Introduction: Recently, ILCOR and AHA have been recommended to treat patients, who returned spontaneous circulation (ROSC) from cardiac arrest, by therapeutic hypothermia. Recently, we have tried to examine predictors, such as internal jugular venous blood oxygen saturation (SjvO2), glucagon, glucose, glial fibrillary acidic protein, procalcitonin, interleukin-8, interleutin-6, S100B and high morbidity group box 1 in serum and/or cerebrospinal fluid (CSF) within 48 hours after ROSC. But those values could not be got within a few hours except for SjvO2. Therefore, we need a predictor to detect the outcome as soon as possible for farther treatments. Methods: This retrospective cohort study included patients with ROSC after CPR who were admitted to our university hospital between January 2000 and May 2011 or an affiliated hospital between January 2006 and May 2011. Clinical parameters recorded on arrival included age (A), arterial blood pH (B), time from CPR to ROSC (C), pupil diameter (D), and initial rhythm (E). Glasgow outcome scale (GOS) was recorded at 6 months and the patients were divided according to favorable or unfavorable neurological outcomes based on GOS score. Multiple logistic regression analysis was performed to derive a formula to predict neurological outcomes based on basic clinical parameters. Results: The regression equation was derived using a teaching dataset consisting of 389 records: EP = 1/(1 + e-x), where EP is the estimated probability of having a favorable outcome, and x = (-0.034 × A) + (4.669 × B) - (0.105 × C) - (0.976 × D) + (2.603 × E) - 28.279. The sensitivity, specificity, and accuracy were 86%, 91% and 91%, respectively, for the validation dataset (n = 100). Conclusions: The 6 month neurological outcomes can be predicted in patients resuscitated from OHCA using clinical parameters that can be easily recorded at the site of CPR.
The intent of this study, based on a global multicenter study of reference values (RVs) for serum analytes was to explore biological sources of variation (SVs) of the RVs among 12 countries around the world. As described in the first part of this paper, RVs of 50 major serum analytes from 13,396 healthy individuals living in 12 countries were obtained. Analyzed in this study were 23 clinical chemistry analytes and 8 analytes measured by immunoturbidimetry. Multiple regression analysis was performed for each gender, country by country, analyte by analyte, by setting four major SVs (age, BMI, and levels of drinking and smoking) as a fixed set of explanatory variables. For analytes with skewed distributions, log-transformation was applied. The association of each source of variation with RVs was expressed as the partial correlation coefficient (rp). Obvious gender and age-related changes in the RVs were observed in many analytes, almost consistently between countries. Compilation of age-related variations of RVs after adjusting for between-country differences revealed peculiar patterns specific to each analyte. Judged from the rp, BMI related changes were observed for many nutritional and inflammatory markers in almost all countries. However, the slope of linear regression of BMI vs. RV differed greatly among countries for some analytes. Alcohol and smoking-related changes were observed less conspicuously in a limited number of analytes. The features of sex, age, alcohol, and smoking-related changes in RVs of the analytes were largely comparable worldwide. The finding of differences in BMI-related changes among countries in some analytes is quite relevant to understanding ethnic differences in susceptibility to nutritionally related diseases.
BACKGROUND AND AIM: Clinical laboratory reference intervals (RI) are supposed to be established and verified in every country. However, few countries carry out their own RI studies due to challenges and cost involved. Ghana diagnostic laboratories rely on reference intervals provided by the manufacturers of in vitro diagnostic instruments. These RIs may have been derived from a population with different characteristics which might not be appropriate. Hence this study aimed at establishing RIs of 40 chemistry and immunochemistry analytes for Ghanaian adults based on IFCC Committee on Reference Intervals and Decision Limits (C-RIDL) protocol. METHOD: A total of 501 healthy volunteers aged ≥18 years were recruited from the northern and southern regions of Ghana. Blood samples were analyzed with Beckman-Coulter AU480 and Centaur-XP/Siemen auto-analyzers. Sources of variations of reference values (RVs) were evaluated by multiple regression analysis (MRA). The need for partitioning RVs by sex and age was guided by the SD ratio (SDR). The RI for each analyte was derived using parametric method with application of the latent abnormal values exclusion (LAVE) method. RESULTS: Using SDR≥0.4 as threshold, RVs were partitioned by sex for most enzymes, creatinine, uric acid, bilirubin, immunoglobulin-M. MRA revealed age and body mass index (BMI) as major source of variations of many analytes. LAVE lowered the upper limits of RIs for alanine/aspartate aminotransferase, γ-glutamyl transaminase and lipids. Exclusion of individuals with BMI≥30 further lowered the RIs for lipids and CRP. After standardization based on value-assigned serum panel provided by C-RIDL, Ghanaian RIs were found higher for creatine kinase, amylase, and lower for albumin and urea compared to other collaborating countries. CONCLUSIONS: The LAVE effect on many clinical chemistry RIs supports the need for the secondary exclusion for reliable derivation of RIs. The differences in Ghanaian RIs compared to other countries underscore the importance of country specific-RIs for improved clinical decision making.
It is necessary to judge the significance of changes in laboratory test results, especially in health screening. For this purpose, the reference change value (RCV) was proposed, which is the (1- α) 100% confidence limit of differences between any two measurements: RCV = √2 z(α) x CV(I), where CV(I) represents intra-individual CV and α = 0.05. However, RCV is not commonly employed because: (1) it assumes constant CV(I) regardless of test levels, and (2) it often results in conservative judgements about the changes due to the blind use of 95% as its confidence probability (CP). Recently, we evaluated the level dependency of CV(I) in common laboratory tests and sought an appropriate CP for computing RCV through systematic analysis of a large long-term health-screening database. The dataset used contained data from approximately 14,000 individuals who underwent annual health-checks repeatedly. None of them were taking any medications or showed unnatural changes in BMI. The level of dependency of CV(I) was clearly observed for test items which showed skewed, logarithmic normal distributions but not for those with normal distributions, indicating the need to compute RCV according to the test level for the former items. To assess the choice of practical CP for RCV, we introduced a metabolic syndrome score (sMS) derived by logistic regression analysis. AsMS was used as an external criterion of changes in the nutritional status of individuals in relation to changes in laboratory tests. We evaluated the sensitivity and specificity of RCV at various CPs for detecting significant changes in ΔsMS. The analysis revealed that CP of 80-90% for computing RCV markedly enhanced the sensitivity of detecting ΔsMS without compromising the specificity. We provide a table listing appropriate RCVs for typical levels of common screening tests obtained from the analysis.
Early prediction of the neurological outcomes of patients with out-of-hospital cardiac arrest is important to select the optimal clinical management. We hypothesized that clinical data recorded at the site of cardiopulmonary resuscitation would be clinically useful.This retrospective cohort study included patients with return of spontaneous circulation after cardiopulmonary resuscitation who were admitted to our university hospital between January 2000 and November 2013 or two affiliated hospitals between January 2006 and November 2013. Clinical parameters recorded on arrival included age (A), arterial blood pH (B), time from cardiopulmonary resuscitation to return of spontaneous circulation (C), pupil diameter (D), and initial rhythm (E). Glasgow Outcome Scale was recorded at 6 months and a favorable neurological outcome was defined as a score of 4-5 on the Glasgow Outcome Scale. Multiple logistic regression analysis was carried out to derive a formula to predict neurological outcomes based on basic clinical parameters.The regression equation was derived using a teaching dataset (total, n = 477; favourable outcome, n = 55): EP = 1/(1 + e-x ), where EP is the estimated probability of having a favorable outcome, and x = (-0.023 × A) + (3.296 × B) - (0.070 × C) - (1.006 × D) + (2.426 × E) - 19.489. The sensitivity, specificity, and accuracy were 80%, 92%, and 90%, respectively, for the validation dataset (total, n = 201; favourable outcome, n = 25).The 6-month neurological outcomes can be predicted in patients resuscitated from out-of-hospital cardiac arrest using clinical parameters that can be easily recorded at the site of cardiopulmonary resuscitation.
