Evidence map›Paper›PMID 39681633›Full record

ArticleScientific reports2024

The Aachen ACLF ICU score predicts ICU mortality in critically ill patients with acute-on-chronic liver failure.

Maike R Pollmanns, Bastian Kister, Samira Abu Jhaisha, Jule K Adams, Elena Kabak, Jonathan F Brozat, Carolin V Schneider, Philipp Hohlstein, Tony Bruns, Lars Küpfer and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

13 authors.

Maike R PollmannsMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Bastian KisterInstitute for Systems Medicine with Focus on Organ Interaction, RWTH Aachen University, Aachen, Germany.
Samira Abu JhaishaMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Jule K AdamsMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Elena KabakMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Jonathan F BrozatMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Carolin V SchneiderMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Philipp HohlsteinMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Tony BrunsMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Lars KüpferInstitute for Systems Medicine with Focus on Organ Interaction, RWTH Aachen University, Aachen, Germany.
Christian TrautweinMedical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Alexander Koch *Medical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
Theresa H Wirtz *Medical Department III, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany. thwirtz@ukaachen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute-on-chronic liver failure (ACLF) defines a heterogeneous syndrome involving acute decompensation in patients with pre-existing liver disease accompanied by (multi-)organ failure. This study aimed to develop a simple, reliable machine learning (ML) model to predict mortality in ACLF patients receiving intensive care unit (ICU) treatment. Data from 206 patients admitted to the ICU at RWTH Aachen University Hospital between 2015 and 2021 were retrospectively analyzed with ICU mortality as the primary outcome. An ICU mortality prediction model was developed by logistic regression and validated by 5-fold cross validation. Performance metrics were assessed to evaluate the model's accuracy and compare to existing mortality scores. ICU mortality was 60%. The chronic-liver-failure-consortium ACLF score (CLIF-C ACLFs) was the best predictor of ICU mortality. ML generated seven models using five to thirteen features. The best-performing model included CLIF-C ACLFs, number of organ failures, Horovitz quotient (FiO

Indexed as

Acute-On-Chronic Liver FailureCritical IllnessHospital MortalityIntensive Care UnitsAdultAgedFemaleHumansMachine LearningMaleMiddle AgedPrognosisRetrospective StudiesSeverity of Illness IndexACLFMachine learningMortalityPrognosis

Identifiers

PMID39681633
PMCPMC11649908

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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.