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Record W4309469229 · doi:10.1159/000527198

Acute and Chronic Cardiovascular Consequences of Acute Kidney Injury: A Systematic Review and Meta-Analysis

2022· review· en· W4309469229 on OpenAlexaboutno aff
Lorenz De Clercq, Tim Ailliet, Hannah Schaubroeck, Eric A. J. Hoste

Bibliographic record

VenueCardiorenal Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute kidney injuryRifleInternal medicineRetrospective cohort studyMeta-analysisProspective cohort studyCohort studyRelative riskMaceIntensive care unitIntensive care medicineAtrial fibrillationConfidence intervalMyocardial infarctionPercutaneous coronary intervention

Abstract

fetched live from OpenAlex

INTRODUCTION: We examined whether patients with acute kidney injury (AKI) have a higher risk of developing atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), and major adverse cardiac events (MACE) in the short and long term compared to patients without AKI, and if that risk is related to the severity of AKI. Furthermore, we investigated the influence of a cardiac event following AKI on the risk of all-cause mortality, length of stay in the intensive care unit and in the hospital. METHODS: We included English and Dutch retrospective and prospective cohort studies on adults (≥15 years) with AKI. Studies lacking epidemiological data, studies not using the consensus definitions (RIFLE, AKIN, KDIGO), animal studies, and studies on children were excluded. Studies were identified using the PubMed and Embase search engines. The last search was performed on the first of August 2021. For assessment of method quality, Newcastle-Ottawa Scale (NOS) for assessing risk of bias was used for cohort studies and heterogeneity was determined by the I2 statistic. Statistical analysis was performed using the Cochrane Review Manager (RevMan 5.3). The risk ratio (RR) and 95% confidence interval (CI) were calculated using the Mantel-Haenszel test. Results were presented in a summary caterpillar plot. RESULTS: We evaluated 14 studies comprising 736,210 patients. AKI was defined according to the RIFLE consensus in 1 article, to the AKIN criteria in 7, and to the KDIGO guidelines in 6. Of the 14 included studies, 4 were prospective and 10 were retrospective. In comparison with patients without AKI, we found that patients with AKI had a 94% increased risk of developing AF in the short term (RR: 1.94, 95% CI 1.35-2.79; p = 0.0004). In the long term, patients with AKI stage 1 had a 59% increased risk of developing HF and a 77% risk of developing ACS (RR: 1.59, 95% CI 1.07-2.34, p = 0.02 and RR: 1.77, 95% CI 1.68-1.88, p < 0.00001, respectively). Patients with AKI stage 2 had a 45% increased risk of ACS development (RR: 1.45, 95% CI 1.11-1.90, p = 0.006). AKI stage 3 was associated with a 164% increased risk of HF and a 95% increased risk of ACS development (RR: 2.64, 95% CI 1.71-4.08, p < 0.00001 and RR: 1.95, 95% CI 1.35-2.82, p = 0.0004, respectively). Analysis of studies not subdividing AKI into groups showed a 74% increased risk of HF, a 12% increased risk of ACS, and a 30% increased risk of developing MACE (RR: 1.74, 95% CI 1.51-2.01, p < 0.00001, RR: 1.12, 95% CI 1.07-1.17, p < 0.00001 and RR: 1.30, 95% CI 1.25-1.35, p < 0.00001, respectively). CONCLUSIONS: Patients who developed AKI have an increased risk of developing AF at short-term follow-up and HF, ACS, and MACE beyond 30 days.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0360.008
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.401
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

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