Fluctuations of consciousness after stroke: Associations with the confusion assessment method for the intensive care unit (CAM-ICU) and potential undetected delirium

Journal Pre-proof Fluctuations of consciousness after stroke: Associations with the confusion assessment method for the intensive care unit (CAMICU) and potential undetected delirium

Michael E. Reznik, Lori A. Daiello, Bradford B. Thompson, Linda C. Wendell, Ali Mahta, N. Stevenson Potter, Shadi Yaghi, Mitchell M. Levy, Corey R. Fehnel, Karen L. Furie, Richard N. Jones PII:

S0883-9441(19)31146-3

DOI:

https://doi.org/10.1016/j.jcrc.2019.12.008

Reference:

YJCRC 53438

To appear in:

Journal of Critical Care

Please cite this article as: M.E. Reznik, L.A. Daiello, B.B. Thompson, et al., Fluctuations of consciousness after stroke: Associations with the confusion assessment method for the intensive care unit (CAM-ICU) and potential undetected delirium, Journal of Critical Care(2019), https://doi.org/10.1016/j.jcrc.2019.12.008

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Fluctuations of Consciousness After Stroke: Associations with the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and Potential Undetected Delirium Michael E. Reznik, MD1,2 ; Lori A. Daiello, PharmD, ScM1 ; Bradford B. Thompson, MD1,2 ; Linda C. Wendell, MD1,2 ; Ali Mahta, MD1,2 ; N. Stevenson Potter, MD, PhD1,2 ; Shadi Yaghi, MD3 ; Mitchell M. Levy, MD4 ; Corey R. Fehnel, MD, MPH5 ; Karen L. Furie, MD, MPH1 ; Richard N. Jones, ScD1,6 Author affiliations: 1

Department of Neurology, Brown University, Alpert Medical School Department of Neurosurgery, Brown University, Alpert Medical School 3 Department of Neurology, New York Langone Health 4 Department of Medicine, Brown University, Alpert Medical School 5 Marcus Institute for Aging Research, Hebrew SeniorLife, Beth Israel Deaconess Medical Center 6 Department of Psychiatry and Human Behavior, Brown University, Alpert Medical School

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Corresponding author and address for reprints:

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Michael Reznik Division of Neurocritical Care, Rhode Island Hospital 593 Eddy Street, APC 712 Providence, RI 02903 Phone: 401-606-8356 / Fax: 401-606-8555 [email protected]

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Key words: Delirium, consciousness, stroke, sepsis

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Abstract word count: 200; Manuscript word count: 2,627 Tables: 5 (+1 supplemental); Figures: 1 supplemental Financial support: LAD is supported by NIH grant R03-AG050232. RNJ is primarily supported by NIH grants R01-AG044518, R01-AG051170, and R01-AG029672, and departmental funds from the Departments of Psychiatry and Neurology at the Alpert Medical School at Brown University. Role of the funding source: None. Conflicts of interest: None declared.

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Abstract

Purpose: To examine associations between fluctuating consciousness and Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) assessments in stroke patients compared to nonneurological patients.

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Materials and Methods: We linked all recorded CAM-ICU assessments with corresponding

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Richmond Agitation Sedation Scale (RASS) measurements in patients with stroke or sepsis from a single-center ICU database. Fluctuating consciousness was defined by RASS variability using

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standard deviations (SD) over 24-hour periods; regression analyses were performed to determine

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associations with RASS variability and CAM-ICU rating.

Results: We identified 16,509 paired daily summaries of CAM-ICU and RASS measurements in

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546 stroke patients and 1,586 sepsis patients. Stroke patients had higher odds of positive (OR

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4.2, 95% CI 3.3-5.5) and “unable to assess” (UTA; OR 5.2, 95% CI 4.0-6.8) CAM-ICU ratings compared to sepsis patients, and CAM-ICU-positive and UTA assessment-days had higher

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RASS variability than CAM-ICU-negative assessment-days, especially in stroke patients. Based on model-implied associations of RASS variability (OR 2.0 per semi-IQR-difference in RASSSD, 95% CI 1.7-2.2) and stroke diagnosis (OR 2.7, 95% CI 2.0-3.7) with CAM-ICU-positive assessments, over one-third of probable delirium cases among stroke patients were potentially missed by the CAM-ICU.

Conclusions: Post-stroke delirium may frequently go undetected by the CAM-ICU, even in the setting of fluctuating consciousness.

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Introduction

Delirium is common in hospitalized patients, especially those who are critically ill, with incidence rates ranging widely depending on patient population and illness severity[1]. Delirium after acute stroke may occur in 10-66% of cases[2-6], and is associated with increased mortality and worse long-term functional outcomes[2, 3]. However, a major challenge in the identification

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of delirium in stroke patients is that their neurological deficits may prevent them from being able

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to complete components of screening instruments such as the Confusion Assessment Method for

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the Intensive Care Unit (CAM-ICU), which are predicated on patients’ ability to follow

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commands if they are otherwise not comatose[7, 8].

However, some components of delirium testing may still be broadly applicable even in

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patients with severe neurological injuries. Specifically, fluctuations in consciousness are a

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hallmark of many delirium identification tools and can be measured by scales such as the Richmond Agitation Sedation Scale (RASS)[9]. Such fluctuations can be seen even in patients

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with severe neurological deficits[10], as the RASS does not depend on following commands or

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any purposeful actions aside from eye opening and/or motor activity. Though impaired consciousness is no longer explicitly featured in the DSM-5 criteria for the diagnosis of delirium, its importance is implicitly acknowledged[11], and there is good concordance between delirium diagnoses using DSM-5 and DSM-IV criteria, the latter of which placed more emphasis on the role of consciousness[12]. Additionally, there is evidence to suggest that abnormal level of arousal may accurately correspond to inattention, a core symptom of delirium[13], and potentially to delirium diagnoses themselves[13-15]. Meanwhile, RASS fluctuations may also be associated with long-term outcomes in certain stroke populations[10].

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Given the uncertain validity of existing delirium screening tools like the CAM-ICU in patients with neurological deficits, we aimed to determine the potential role of fluctuating consciousness in delirium identification. We hypothesized that a high degree of fluctuating consciousness corresponded not only to positive CAM-ICU assessments but also those that were rated “unable to assess” (UTA). We further hypothesized that patients with stroke were more likely to be rated UTA than patients presumed to be without focal neurological deficits, and that

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stroke patients rated UTA would have a similar degree of fluctuating consciousness as those with

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positive CAM-ICU assessments. We therefore designed this retrospective cohort study to test our

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hypotheses.

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Materials and Methods

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Data source

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Data for this study were obtained from a publicly-available, single-center ICU database called

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the Medical Information Mart for Intensive Care III (MIMIC-III)[16]. MIMIC-III is an initiative from the Laboratory of Computational Physiology at the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC). The available data contain deidentified health-related information for over 40,000 patients admitted to the ICUs at BIDMC from 2001-2012, including demographics, hospital administrative data, vital signs, laboratory tests, and nursing flowsheets. The Institutional Review Boards of MIT and BIDMC have extended approval for the use of the MIMIC-III database for research purposes. Raw data were extracted and all analyses were performed using Stata/MP 11 (College Station, TX).

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Study population

We identified all patients admitted to a BIDMC ICU with acute stroke using validated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes[17]. This algorithm has been shown to have high sensitivity and specificity in identifying

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patients with acute ischemic stroke (433.x1, 434.x1, or 436), intracerebral hemorrhage (431.x),

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and subarachnoid hemorrhage (430.x) in the absence of a primary code for traumatic brain injury

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(800-804, 850-854) or rehabilitation care (V57). We then used validated ICD-9-CM codes to

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identify ICU patients with sepsis or severe sepsis (038.x, 785.52, 995.91, 995.92, 003.1, 020.2, 022.3, 036.2, 036.3, 054.5, 098.89, 112.5)[18-20]. We chose patients with sepsis as a comparison

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because they are a relatively heterogeneous and representative ICU population, and one that is

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relatively unlikely to have had concurrent new focal neurological deficits (though global features of neurological sequelae, such as sepsis-associated encephalopathy, are not uncommon). Patients

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who had ICD-9-CM codes for both acute stroke and sepsis were excluded. Though MIMIC-III

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contains data on patients admitted from 2001-2012 and CAM-ICU data was available over this entire time period, RASS measurements were only available from 2008-2012, so only patients from these years were included.

Measurements

The CAM-ICU identifies patients with delirium using a combination of four defining features: the presence of both a change in baseline mental status (Feature 1) and inattention (Feature 2),

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along with either an altered level of consciousness (Feature 3) or disorganized thinking (Feature 4)[21]. However, its method for testing inattention and disorganized thinking requires intact language ability, and may also prove challenging to use in patients with other neurological deficits (Supplementary Table 1). Per its explicit instructions, only patients who are comatose should be rated UTA, while other patients who cannot perform the delineated tasks should be rated CAM-ICU-positive. In clinical practice, however, UTA designations are often made in

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other cases and in situations that may be inappropriate[22, 23]. Meanwhile, the RASS is a

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straightforward 10-point scale measuring level of consciousness, with measurements ranging

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from -5 (unarousable) to +4 (combative)[9]. It is often used as part of the CAM-ICU, and has

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high interrater reliability[24].

We extracted all documented CAM-ICU and RASS measurements for patients in each

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patient group. All assessments were performed by bedside nurses as part of clinical care and

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available in the MIMIC-III database, with CAM-ICU assessments documented approximately every 12 hours and RASS assessments documented approximately every 2-4 hours in most cases.

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Patient-days that did not have both CAM-ICU and RASS assessments that were documented

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were not included in the analysis.

Summaries of maximum daily CAM-ICU scores were obtained with the following priority: if a patient had any CAM-ICU assessment that was positive over a 24-hour period, they were considered CAM-ICU-positive for the day; if there was no positive assessment but there was a negative assessment, they were considered CAM-ICU-negative for the day; and if all assessments were marked as “unable to assess” (UTA), then they were considered CAM-ICUUTA for the day. RASS measurements were summarized over 24-hour time periods as means

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and standard deviations (SD), which were then linked with corresponding daily summaries of CAM-ICU assessments. We also extracted patient demographics, Elixhauser comorbidities (combining subcategories for anemia, hypertension, diabetes, and malignancy for the purpose of simplicity), and clinical factors suggestive of critical illness, including utilization of mechanical ventilation, renal replacement therapy (RRT), and vasoactive and inotropic medications, as well as

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cumulative 24-hour doses of commonly used sedative medications.

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Statistical analysis

We used standard descriptive statistics to report patient characteristics. Comparisons between

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groups were performed using t-tests, Wilcoxon rank-sum tests, or chi-squared tests as

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appropriate. This included comparisons of mean and SD of 24-hour RASS scores across diagnosis groups and CAM-ICU assignments.

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We used multinomial logistic regression with correction for clustering within patients

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using a robust variance estimator to determine the effects of stroke diagnosis on the likelihood of obtaining the categorical outcomes represented by CAM-ICU-positive, CAM-ICU-negative, and UTA assessments, including age, comorbidities, clinical factors suggestive of critical illness, and cumulative 24-hour doses of commonly used sedative medications as covariates in our model. We then used linear regression to determine whether 24-hour RASS variability (SD) was higher with CAM-ICU-positive and UTA assessments (as compared to CAM-ICU-negative assessments), with similar correction for clustering within patients. We included an additional linear regression model that adjusted for diagnosis group (stroke vs. sepsis) and an interaction

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term between CAM-ICU assignment and diagnosis group, along with the other covariates previously mentioned. Finally, we performed logistic regression to determine associations between 24-hour RASS SD, diagnosis group (stroke vs. sepsis), and a dichotomized outcome, CAM-ICU-positive vs. CAM-ICU-negative, while excluding UTA assessments. An interaction term combining diagnosis group and 24-hour RASS SD was included in our model, along with age,

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comorbidities, sedative medication use, and critical illness covariates. Parameter estimates were

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used to calculate the model-implied prevalence of CAM-ICU-positive assignment—and by

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extension, probable delirium—among patients rated CAM-ICU-UTA. All hypothesis-testing was

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two-sided, and exact confidence intervals (CI) were included with all analyses. Significance level

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was set at alpha = 0.05.

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Results

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We identified 2,132 total patients with stroke or sepsis over the years 2008-2012, including 546

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with stroke (55% hemorrhagic, 45% ischemic; Supplementary Table 2) and 1,586 with sepsis, after excluding 40 patients who had both diagnoses during their admission. Stroke patients had a higher proportion of women and were significantly more likely to have had at least one CAMICU-positive assessment during their admission (Table 1). Meanwhile, sepsis patients had a higher prevalence of most comorbidities, longer ICU length of stay, and were more likely to require mechanical ventilation, vasopressor use, and RRT utilization, suggesting that they had a higher overall illness burden than stroke patients. (Note also that 22% of sepsis patients had a

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diagnosed chronic neurological disorder such as dementia, prior stroke, demyelinating disease, or epilepsy.) However, hospital mortality was similar between groups. Among all patients in our study cohort, we extracted data for 16,509 patient-days in which 24-hour summaries of RASS measurements were linked with CAM-ICU assessments. There was a significantly higher proportion of CAM-ICU-positive (18% vs. 9%) and UTA assessments (32% vs. 13%) in patients with stroke compared to those with sepsis (Table 2). This

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difference was further magnified after adjusting for comorbidities, sedative medications, and

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factors suggestive of critical illness (adjusted odds ratio [OR] 4.2, 95% CI 3.3-5.5 for CAM-

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ICU-positive; adjusted OR 5.2, 95% CI 4.0-6.8 for UTA).

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The mean of 24-hour RASS variability (SD) was significantly higher on patient-days that were CAM-ICU-positive or UTA as compared to CAM-ICU-negative days (estimated mean-

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difference 0.41 [95% CI 0.38-0.45] and 0.60 [95% CI 0.54-0.66], respectively). After further

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stratifying patients based on diagnosis, we found that 24-hour RASS SD was significantly higher in stroke patients than those with sepsis when their CAM-ICU assessments were positive or UTA

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(Figure 1). An adjusted linear regression model that included diagnosis group and interaction

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terms combining CAM-ICU assignment and diagnosis group (plus age, comorbidities, sedative medications, and critical illness covariates) confirmed that stroke patients had higher RASS variability beyond what was attributable to their corresponding CAM-ICU-positive or UTA assessments alone (Table 3). We subsequently found that 24-hour RASS variability was associated with CAM-ICUpositive assessments (OR 2.0 per semi-interquartile range difference in RASS SD, 95% CI 1.72.2) in an adjusted logistic regression model that excluded UTA assessments. Patients with stroke were also more likely to be CAM-ICU-positive independent of RASS variability (OR 2.7,

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95% CI 2.0-3.7), while the interaction term combining 24-hour RASS SD with diagnosis group was also significant (OR 0.6, 95% CI 0.4-0.8), suggesting that RASS variability may predict delirium differently in patients with stroke and sepsis. Finally, we used the results of this logistic regression model to estimate the modelimplied proportion of patients whose CAM-ICU was UTA but who would have had probable delirium based on their RASS variability, age, comorbidities, sedative medication use, critical

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illness covariates, and diagnosis group (stroke vs. sepsis). We found that 48% (497/1036) of

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CAM-ICU UTA assessments in stroke patients would have corresponded to probable delirium

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based on RASS variability. This amounts to an additional 108 stroke patients with probable

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delirium, suggesting that 38% (108/284) of all stroke patients with probable delirium in this

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group may have been missed.

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Discussion

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We found that fluctuations of consciousness, as manifested by variability in RASS scores,

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correspond to CAM-ICU assessments in several important ways, and that these effects may differ based on whether patients are admitted to the ICU because of stroke or sepsis. First, we observed that fluctuations of consciousness were significantly more pronounced when CAM-ICU assessments were rated positive, and that they were similarly pronounced when patients were rated UTA, suggesting that some of the patients in the study cohort may have had delirium that was undetected. In both cases, the degree of fluctuation was notably most prominent in stroke patients. Meanwhile, we also found that stroke patients were significantly more likely to be rated CAM-ICU-positive or UTA compared to sepsis patients, with odds that

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were over 4 times higher in each case. The high rate of UTA assessments is especially concerning as our projections suggest that, based on RASS variability, up to half of UTA assessments in stroke patients may mask an undetected delirium. On a patient level, this may translate to over one-third of cases of probable delirium that were missed. Our findings suggest that stroke patients face a high risk of having delirium that is not detected by the CAM-ICU, but that they may still exhibit fluctuations of consciousness that may

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be captured by the RASS. Along with other similar instruments, the CAM-ICU is limited by its

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inability to account for patients with aphasia, executive dysfunction, and other neurological

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deficits. Indeed, one study attempted to validate the CAM-ICU in stroke patients, but it suffered

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from a high exclusion rate of patients due to neurological impairment that was deemed to be too severe[25]. Meanwhile, a study investigating the real-world utility of the CAM-ICU found that,

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in patients with neurological injury, ICU nurses were able to accurately detect only one-sixth of

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delirium cases diagnosed via expert assessment[26]. Though a modified approach to the CAMICU has been proposed in such patients to account for a new neurological baseline[27], such an

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approach still does not account for the obstacles posed by these patients’ neurological limitations

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in performing other aspects of the CAM-ICU. In general, existing delirium screening tools are limited in their ability to account for a baseline mental status that is no longer normal; in many cases, subtly fluctuating symptoms may be the only clue to diagnosing delirium in a patient with neurological deficits. Current tools may also be overly reliant on traditional verbal-based methods of cognitive testing, which may not be feasible in many patients with severe deficits. As a result, novel delirium screening tools are warranted in the neurological patient population.

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Our study is specific to stroke and sepsis patients in the ICU setting, and has several limitations. First, since we relied on data from a single clinical center, there may be institutional practice patterns and demographics that are not readily generalizable. However, our study cohort is large, consists of a multi-ethnic cohort from a large urban area, and likely represents the assessments of numerous examiners. Second, these assessments were performed by ICU nurses rather than by trained delirium experts, leading to the possibility that some assessments may

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have been incorrect or inappropriate; indeed, there is some suggestion that CAM-ICU

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assessments may often be rated UTA inappropriately even in patients without neurological

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impairment[22, 23]. However, data from BIDMC overlapping with the time period of our study

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suggests a reasonably high accuracy for ICU nurses performing the CAM-ICU[28]. Third, the two groups in our study represent heterogeneous cohorts, with the possibility of bias being

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introduced based on uncontrolled clinical differences. However, we attempted to control for

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clinically relevant factors with our regression models. Fourth, RASS variability may represent an imperfect marker in patients receiving sedation, as targeted sedation titration protocols may on

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one hand lead to the desirable (and purposeful) effect of low levels of RASS variability, or on the

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other hand incentivize nurses to document a RASS within the stated goal rather than the clinically assessed RASS score. Fifth, there may have been other factors contributing to fluctuations in consciousness which we did not control for, including other psychoactive medications and changes in clinical status due to hospital complications. However, these factors also play a role in the development of delirium and should therefore not be implicated in the performance of the delirium assessments themselves. Finally, we were limited in our ability to select a comparison group comprising all ICU patients without acute neurological illness.

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Though we chose patients with sepsis as a representative ICU population, choosing a different group of patients may have produced different results.

Conclusion

Our findings suggest that stroke patients face a substantial risk of having delirium that is not

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detected. Prospective studies are needed to further characterize the challenges such patients may

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present in their assessment, and to develop novel diagnostic tools that are broadly applicable in

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patients with stroke and other forms of brain injury. Based on our findings, fluctuations in

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consciousness may represent one potential focus of future studies.

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Acknowledgements: None.

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Table & Figure Legend

Table 1. Characteristics for ICU patients hospitalized with acute stroke and patients hospitalized with sepsis.

Table 2. Distribution of CAM-ICU assessments for patients with stroke or sepsis. Associated

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odds ratios (OR) compare the likelihood of obtaining a CAM-ICU assessment that is positive or

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“unable to assess” (UTA) in stroke patients relative to patients with sepsis using multinomial

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logistic regression.

Table 3. Results of linear regression testing the effects of CAM-ICU assignment group and the

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standard deviation.

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interaction between stroke diagnosis and CAM-ICU assignment on corresponding 24-hour RASS

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Figure 1. Comparison of RASS score measures corresponding to daily summaries of CAM-ICU assessments for patients with sepsis and stroke: (a) 24-hour mean of RASS scores, and (b) 24hour standard deviation of RASS scores.

Supplementary Table 1. Implications of various stroke-related deficits on performance of the CAM-ICU.

Journal Pre-proof Supplementary Table 2. Distribution of stroke patients identified based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes listed in

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primary position.

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Table 1. Characteristics for ICU patients hospitalized with acute stroke and patients hospitalized with sepsis Stroke patients (n = 546)

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Sepsis patients (n = 1586) p-value 0.37 69 (4%) 180 (11%) 453 (28%) 499 (31%) 385 (24%) 887 (56%) 0.015 458 (29%) 0.020 245 (15%) 0.034 972 (61%) 0.001 5.9 (10.3) < 0.001 1,062 (67%) < 0.001 261 (16%) < 0.001

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15 (3%) 54 (10%) 156 (29%) 185 (34%) 136 (25%) 272 (50%) 187 (34%) 64 (12%) 289 (53%) 3.4 (6.7) 179 (33%) 25 (4.6%) 25%

16%

2026 (669–4521) 8%

2021 (606–5056) 24%

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Age, n (%) 18-35 35-50 50-65 65-80 > 80 Male, n (%) Nonwhite race, n (%) Non-English primary language, n (%) Mechanical ventilation, n (%) Mechanical ventilation days, mean (SD) Vasopressor/inotrope use, n (%) Renal replacement therapy, n (%) Sedative administration % patient-days with propofol use Cumulative 24-hour propofol dose (when administered, mg), median (IQR) % patient-days with midazolam use Cumulative 24-hour midazolam dose (when administered, mg), median (IQR) % patient-days with fentanyl use Cumulative 24-hour fentanyl dose (when administered, mcg), median (IQR) ICU length of stay, mean (SD), days Hospital mortality, n (%) Comorbidities, n (%) Alcohol abuse Anemia Arrhythmia Cancer Chronic lung disease Coagulopathy Congestive heart failure Diabetes Drug abuse Electrolyte abnormalities HIV/AIDS Hypothyroidism Hypertension Liver disease

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Characteristic

< 0.001

< 0.001 8.5 (2.0–33.3) 18%

18.2 (3.4–64.9) 39%

200 (75–1250) 7.0 (9.0) 112 (21%)

825 (150–3340) 11.0 (13.9) 325 (20%)

< 0.001 > 0.99

43 (7.9%) 102 (19%) 189 (35%) 51 (9.3%)` 79 (15%) 59 (11%) 96 (18%) 141 (26%) 16 (2.9%) 166 (30%) 2 (0.4%) 61 (11%) 400 (73%) 27 (4.9%)

157 (10%) 704 (44%) 627 (39%) 290 (18%) 405 (26%) 583 (37%) 546 (34%) 555 (35%) 80 (5.0%) 1156 (73%) 19 (1.2%) 239 (15%) 1020 (64%) 273 (17%)

0.17 < 0.001 0.046 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.042 < 0.001 0.13 0.027 < 0.001 < 0.001

< 0.001

Journal Pre-proof Peripheral vascular disease Psychotic disorder Pulmonary hypertension Renal failure Rheumatologic disease Valvular disease CAM-ICU positive during admission*, n (%) 24-hour RASS score mean, median (IQR) 24-hour RASS score SD, median (IQR)

78 (14%) 15 (2.7%) 38 (7.0%) 68 (12%) 14 (2.6%) 57 (10%) 176 (32%) -0.5 (-1.9–0) 0.6 (0–1.3)

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257 (16%) 92 (5.8%) 220 (14%) 428 (27%) 94 (5.9%) 232 (15%) 327 (21%) -0.3 (-1.6–0) 0.5 (0–0.9)

*At least 1 patient-day with a documented CAM-ICU positive assessment.

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Abbreviations: SD, standard deviation; IQR, interquartile range; mg, milligrams; mcg, micrograms; HIV, human immunodeficiency virus; AIDS, acquired immune deficiency syndrome; CAM-ICU, Confusion Assessment Method for the Intensive Care Unit; RASS, Richmond Agitation Sedation Scale.

0.31 0.004 < 0.001 < 0.001 0.001 0.014 < 0.001 < 0.001 < 0.001

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Table 2. Distribution of CAM-ICU assessments for patients with stroke or sepsis. Associated odds ratios (OR) compare the likelihood of obtaining a CAM-ICU assessment that is positive or “unable to assess” (UTA) in stroke patients relative to patients with sepsis using multinomial logistic regression Patient group Sepsis (n = 13,224) Stroke (n = 3,285) OR (95% CI) Adjusted ORa (95% CI)

Negative 78% 50% (reference) (reference)

CAM-ICU assignment Positive UTA 9% 13% 18% 32% 3.1 (2.8-3.5) 3.8 (3.4-4.1) 4.2 (3.3-5.5) 5.2 (4.0-6.8)

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Abbreviations: CAM-ICU, Confusion Assessment Method for the Intensive Care Unit; RASS, Richmond Agitation Sedation Scale; CI, confidence interval.

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Table 3. Results of linear regression testing the effects of CAM-ICU assignment group and the interaction between stroke diagnosis and CAM-ICU assignment on corresponding 24-hour RASS standard deviation* Assignment group CAM-ICU: Positive CAM-ICU: UTA Interaction group Stroke & CAM-ICU: Positive Stroke & CAM-ICU: UTA

Estimated mean difference (95% CI) 0.32 (0.28-0.37) 0.41 (0.35-0.46)

p-value < 0.001 < 0.001

0.12 (0.04-0.19) 0.34 (0.22-0.45)

0.004 < 0.001

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*Model adjusted for age, comorbidities, sedative medication dosing, mechanical ventilation, use of pressors, use of renal replacement therapy, and ICU length of stay.

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Abbreviations: CAM-ICU, Confusion Assessment Method in the ICU; RASS, Richmond Agitation Sedation Scale; CI, confidence interval; UTA, unable to assess.

Journal Pre-proof Figure 1. Comparison of RASS score measures corresponding to CAM-ICU assessments for patients with sepsis and stroke: (a) 24-hour mean of RASS scores, and (b) 24-hour standard deviation of RASS scores (as a surrogate for degree of RASS fluctuation)

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Journal Pre-proof Highlights

CAM-ICU assessments are often rated “unable to assess” (UTA) in real-world settings

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Stroke patients are more likely to be rated UTA on the CAM-ICU than sepsis patients

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Fluctuating consciousness is associated with both CAM-ICU positive and UTA ratings

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Stroke patients rated UTA have especially high degrees of fluctuating consciousness

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Stroke patients may face a high risk of having delirium not detected by the CAM-ICU

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Figure 1