Evidence map›Paper›PMID 30563915›Full record

Observational studyJournal of the American Society of Nephrology : JASN2019

Simple Postoperative AKI Risk (SPARK) Classification before Noncardiac Surgery: A Prediction Index Development Study with External Validation.

Sehoon Park, Hyunjeong Cho, Seokwoo Park, Soojin Lee, Kwangsoo Kim, Hyung Jin Yoon, Jiwon Park, Yunhee Choi, Suehyun Lee, Ju Han Kim and 6 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Journal of the American Society of Nephrology : JASN, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

60 citing papers in PubMed, 117 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 2 countries.

Sehoon ParkDepartment of Biomedical Sciences.
Hyunjeong ChoDepartment of Biomedical Sciences.
Seokwoo ParkDepartment of Biomedical Sciences.
Soojin LeeDepartment of Internal Medicine.
Kwangsoo KimDivision of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea.
Hyung Jin YoonDepartment of Biomedical Engineering.
Jiwon ParkMedical Research Collaborating Center, and.
Yunhee ChoiMedical Research Collaborating Center, and.
Suehyun LeeDepartment of biomedical informatics, College of Medicine, Konyang University, Daejeon, Korea; and.
Ju Han KimDivision of Biomedical Informatics.
Sejoong KimDepartment of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea.
Ho Jun ChinDepartment of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea.
Dong Ki KimDepartment of Internal Medicine.
Kwon Wook JooDepartment of Internal Medicine.
Yon Su KimDepartment of Biomedical Sciences.
Hajeong LeeDepartment of Internal Medicine, mdhjlee@gmail.com.
Institute of Biomedical Science · GBSeoul National University · KRKonyang University · KRSeoul National University Bundang Hospital · KRSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResearchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed.

methodsWe performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (≥1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ≥2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI.

resultsThe study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI.

conclusionsClinicians may consider implementing the SPARK index and classifications to stratify patients' PO-AKI risks before performing noncardiac surgery.

Indexed as

Acute Kidney InjuryAgedAge DistributionCohort StudiesFemaleHumansIncidenceKidney Function TestsMaleMiddle AgedPostoperative ComplicationsPredictive Value of TestsPreoperative CareReproducibility of ResultsRepublic of KoreaRetrospective Studiesacute kidney injurycreatininesurgery

Identifiers

PMID30563915
PMCPMC6317608
OpenAlexW2904809237

What OpenQuestion holds

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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.