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Archivos de Medicina de Urgencia de México

2014, Number 1

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Arch Med Urg Mex 2014; 6 (1)

Strong ion difference (SID) calculated by the Fencl-Stewart simplified method, is a mortality predictor in patients with septic shock

Sánchez DJS, Meneses OC, Monares ZE, Torres GA, Aguirre SJ, Franco GJ

Language: Spanish
References: 16
Page: 5-11
PDF size: 197.21 Kb.


ABSTRACT

Introduction: The Stewart model (theory of strong ion difference), to calculate the acid-base balance in critically ill patients is a method that can be used in the Intensive Care Unit for a more accurate assessment of acid base equilibrium. This methodology has been limited in daily practice so complex their formulas and the number of tests involving laboratory. Objective: Demonstrate that the Fencl-Stewart Method Simplified useful to predict mortality in septic shock patients. Material and methods: This was a retrospective, observational and descriptive study, conducted in an Intensive Care Unit of a tertiary care hospital. We included all patients with septic shock admitted to the ICU from January 2005 to December 2012. The next measures were collected: Sequential Organ Failure Assessment (SOFA), Central Venous O2, Saturation (ScvO2) , shock index (SI), pH, base (B), strong ion gap (SIG), cardiac index (CI), anions not measured (UMA) were measured at time of admission and 24 hours later. Normally-distributed variables were compared with an Independent T-Test; Threshold UMA predictive value to predict mortality was obtained to maximize the ROC. Results: N = 41, M/F n = 24 (58%)/17 (42%); surviving 21 (51%), non survivors: n = 20 (49%). The evaluation of the probability of death pre-test was 49%. The cutoff for the non measured anions was -2 mEq/L. Conclusions: The non measured anions calculated by the Fencl-Stewart simplified method identifies septic shock patients with decreased risk of mortality at admission. It is likely that acid-base disturbances could be conditioned by the solutions used during the resuscitation of patients in septic shock decreasing the predictive value.


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