ArticleThe Journal of surgical research2022
Predictive Capability of Metabolic Panels for Postoperative Atrial Fibrillation in Cardiac Surgery Patients.
Article in The Journal of surgical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Prediction Models for Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Critical Appraisal.Journal of clinical medicine · 2026Review
- Development and internal validation of an OPCABG-specific prediction model for postoperative atrial fibrillation in Chinese patients: a retrospective cohort study.BMC cardiovascular disorders · 2025Article
- Relevance of Targeting Oxidative Stress, Inflammatory, and Pro-Resolution Mechanisms in the Prevention and Management of Postoperative Atrial Fibrillation.Antioxidants (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
introductionPostoperative atrial fibrillation (POAF) occurs in up to 65% of cardiac surgery patients and is associated with an increased risk for stroke and mortality. Electrolyte disturbances in sodium (Na
methodsMetabolic panels of patients with no prior history of atrial fibrillation who did (n = 763) and did not develop POAF (n = 2144) after cardiac surgery were obtained from the Carilion Clinic electronic medical record system. We initially evaluated serum Na
resultsNeither scatter nor spaghetti plots of electrolyte data revealed a significant difference between those who did and did not develop POAF. Two logistic regression models and two random forest models with POAF status as the outcome were generated using the first observation for each electrolyte and the coefficient of the linear regression, which was obtained from a linear fit of the scatter plot. The random forest model using the first observation had a sensitivity of only 12.2%, but all four models had specificities more than 97%.
conclusionsNeither of the two logistic regression nor two random forest models were able to effectively predict the development of POAF from plasma ionic concentrations, but the random forest models effectively classified patients who would not develop POAF.
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What OpenQuestion holds
Registered trials
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