Evidence map›Paper›PMID 37107060›Full record

ArticleAntibiotics (Basel, Switzerland)2023

External Validation of the Augmented Renal Clearance Predictor in Critically Ill COVID-19 Patients.

Chao-Yuan Huang, Fabian Güiza, Matthias Gijsen, Isabel Spriet, Dieter Dauwe, Yves Debaveye, Marijke Peetermans, Joost Wauters, Greet Van den Berghe, Geert Meyfroidt and 1 more

Abstract read
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2023. 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

11 authors.

Chao-Yuan HuangLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Fabian GüizaDepartment of Intensive Care Medicine, University Hospitals Leuven, 3000 Leuven, Belgium.ORCID 0000-0001-7026-0957
Matthias GijsenPharmacy Department, University Hospitals Leuven, 3000 Leuven, Belgium.ORCID 0000-0003-1378-3772
Isabel SprietPharmacy Department, University Hospitals Leuven, 3000 Leuven, Belgium.
Dieter DauweLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.ORCID 0000-0002-9771-2543
Yves DebaveyeLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Marijke PeetermansLaboratory for Clinical Infectious and Inflammatory Disorders, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Joost WautersLaboratory for Clinical Infectious and Inflammatory Disorders, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Greet Van den BergheLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.ORCID 0000-0002-5320-1362
Geert MeyfroidtLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.
Greet De VliegerLaboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven, 3000 Leuven, Belgium.

Funding

KU Leuven PDMT2/21/077
6 · The paper itself

Abstract

The ARC predictor is a prediction model for augmented renal clearance (ARC) on the next intensive care unit (ICU) day that showed good performance in a general ICU setting. In this study, we performed a retrospective external validation of the ARC predictor in critically ill coronavirus disease 19 (COVID-19) patients admitted to the ICU of the University Hospitals Leuven from February 2020 to January 2021. All patient-days that had serum creatinine levels available and measured creatinine clearance on the next ICU day were enrolled. The performance of the ARC predictor was evaluated using discrimination, calibration, and decision curves. A total of 120 patients (1064 patient-days) were included, and ARC was found in 57 (47.5%) patients, corresponding to 246 (23.1%) patient-days. The ARC predictor demonstrated good discrimination and calibration (AUROC of 0.86, calibration slope of 1.18, and calibration-in-the-large of 0.14) and a wide clinical-usefulness range. At the default classification threshold of 20% in the original study, the sensitivity and specificity were 72% and 81%, respectively. The ARC predictor is able to accurately predict ARC in critically ill COVID-19 patients. These results support the potential of the ARC predictor to optimize renally cleared drug dosages in this specific ICU population. Investigation of dosing regimen improvement was not included in this study and remains a challenge for future studies.

Indexed as

augmented renal clearanceCOVID-19external validationintensive care unitsmachine learningrenal elimination

Identifiers

PMID37107060
PMCPMC10135364

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.