Evidence map›Paper›PMID 39149454›Full record

ArticleResearch square2024

APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model.

Miguel Contreras, Brandon Silva, Benjamin Shickel, Andrea Davidson, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Ziyuan Guan, Jeremy Balch, Jiaqing Zhang, Sabyasachi Bandyopadhyay and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

15 authors.

Miguel ContrerasDepartment of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Brandon SilvaDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Benjamin ShickelDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0002-5304-7027
Andrea DavidsonDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.
Tezcan Ozrazgat-BaslantiDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.
Yuanfang RenDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.
Ziyuan GuanDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0009-0009-4824-6927
Jeremy BalchDepartment of Surgery, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0002-1826-7884
Jiaqing ZhangDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Sabyasachi BandyopadhyayDepartment of Medicine, Stanford University, Stanford, CA, USA.
Tyler LoftusDepartment of Surgery, University of Florida, Gainesville, FL, USA.
Kia KhezeliDepartment of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Subhash NerellaDepartment of Biomedical Engineering, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0003-2940-6517
Azra BihoracDivision of Nephrology, Department of Medicine, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0002-5745-2863
Parisa RashidiDepartment of Biomedical Engineering, University of Florida, Gainesville, FL, USA.

Funding

ADAPT: Autonomous Delirium Monitoring and Adaptive PreventionR01NS120924 · NINDS · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2021 to 2025
$2.9M
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-makingR01EB029699 · NIBIB · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2021 to 2024
$2.4M
NIBIB NIH HHS R01 EB029699NINDS NIH HHS R01 NS120924
6 · The paper itself

Abstract

On average, more than 5 million patients are admitted to intensive care units (ICUs) in the US, with mortality rates ranging from 10 to 29%. The acuity state of patients in the ICU can quickly change from stable to unstable, sometimes leading to life-threatening conditions. Early detection of deteriorating conditions can assist in more timely interventions and improved survival rates. While Artificial Intelligence (AI)-based models show potential for assessing acuity in a more granular and automated manner, they typically use mortality as a proxy of acuity in the ICU. Furthermore, these methods do not determine the acuity state of a patient (

Indexed as

acuityintensive care unitmortalitystate spacetransformers

Identifiers

PMID39149454
PMCPMC11326394

What OpenQuestion holds

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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.