Evidence map›Paper›PMID 33508763›Full record

ArticleJournal of critical care2021

Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission.

Emma Schwager, Stephanie Lanius, Erina Ghosh, Larry Eshelman, Kalyan S Pasupathy, Erin F Barreto, Kianoush Kashani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of critical care, 2021. 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. Review
  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

7 authors.

Emma SchwagerPhilips Research North America, Cambridge, MA, USA.
Stephanie LaniusPhilips Research North America, Cambridge, MA, USA.
Erina GhoshPhilips Research North America, Cambridge, MA, USA.
Larry EshelmanPhilips Research North America, Cambridge, MA, USA.
Kalyan S PasupathyHealthcare Policy and Research, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Erin F BarretoRobert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Department of Pharmacy, Mayo Clinic, Rochester, MN, USA.
Kianoush KashaniDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: Kashani.Kianoush@mayo.edu.

Funding

Beta-lactam individualization for critically ill patientsK23AI143882 · NIAID · MAYO CLINIC ROCHESTER · PI BARRETO, ERIN FRAZEE · 2020 to 2024
$986k
NIAID NIH HHS K23 AI143882
6 · The paper itself

Abstract

purposeAcute kidney injury (AKI) is a prevalent and detrimental condition in intensive care unit patients. Most AKI predictive models only predict creatinine-triggered AKI (AKI MATERIALS AND

methodsThree types of models were trained: 1) pAKI

resultsThe pAKI

conclusionIgnoring urine output in the outcome during model training resulted in models that are unlikely to predict AKI

Indexed as

Acute Kidney InjuryCreatinineCritical CareHospitalizationHumansMachine LearningCreatinineAcute kidney injuryClinical decision support system

Identifiers

PMID33508763
PMCPMC8534813

What OpenQuestion holds

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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.