Here is the design of a clinical trial:

Official Title: Cerebral Oxygenation and Neurological Outcomes FOllowing CriticAL Illness-2

Included conditions:
- Critical Illness
- Respiratory Failure
- Shock
- Delirium

Study Armgroups:
- {'label': 'Respiratory failure and/or shock', 'description': 'All enrolled patients will undergo 72 hours of monitoring of cerebral oxygenation with near-infrared spectroscopy.'}

Interventions:

Primary Outcomes:
- {'measure': 'Regional cerebral oxygenation (rSO2) and delirium', 'description': 'Multivariate linear regression will be used to characterize the association between poor cerebral perfusion (as measured using duration of time (minutes) outside of MAPOPT, mean rSO2, and duration of disturbed cerebral autoregulation) and delirium severity to determine if poor cerebral perfusion is an independent predictor of delirium severity. We will estimate the unadjusted effect of each individual predictor on delirium severity (i.e., cumulative CAM-7 scores per patient). The simultaneous multivariate regression model will adjust for the following covariates (sex, a history of hypertension, a history of alcohol abuse, total sedative dose (in midazolam equivalents), total narcotic dose (fentanyl equivalents), severity of illness (APACHE II scores), pre-existing cognitive impairment (CDR score), length of ICU stay, and blood urea nitrogen. The multivariable model will provide the adjusted regression coefficients after controlling for all predictors included in the model.', 'timeFrame': '30 days'}

Please estimate the sample size based on the assumption: 
The study assumes a significance level (alpha) of 0.050, 90% power, and accounts for a 20% dropout rate for the primary outcome and a 30% mortality rate for secondary outcomes. A Bonferroni correction (0.0056) is used for multiple testing in secondary outcomes.
Sample size calculation
        Primary outcome: Our overall hypothesis is that poor cerebral perfusion contributes to delirium and long-term cognitive impairment. For study purposes, we define poor cerebral perfusion as the composite of (1) low mean rSO2, (2) duration of impaired cerebral autoregulation and (3) time outside individualised optimal MAP MAPOPT, which will be discussed in more detail in the statistical plan section. We acknowledge that this is an imperfect measure of cerebral perfusion. However, this is a comprehensive, continuous and non-invasive assessment of cerebral perfusion. For our primary outcome (CAM-7 delirium severity score), we will enrol a total of 500 patients, as our prior work has demonstrated that ~20% of patients remain comatose (RASS=â4 or â5) during their entire ICU stay,16 and cannot be assessed for delirium. Therefore, using our pilot data, we estimate that ~100 patients will be remain comatose resulting in approximately 400 patients to assess our primary outcome, which will allow for 10 df for our 3 measures of perfusion (ie, mean rSO2, duration of disturbed cerebral autoregulation, duration outside MAPOPT) and controlling for the nine covariates (see below). The 10 df will allow us to model non-linear relationships between the 3 measures of cerebral perfusion and delirium severity. This sample size achieves 90% power to detect an R2 of 0.050 collectively among these measures of cerebral perfusion and using an F-test with a significance level (alpha) of 0.050 (see figure 5).
        
          Figure 5
          
            A power curve indicating the study sample size, and the respective statistical power, to asses the primary study outcome. Red dots represent the sample size needed for a given statistical power. The primary sample size was calculated using the following multivariate regression model parameters: 10 independent variables tested, controlling for 9 additional covariates, power=0.90, R2=0.050, Î±=0.05, which would require a sample size of 400.
          
          
        
        
          Secondary outcomes: physiological determinants of rSO2 and neurological outcomes
          For evaluating the determinants of the rSO2 signal during critical illness, we will assess the association between each of the nine prespecified candidate predictors of rSO2 after controlling for the four covariates (see below for covariates). We will use a Bonferroni correction (0.05/9=0.0056) to control for multiple testing. With the total 500 patients recruited, and a multivariate regression model that includes 13 independent variables, this testing strategy will provide 90% power to identify any predictor that explains an additional 3.2% of the variance of rSO2 after controlling for the other variables in the model. This sample size is sufficient to identify independent significant predictors that account for a small-moderate degree of variance in the overall rSO2 signal. However, our pilot data indicated a 30% mortality rate. Given our overall sample size of 500 patients recruited, we are anticipating ~350âICU survivors (ie, 500â150) to return for follow-up assessment. This cohort will provide sufficient power to detect important predictors of long-term neurological outcomes. However, these predictors have been intentionally not specified a priori, as this analysis will be dependent on our findings related to cerebral perfusion and delirium.
          All sample size calculations were conducted using Power Analysis and Sample Size Software (V.15).37

          The actual start date at KHSC began on 26 January 2018 and our estimated primary completion data is June 2022. Due to our 12-month follow-up, we expect the study to be completed June 2023.