Evidence map›Paper›PMID 39851257›Full record

ArticleClinical and translational gastroenterology2025

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

Niwen Kong, Patrick Chang, Ira A Shulman, Ubayd Haq, Maziar Amini, Denis Nguyen, Farhaad Khan, Rachan Narala, Nisha Sharma, Daniel Wang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and translational gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

15 authors.

Niwen KongDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Patrick ChangDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Ira A ShulmanDepartment of Pathology, University of Southern California, Los Angeles, California, USA.
Ubayd HaqDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Maziar AminiDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Denis NguyenDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Farhaad KhanDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Rachan NaralaDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Nisha SharmaDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Daniel WangDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Tiana ThompsonDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Jonathan SadikDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.
Cameron BrezeAriel Precision Medicine, Pittsburgh, Pennsylvania, USA.
David C WhitcombAriel Precision Medicine, Pittsburgh, Pennsylvania, USA.
James L BuxbaumDivision of Gastroenterology, Department of Medicine, University of Southern California, Los Angeles, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAriel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations vs standard management impacted clinical outcomes in a large prospective cohort.

methodsWe analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 and November 2022 whose course was richly characterized by capturing more than 100 clinical variables. We inputted these data into the ADAPT system to generate resuscitation fluid recommendations and compared with the actual fluid resuscitation within the first 24 hours from presentation. The primary outcome was the difference in organ failure in those who were over-resuscitated (>500 mL) vs adequately resuscitated (within 500 mL) with respect to the ADAPT fluid recommendation. Additional outcomes included intensive care unit admission, systemic inflammatory response syndrome (SIRS) at 48 hours, local complications, and pancreatitis severity.

resultsAmong the 1,083 patients evaluated using ADAPT, 700 were over-resuscitated, 196 were adequately resuscitated, and 187 were under-resuscitated. Adjusting for pancreatitis etiology, gender, and SIRS at admission, over-resuscitation was associated with increased respiratory failure (odd ratio [OR] 2.73, 95% confidence interval [CI] 1.06-7.03) as well as intensive care unit admission (OR 2.40, 1.41-4.11), more than 48 hours of hospital length of stay (OR 1.87, 95% CI 1.19-2.94), SIRS at 48 hours (OR 1.73, 95% CI 1.08-2.77), and local pancreatitis complications (OR 2.93, 95% CI 1.23-6.96). DISCUSSION: Adherence to ADAPT fluid recommendations reduces respiratory failure and other adverse outcomes compared with conventional fluid resuscitation strategies for acute pancreatitis. This validation study demonstrates the potential role of dynamic machine learning tools in acute pancreatitis management.

Indexed as

Fluid TherapyMachine LearningPancreatitisResuscitationAcute DiseaseAdultAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedProspective StudiesRespiratory InsufficiencySeverity of Illness IndexSystemic Inflammatory Response Syndromeartificial intelligencemachine learningpancreatitisrespiratory insufficiencyresuscitationsystemic inflammatory response syndrome

Identifiers

PMID39851257
PMCPMC12020695

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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.