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Título:	Acid base variables predict survival early in the course of treatment with continuous venovenous hemodiafiltration
Autor(es):	Ribeiro, Juliana Caldas
Palavras-chave:	Acidosis. Acute kidney injur. Hemodiafiltration, Quantitative analysis
Data do documento:	2018
Descrição:	Metabolic acid–base disorders, especially metabolic acidosis, are common in critically ill patients who require renal replacement therapy. Continuous veno-venous hemodiafiltration (CVVHDF) achieves profound changes in acid–base status, but metabolic acidosis can remain unchanged or even deteriorate in some patients. The objective of this study is to understand the changes of acid–base variables in critically ill patients with septic associated acute kidney injury (SA-AKI) during CVVHDF and to determine how they relate to clinical outcome. Observational study of 200 subjects with SA-AKI treated with CVVHDF for at least 72 hours. Arterial blood gases and electrolytes and other relevant acid–base variables were analyzed using quantitative acid–base chemistry. Survivors and nonsurvivors had similar demographic characteristics and acid–base variables on day one of CVVHDF. However, during the next 48 hours, the resolution of acidosis was significantly different between the 2 groups, with an area under the ROC curve for standard base excess (SBE) and mortality of 0.62 (0.54–0.70), this was better than APACHE II score prediction power. Quantitative physicochemical analysis revealed that the majority of the change in SBE was due to changes in Cl and Na concentrations. Survivors of SA-AKI treated with CVVHDF recover hyperchloremic metabolic acidosis more rapidly than nonsurvivors. Further study is needed to determine if survival can be improved by measures to correct acidosis more rapidly. Abbreviations: AG = anion gap, APACHE = Acute Physiology and Chronic Health Evaluation, CVVHDF = continuous venovenous hemodiafiltration, ROC = receiver operator characteristics, SA-AKI = sepsis associated acute kidney injury, SIDa, SIDe = strong ion difference, SIG = strong ion gap.
URI:	http://www7.bahiana.edu.br//jspui/handle/bahiana/2492
Aparece nas coleções:	Aula, Videoaula, Modelagem Computacional em Saúde etc.

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