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1

Febuxostat Impact on Blood Pressure

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The primary endpoint of the present subanalysis was CV of systolic BP during the study period. SD of systolic BP and mean systolic BP during the study period, and at each timepoint, were also evaluated. As an exploratory analysis, mean PR and PR variability were investigated.
Statistical analysis was performed by an independent, professional biostatistician with R statistical software V.4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) in a modified intention-to-treat manner.13 (link) Data are expressed as median (IQR) or frequency (%). The distributions of baseline characteristics were evaluated with standardised mean difference. CV of systolic BP during the study period was calculated as abovementioned. Effect of febuxostat versus control in mean systolic BP and PR and SD and CV of systolic BP and PR were estimated using a linear regression model, adjusted with age, sex and a baseline systolic BP or PR value, and expressed with 95% CIs. A value of p<0.05 was considered statistically significant.

Saito Y., Tanaka A., Koide Y., Yoshida H., Uchida D., Matsunaga K., Yokota N., Ueyama C., Kobayashi Y, & Node K. (2022). Impact of febuxostat on visit-to-visit blood pressure variability: insights from the randomised PRIZE Study. RMD Open, 8(2), e002505.

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Statistical Analysis of Non-Parametric Data

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The normality of the data was assessed using the Shapiro–Wilk test. Because of the lack of normality distribution for most of the analyzed parameters and limited observations, nonparametric tests were applied. The differences in median values were tested using the Mann–Whitney U-test along with an estimation of the Glass rank biserial correlation coefficient as the metric of a size effect (r_G). Correlation between numerical data was assessed using Spearman’s rank test. A contingency table was used to test the frequency distribution of the variables. The significance of the association between the two kinds of classification was assessed using the Pearson χ² test (or Fisher exact test). The receiver operator characteristic curves with area under the curve (AUC) scores were used to determine the cutoff values for biomarkers for the prediction of in-hospital mortality. The level of significance was set at 0.05 in all analyses. Statistical analysis was performed using STATISTICA 13 (Tibco, Palo Alto, USA) and R Statistical Software (v.4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Data are presented as median (first–third quartile) unless indicated otherwise.

Burzyńska M., Uryga A., Kasprowicz M., Czosnyka M., Goździk W, & Robba C. (2023). Cerebral Autoregulation, Cerebral Hemodynamics, and Injury Biomarkers, in Patients with COVID-19 Treated with Veno-Venous Extracorporeal Membrane Oxygenation. Neurocritical Care, 39(2), 425-435.

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3

Neuromonitoring and Hemodynamic Correlation

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The normality of the data was assessed using the Shapiro–Wilk test. Because of the lack of normality distribution for most of the analyzed parameters and limited observations, non–parametric tests were applied, and data are presented in the text and in the tables as median and interquartile range.
Comparisons between different variables at baseline and PLR were made by paired Wilcoxon signed-rank test. The difference between patients regarding changes in CI (10% threshold) was tested using U Mann–Whitney test. A rank-biserial correlation (rrb) coefficient was used as the metric of a size effect. The correlation coefficients (95% confidence interval (CI)) between systemic and the different neuromonitoring variables were assessed using Spearman’s rank test. The rectangles around the plot of the correlation matrix are based on the results of hierarchical clustering. The level of significance was set at 0.05 in all analyses.
Statistical analysis was performed using STATISTICA 13 (Tibco, Palo Alto, USA) and R Statistical Software (v.4.0.2; R Foundation for Statistical Computing, Vienna, Austria) using ‘ggstatsplot’ [10 (link)]. Data are presented as median (first–third quartile) unless indicated otherwise. No significant differences were found in systemic hemodynamic parameters.

Messina A., Uryga A., Giardina A., Ciliberti P., Battaglini D., Patroniti N., Czosnyka M., Monnet X., Cecconi M, & Robba C. (2024). The effect of passive leg raising test on intracranial pressure and cerebral autoregulation in brain injured patients: a physiological observational study. Critical Care, 28, 23.

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4

Neuronal Dynamics Analysis via DSF and FIBSI

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Following the quantitative DSF analysis with the automated FIBSI program, inter-event intervals (IEI) between the DSF peak-to-peak time points in each neuron were measured to determine whether groups of neurons (i.e., with SA and without SA, having dermatomes with and without pain) exhibited preferred IEI values. To identify the dominant frequencies of voltage fluctuations in neurons, the voltage time series of each neuron was normalized to its sliding median (an optional output of the FIBSI program that eliminates linear trends) and analyzed using the periodogram function (TSA: Time Series Analysis package v1.3) in R statistical software v4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Finally, autocorrelations of each neuron with up to a 5 s lag were used to assess randomness (cyclic periods of high and low correlation would suggest that non-random components exist within the signal). The normalized time series of each neuron was analyzed using the acf (autocorrelation) function in R. Default settings for the periodogram and acf functions were used.

North R.Y., Odem M.A., Li Y., Tatsui C.E., Cassidy R.M., Dougherty P.M, & Walters E.T. (2022). Electrophysiological alterations driving pain-associated spontaneous activity in human sensory neuron somata parallel alterations described in spontaneously active rodent nociceptors. The journal of pain, 23(8), 1343-1357.

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5

Statistical Analysis of Non-Parametric Data

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The normality of the data was assessed using the Shapiro–Wilk test. Because of the lack of normality distribution for most of the analyzed parameters and limited observations, nonparametric tests were applied. The differences in median values were tested using the Mann–Whitney U-test along with an estimation of the Glass rank biserial correlation coefficient as the metric of a size effect (r_G). Correlation between numerical data was assessed using Spearman’s rank test. A contingency table was used to test the frequency distribution of the variables. The significance of the association between the two kinds of classification was assessed using the Pearson χ² test (or Fisher exact test). The receiver operator characteristic curves with area under the curve (AUC) scores were used to determine the cutoff values for biomarkers for the prediction of in-hospital mortality. The level of significance was set at 0.05 in all analyses. Statistical analysis was performed using STATISTICA 13 (Tibco, Palo Alto, USA) and R Statistical Software (v.4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Data are presented as median (first–third quartile) unless indicated otherwise.

Burzyńska M., Uryga A., Kasprowicz M., Czosnyka M., Goździk W, & Robba C. (2023). Cerebral Autoregulation, Cerebral Hemodynamics, and Injury Biomarkers, in Patients with COVID-19 Treated with Veno-Venous Extracorporeal Membrane Oxygenation. Neurocritical Care, 39(2), 425-435.

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6

Quantile and Linear Regression Analysis

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We compared median values between the two clinics using quantile regression. Mean values between the clinics were calculated using linear regression with robust standard errors.
A detailed explanation of assumptions and calculations is available in the appendix. Data management, cleaning, and analysis were performed using STATA 11 (Stata Corp., College Station, TX, USA) and R Statistical Software v4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).

Mukora R., Thompson R.R., Hippner P., Pelusa R., Mothibi M., Lessells R., Grant A.D., Fielding K., Velen K., Charalambous S., Dowdy D.W, & Sohn H. (2023). Human resource time commitments and associated costs of Community Caregiver outreach team operations in South Africa. PLOS ONE, 18(3), e0282425.

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R statistical software v 4.0.2

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6 protocols using r statistical software v 4.0.2

Febuxostat Impact on Blood Pressure

Statistical Analysis of Non-Parametric Data

Neuromonitoring and Hemodynamic Correlation

Neuronal Dynamics Analysis via DSF and FIBSI

Statistical Analysis of Non-Parametric Data

Quantile and Linear Regression Analysis

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