Evidence map›Paper›PMID 42092179›Full record

ArticleNPJ digital medicine2026

AI Guided Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation.

Abdel Badih El Ariss, Thanaphong Phongpreecha, Marc Ghanem, Mehrdad Kimari, Tomin James, Elise Freche, John A Kerner, Brice Gaudillière, Martin S Angst, Shabnam Gaskari and 3 more

Abstract read
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Article in NPJ digital medicine, 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

13 authors.

Abdel Badih El ArissDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Thanaphong PhongpreechaDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Marc GhanemDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Mehrdad KimariDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Tomin JamesDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Elise FrecheStanford Healthcare, Stanford, CA, USA.
John A KernerDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Brice GaudillièreDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Martin S AngstDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA.
Shabnam GaskariDepartment of Pharmacy, Lucile Packard Children's Hospital, Palo Alto, CA, USA.
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Melody SmithDivision of Blood and Marrow Transplantation and Cellular Therapy, Stanford University, Stanford, CA, USA.
Nima AghaeepourDepartment of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, CA, USA. naghaeep@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adults undergoing hematopoietic cell transplantation often develop serious complications that cause rapid nutritional decline. We developed and evaluated an AI approach to standardize intravenous nutrition (total parenteral nutrition, TPN) during this vulnerable period. Using real-world records from Stanford Health Care (6402 transplants, 2008-2025), we analyzed 1473 adults who received TPN, totaling 27,447 patient-days, linking each day's clinical state to the next day's prescription. We created a library of 30 standardized TPN regimens and trained a model to recommend next-day dose adjustments based on laboratory data and the existing prescription. (Pearson r ≈ 0.71). We then assessed an AI policy learned from past care and found that the Reinforcement learning agent selected dose adjustments with a higher composite score than the existing clinical policy. These results show that AI-guided TPN is feasible and may enhance bedside decision-making for adult transplant care, warranting prospective evaluation.

Identifiers

PMID42092179
PMCPMC13357808

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Registered trials

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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.