Introduction
Stroke continues to be the second leading cause of death and disability globally (Krishnamurthi et al., Reference Krishnamurthi, Ikeda and Feigin2020), with 30-40% of stroke-survivors experiencing persistent cognitive deficits (McDonald et al., Reference McDonald, Black, Copland, Corbett, Dijkhuizen and Farr2019). Post-stroke cognitive impairment impacts individual quality of life (Barker-Collo et al., Reference Barker-Collo, Feigin, Parag, Lawes and Senior2010; Nys et al., Reference Nys, Van Zandvoort, Van Der Worp, De Haan, De Kort, Jansen and Kappelle2006) and function (Tatemichi et al., Reference Tatemichi, Desmond, Stern, Paik, Sano and Bagiella1994) and increases burden on communities (Krishnamurthi et al., Reference Krishnamurthi, Ikeda and Feigin2020). There is a need to understand cognitive recovery patterns post-stroke to compare efficacy of therapeutic interventions against the rate of natural post-stroke improvement (Duncan et al., Reference Duncan, Min Lai and Keighley2000). A growing body of literature has described the long-term trajectory of post-stroke cognitive change (Levine et al., Reference Levine, Galecki, Langa, Unverzagt, Kabeto, Giordani and Wadley2015; Lo et al., Reference Lo, Crawford, Desmond, Bae, Lim and Godefroy2021; Mahon et al., Reference Mahon, Parmar, Barker-Collo, Krishnamurthi, Jones, Theadom and Feigin2017; Zheng et al., Reference Zheng, Yan, Zhong, Yang and Xie2019) with general consensus that early cognitive deficits are complex and multifactorial due to processes of neuro-vascular injury and recovery, delirium, fatigue, fluctuating alertness and emotional distress (Lezak, Reference Lezak2012; McDonald et al., Reference McDonald, Black, Copland, Corbett, Dijkhuizen and Farr2019). However, there is limited information on the trajectory of cognitive recovery over the first week post-stroke, when cognition is impacted by the acute effects of stroke, despite the clinical requirement of early assessment to support critical decisions such as discharge disposition and referral to rehabilitation services (Australian Commission on Safety & Quality in Health Care, 2019; Lindsay et al., Reference Lindsay, Furie, Davis, Donnan and Norrving2014).
Cognitive screening post mild-stroke is essential as cognitive impairment is pervasive (Boulanger et al., Reference Boulanger, Lindsay, Gubitz, Smith, Stotts and Foley2018; Lanctôt et al., Reference Lanctôt, Lindsay, Smith, Sahlas, Foley and Gubitz2020; Pendlebury et al., Reference Pendlebury, Klaus, Thomson, Mehta, Wharton and Rothwell2015; Stroke Foundation, 2022) and can result in reduced participation (Adamit et al., Reference Adamit, Maeir, Ben Assayag, Bornstein, Korczyn and Katz2014; Tellier & Rochette, Reference Tellier and Rochette2015). With short length-of-stay in acute care, screening measures are conducted expeditiously post-stroke (Bertolin et al., Reference Bertolin, Van Patten, Greif and Fucetola2018; Verdelho et al., Reference Verdelho, Wardlaw, Pavlovic, Pantoni, Godefroy, Duering, Charidimou, Chabriat and Biessels2021). Brief screening and more extensive neuropsychological testing are feasible early post-stroke (Nys et al., Reference Nys, Van Zandvoort, De Kort, Jansen, Van Der Worp, Kappelle and De Haan2005; van Zandvoort et al., Reference van Zandvoort, Kessels, Nys, de Haan and Kappelle2005), with screening tests more efficient and better tolerated by patients in acute care. However, there is no gold standard for cognitive screening (Hachinski et al., Reference Hachinski, Iadecola, Petersen, Breteler, Nyenhuis and Black2006; Quinn et al., Reference Quinn, Elliott and Langhorne2018; Quinn et al., Reference Quinn, Richard, Teuschl, Gattringer, Hafdi and O’Brien2021), with measures needing to cover a broad range of cognitive domains, be valid, feasible and sensitive for identification of impairments post-stroke (Chan et al., Reference Chan, Altendorff, Healy, Werring and Cipolotti2017; Chan et al., Reference Chan, Khan, Oliver, Gill, Werring and Cipolotti2014; Stolwyk et al., Reference Stolwyk, O’Neill, McKay and Wong2014). They also only provide a ‘snapshot’ at a single point in time and should be responsive to change for monitoring of cognitive recovery (Skirrow et al., Reference Skirrow, Cashdollar, Granger, Jennings, Baker, Barnett and Cormack2021), making it difficult for one tool to meet all requirements. Exploring new technologies could provide alternatives for measuring cognition in acute stroke.
Computerised cognitive assessments offer sensitive continuous measures that can be customised for select subtests and repeated to mark changes over short epochs of cognitive recovery (Aslam et al., Reference Aslam, Bates, Dundar, Hounsome, Richardson and Krishan2018; Pettigrew et al., Reference Pettigrew, Soldan, Brichko, Zhu, Wang and Kutten2021; Zygouris & Tsolaki, Reference Zygouris and Tsolaki2015), such as the acute phase post-stroke (Bernhardt et al., Reference Bernhardt, Hayward, Kwakkel, Ward, Wolf and Borschmann2017). Computerised cognitive assessment platforms are feasible as research measures acutely post-stroke (Cumming et al., Reference Cumming, Brodtmann, Darby and Bernhardt2012; Shopin et al., Reference Shopin, Shenhar-Tsarfaty, Ben Assayag, Hallevi, Korczyn, Bornstein and Auriel2013) and are designed for serial measurement of cognition over short time intervals (Cambridge Campos-Magdaleno et al., Reference Campos-Magdaleno, Leiva, Pereiro, Lojo-Seoane, Mallo, Facal and Juncos-Rabadán2021; Cognition, 2022; Skirrow et al., Reference Skirrow, Cashdollar, Granger, Jennings, Baker, Barnett and Cormack2021), but have not been used in both capacities in the acute post-stroke period. We aimed to map the trajectory of cognitive recovery during the first week post-stroke and up to 90-day follow-up using serial computerised cognitive assessment.
Materials and methods
Patients and setting
A prospective, single-centre, observational, cohort study was conducted in the Acute Stroke Unit of the Sunshine Coast University Hospital. Ethical approval was obtained from: The Prince Charles Hospital Human Research Ethics Committee, HREC/17/QPCH/163; the University of Queensland (#2017001149); and from University of the Sunshine Coast (S/17/1091). Data were collected from January to September 2018.
Participant screening included all patients diagnosed with stroke by senior medical consultants. Eligible patients were aged 18 years or older and within 48 hours of stroke onset. Patients were excluded if they: were palliative in treatment intent; previously had diagnoses of dementia or extensive mental illness; were blind or were anticipated to be unavailable for follow-up. The Principal Investigator provided study information to all consecutive eligible patients and invited them to participate. Signed consent was obtained from participants or substitute decision-makers.
Data collection tools
Demographic information - age, gender, premorbid function, education, occupation and comorbid medical conditions - were gathered from medical records and participant interview. Admission medical entries and imaging (Computerised Tomography, CT and Magnetic Resonance Imaging, MRI) provided stroke-specific information: National Institutes of Health Stroke Scale (NIHSS) (Brott et al., Reference Brott, Adams, Olinger, Marler, Barsan and Biller1989) for stroke severity (Adams et al., Reference Adams, Davis, Leira, Chang, Bendixen and Clarke1999), stroke type, hemisphere, symptoms and reperfusion therapies.
Participants were screened for cognitive confounders: the Depression, Anxiety and Stress Scale-21 assessed for clinical symptoms of anxiety and depression (Ng et al., Reference Ng, Trauer, Dodd, Callaly, Campbell and Berk2007) and was provided to the patient to read while questions were read through with the primary investigator; the Wechsler Test for Adult Reading (WTAR) estimated pre-stroke intellectual capacity (Full Scale intelligence Quotient, FSIQ) (Steward et al., Reference Steward, Novack, Kennedy, Crowe, Marson and Triebel2016) and the 4-A’s Test screened for delirium (4-AT) (Lees et al., Reference Lees, Corbet, Johnston, Moffitt, Shaw and Quinn2013).
Data were collected from Version 7.1 of the Montreal Cognitive Assessment (MoCA); routinely used by local clinicians to screen cognitive function for assessment of rehabilitation needs post-stroke. The MoCA screens cognitive domains of Executive Function, Naming, Attention, Language, Abstraction, Delayed Recall and Orientation (Nasreddine et al., Reference Nasreddine, Phillips, Bã©Dirian, Charbonneau, Whitehead and Collin2005). It has been shown to predict long-term cognitive and functional outcomes, and is valid and feasible in the acute phase post-stroke (Abzhandadze et al., Reference Abzhandadze, Rafsten, Lundgren Nilsson, Palstam and Sunnerhagen2019; Chiti & Pantoni, Reference Chiti and Pantoni2014; Koski, Reference Koski2013; Zietemann et al., Reference Zietemann, Georgakis, Dondaine, Müller, Mendyk and Kopczak2018).
Cambridge neuropsychological test automated battery (CANTAB)
The CANTAB (Connect version) was the primary cognitive outcome measure. The CANTAB is a computerised, portable (tablet-based), cognitive assessment battery that presents parallel versions and randomised stimuli to minimise practice effects for extensive serial assessment (Cambridge Cognition, 2021). Automated scoring and instruction remove human error, reduce variance in administration and allow non-specialist staff to administer tests. Additionally, on reaction time-based measures, time to move is partitioned from cognitive processing time to decide and react, enabling the separation of cognitive speed from impaired motor functions post-stroke. The subtests primarily utilise visual stimuli, reducing language dependent effects and allowing those with language impairment to participate in testing (Cacciamani et al., Reference Cacciamani, Salvadori, Eusebi, Lisetti, Luchetti, Calabresi and Parnetti2018; Crivelli et al., Reference Crivelli, Spinosa, Angelillo and Balconi2021). Additionally, the CANTAB platform uses embedded practice sessions to ensure participants can successfully perform the task before proceeding to the assessment phase of the test.
The CANTAB features 23 modular subtests, with studies on subtest properties and availability of normative data confined to a limited set (Aslam et al., Reference Aslam, Bates, Dundar, Hounsome, Richardson and Krishan2018). Subtest selection was determined by domains of post-stroke cognitive impairment identified in previous research (Cumming et al., Reference Cumming, Marshall and Lazar2013; Mole & Demeyere, Reference Mole and Demeyere2020) and limited by overall time to perform the customised battery, accommodating for participants’ reduced tolerance early post-stroke. Verbal measures were not selected, for inclusivity of people with aphasia and as aphasia is routinely screened separately. The selected CANTAB measures are outlined in Table 1, in order of presentation for testing.
Table 1. CANTAB Subtests and Key Measures
Procedure
Following enrolment, participant demographic and stroke-specific information were collected. CANTAB assessment was performed daily for 7 days, with follow-up at 14, 30 and 90-days post-stroke (+3 days for 30 and 90-day follow-up) in hospital and subsequent community settings post-discharge. The MoCA, version 7.1 was administered within the first week as per standard clinical practice. The 4-AT was conducted from the date of enrolment, and the DASS-21and WTAR were administered in the first four days with DASS-21 repeated at 14 days. For participants exhibiting symptoms of delirium (scoring ≥4), the 4-AT was repeated, and the number of days with clinically recognised delirium was recorded.
Statistical analysis
Analyses were performed using IBM SPSS for Windows (version 27.0). Participants with missing data for all CANTAB subtests in a single assessment timepoint were removed from analysis (incomplete data). Participants with missing data for some (not all) CANTAB subtests within a timepoint or over multiple timepoints were retained.
Independent samples t-test and Chi-Square were used to compare participants who completed all timepoints with those who did not across key demographic variables. Variables from similar cognitive domains were entered into a repeated measures MANOVA to control for study-wide type I error rate. Significant multivariate tests were followed by repeated measures ANOVAs for each of the variables corrected for family-wise error, to determine significant change over time. Unrelated variables were analysed using independent repeated measures ANOVAs. Post-hoc analyses of change over time were examined by Least Square Differences comparison (p < .05).
Results
Participant information
Of the 88 patients determined eligible to participate, 17 declined and 71 participants were recruited. Thirteen of the 71 participants withdrew prior to 90-day follow-up: four were not accessible for follow-up, two had ongoing medical issues, and 7 declined to participate further. Two participants died, seven missed an entire assessment time point due to technology errors and one failed to attend an appointment. A final sample of 48 participants was available for trajectory analysis (Fig. 1).
Figure 1. Participant flow diagram.
Demographic data
The 48 participants were equally distributed for gender and stroke hemisphere, with a mean age of 73 years, and the majority sustained a mild, ischaemic stroke (NIHSS <6). Average length of stay in acute care approached 5 days with 25% of participants exhibiting symptoms of delirium. More than 40% exhibited symptoms of depression the first week, dropping to 30% by day 14. As per usual clinical practice, participants with functional language deficits were screened using the Western Aphasia Battery. Three participants had moderate and three mild aphasia, with one omitted from analysis due to technology error. Three of these participants could perform the WTAR at 14 days. One was also unable to complete DASS-21 or MoCA the first week but could participate at day 14. All could perform the CANTAB. MoCA mean score was 22.7 with over half scoring at or above 23 (Table 2).
Table 2. Demographic and Clinical Characteristics of Participants with Data for All Timepoints vs Participants with Missing Data in One or More Timepoints Post-stroke (n = 71)
†Data p values determined by Chi-square for categorical variables (Gender, Delirium, Stroke Hemisphere, Stroke Severity, Stroke Type, Depression, Anxiety, Stress) and Independent-Samples t-tests, for the remainder of the comparisons.
‡Where cell is blank, insufficient n for analysis.
§21 year-old outlier removed.
‖Academic professor with multiple PhD’s removed as outlier.
¶Ischaemic includes stroke w/ haemorrhagic transformation.
* Significant at p < 0.05, two-tailed.
The only significant demographic differences between participants analysed and those excluded from analysis was acute care length of stay which was nearly twice as long for those excluded. There was a trend toward greater rates of delirium in excluded participants, however mean MoCA scores were similar between groups suggesting attention difficulties associated with delirium were not severe enough to adversely impact a cognitive screening measure. Three participants in the excluded group had delirium for >10 days. For included participants, delirium clinically resolved an average of 4 days post-stroke. Lastly, a significantly higher proportion of participants with moderate to severe stroke had incomplete data (74%) compared to mild stroke (26%) (Table 2).
Trajectory data
Raw mean scores are summarised in Table 3. Standard scores were available for PALTEA and RVP A’, which indicate that participants’ average scores were trending below a healthy adult population at all time-points for these subtests.
Table 3. CANTAB Subtest Mean Scores by Day Post-stroke
†In n and mean (standard error).
* Indicates significant point of change starting from day 2, with subsequent asterisks in same column indicating a significant point of change from the previous significant point of change.
As missing values occurred at random intervals in the data, they were replaced by the sample mean for that subtest at that timepoint so as not to impact on the overall mean and standard deviation of the sample at that timepoint. Sixteen participants had missing subtest values replaced: seven at day 2; four at day 3; four at day 4; one at day 5; two at day 6; two at day 7; five at day 8; one at day 14; two at day 30; zero at day 90 – representing 1.9% of all values. Out of 2400 subtests administered, 32 subtests were not completed due to participant factors (i.e. fatigue, nausea, difficulties sustaining attention) and 17 were completed but not recorded due to technology errors.
All CANTAB measures correlated with age and DASS-21 scores at r < 0.40; thus, age and depression were not meaningful covariates for any variable. Subtest key measures within the same cognitive test (i.e. RTI and SWM subtests) were significantly correlated with each other, and repeated measures MANOVA was performed for within subtest key measures to control for family-wise error.
Multi-tasking Test
For MTT mean latency on the incongruent trial (MTTLNOM), a significant change over time was found (F (9,423) = 5.557, p. < 0.001, η2 p = 0.106, power = 1.00) (Fig. 2). Post-hoc Least Square Differences comparison of timepoints identified 22 differences (p < 0.05) with performances slower to demonstrate improvements initially, but then readily improving from day 5 until day 8 post-stroke when performances stabilised (Table 4).
Figure 2. The trajectories of estimated marginal mean scores for CANTAB key measures plotted on graphs over ten timepoints from 2 to 90 days post-stroke, where all graphs show a significant trend towards improvement that stabilises from 6-14 days post-stroke, apart from the Spatial Working Memory Strategy subtest which does not show significant change.a
Table 4. Time Points of Significant Change in CANTAB Key Measures*
*p < 0.05
Time points of significant change identified in post-hoc Least Square Differences for CANTAB key measures (p < .05) with: 22 significant points of change for Multitasking Test Incongruent Trial Mean Latency (MTTLNOM), a measure of mental flexibility and executive function; 18 significant points of change for Paired Associates Learning Total Errors Adjusted (PALTEA), a measure of learning and memory; 5 significant points of change for RTI Mean 5-Choice Movement Time (RTIFMMT), a measure of upper limb motor speed; 13 significant points of change for RTI Mean 5-Choice Reaction Time (RTIFMRT), a measure of reaction time; 10 significant points of change for Spatial Working Memory Total Errors (SWMTE), a measure of spatial working memory; 0 significant points of change for Spatial Working Memory Strategy (SWMS), a measure of strategy use and executive function; 37 significant points of change for Rapid Visual Processing ‘A’ Prime (RVPA’), a measure of sustained visual attention.
Paired associates learning
Repeated measures ANOVA was conducted for total errors adjusted on the Paired Associates Learning task (PALTEA) (Fig. 2) identifying a significant change over time (F (9,39) = 4.656, p. < 0.001, η2 p = 0.518, power = 0.994). Post-hoc Least Square Differences comparison of timepoints identified 18 differences (p < 0.05) with performances improving significantly from day 2 to 3 with ongoing improvements until day 14 post-stroke, after which performances stabilised (Table 4). For z-score analysis, one-way repeated measures ANOVA was significant, F (9,423) = 6.104, p. < 0.001, η2 p = 0.115, power = 1.0). With results otherwise identical to raw score analysis.
Five-choice reaction time
Across both five-choice RTI measures (movement time and reaction time), repeated measures MANOVA returned a significant effect of time (F (18,30) = 2.656, p. = 0.009, η2 p = 0.614, power = 0.964). Follow-up repeated measures ANOVAs for each of the subtests, corrected for family-wise error, were performed. For RTI five-choice movement time (RTIFMMT), a significant change over time was found (F (9,423) = 1.883, p. = 0.053, η2 p = 0.039, power = 0.828) (Fig. 2). For RTI five-choice mean reaction time (RTIFMRT), a significant change over time was also identified (F (9,423) = 3.189, p. = 0.001, η2 p = 0.064, power = 0.980) (Fig. 2). Post-hoc Least Square Differences comparison of timepoints identified 13 differences (p < .05) in RTIFMRT and 5 in RTIFMMT. Movement time (RTIFMMT) performances improved steadily until day 8 post-stroke. Reaction time (RTIFMRT) showed significant improvements to day 6 after which performances stabilised (Table 4).
Spatial working memory
For SWM, a slightly different picture emerged. Repeated measures MANOVA of both SWM strategy (SWMS) and SWM total errors (SWMTE), to control for family-wise error, identified a significant effect of time (F (18,30) = 2.989, p. = 0.004, η2 p = 0.642, power = 0.981). Follow-up repeated measures ANOVAs for each of the subtests corrected for family-wise error returned mixed results. For SWMS, no significant change over time was found (F (9,423) = 1.443, p. = 0.167, η2 p = 0.030, power = 0.693), while for SWMTE a significant change over time was found (F (9,423) = 2.452, p. = 0.010, η2 p = 0.050, power = 0.928) (Fig. 2). Post-hoc Least Square Differences comparison of timepoints identified 10 differences in SWMTE (p < 0.05) with performances improving until day 14 post-stroke, after which performances stabilised (Table 4).
Rapid visual processing
Lastly, for RVP A’, a repeated measures ANOVA identified a significant change over time (F(9,423) = 12.922, p. < 0.001, η2 p = 0.749, power = 1.00) (Fig. 2). Post-hoc Least Square Differences comparison of timepoints identified 37 differences (p < 0.05) with performances improving until day 6 where they slowed slightly but continued to improve until day 14 post-stroke, after which performances stabilised (Table 4). For z-score analysis, one-way repeated measures ANOVA was significant (F (9,423) = 26.015, p. < 0.001, η2 p = 0.356, power = 1.0). With results otherwise identical to raw score analysis.
In summary, post-hoc analyses of change over time for MTTLNOM, PALTEA, RTIFMRT, RTIFMMT, and RVP A’ showed a significant change within the first week of measurement, to 8 days post-stroke (Table 3). The SWMTE post-hoc analysis did not identify significant change until day 14 post-stroke (Table 3). The SWMS measure did not show any significant change (Table 3). All measures had stabilised by day 14 post-stroke. Raw mean scores are available in Table 4. Standardised scores and percentile scores are available for PALTEA and RVP A’and reveal participant’s mean scores fall below a healthy adult normative population
Discussion
Mapping the trajectory of cognitive recovery post-stroke
We found that cognitive recovery occurs steadily over the first 2 weeks post-stroke, a period we identify as post-stroke delirium, which has significant clinical implications for cognitive assessment in the acute stroke unit. Quality statements for acute stroke clinical care standards recommend that stroke survivors be assessed for rehabilitation needs, including cognitive status, within the first few days of admission to an acute stroke unit (Lindsay et al., Reference Lindsay, Furie, Davis, Donnan and Norrving2014). Current practice in the acute stroke unit is screening cognition at a single time-point during a period of early, predictable global improvement (Quinn et al., Reference Quinn, Elliott and Langhorne2018). Cognitive assessment results in this early phase, therefore, should be used as a marker for monitoring improvement and to direct acute clinical decisions, rather than an indicator of long-term impairment.
Monitoring cognitive improvement with pen and paper cognitive screening assessments poses a problem as alternate forms are largely not validated for serial testing over short time intervals or have few alternate versions (Costa et al., Reference Costa, Fimm, Friesen, Soundjock, Rottschy and Gross2012; Nasreddine & Patel, Reference Nasreddine and Patel2016). In contrast, many computerised cognitive assessments, such as the CANTAB, are designed and validated for serial testing over short periods of time (Zygouris & Tsolaki, Reference Zygouris and Tsolaki2015); and, when used in conjunction with functional clinical assessment (Aslam et al., Reference Aslam, Bates, Dundar, Hounsome, Richardson and Krishan2018), can be used to measure improvements during this period of acute recovery.
Our second finding is that early cognitive recovery largely stabilises by 6 to 14 days post mild stroke. Measures of visual episodic learning (PAL), impulse control and decision making (MTT), information processing speed (RTIFMRT), motor speed (RTIFMMT) and complex sustained attention (RVP) demonstrated steady significant change in the first week. Visual working memory (SWM) was slower to recover in the first week but showed significant change by day 14 when all measures stabilised. These results are consistent with a study that demonstrated generalised rather than domain specific cognitive improvement measured by traditional neuropsychological testing in the acute phase and followed up 12-24 months later (van Zandvoort et al., Reference van Zandvoort, Kessels, Nys, de Haan and Kappelle2005). Additionally, differences in pace of recovery for cognitive processing speed (RTIFMRT), which significantly increased to day 6, and motor speed (RTIFMMT), which demonstrated slightly slower recovery to day 8, confirms the importance of partitioning the two during early testing. Our results indicate that early cognitive recovery begins to stabilise from days 6 to 14 in mild ischaemic stroke survivors, suggesting that formal neuropsychological assessment may occur significantly earlier than previously considered to be clinically appropriate (De Haan et al., Reference De Haan, Nys and Van Zandvoort2006).
Domain-specific testing
Improved understanding of domain-specific patterns of recovery is clinically useful. Targeted cognitive assessment of a single domain (processing speed), found to be linked to long-term cognitive and functional impairment, has previously been suggested as time-efficient (Cumming et al., Reference Cumming, Brodtmann, Darby and Bernhardt2012). Our results indicate that specific CANTAB subtests display high levels of sensitivity to early cognitive change following mild ischaemic stroke. The RVP A’ measures complex sustained attention using a target sensitivity paradigm. Performances on RVP A’ displayed steady, consistent improvement, showing significant daily change from very early post-stroke, day 4 through day 14, with flattening of the trajectory out to 30 and 90 days. The measure displayed narrow variance across all time-points indicating a high degree of reliability of measurement.
The only measure to demonstrate no change over time was the Spatial Working Memory Strategy (SWMS) task, which involves use of strategy to minimise working memory errors when attempting to recall and manipulate visuospatial information (Cambridge Cognition, 2022). A small study of 25 participants with amnestic mild cognitive impairment found a practice effect for the SWMS task when testing was separated by 6 months (Cacciamani et al., Reference Cacciamani, Salvadori, Eusebi, Lisetti, Luchetti, Calabresi and Parnetti2018). However, our participants failed to demonstrate any significant change in score despite daily testing. Executive dysfunction is poorly predicted by stroke and is often present prior to stroke. (Heshmatollah et al., Reference Heshmatollah, Dommershuijsen, Fani, Koudstaal, Ikram and Ikram2021; Kliper et al., Reference Kliper, Ben Assayag, Tarrasch, Artzi, Korczyn and Shenhar-Tsarfaty2014; Veldsman et al., Reference Veldsman, Werden, Egorova, Khlif and Brodtmann2020). Possibly this measure reflects a high degree of stable pre-stroke executive dysfunction that did not change due to the chronic nature of the impairment. The non-significance observed on this measure suggests that SWMS is a less reliable method to detect early change following mild stroke.
Post-stroke delirium
We propose that this early improvement across a broad sample of cognitive domains is evidence of a distinct state of acute stroke-induced delirium that precedes a slower neuroplastic recovery process, consistent with clinical observations of this population. This acute recovery is expected to be due to secondary effects of the stroke resolving, rather than resolution of primary effects of neuronal death, which are not likely to demonstrate such quick restitution (Gottesman & Hillis, Reference Gottesman and Hillis2010; McDonald et al., Reference McDonald, Black, Copland, Corbett, Dijkhuizen and Farr2019).
In keeping with a delirium state, the effect of post-stroke delirium appears to be generalised across cognitive domains rather than domain specific. All CANTAB measures (Fig. 2) demonstrated pronounced cognitive improvement, with the notable exception of SWMS which did not show significant change. Other studies have revealed that when detailed neuropsychological testing is performed early post-stroke, the cognitive profile remains similar 6-24 months later, with the same constellation of impaired versus unimpaired domains (Nys et al., Reference Nys, Van Zandvoort, De Kort, Jansen, Van Der Worp, Kappelle and De Haan2005; Wolf & Rognstad, Reference Wolf and Rognstad2013; van Zandvoort et al., Reference van Zandvoort, Kessels, Nys, de Haan and Kappelle2005). Two of these studies assessed cognition 4-20 days post-stroke, and found generalised improvements, and the third study by Wolf & Rognstad (Reference Wolf and Rognstad2013) assessed cognition at 3 weeks and found a stable cognitive profile. This generalised global recovery was captured when testing occurred at <3 weeks post-stroke, which is consistent with our findings. However, our findings suggest an earlier stabilisation of post-stroke delirium between 6 and 14 days post mild stroke.
Other studies have suggested that acute stroke causes a delirium-state (Maldonado, Reference Maldonado2018; McManus et al., Reference McManus, Pathansali, Hassan, Ouldred, Cooper and Stewart2009; Stokholm et al., Reference Stokholm, Steenholt, Csilag, Kjær and Christensen2019), but acknowledged difficulty in distinguishing which factors were associated with delirium. Stroke severity is a known predictor of delirium (Oldenbeuving et al., Reference Oldenbeuving, de Kort, Jansen, Algra, Kappelle and Roks2011; Qu et al., Reference Qu, Chen, Luo, Zhong, Xiao and Yin2018a), which supports the premise that stroke induces a delirium state in the acute phase. A systematic review of delirium assessment tools used in stroke and variations in reported delirium, suggested that acute stroke may cause delirium-like symptoms (Stokholm et al., Reference Stokholm, Steenholt, Csilag, Kjær and Christensen2019). A comparison of two delirium screening tools concluded that delirium detected within four days of stroke was precipitated by the stroke as no other cause was evident (Caeiro et al., Reference Caeiro, Ferro, Albuquerque and Figueira2004). Another found higher incidence of delirium in an acute stroke population when compared to acute coronary patients and concluded that delirium was likely caused by stroke (Mc Manus et al., Reference Mc Manus, Pathansali, Hassan, Ouldred, Cooper and Stewart2009). However, these studies did not directly demonstrate early, global cognitive recovery separate from clinically recognised delirium.
One quarter of our participants had observable symptoms of delirium, captured on the 4-AT, which is validated for use in acute stroke (Lees et al., Reference Lees, Corbet, Johnston, Moffitt, Shaw and Quinn2013; Qu et al., Reference Qu, Chen, Luo, Zhong, Xiao and Yin2018b). This proportion is consistent with prior research reporting delirium to be present in 13-48% of stroke survivors (Oldenbeuving et al., Reference Oldenbeuving, De Kort, Jansen, Roks and Kappelle2007). However, using the 4-AT, most participants with symptomatic delirium appeared to resolve by day four post-stroke. When testing with a highly sensitive, computerised cognitive battery, the steady recovery we were able to measure implies ongoing resolution of this delirium state, beyond the days captured by the 4-AT. Sub-syndromal delirium has been described in the acute stroke population (Pasinska et al., Reference Pasinska, Kowalska, Klimiec, Szyper-Maciejowska, Wilk and Klimkowicz-Mrowiec2018), which suggests that post-stroke delirium is on a continuum of severity made apparent by the more sensitive computerised testing used in our study. For mild post-stroke delirium, fluctuations in attention, a feature used in the clinical diagnosis of delirium (American Psychiatric & American Psychiatric Association, 2013), may be less apparent and therefore not captured by the delirium screening. These results illustrate that cognitive impairment is pervasive acutely post-stroke.
Strengths
Serial computerised cognitive assessment was sensitive enough to capture detailed information on domain-specific cognitive impairment and demonstrate significant recovery in a predominantly mild, ischaemic stroke cohort. These computerised assessments are highly reproducible and protect against practice effect ensuring the findings are likely to be reliable. We were also able to include stroke patients with aphasia who are often excluded in cognitive research (Wall et al., Reference Wall, Isaacs, Copland and Cumming2015). Further, we compared sensitive cognitive testing with a validated delirium screen to highlight limitations of delirium screening in a mild stroke cohort. Lastly, we gathered data up to 90 days to ensure we captured the period of greatest change post-stroke.
Limitations
Participants were classified as predominantly mild stroke and results less generalisable to a severe stroke cohort. Our selected subtests do not include assessment of language and Verbal Paired Associates could be added to address this commonly impaired domain. As well, this is a single-centre study in a dedicated acute stroke unit and results may differ in alternate settings. Lastly, our cohort of 48 participants was small and additional research with larger numbers will be required to verify these results.
Suggestions for further research
Further research is recommended to confirm the stability of domain-specific patterns of post-stroke cognitive impairment which would assist early, targeted cognitive interventions. Large studies of computerised testing could define more sensitive thresholds for identifying delirium than current screening tools. Further, exploring a broader range of computerised measures may reveal tests optimal for both screening and identification of specific clinically relevant deficits.
Some studies have found that cognition continues to recover months to years post-stroke (Hofgren et al., Reference Hofgren, Björkdahl, Esbjörnsson and Stibrant-Sunnerhagen2007; Hurford et al., Reference Hurford, Charidimou, Fox, Cipolotti and Werring2012), while others demonstrate a decline in cognitive function (Jokinen et al., Reference Jokinen, Melkas, Ylikoski, Pohjasvaara, Kaste, Erkinjuntti and Hietanen2015; Levine et al., Reference Levine, Galecki, Langa, Unverzagt, Kabeto, Giordani and Wadley2015). Hochstenbach et al. (Reference Hochstenbach, Den Otter and Mulder2003) found that most stroke survivors showed no change in cognitive performance over a 2-year period, while some declined and others improved. The stroke survivors that improved accounted for the general upward trend of cognitive function. Further research may reveal different patterns of acute post-stroke recovery, which may be useful in predicting individual cognitive trajectories.
Conclusion
Serial computerised cognitive assessment is able to illustrate early generalised recovery over 14 days that is evidence for a distinct post-stroke delirium state. Post-stroke delirium appears to be far more common than is currently appreciated, and can be measured, even in mild stroke. Computerised cognitive assessment may be a more sensitive tool to identify delirium than current clinical screening tools. We have identified stabilisation of this acute cognitive recovery between 6 and 14 days for mild-stroke, indicating that more detailed neuropsychological testing may be performed earlier than previously understood. Further exploration of serial cognitive performance in delirium states could help us better understand this common and costly complication.
Data availability statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Acknowledgements
The authors gratefully thank all the participants. They acknowledge the valued contributions of the Allied Health Assistants, Christopher Duffy and Michael Lee, who assisted with data collection. The authors also thank staff of the Acute Stroke and Rehabilitation Units of the Sunshine Coast University Hospital for their support.
Authors’ contributions
AC, MS, LG and RG developed the concept and design. AC, LG, IR and RG prepared the protocol and ethics application. RG, LG and IR obtained funding. AC collected the data. MS and AC performed the statistical analysis. AC drafted the manuscript. All authors participated in the critical revisions of the manuscript.
Funding
This work was supported by the Sunshine Coast Hospital and Health Service – Transformation and Transition Funding.
Conflicts of interest
None.
Ethics approval
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Ethical approval was obtained from The Prince Charles Hospital Human Research Ethics Committee, HREC/17/QPCH/163; the University of Queensland (#2017001149) and from University of the Sunshine Coast (S/17/1091).
Consent to participate
Informed consent was obtained from individual participants and/or substitute decision-makers for all participants included in the study.
Consent for publication
Participants or their substitute decision-makers signed informed consent regarding publishing their data.
Open access
