Evidence map›Paper›PMID 37844555›Full record

ReviewAmerican journal of nephrology2024

Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit.

Louis Baeseman, Samantha Gunning, Jay L Koyner

Open access · greenAbstract readReview
In one paragraph

Review in American journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 12 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

3 authors at 1 institution in 1 country.

Louis BaesemanSection of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Samantha GunningSection of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Jay L KoynerSection of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
University of Chicago · US

Funding

Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney InjuryR01DK126933 · NIDDK · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL, KOYNER, JAY L · 2021 to 2025
$3.4M
NIDDK NIH HHS R01 DK126933
6 · The paper itself

Abstract

backgroundSepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output. SUMMARY: Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI. KEY MESSAGES: Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.

Indexed as

Acute Kidney InjurySepsisArtificial IntelligenceBiomarkersCreatinineCritical IllnessHumansIntensive Care UnitsBiomarkersCreatinineAcute kidney injuryBiomarkersClinical trialsEndotypesOutcomesPhenotypesRenal replacement therapySepsis

Identifiers

PMID37844555
PMCPMC10872813
OpenAlexW4387664707

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.