Novović Miloš N, Jevdjićt Jasna
Vojnosanit Pregl. 2014 Oct;71(10):936-41.
BACKGROUND/AIM: Acid-base disorders are common within critically ill patients. Physicochemical approach described by Stewart and modified by Figge gives precise quantification method of metabolic acidosis and insight into its main mechanisms, as well as influence of unmeasured anion on metabolic acidosis. The aims of this study were to determine whether the conventional acid-base variables are connected with survival rate of critically ill patients at Intensive care unit; whether strong ion difference/strong ion gap (SID/SIG) is a better predictor of mortality rate comparing to conventional acid-base variables; to determine all significant predictable parameters for the 28-day mortality rate at intensive care units.
This retrospective observational analytic study included 142 adult patients requiring mechanical ventilation, survivors (n = 68) and nonsurvivors (n = 74). Apparent strong ion difference (SIDapp), effective strong ion difference (SIDeff) and SIG values were calculated with the Stewart-Figge's quantitative biophysical method. Descriptive and analytical statistical methods were used in the study [t-test, Mann-Whitney U test, χ2-test, binary logistic regression, Reciever operating characteristic (ROC) curves, calibration].
Age, Na+, acute physiology and chronic health evaluation (APACHE II), Cl-, albumin, SIG, SID app, SIDeff, and aninon gap (AG) were statistically significant predictors. AG represented a model with imprecise calibration, i.e. a model with little predictive power. APACHE II had p-value more than 0.05 if it was near it, and therefore it could be considered potentially unreliable for outcome prediction. SIDeff and SIG represented models with well-defined calibration. ROC analysis results showed that APACHE II, Cll-, albumin, SIDeff, SIG i AG had the largest area bellow the curve. By creation of logistic models with calibration methods, we found that outcome depends on SIG and APACHE II score.
Based on our data, unmeasured anions provide prediction of mortality of critically ill patients on mechanical ventilation, unlike the traditional acid-base variables which are not accurate predictors of the 28-day mortality rate.
背景/目的：酸碱平衡紊乱在重症患者中很常见。Stewart描述并经Figge修改的物理化学方法给出了代谢性酸中毒的精确量化方法，并深入了解其主要机制以及未测定阴离子对代谢性酸中毒的影响。本研究的目的是确定传统酸碱变量是否与重症监护病房（ICU）重症患者的生存率相关；与传统酸碱变量相比，强离子差/强离子间隙（SID/SIG）是否是更好的死亡率预测指标；确定ICU 28天死亡率的所有重要预测参数。
这项回顾性观察分析研究纳入了142例需要机械通气的成年患者，包括幸存者（n = 68）和非幸存者（n = 74）。采用Stewart - Figge定量生物物理方法计算表观强离子差（SIDapp）、有效强离子差（SIDeff）和SIG值。研究中使用了描述性和分析性统计方法[t检验、Mann - Whitney U检验、χ2检验、二元逻辑回归、受试者操作特征（ROC）曲线、校准]。
年龄、Na +、急性生理与慢性健康状况评估系统（APACHE II）、Cl -、白蛋白、SIG、SIDapp、SIDeff和阴离子间隙（AG）是具有统计学意义的预测指标。AG代表校准不精确的模型，即预测能力较弱的模型。APACHE II接近临界值时p值大于0.05，因此对于结局预测可能不可靠。SIDeff和SIG代表校准明确的模型。ROC分析结果显示，APACHE II、Cl -、白蛋白、SIDeff、SIG和AG在曲线下面积最大。通过校准方法创建逻辑模型，我们发现结局取决于SIG和APACHE II评分。
基于我们的数据，与传统酸碱变量不同，未测定阴离子可预测机械通气重症患者的死亡率，传统酸碱变量不是28天死亡率的准确预测指标。
