Missed postoperative metabolic acidosis associated with sodium-glucose transporter 2 inhibitors in cardiac surgery patients: a retrospective analysis

Autor: Hyeon A Kim, Joo Yeon Kim, Young Hwan Kim, Young Tak Lee, Pyo Won Park
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Druh dokumentu: article
ISSN: 2045-2322
99328674
DOI: 10.1038/s41598-024-58853-7
Popis: Abstract The increasing use of sodium glucose transporter 2 inhibitors (SGLT2i) for treating cardiovascular (CV) diseases and type 2 diabetes (T2D) is accompanied by a rise in euglycemic diabetic ketoacidosis occurrences in cardiac surgery patients. Patients undergoing cardiac surgery, due to their pre-existing CV disease which often requires SGLT2i prescriptions, face an increased risk of postoperative metabolic acidosis (MA) or ketoacidosis (KA) associated with SGLT2i, compounded by fasting and surgical stress. The primary aim of this study is to quantify the incidence of SGLT2i-related postoperative MA or KA and to identify related risk factors. We analyzed data retrospectively of 823 cardiac surgery patients, including 46 treated with SGLT2i from November 2019 to October 2022. Among 46 final cohorts treated preoperatively with SGLT2i, 29 (63%) developed postoperative metabolic complications. Of these 46 patients, stratified into two categories based on postoperative laboratory findings, risk factor analysis were conducted and compared. Analysis indicated a prescription duration over one week significantly elevated the risk of complications (Unadjusted OR, 11.7; p = 0.032*; Adjusted OR, 31.58; p = 0.014*). A subgroup analysis showed that a cardiopulmonary bypass duration of 60 min or less significantly raises the risk of SGLT2i-related postoperative MA in patients with a sufficient prescription duration. We omitted the term "diabetes" in describing complications related to SGLT2i, as these issues are not exclusive to T2D patients. Awareness of SGLT2i-related postoperative MA or KA can help clinicians distinguish between non-life-threatening conditions and severe causes, thereby preventing unnecessary tests and ensuring best practice.
Databáze: Directory of Open Access Journals
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