We planned to enrol 40 patients in accordance with the inclusion and exclusion criteria, as there were no previous studies reporting agreement between JB and CSF lactate levels in patients with OHCA. Continuous variables are reported as medians with interquartile ranges or means and standard deviations, depending on the normal distribution. Categorical variables are reported as frequencies and percentages. Comparisons between the arterial, JB, and CSF lactate levels were made using the Wilcoxon signed-rank test, Kendall’s tau correlation analysis. We divided the lactate levels into intervals based on the interquartile range, and conducted a reliability analysis using using Cohen’s kappa to evaluate agreement between arterial, CSF, and JB lactate levels. All statistical analyses were performed using the PASW-SPSS software version 18 (IBM, Armonk, NY) and MedCalc 15.2.2 (MedCalc Software, Mariakerke, Belgium). Results were considered statistically significant at p < 0.05 (two-tailed).
Pasw spss software version 18
Manufactured by IBM
Sourced in Belgium, United States
PASW/SPSS software, version 18, is a data analysis and statistical software package developed by IBM. It provides a comprehensive set of tools for data management, analysis, and visualization. The software supports a wide range of data types and statistical methods, enabling users to explore, analyze, and interpret data effectively.
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Lab products found in correlation
5 protocols using pasw spss software version 18
1
Lactate Levels Correlation in OHCA
2
Neurological Prognostic Factors Analysis
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Continuous variables are reported as medians with interquartile ranges or means and standard deviations, depending on the normal distribution. Categorical variables are reported as frequencies and percentages. Comparisons between groups were performed using the chi-square test, Fisher’s exact test, Mann–Whitney U test, independent t-test, Kruskal–Wallis test, or ANOVA test. The area under the receiver operating characteristic curve was used to identify the prognostic value of lactate, pyruvate, and LP ratio for predicting the neurological prognosis. All statistical analyses were performed using PASW/SPSS software version 18 (IBM, Armonk, NY, USA) and MedCalc 15.2.2 (MedCalc Software, Mariakerke, Belgium). Statistical significance was set at p < 0.05 (two-tailed).
3
Serum NSE Predicts Neurologic Outcomes
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Continuous variables were reported as medians with interquartile ranges or means and standard deviations, depending on normal distribution. Categorical variables were reported as frequencies and percentages. Comparisons between the two groups were made using the chi-squared test, Fisher’s exact test, the Mann–Whitney U test, or the two-tailed t-test. The AUROC was used to identify cut-off values of serum NSE in patients with and without severe BBB disruption for predicting neurologic outcomes. The correlation between Qa, serum, and CSF NSE was analysed using Kendall tau. All statistical analyses were performed using PASW/SPSS software, version 18 (IBM, Armonk, NY) and MedCalc 15.2.2 (MedCalc software, Mariakerke, Belgium). Results were considered statistically significant at P < 0.05 (two-tailed).
4
Predictors of In-Hospital Mortality in Trauma Patients
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Continuous variables that did not satisfy the normality test are presented as median values with interquartile ranges. Categorical variables are presented as frequencies and percentages. We assessed differences between the two groups using the Mann-Whitney U-test for continuous variables. The Fisher exact test or chi-square test was used to compare categorical variables, as appropriate. Furthermore, we conducted a multivariate analysis using logistic regression of relevant covariates to predict in-hospital mortality. Variables with P-values <0.20 in the univariate analysis were included in the multivariate regression model. We used a backward stepwise approach and sequentially eliminated variables with P-values >0.10 to build a final adjusted regression model.
The results of logistic regression analysis are presented as odd ratios (OR) and 95% confidence intervals (CI). Receiver operating characteristic curve (ROC) analysis was performed to examine the prognostic performance of GCS score ≤12, ISS, hemoglobin level, and PLR for in-hospital mortality. Comparison of dependent ROC curves was performed using the DeLong method.
14 (link) We performed all analyses using PASW/SPSS software, version 18 (IBM Inc., Chicago, IL) and MedCalc version 19.0 (MedCalc Software, bvba, Ostend, Belgium). A two-sided significance level of 0.05 was defined as a statistically significant value.
The results of logistic regression analysis are presented as odd ratios (OR) and 95% confidence intervals (CI). Receiver operating characteristic curve (ROC) analysis was performed to examine the prognostic performance of GCS score ≤12, ISS, hemoglobin level, and PLR for in-hospital mortality. Comparison of dependent ROC curves was performed using the DeLong method.
14 (link) We performed all analyses using PASW/SPSS software, version 18 (IBM Inc., Chicago, IL) and MedCalc version 19.0 (MedCalc Software, bvba, Ostend, Belgium). A two-sided significance level of 0.05 was defined as a statistically significant value.
5
Predicting In-Hospital Mortality in Trauma Patients
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Continuous variables that did not satisfy the normality test are presented as median values with interquartile ranges. Categorical variables are presented as frequencies and percentages. Differences between the two groups were assessed using a Mann–Whitney U-test for continuous variables. Fisher exact test or Chi-square test was used to compare categorical variables, as appropriate. Furthermore, we conducted a multivariate analysis using logistic regression of relevant covariates to predict in-hospital mortality. Variables with p<0.20 in the univariate analysis were included in the multivariate regression model. We used a backward stepwise approach and sequentially eliminated variables with p>0.10 to build a final adjusted regression model. We presented logistic regression analysis results as odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristics (ROC) curve analysis was performed to examine the prognostic performance of ISS, RTS, and pre-hospital NEWS for in-hospital mortality. The comparison of dependent ROC curves was performed using the DeLong method.[11 (link)] All analyses were performed using the PASW/SPSS™ software, version 18 (IBM Inc., Chicago, IL, USA) and MedCalc version 19.0 (MedCalc Software, bvba, Ostend, Belgium). A two-sided significance level of 0.05 was defined as a statistically significant value.
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