Evidence map›Paper›PMID 33618695›Full record

ArticleBMC nephrology2021

Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

Wen En Joseph Wong, Siew Pang Chan, Juin Keith Yong, Yen Yu Sherlyn Tham, Jie Rui Gerald Lim, Ming Ann Sim, Chai Rick Soh, Lian Kah Ti, Tsong Huey Sophia Chew

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

Article in BMC nephrology, 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
2.3field-weighted citation impact, top 12% 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, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Machine learning in perioperative medicine: a systematic review.Journal of anesthesia, analgesia and critical care · 2022
    Review
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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

9 authors at 3 institutions in 2 countries.

Wen En Joseph WongYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore. josephwong5@hotmail.com.ORCID 0000-0003-4226-4752
Siew Pang ChanYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Juin Keith YongYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Yen Yu Sherlyn ThamYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Jie Rui Gerald LimYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Ming Ann SimDepartment of Anaesthesia, National University Health System, 5 Lower Kent Ridge Road, Singapore, 119074, Singapore.
Chai Rick SohDepartment of Anaesthesiology, Singapore General Hospital, 20 College Road, Singapore, 169856, Singapore.
Lian Kah TiYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
Tsong Huey Sophia ChewYong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
National University of Singapore · SGNational University Health System · SGSingapore General Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high risk for acute kidney injury. Its secondary objective is to examine the effect of acute kidney injury on a patient's prognosis during and after the ICU admission.

methodsA retrospective cohort of patients admitted to a Singaporean surgical ICU between 2015 to 2017 was collated. Patients undergoing chronic dialysis were excluded. The outcomes were occurrence of ICU acute kidney injury, hospital mortality and one-year mortality. Predictors were identified using decision tree algorithms. Confirmatory analysis was performed using a generalized structural equation model.

resultsA total of 201/940 (21.4%) patients suffered acute kidney injury in the ICU. Low ICU haemoglobin levels, low ICU bicarbonate levels, ICU sepsis, low pre-ICU estimated glomerular filtration rate (eGFR) and congestive heart failure was associated with the occurrence of ICU acute kidney injury. Acute kidney injury, together with old age (> 70 years), and low pre-ICU eGFR, was associated with hospital mortality, and one-year mortality. ICU haemoglobin level was discretized into 3 risk categories for acute kidney injury: high risk (haemoglobin ≤9.7 g/dL), moderate risk (haemoglobin between 9.8-12 g/dL), and low risk (haemoglobin > 12 g/dL).

conclusionThe occurrence of acute kidney injury is common in the surgical ICU. It is associated with a higher risk for hospital and one-year mortality. These results, in particular the identified haemoglobin thresholds, are relevant for stratifying a patient's acute kidney injury risk.

Indexed as

Machine LearningAcute Kidney InjuryAgedCohort StudiesFemaleHospital MortalityHumansMaleMathematical ConceptsMiddle AgedModels, TheoreticalRetrospective StudiesRisk AssessmentAcute kidney injuryCritically illHemoglobinMortalitySurgery

Identifiers

PMID33618695
PMCPMC7898752
OpenAlexW3129750681

What OpenQuestion holds

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LicenceCC BY
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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.