Evidence map›Paper›PMID 34424303›Full record

ArticleJAMA network open2021

Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery.

Todd A Wilson, Lawrence de Koning, Robert R Quinn, Kelly B Zarnke, Eric McArthur, Carina Iskander, Pavel S Roshanov, Amit X Garg, Brenda R Hemmelgarn, Neesh Pannu and 1 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

6 citing papers in PubMed, 10 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

11 authors at 5 institutions in 1 country.

Todd A WilsonDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Lawrence de KoningDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Robert R QuinnDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Kelly B ZarnkeDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Eric McArthurICES, Toronto, Ontario, Canada.
Carina IskanderICES, Toronto, Ontario, Canada.
Pavel S RoshanovDepartment of Medicine, Western University, London, Ontario, Canada.
Amit X GargDepartment of Medicine, Western University, London, Ontario, Canada.
Brenda R HemmelgarnDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Neesh PannuDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Matthew T JamesDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
University of Calgary · CAInstitute for Clinical Evaluative Sciences · CAUniversity of Alberta · CAWestern University · CACalgary Laboratory Services · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Severe acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk. Objective: To derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery. Design, Setting, and Participants: In this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021. Exposures: Demographic characteristics, surgery type, laboratory measures, and comorbidities before surgery. Main Outcomes and Measures: Acute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort. Results: The derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater. Conclusions and Relevance: The findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

Indexed as

Practice Guidelines as TopicAcute Kidney InjuryAdolescentAdultAgedAged, 80 and overAlbertaCohort StudiesFemaleForecastingHumansMaleMiddle AgedOdds RatioPostoperative ComplicationsPrognosis

Identifiers

PMID34424303
PMCPMC8383136
OpenAlexW3193289809

What OpenQuestion holds

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LicenceCC BY
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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.