washington, DC.
Robert B KellyDivision of Critical Care, Children's Hospital of Orange County, Orange, CA.
Andrew MacfadyenDivision of Critical Care, Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE.
David S CopperDivision of Cardiology, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH.
Joshua W SalvinDivision of Cardiology, Department of Pediatrics, Harvard Medical School, Boston, MA.
Adam SzadkowskiDivision of Critical Care, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI.
Santiago BorasinoDepartment of Pediatric Cardiology, Section of Cardiac Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL.
Joshua WolovitsDivision of Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX.
Hayden J ZaccagniDivision of Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAcidemia frequently evolves in pediatric critical care patients, especially with congenital heart defects. Worsening acidemia secondary to inadequate systemic oxygen delivery can be detrimental to patients' outcomes and the ability to predict it has the potential to prompt early interventions to improve the clinical state. We aimed to evaluate the association of a novel near real-time predictive analytics algorithm with acidemia (ACD) (arterial pH < 7.25) in pediatric patients admitted to a critical care unit. STUDY

designRetrospective observational study in nine tertiary institutions in the United States.

settingMajority of patients were admitted to the cardiac ICU. Using Etiometry platform data (Etiometry, Boston, MA), acidemia (ACD) index was validated. PATIENTS: Patients 12 years old or younger were admitted to an ICU between February 1, 2018, and November 31, 2020.

interventionA total of 24,431 arterial blood pH measurements from 1858 patients were included in the validation dataset. The ACD index was calculated using a physiologic algorithm that incorporates patients' variables including laboratory and clinical data. Based on the previous assessment of the physiologic state of the patient, the physiologic algorithm interprets the new data in a real-time manner using Bayes' theorem. MEASUREMENT AND MAIN

resultsBased on a complete dataset, the area under the receiver operating characteristic curve of the ACD index was 0.93. As the index value increased, the likelihood of having acidemia increased (p < 0.01). The relative risk of having acidemia when the ACD index is less than 1 was 0.11 (95% CI, 0.07-0.15), and the relative risk of not having acidemia when the ACD index was greater than 99 was 0.38 (95% CI, 0.32-0.46).

conclusionsIn this large pediatric cohort, higher ACD index values were associated with a higher likelihood of having acidemia. Consequently, this novel index has the potential to identify severe changes in clinical status. Prospective analysis of the ACD index is important to understand its utility in the management of pediatric critical illness.

Indexed as

AcidosisAlgorithmsCritical CareChildChild, PreschoolFemaleHumansHydrogen-Ion ConcentrationInfantIntensive Care Units, PediatricMaleRetrospective StudiesRisk AssessmentUnited Statesacidemiaacidosispediatric cardiac surgerypredictive analyticsrisk estimationserum pH

Identifiers

PMID40561190
PMCPMC12200221

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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.