Evidence map›Paper›PMID 35859926›Full record

ArticleBiomarker insights2022

Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit.

Kirby Tong-Minh, Yuri van der Does, Joost van Rosmalen, Christian Ramakers, Diederik Gommers, Eric van Gorp, Dimitris Rizopoulos, Henrik Endeman

Open access · goldAbstract read
In one paragraph

Article in Biomarker insights, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Kirby Tong-MinhDepartment of Emergency Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0002-8373-0011
Yuri van der DoesDepartment of Emergency Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
Joost van RosmalenDepartment of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Christian RamakersDepartment of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, The Netherlands.
Diederik GommersDepartment of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
Eric van GorpDepartment of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
Dimitris RizopoulosDepartment of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Henrik EndemanDepartment of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
Erasmus MC · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19. Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period. Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively. Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.

Indexed as

biomarkersCOVID-19intensive care unitjoin modelsprediction modelrepeated measurements

Identifiers

PMID35859926
PMCPMC9290097
OpenAlexW4285411274

What OpenQuestion holds

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LicenceCC BY
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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.