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￭♣ S-455 ￭
Phosphate is a Biomarker of Disease Severity and Predicts Outcomes in AKI Patient undergoing CRRT
연세대학교 신촌세브란스 신장내과
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권재열, 정수영, 박서현, 지종현, 윤해룡, 김형래, 기연경, 윤창연, 한승혁
Background: Acute kidney injury commonly occurs in critically ill patients with intensive care unit (ICU) and AKI patients had higher serum phos- phate levels. Although phosphate levels are known to be associated with adverse outcome in patients with acute kidney injury, it is not well known whether serum high phosphate levels are related to disease severity in patients undergoing CRRT. In this study, we aimed to investigate the correlation of phosphate level with disease severity and prognosis. Methods: Patients who underwent CRRT at Yonsei University Health System from January 2011 to December 2016 were enrolled. Patients were include if they met the following criteria: injury stage of RIFLE(risk, injury, failure, loss, end-stage) criteria or more (＞2-fold increase in the serum creatinine or urine ouput ＜0.5 ml/kg/hr for 12 hours). Patients were excluded if the met the following criteria: younger than 18 years of age, life expectancy less than 3 months, pregnancy or lactation, and history of dialysis prior to the study.
Thus, a total of 1144 patients were enrolled. Results: Phosphate levels at 24 h after CRRT were positively associated with APACHE II and SOFA score (r＝0.134; p＜0.001, r＝0.161; p＜0.001, respectively). The 28-day and 90-day mortality were significantly higher in patients with hyperphosphatemia.
Conclusions: This study showed that high phosphate level has emerged as a potential biomarker of disease severity and may predict poor prognosis in AKI patients undergoing CRRT.
Table. Cox proportional hazard regression analysis for 28- and 90-day mortality before starting CRRT with all-cause mortality Phosphate as a categorical variable
( ≥ 4.5 vs. ＜4.5 mg/dL)a
Phosphate as a continuous variable (Per 1 mg/dL increase)
28-day HR (95% CI) P HR (95% CI) P
Model 1 1.28 (1.01-1.61) 0.04 1.04 (1.00-1.09) 0.04
Model 2 1.29 (1.03-1.63) 0.03 1.05 (1.00-1.09) 0.03
Model 3 1.38 (1.09-1.75) 0.01 1.07 (1.02-1.12) 0.004
90-day HR (95% CI) P HR (95% CI) P
Model 1 1.31 (1.06-1.63) 0.01 1.04 (1.00-1.08) 0.05
Model 2 1.32 (1.07-1.64) 0.01 1.04 (1.00-1.08) 0.04
Model 3 1.43 (1.14-1.78) 0.002 1.07 (1.02-1.11) 0.003
Model 1 : unadjusted
Model 2 : Age, gender, BMI at ICU admission Model 3 : Model 2 + CCI, SOFA score, eGFR (0h)
￭♣ S-456 ￭
Variation in Prevalence Rates of Chronic Disease by Income Level over the Past 6 Years
인제대학교 부속 서울백병원, 내과
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김승혁, 김준호, 윤원의, 구호석
Introduction: Although it is known that the prevalence rates of chronic disease depend on income level, little research has been undertaking their an- nual changes. In this study, we analyzed the variation in the prevalence rates of chronic disease based on annual income level over the past 6 years.
Methods: We analyzed the prevalence rates of chronic diseases such as hypertension, diabetes and chronic kidney disease(CKD) in relation to annual income levels with the results of the Korea National Health and Nutrition Examination Survey obtained over the past 6 years. We also discussed both the variation and its yearly changes in prevalence rates of chronic disease. Result: The data of 30,107 persons were used for analysis. Although the prevalence rates of diabetes have increased, the average rates of HbA1c have been gradually dropping. A significant increase has been shown on the prevalence rates of hypertension patients, although it had seemed to decrease. The prevalence rates of chronic kidney disease stayed unchanged. The management rates of chronic disease by income level have been growing while the gap of the management rates was determined between a higher and lower income group. As for hypertension and diabetes, the management rates have increased and the gap decreased whereas those of CKD have in- creased while the gap is ever growing by income groups. Further, not only the rates of chronically ill patients with no proper hospital service have de- clined by each income groups, but the gap between patients using medical service by their income levels also has been narrowed. It proved that one of the main reasons that the patients are not properly treated was the difficulty visiting hospitals for financial reasons. In the regression analysis results on prevalence rates of chronic diseases by income level, lower income group tend to have higher odds ratio of chronic diseases. Conculusion: Our results suggest that the gap of the prevalence rates of chronic disease by income level has decreased. However, the management rates of their chronic disease still remained low when it comes to the lower income group. It proved that their financial burdens might play the essential role in the management of their disease. .
