PURPOSE: This study was performed to determine whether the anion gap, the base excess, the lactate, and the strong ion gap obtained in the emergency department correlate with the prognosis and whether the strong ion gap is the most useful marker compared to the prognostic ability of the anion gap, the base excess, and the lactate. METHODS: We reviewed the records of 106 patients admitted to the intensive care unit via the emergency department. We measured the anion gap, the base excess, and the lactate and we calculated strong anion gap by using a formula. We divided the patients into survivors and nonsurvivors and compared the prognostic abilities of the four variables by using the Student's t-test and receiver operator characteristic curves. RESULTS: The mean age of the patients was 67+/-14, and the numbers of males and females were similar (58 males vs 48 females). The number of survivors was 92 (86.7%), and that of nonsurvivors was 14 (3.2%). The anion gap ( 24.8+/-8.8 vs. 16.4+/-4.8 mmol/L, p value=0.000), the base excess (-11.9+/-8.7 vs. -3.49+/-6.5 mmol/L, p value = 0.001), the lactate (9.1+/-7.7 vs. 4.5+/-3.1 mmol/L, p value = 0.011 ) and the strong ion gap (16.6+/-3.6 vs. 10.9+/-3.7, p value=0.000) of the nonsurvivors were higher. All of the four varibles were associated with the prognosis, but among them, the strong ion gap discriminated most strongly with an area under the receiver operator characteristic curve of 0.866 (95% confidence interval, 0.787 to 0.92). CONCLUSION: The initial emergency-department acid-base variables, the anion gap, the base excess, the lactate and the strong ion gap have prognostic abilities, but the strong ion gap is the variable that most strongly predicts of mortality.
Acid-Base Equilibrium ; Critical Illness* ; Emergencies* ; Emergency Service, Hospital* ; Female ; Humans ; Intensive Care Units ; Lactic Acid ; Male ; Mortality ; Prognosis ; Survivors

PURPOSE: To determine whether initial corrected anion gap (C(o)AG), base excess caused by unmeasured anions (BEua), and strong ion gap (SIG) can predict the morbidity of critically ill patients admitted to emergency department (ED). METHODS: 138 patients who visited the critical section of the ED and were admitted to intensive care unit (ICU) were enrolled. We calculated the C(o)AG, BEua, and SIG from the initial blood samples of the patients and initial logistic organ dysfunction score (LODS) also. Then we measured the LODS at the last day of ICU stay again. Comparing with the initial LODS, we divided the patients into two groups based on the changes of the values: favorable group and poor group. RESULTS: There was a significant difference in the mean AGcorr (p=0.007), BEua (p=0.008), SIG (p=0.037) between favorable and poor group. The area under the receiver operating characteristic (AUROC) curves for morbidity prediction were relatively small: 0.66 (95% CI, 0.56-0.77) for C(o)AG, 0.65 (95% CI, 0.54-0.76) for BEua, and 0.59 (95% CI, 0.49-0.70) for SIG. CONCLUSION: We found the initial unmeasured anions at the ED of the patients who eventually showed improved LODS during ICU period are significantly different to those of the other patients. But they failed to show enough capability of discriminating the morbidities between two groups.
Acid-Base Equilibrium ; Anions* ; Critical Illness* ; Emergencies* ; Emergency Service, Hospital* ; Humans ; Intensive Care Units ; Organ Dysfunction Scores ; Organization and Administration ; ROC Curve

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.