Very Early Changes In Serum Creatinine Are Associated With 30-Day Mortality After Cardiac Surgery A Cohort Study
EUROPEAN JOURNAL OF ANAESTHESIOLOGY(2020)
摘要
BACKGROUND Acute kidney injury predicts adverse outcomes after cardiac surgery. OBJECTIVES To determine whether ultra-short-term changes (within 120 min) in serum creatinine (SCrea) levels after cardiac surgery predict clinical outcomes (30-day mortality). DESIGN Observational cohort study. SETTING Austrian tertiary referral centre. PATIENTS A total of 7651 patients scheduled to undergo elective cardiac surgery. MAIN OUTCOME MEASURES We analysed SCrea levels measured pre-operatively (baseline) and within 120 min after surgery. We also adjusted the postoperative SCrea levels for fluid balance. Patients were grouped according to the difference between the pre and postoperative SCrea levels (Delta SCrea(AdmICU)). We performed univariable and multivariable analyses to determine the association between changes in SCrea levels and 30-day mortality. RESULTS After cardiac surgery, the SCrea level decreased in 5923 patients and increased in 1728 patients. Increased SCrea levels were associated with a 21% increase in 30-day mortality. Even minimal increases in SCrea (0 to <26.5 mu mol l(-1)) were significantly associated with 30-day mortality [hazard ratio (HR), 1.98; 95% confidence interval (CI), 1.54 to 2.55; P < 0.001]. Adjustments for fluid balance strengthened the above association (increases of 0 to <26.5 mu mol l(-1): HR, 1.78; 95% CI, 1.40 to 2.26; P < 0.001; increases of at least 26.5 mu mol l(-1): HR, 2.40; 95% CI, 1.68 to 3.42; P < 0.001). CONCLUSION Even minimal, ultra-short-term increases in SCrea levels after cardiac surgery are associated with increased 30-day mortality. Adjustment for fluid balance strengthens this association. The change in SCrea between baseline and after admission to the Intensive Care Unit (Delta SCrea(AdmICU)) can serve as a simple, cheap and widely available marker for very early risk stratification after cardiac surgery.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络