Evidence map›Paper›PMID 35072041›Full record

ArticleKidney medicine2022

Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model.

Daniel Murphy, Scott Reule, David Vock, Paul Drawz

Open access · goldAbstract read
In one paragraph

Article in Kidney medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. What is Outpatient AKI? Insights from the SPRINT AKI Cohort.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Cigarette Smoking and Risk of Hospitalization With Acute Kidney Injury: The Atherosclerosis Risk in Communities (ARIC) Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024
    Observational
  7. 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

4 authors at 2 institutions in 1 country.

Daniel MurphyDepartment of Medicine, University of Minnesota, Minneapolis, MN.
Scott ReuleDepartment of Medicine, Minneapolis Veterans Health Administration, Minneapolis, MN.
David VockDepartment of Medicine, and School of Public Health, University of Minnesota, Minneapolis, MN.
Paul DrawzDepartment of Medicine, University of Minnesota, Minneapolis, MN.
University of Minnesota · USMinneapolis VA Health Care System · US

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
NCATS NIH HHS UL1 TR002494
6 · The paper itself

Abstract

RATIONALE &

objectiveRisk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ. STUDY

designPredictive model development and external validation using observational electronic health record data. SETTING &

participantsPatients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease. NEW PREDICTORS & ESTABLISHED PREDICTORS: Established predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI. OUTCOMES: A ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI. ANALYTICAL APPROACH: Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention.

resultsOutpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807). LIMITATIONS: Only surviving patients with measured creatinine levels during a baseline period and outcome period were included.

conclusionsThe outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.

Indexed as

acute kidney injuryambulatoryoutpatientrenal failurerisk prediction

Identifiers

PMID35072041
PMCPMC8767133
OpenAlexW3205655135

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.