Evidence map›Paper›PMID 37906980›Full record

ArticleAmerican journal of nephrology2024

Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell Disease.

Rima S Zahr, Akram Mohammed, Surabhi Naik, Daniel Faradji, Kenneth I Ataga, Jeffrey Lebensburger, Robert L Davis

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 58% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Rima S ZahrDivision of Pediatric Nephrology and Hypertension, University of Tennessee Health Science Center Memphis, Memphis, Tennessee, USA.
Akram MohammedCenter for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Surabhi NaikDepartment of Surgery, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Daniel FaradjiCollege of Medicine, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Kenneth I AtagaCenter for Sickle Cell Disease, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
Jeffrey LebensburgerDivision of Pediatric Hematology and Oncology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Robert L DavisCenter for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
University of Tennessee Health Science Center · USUniversity of Alabama at Birmingham · US

Funding

Genetic modifiers of Sickle Cell Kidney DiseaseK23HL157554 · NHLBI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI Rima Zahr · 2022 to 2026
$849k
NHLBI NIH HHS K23 HL157554
6 · The paper itself

Abstract

introductionAcute kidney injury (AKI) is common among hospitalized patients with sickle cell disease (SCD) and contributes to increased morbidity and mortality. Early identification and management of AKI is essential to preventing poor outcomes. We aimed to predict AKI earlier in patients with SCD using a machine-learning model that utilized continuous minute-by-minute physiological data.

methodsA total of6,278 adult SCD patient encounters were admitted to inpatient units across five regional hospitals in Memphis, TN, over 3 years, from July 2017 to December 2020. From these, 1,178 patients were selected after filtering for data availability. AKI was identified in 82 (7%) patient encounters, using the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The remaining 1,096 encounters served as controls. Features derived from five physiological data streams, heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from bedside monitors were used. An XGBoost classifier was used for classification.

resultsOur model accurately predicted AKI up to 12 h before onset with an area under the receiver operator curve (AUROC) of 0.91 (95% CI [0.89-0.93]) and up to 48 h before AKI with an AUROC of 0.82 (95% CI [0.80-0.83]). Patients with AKI were more likely to be female (64.6%) and have history of hypertension, pulmonary hypertension, chronic kidney disease, and pneumonia than the control group.

conclusionXGBoost accurately predicted AKI as early as 12 h before onset in hospitalized SCD patients and may enable the development of innovative prevention strategies.

Indexed as

Acute Kidney InjuryAnemia, Sickle CellAdultFemaleHumansKidneyMachine LearningMaleRetrospective StudiesRisk AssessmentAcute kidney injuryMachine learningPredictive analyticsSickle cell diseaseTime-series data

Identifiers

PMID37906980
PMCPMC10872356
OpenAlexW4388038492

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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.