Evidence map›Paper›PMID 36253027›Full record

ReviewAdvances in chronic kidney disease2022

Can Artificial Intelligence Assist in Delivering Continuous Renal Replacement Therapy?

Nada Hammouda, Javier A Neyra

Open access · greenAbstract readReview
In one paragraph

Review in Advances in chronic kidney disease, 2022. 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
2.0field-weighted citation impact, top 13% 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, 15 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

2 authors at 2 institutions in 1 country.

Nada HammoudaDepartment of Applied Clinical Research, University of Texas, Southwestern, Dallas, TX.
Javier A NeyraDepartment of Medicine, Division of Nephrology, University of Alabama at Birmingham, Birmingham, AL. Electronic address: jneyra@uabmc.edu.
The University of Texas Southwestern Medical Center · USUniversity of Alabama at Birmingham · US

Funding

UAB-UCSD O'Brien Core Center for Acute Kidney Injury ResearchP30DK079337 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI AGARWAL, ANUPAM · 2008 to 2022
$18.3M
Blood and Urine Biomarkers for Predicting Long-Term Adverse Kidney and Cardiovascular Outcomes after Cardiac SurgeryR01HL148448 · NHLBI · UT SOUTHWESTERN MEDICAL CENTER · PI FOX, AMANDA A · 2019 to 2023
$3.7M
Artificial Intelligence to Predict Outcomes in Patients with Acute Kidney Injury on Continuous Renal Replacement TherapyR01DK133539 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Girish Nitin Nadkarni, Javier A. Neyra · 2023 to 2026
$2.6M
The Relation of Soluble Klotho with Cardiovascular Disease, Chronic Kidney Disease Progression, and Blood Pressure in the Systolic Blood Pressure Intervention TrialR01DK128208 · NIDDK · TUFTS MEDICAL CENTER · PI DREW, DAVID ALAN, NEYRA, JAVIER A. · 2021 to 2025
$1.6M
Artificial Intelligence to Predict Outcomes in Patients with Acute Kidney Injury on Continuous Renal Replacement TherapyR56DK126930 · NIDDK · UNIVERSITY OF KENTUCKY · PI NADKARNI, GIRISH NITIN, NEYRA, JAVIER A. · 2020 to 2020
$100k
NHLBI NIH HHS R01 HL148448NIDDK NIH HHS P30 DK079337NIDDK NIH HHS R01 DK128208NIDDK NIH HHS R01 DK133539NIDDK NIH HHS R56 DK126930
6 · The paper itself

Abstract

Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools in patients receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery. All innovations were in the design/early validation phase of development. Primary research interests focused on early indicators of CRRT need, prognostication of mortality and kidney recovery, and identification of risk factors for mortality. Secondary research priorities included dynamic CRRT monitoring, predicting CRRT-related complications, and automated data pooling for point-of-care analysis. Literature gaps included prospective validation and implementation, biases ascertainment, and evaluation of AI-generated health care disparities. Research on AI applications to enhance CRRT delivery has grown exponentially in the last years, but the field remains premature. There is a need to evaluate how these applications could enhance bedside decision-making capacity and assist structure and processes of CRRT delivery.

Indexed as

Acute Kidney InjuryContinuous Renal Replacement TherapyArtificial IntelligenceCritical IllnessHumansRenal Replacement TherapyArtificial intelligenceContinuous renal replacement therapyCRRTInformation technologyMachine learning

Identifiers

PMID36253027
PMCPMC9586461
OpenAlexW4306247339

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.