Observational studyPloS one2026
Association between acute respiratory acidosis and hyperkalemia during esophageal cancer surgery in the prone position: A multicenter retrospective observational study protocol.
Observational study in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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4 authors.
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Abstract
introductionEsophagectomy in the prone position can induce acute respiratory acidosis. While it is known that metabolic acidosis can significantly elevate serum potassium, the effect of respiratory acidosis is conventionally considered to cause minimal or no elevation. However, clinical practice in this surgical setting sometimes reveals a greater-than-expected degree of hyperkalemia. The objective of this study is to investigate the association between this acute respiratory acidosis and hyperkalemia, and to explore its clinical implications. METHODS AND ANALYSIS: This is a retrospective, two-center observational study of adult patients who underwent thoracoscopic esophagectomy in the prone position at two major Japanese institutions between January 2022 and December 2024. The primary outcome is the mean difference in serum potassium levels between the supine (baseline) and prone-position phases, analyzed as a paired-design within each patient. Key secondary outcomes include: 1) a multivariable analysis to identify factors associated with the magnitude of potassium increase; 2) a sensitivity analysis of the primary outcome after excluding cases with significant metabolic acidosis (e.g., base excess < -5 mmol/L); and 3) correlation analyses between the change in potassium and various physiological parameters, including PaCO₂ and markers of metabolic stress. Analyses will include the paired t-test, correlation analyses, and multivariable regression models.
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