# Prevalence of poststroke anxiety and its associations with global cognitive impairment: An individual participant data analysis.

Florine Ruthmann, J. W. Lo, Anne Marie Bordet, Etienne Allart, S. Köhler, A. Klimkowicz-Mrowiec, J. Staals, P. S. Sachdev, Regis Bordet, Thibaut Dondaine

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## To cite this version:

Florine Ruthmann, J. W. Lo, Anne Marie Bordet, Etienne Allart, S. Köhler, et al.. Prevalence of poststroke anxiety and its associations with global cognitive impairment: An individual participant data analysis.. Journal of Affective Disorders, 2024, J Affect Disord, 369, pp.1136-1144. 10.1016/j.jad.2024.10.099. hal-04977485

HAL Id: hal-04977485

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Submitted on 5 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Research paper

# Prevalence of poststroke anxiety and its associations with global cognitive impairment: An individual participant data analysis

Florine Ruthmann<sup>a</sup>, Jessica W. Lo<sup>b</sup>, Anne-Marie Mendyk-Bordet<sup>a</sup>, Etienne Allart<sup>a</sup>, Sebastian Köhler<sup>c</sup>, Aleksandra Klimkowicz-Mrowiec<sup>d</sup>, Julie Staals<sup>e</sup>, Perminder S. Sachdev<sup>b</sup>, Régis Bordet<sup>a</sup>, Thibaut Dondaine<sup>a, *, f</sup>, for the STROKOG Collaboration

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## ARTICLE INFO

**Keywords:**
Stroke
Anxiety
Psychiatry
Cognition
Multicentre study

## ABSTRACT

**Background and purpose:** Anxiety is frequent after stroke; however, little is known about its determinants. This study aims to assess the prevalence and correlates of post stroke anxiety (PSA) within 3–6 months following ischemic stroke.

**Methods:** Three cohort studies from the STROKOG consortium were involved. Demographic and clinical data were standardized. PSA and PSD were assessed using inventories. The criteria for post-stroke cognitive impairment (PSCI) were at least one cognitive domain impaired if applicable, or a Montreal Cognitive Assessment (MoCA) score. Descriptive analyses were conducted to ascertain the prevalence of anxiety. Comparisons between anxious and non-anxious patients in the total sample were made using $\chi^2$ and t-tests. A two-step individual participant data (IPD) meta-analysis was employed to identify factors associated with PSA.

**Results:** 584 patients were included. The total prevalence of PSA was 35 % (95%CI = [31.23;38.97]) and ranged from 27 % to 45 % across cohorts. In the total sample, there was a higher proportion of females in the anxiety group than the non-anxiety group ($\chi^2$ = 19.62; *p* < 0.001). Anxious patients had lower education, ($\chi^2$ = 6.59; *p* = 0.03), higher stroke severity (*t* = 2.77; *p* = 0.002), and higher rates of PSD ($\chi^2$ = 118.09; *p* < 0.001), and PSCI ($\chi^2$ = 23.81, *p* < 0.001). The analysis demonstrates that the odds of presenting with PSA is larger in patients with PSCI (OR = 1.84, 95%CI = [1.14; 2.91]).

**Conclusions:** Anxiety is frequent after stroke, especially in females, and is associated with depression and cognitive impairment.

## 1. Introduction

Psychiatric disorders frequently occur following a stroke and can significantly hinder the recovery process (Hackett et al., 2014). However, there remains a paucity of research on PSA*.

Anxiety encompasses subjective experiences, physiological responses, and action tendencies triggered by perceived threats or negative events (Barlow et al., 2003). Anxiety disorders also manifest through physiological and cognitive symptoms.

Anxiety has been shown to be more prevalent in stroke patients than

in the general population (Cumming et al., 2011). Indeed, PSA emerges as a common complication, affecting approximately 1/5 to 1/4 of patients (Knapp et al., 2020). This condition has negative effects on patients' quality of life and their ability to reintegrate into society (Tang et al., 2013). Notably, anxiety endures into the chronic phase of stroke, with approximately 29 % of patients reporting its presence 5 years after stroke (Lincoln et al., 2013).

Despite the increasing number of studies exploring the psychiatric consequences of stroke, PSA has received comparatively less attention and has mainly been studied as a coexisting factor in PSD. PSA and

<footnote><sup>a</sup> Lille Neuroscience et Cognition, Degenerative and Vascular Cognitive Disorders Team, UMR-S 1172, INSERM, Lille F-59000, France</footnote>

<footnote><sup>b</sup> Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia</footnote>

<footnote><sup>c</sup> Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Alzheimer Center Limburg, Maastricht University, the Netherlands</footnote>

<footnote><sup>d</sup> Department of Internal Medicine and Gerontology, Jagiellonian University Medical College, Krakow, Poland</footnote>

<footnote><sup>e</sup> Department of Neurology, School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands</footnote>

<footnote>* Corresponding author at: Service de pharmacologie, Faculté de Médecine, Pôle Recherche, 1, place de Verdun, 59045 CEDEX Lille, France.
E-mail address: thibaut.dondaine2@univ-lille.fr (T. Dondaine).</footnote>
PSD** have been frequently observed as co-morbid conditions (Rabat et al., 2021), yet the pathophysiological mechanisms for these two consequences appear to diverge (Nutt, 2004). It is intriguing to investigate the interplay between anxiety and depression following a stroke, particularly in relation to cognitive impairments, as separate considerations. A more profound comprehension of PSA is imperative to enhance its detection, treatment, and, ultimately, post-stroke outcomes.

While the strong link between depression and anxiety following stroke is well-established (Bergersen et al., 2010; Schöttke and Giabbiconi, 2015) further investigation is needed to pinpoint reliable correlates of PSA (Wright et al., 2017). Some studies suggested that younger age may be associated with PSA (Kapoor et al., 2019) but conversely, older age has also been identified as a potential risk factor (Menlove et al., 2015). Regarding sex, studies suggest that females exhibit a higher prevalence of PSA (Morrison et al., 2005). Lesion location has also been explored as a contributing factor to emotional disorders post-stroke, but the role of lesion lateralization remains a topic of debate (Morrison et al., 2005; Robinson and Jorge, 2016).

*PSA: post stroke anxiety.

**PSD: post stroke depression.

Post-stroke patients face a notably elevated risk of developing PSCI*** even following a successful clinical recovery (Jokinen et al., 2015a). While PSCI has been recognized as a factor in PSD (Robinson and Jorge, 2016), there exist limited and conflicting findings regarding the relationship between PSA and PSCI.

Although some studies reported an association of PSA with global or specific cognitive impairment, there is yet no consensus (Barker-Collo, 2007; Williams and Demeyere, 2021; Hama et al., 2020). Previous studies in healthy and pathological populations demonstrated that anxiety and cognition are closely related (Castaneda et al., 2008; Vytal et al., 2012). Ultimately, higher rates of PSCI and dementias have been reported in anxious individuals (Gulpers et al., 2016). Investigating the relationship between anxiety and cognition is therefore crucial in the as-risk population of post-stroke patients.

Further research is essential to explore PSA determinants, given the diversity of results and the limited number of identified predictors.

Building on recent findings in the literature (Menlove et al., 2015; Sanner Beauchamp et al., 2020; Rafsten et al., 2018; Almhdawi et al., 2021), we hypothesized that PSA is linked to PSCI. Consequently, the objective of this study was to determine PSA prevalence and to investigate the demographic, clinical, and cognitive factors associated with PSA in three cohorts of ischemic stroke patients drawn from the STROKOG consortium.

# 2. Methods

## 2.1. Contributing studies

Three cohorts from the Stroke and Cognition Consortium (STROKOG) were included in the analyses (Sachdev et al., 2017). The inclusion criteria encompassed hospital-based studies that had shared data in 2021 and reported demographic, clinical and cognitive data of patients at either three or six months following a stroke. The procedures of the consortium have received approval from the University of New South Wales Human Research Ethics Committee (reference HC14359). Additionally, each individual study within the consortium obtained approval from its respective local ethics committee.

***PSCI = post stroke cognitive impairment.

## 2.2. Cohort characteristics and patients’ inclusion

The CASPER study was conducted in the Netherlands between 2014 and 2018, with our analysis centred on the three-months baseline evaluation. Meanwhile, the STROKDEM study spanned from 2011 to 2018, with our analysis centred on the six-month baseline evaluation. Lastly, the PROPOLIS study was carried out in Poland from 2014 to

2016, with our analysis centred on the three-month baseline evaluation. All patients from the three cohorts provided informed consent.

In the CASPER and STROKDEM cohorts, pre-stroke dementia was assessed using clinical scales (DSM-5, NINCDS-ADRDA, NINDS-AIREN for CASPER, and IQ-CODE for STROKDEM). Detailed descriptions of exclusion and inclusion criteria, as well as study designs, can be found in previous publications (Klimiec et al., 2015; Zietemann et al., 2018; Douven et al., 2016).

The inclusion criteria for this study encompassed patients aged >18 years who had experienced ischemic stroke. We excluded individuals with transient ischemic attacks (TIAs), haemorrhagic strokes, infratentorial strokes. We also excluded patients with pre-stroke dementia, and patients who have not received a neuropsychological evaluation (including PSA, PSD and PSCI) at the three or six-months evaluation points.

Based on these criteria, we included a total of 196 patients (78 %) from the CASPER cohort, 138 patients (67 %) from the STROKDEM cohort, and 250 patients (33 %) from the PROPOLIS cohort in this study. Comparative analyses between included and non-included patients are presented in the supplemental data (Supplementary analyses, table1).

## 2.3. Data harmonization

### 2.3.1. Demographics

Demographic characteristics of the patients under consideration included biological sex, age, and education. Educational level was collected with different modalities across cohorts, therefore we categorized patients into three groups: <12 years, 12 years, and > 12 years of education. Demographic data were summarized for continuous variables (mean, SD) and categorical variables (N%), both for the combined and individual samples.

### 2.3.2. Clinical data

Lesion localizations were categorized as, left, right, and other. Stroke aetiology was determined based on the TOAST classification, but this classification was available only to the STROKDEM and PROPOLIS studies (Adams et al., 1993). These characteristics were established through imaging (CT scan or MRI) and/or neurological examination. In all three samples, medical history data included hypertension, diabetes, and smoking status. The evaluation of the severity of neurological deficits, assessed using the NIH Stroke Scale (Brott et al., 1989), was conducted in the PROPOLIS and STROKDEM studies.

### 2.3.3. Neuropsychological evaluation: PSA, PSD and PSCI

Clinical scales were employed to assess the presence or absence of anxiety and depression. Specific tests and their associated cut-off values are elaborated in Table 1. The selection of these cut-offs for the present study was guided by test instructions or normative data relevant to the populations of interest, which included the patients’ country of origin

<table><caption>**Table 1**
Tests and cut-offs used to assess PSA, PSD and PSCI.</caption><thead><tr><th></th><th>CASPER</th><th>STROKDEM</th><th>PROPOLIS</th></tr></thead><tbody><tr><td>PSA</td><td>Hospital Anxiety and Depression Scale, anxiety subscale (HADS-A) > 7 <sup>1</sup></td><td>Hamilton Anxiety Rating Scale (HARS) ≥ 8 <sup>2</sup></td><td>State Trait Anxiety Inventory, trait subscale (STAI-T) ≥ 45 <sup>3</sup></td></tr><tr><td>PSD</td><td>Hospital Anxiety and Depression Scale (HADS-D) Cut-off >7 <sup>1</sup></td><td>Center for Epidemiologic Studies Depression Scale (CES-D) ≥ 16 <sup>4</sup></td><td>Patient Health Questionnaire (PHQ-9) Cut-off ≥10 <sup>5</sup></td></tr><tr><td>PSCI</td><td>Cognitive z-scores Cut-off ≥1.65 standard deviation below the mean score for at least one cognitive domain</td><td>Cognitive z-scores Cut-off ≥1.65 standard deviation below the mean score for at least one cognitive domain</td><td>Montreal Cognitive Assessment (MoCA) ≤ 23 <sup>7</sup></td></tr></tbody></table>
and post-stroke individuals when applicable. Additionally, the chosen cut-offs were designed to provide sensitivity and to encompass patients with varying degrees of disorder, ranging from probable to mild, moderate, and severe.

The variable “cognitive impairment” was coded using different methods depending on the available data in each study (see Table 1). In CASPER and STROKDEM studies, harmonized cognitive scores were calculated in five cognitive domains: memory, attention, executive function, language and perceptual motor (Lo et al., 2020a). Patients with at least one domain-specific Z-score falling below −1.65 were classified as cognitively impaired. In the PROPOLIS study, cognitive impairment was determined based on the MoCA score and using established cut-off (Magierska et al., 2012).

## 2.4. Standard protocol approvals

Procedures of the consortium have been approved by the University of New South Wales Human Research Ethics Committee (reference HC14359). All studies had ethics approval from local institutional review boards.

## 2.5. Statistical analyses

We conducted comparative analyses employing $\chi^2$ test and t-test. We first compared data from PROPOLIS, CASPER, and STROKDEM patients to identify potential differences between cohorts. We then compared anxious and non-anxious patients to identify differences in demographic and clinical variables. These results are reported for the total sample and for each sex separately.

To investigate the factors associated with PSA, we employed a two-stage individual participant data (IPD) meta-analysis (Stewart et al., 2012; Veroniki et al., 2023).

Firstly, logistic regressions were implemented in each one of the three cohorts to identify the factors contributing to the presence of PSA. The factors considered in each model were PSCI (binary variable), PSD (binary variable), age (continuous variable), biological sex (binary variable), education (ordinal variable), history of stroke (all binary variables), and cardiovascular risk factors if available. McFadden's pseudo-R² values were calculated to determine the predictive power of these logistic regressions.

These univariable regressions were performed using the R function 'lme' with the logit method in the lme4 package.

In the second stage of the IPD analysis, we employed meta-analytic fixed-effects models using the coefficient and its associated standard error obtained from the logistic regression model. This stage was carried out separately for each factor that was significantly associated with PSA in at least one cohort during the first stage. These fixed-effects models were implemented using the 'rma' function from the metafor package in R.

The fixed-effects model was evaluated using the $I^2$ statistic. The $I^2$ statistic quantifies the proportion of total variation attributed to heterogeneity, with values exceeding 60 % indicating substantial heterogeneity. The $H^2$ statistic measures heterogeneity, with a value close to 1 indicating greater homogeneity (Knapp et al., 2020; Higgins and Thompson, 2002). The forest plots generated illustrate the observed effects and the corresponding 95 % confidence intervals for each study, as well as the pooled results.

All statistical analyses were performed using R Studio version 2022.07.2–576. The threshold for statistical significance was defined as $p < 0.05$ in two-sided tests.

## 2.6. Data availability

Anonymized data and the scripts used for statistical analysis will be shared on request from any qualified investigator.

# 3. Results

## 3.1. Population characteristics and anxiety prevalence

A total of 584 participants, with a mean age of 67.6 ± 12.3 years, were included in the analyses. Among them, 344 (59 %) were male with an average age of 65.7 ± 11.7 years, while 240 (41 %) were female with an average age of 70.3 ± 12.5 years.

The prevalence of anxiety was 27 % (95%CI = [20.8; 33.2]) in the CASPER study, 28 % (95%CI = [20.5, 35.5]) in the STROKDEM study, and 45 % (95%CI = [38.8; 51.2]) in the PROPOLIS study. In the total sample, 205 patients experienced PSA (prevalence of 35 %, 95%CI = [31.23; 38.97]), 147 patients experienced PSD (prevalence of 25 %, 95% CI = [21.7, 28.7]), and 229 patients were cognitively impaired (prevalence of 39.2 %; 95%CI = [35.3, 43.2]).

Patient characteristics and variations among cohorts are outlined in Table 2. The data exhibited differences across the cohorts. Notably, individuals from the PROPOLIS cohort were older, had a higher frequency of prior stroke history, experienced more severe strokes (in comparison to STROKDEM patients), and displayed the highest rates of both PSCI and PSA.

## 3.2. Differences between anxious and non-anxious patients

Table 3 presents comparative analyses between anxious and non-anxious patients from the total sample. Within the PSA group, a higher proportion of females was observed ($\chi^2$ = 19.62; $p < 0.001$), and patients had fewer years of education ($\chi^2$ = 6.59; $p = 0.03$). Additionally, anxious patients exhibited higher NIHSS scores ($t$ = 2.77; $p = 0.002$), a greater prevalence of PSD ($\chi^2$ = 118.09; $p < 0.001$), and a higher incidence of PSCI ($\chi^2$ = 23.81; $p < 0.001$).

Anxious females displayed higher prevalence of depression ($\chi^2$ = 56.2; $p < 0.001$) and PSCI ($\chi^2$ = 13, $p < 0.001$) than non-anxious females. Anxious males were less educated ($\chi^2$ = 6.98, $p = 0.03$) displayed higher prevalence of PSD ($\chi^2$ = 57.6, $p < 0.001$) and PSCI ($\chi^2$ = 10.2, $p = 0.001$) than non-anxious males.

## 3.3. Factors associated with PSA

### 3.3.1. Logistic regression by cohorts

Table 4 presents the associations between demographic and clinical factors with PSA.

The logistic regressions conducted in each cohort included the following variables: PSD, sex, age, education, history of stroke, PSCI and cardiovascular variables if available.

The predictive power of the model was deemed acceptable in PROPOLIS (McFadden's pseudo-R² = 0.317) and CASPER (pseudo-R² = 0.226). In contrast, the predictive power of the STROKDEM model was modest (pseudo-R² = 0.157).

The factors significantly associated with PSA were PSD, female sex (in CASPER only), and previous stroke (in PROPOLIS only). These three factors, as well as CI, which is the focus of our hypothesis, were further examined in an IPD analysis.

### 3.3.2. Association with cognitive impairment

The pooled OR demonstrated an association between PSCI (using the absence of impairment as the reference) and PSA, with an OR of 1.83 (95 % IC = [1.14.2.91] (Fig. 2 a)). The z-value for this estimate is 2.53, yielding a p-value of 0.01. There was no evidence for heterogeneity among the studies ($I^2$ = 0 % and $Q(df = 2) = 0.46, p = 0.80$), and the H value indicated that 1 % of the total variability can be attributed to sampling variability.

### 3.3.3. Association with depression

The pooled OR demonstrated an association between PSD (using the absence of depression as the reference) and PSA, with an OR of 12.45
<table><caption>**Table 2**
Comparison of demographic and clinical data across the three cohorts.</caption><thead><tr><th></th><th>CASPER</th><th>PROPOLIS</th><th>STROKDEM</th><th>Total</th><th>Tests for difference between cohorts</th></tr></thead><tbody><tr><td><strong>N</strong></td><td>196</td><td>250</td><td>138</td><td>584</td><td></td></tr><tr><td>Age mean (SD)</td><td>68.1<br>(11.6)</td><td>69.6<br>(12.1)</td><td>63.1 (12.6)</td><td>67.6<br>(12.3)</td><td>F =<br>13.63<sup>***</sup></td></tr><tr><td>Female n (%)</td><td>71<br>(36.2)</td><td>121 (48.4)</td><td>48 (34.8)</td><td>240<br>(41.1)</td><td>χ<sup>2</sup> =<br>9.70<sup>**</sup></td></tr><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;12 years n (%)</td><td>81<br>(41.3)</td><td>143(57.2)</td><td>82(59.4)</td><td>306<br>(52.4)</td><td>χ<sup>2</sup> =<br>25.24<sup>***</sup></td></tr><tr><td>12 n (%)</td><td>60<br>(30.6)</td><td>38(15.2)</td><td>17(12.3)</td><td>115<br>(19.7)</td><td></td></tr><tr><td>&gt;12 n (%)</td><td>55<br>(28.1)</td><td>69 (27.6)</td><td>39(28.3)</td><td>163<br>(27.9)</td><td></td></tr><tr><td><strong>Stroke location</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Left n (%)</td><td>87<br>(44.4)</td><td>116(46.4)</td><td>69(50.0)</td><td>272<br>(52.7)</td><td>χ<sup>2</sup> =<br>48.45<sup>**</sup></td></tr><tr><td>Right n (%)</td><td>107<br>(54.6)</td><td>98 (39.2)</td><td>69(50.0)</td><td>274<br>(47.3)</td><td></td></tr><tr><td>Other n (%)</td><td>2 (1.00)</td><td>36 (14.4)</td><td>0 (0)</td><td>38</td><td></td></tr><tr><td><strong>Vascular risk factors</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>History of stroke n(%)</td><td>12 (6.1)</td><td>42 (16.8)</td><td>14 (10.1)</td><td>68<br>(11.6)</td><td>χ<sup>2</sup> =<br>12.57<sup>**</sup></td></tr><tr><td>Diabetes mellitus n(%) (%)</td><td>28<br>(14.3)</td><td>72(28.8)</td><td>75(54.3)</td><td>175<br>(30)</td><td>χ<sup>2</sup> =<br>62.21<sup>***</sup></td></tr><tr><td>Hypertension n (%)</td><td>141<br>(71.9)</td><td>177(70.8)</td><td>75(54.3)</td><td>393<br>(67.3)</td><td>χ<sup>2</sup> =<br>13.83<sup>***</sup></td></tr><tr><td>Smoking n (%)</td><td>32<br>(16.3)</td><td>72(28.8)</td><td>29 (21.0)</td><td>133<br>(22.8)</td><td>χ<sup>2</sup> =<br>10.04<sup>***</sup></td></tr><tr><td><strong>Stroke aetiology</strong></td><td></td><td></td><td></td><td></td><td>χ<sup>2</sup> = 6.76</td></tr><tr><td>Large artery % (n)</td><td>Na.<sup>1</sup></td><td>20 (8.0)</td><td>16</td><td>40<br>(10.3)</td><td></td></tr><tr><td>Small vessel % (n)</td><td>Na<sup>1</sup></td><td>16 (6.4)</td><td>7 (5.1)</td><td>23<br>(5.9)</td><td></td></tr><tr><td>Cardioembolic % (n)</td><td>Na<sup>1</sup></td><td>81 (32.4)</td><td>32 (23.2)</td><td>113<br>(29.1)</td><td></td></tr><tr><td>Undetermined or Other determined % (n)</td><td>Na<sup>1</sup></td><td>133 (53.2)</td><td>79 (57.2)</td><td>212<br>(54.6)</td><td></td></tr><tr><td>NIHSS mean (SD)</td><td>Na<sup>1</sup></td><td>6.18 (5.6)</td><td>0.67 (1.3)</td><td>4.22<br>(5.3)</td><td>t =<br>14.75<sup>***</sup></td></tr><tr><td>PSA n (%)</td><td>53 (27)</td><td>113 (45.2)</td><td>39 (28.3)</td><td>205<br>(35.1)</td><td>χ<sup>2</sup> =<br>19.62<sup>***</sup></td></tr><tr><td>PSD n (%)</td><td>48<br>(24.5)</td><td>60 (24.0)</td><td>39 (28.3)</td><td>147<br>(25.7)</td><td>χ<sup>2</sup> = 0.93</td></tr><tr><td>PSCI n (%)</td><td>65<br>(33.2)</td><td>124 (50.0)</td><td>40 (29.0)</td><td>229<br>(39.3)</td><td>χ<sup>2</sup> =<br>20.76<sup>***</sup></td></tr></tbody></table>

N.a. = Not Available.

(95 % CI = [6.77; 22.88]) (Fig. 2b). The z-value for this estimate is 8.12, yielding a *p*-value of 0.001. There was no evidence for heterogeneity among the studies (I = 24.99 % and Q(df = 2) =2.74, *p* = 0.25), and the H value indicated that 1.33 % of the total variability can be attributed to sampling variability.

### 3.3.4. Association with sex

The pooled effect results failed to demonstrate the association between female sex (with males as the reference group) and PSA, with an OR of 1.98 (95 % CI = [0.89.4.40] (Fig. 2c). The z-value for this estimate is 1.68, resulting in a *p*-value of 0.09. There was some evidence for

<table><caption>**Table 3**
Differences between anxious and non-anxious groups in the combined sample.</caption><thead><tr><th></th><th>Non-anxious</th><th>Anxious</th><th>t, F or X<sup>2</sup> values</th></tr></thead><tbody><tr><td><strong>N</strong></td><td>379</td><td>205</td><td></td></tr><tr><td>Age mean (SD)</td><td>67.4 (12.4)</td><td>67.95 (12)</td><td>t = 2.64</td></tr><tr><td>Female n (%)</td><td>133<br>(35.10)</td><td>107<br>(52.2)</td><td>χ<sup>2</sup> = 19.62<sup>***</sup></td></tr><tr><td><strong>Education</strong></td><td></td><td></td><td></td></tr><tr><td>&lt;12 years n (%)</td><td>184 (48.5)</td><td>122<br>(59.5)</td><td>χ<sup>2</sup> = 6.59<sup>*</sup></td></tr><tr><td>12 years n (%)</td><td>79 (20.8)</td><td>36 (17.6)</td><td></td></tr><tr><td>&gt;12 years n (%)</td><td>116 (30.6)</td><td>47 (22.9)</td><td>χ<sup>2</sup> = 0.20</td></tr><tr><td><strong>Stroke side</strong></td><td></td><td></td><td></td></tr><tr><td>Left n (%)</td><td>179 (47.2)</td><td>93 (45.4)</td><td></td></tr><tr><td>Right n (%)</td><td>176 (46.4)</td><td>98 (47.8)</td><td></td></tr><tr><td>Other n (%)</td><td>24 (6.3)</td><td>14 (6.8)</td><td></td></tr><tr><td><strong>Vascular risk factors</strong></td><td></td><td></td><td></td></tr><tr><td>History of stroke n (%)</td><td>38 (10.0)</td><td>30 (14.6)</td><td>χ<sup>2</sup> = 2.75</td></tr><tr><td>Diabetes mellitus n (%)</td><td>115 (30.3)</td><td>60 (29.3)</td><td>χ<sup>2</sup> = 0.07</td></tr><tr><td>Hypertension n (%)</td><td>255 (67.3)</td><td>138<br>(67.3)</td><td>χ<sup>2</sup> &lt; 0.001</td></tr><tr><td>Smoking n (%)</td><td>81(60.9)</td><td>52 (39.1)</td><td>χ<sup>2</sup> = 0.99<br>χ<sup>2</sup> = 4.5</td></tr><tr><td><strong>Stroke aetiology<sup>1</sup></strong></td><td></td><td></td><td></td></tr><tr><td>Large artery n (%)</td><td>30 (12.7)</td><td>10 (6.4)</td><td></td></tr><tr><td>Small vessel n (%)</td><td>14 (5.9)</td><td>9 (5.9)</td><td></td></tr><tr><td>Cardioembolic n (%)</td><td>63 (26.7)</td><td>50 (32.9)</td><td></td></tr><tr><td>Other determined/ Undetermined n (%)</td><td>129 (54.7)</td><td>83 (54.6)</td><td></td></tr><tr><td>NIHSS<sup>1</sup> mean (SD)</td><td>3.6 (5.1)</td><td>5.2 (5.4)</td><td>t = 2.77<sup>**</sup></td></tr><tr><td>PSD n (%)</td><td>41 (10.8)</td><td>106<br>(51.7)</td><td>χ<sup>2</sup> = 118.09<sup>***</sup></td></tr><tr><td>PSCI n (%)</td><td>121 (32.0)</td><td>108<br>(52.2)</td><td>χ<sup>2</sup> = 23.81<sup>***</sup></td></tr></tbody></table>

heterogeneity among the studies (I = 63.71 % and Q(df = 2) =5.19, *p* = 0.07), and the H value indicated that 2.76 % of the total variability can be attributed to sampling variability.

### 3.3.5. Association with a history of stroke

The pooled effect results did not reveal association between a history of stroke (using the absence of a stroke history as the reference) and PSA, with an OR of 0.75 (95%CI = [0.22;2.57] (refer to Fig. 2d). The z-value for this estimate is -0.45, resulting in a *p*-value of 0.65. There was evidence for heterogeneity among the studies (I = 49.75 %) and Q(df = 2) =6.47, *p* = 0.04), and the H value indicated that 1.99 % of the total variability can be attributed to sampling variability.

## 4. Discussion

The aim of the present study was to determine the prevalence and correlates of PSA. A total of 584 patients across three distinct cohorts of the STROKOG consortium were included in the analyses. PSA was frequent, especially in females. A 2-step IPD meta-analysis revealed that PSD and global PSCI were associated with PSA.

### 4.1. PSA prevalence

The prevalence of PSA was 35 % (95%CI = [31.23; 38.97]), in the total sample. CASPER and STROKDEM exhibited PSA prevalence of 27 % (95%CI = [20.82;33.26]) and 28 % (95%CI = [20.75; 35.77]), respectively, aligning with a recent meta-analysis of 97 studies which reported a prevalence of 25 % anxious patients, when assessed using anxiety rating scales (Knapp et al., 2020). However, PROPOLIS displayed a notably higher PSA prevalence of 45 % (95%CI = [39.03; 51.37]). Various factors, including differences in patients' characteristics, stroke onset delay, and testing methodologies, could account for

<footnote n="1">¹ CASPER used a stroke classification that could not be harmonized to the TOAST stroke subtype categories.</footnote>

<footnote>** p < 0.01.</footnote>

<footnote>*** p < 0.001 between cohorts.</footnote>

<footnote>* p < 0.05.</footnote>

<footnote>** p < 0.01.</footnote>

<footnote>*** p < 0.001.</footnote>
<table><caption>**Table 4**
Association between each demographic and clinical factors with PSA.</caption><thead><tr><th rowspan="2"></th><th colspan="2">CASPER</th><th colspan="2">PROPOLIS</th><th colspan="2">STROKDEM</th></tr><tr><th>OR</th><th>95%CI</th><th>OR</th><th>95%CI</th><th>OR</th><th>95%CI</th></tr></thead><tbody><tr><td>PSCI</td><td>1.54</td><td>0.65–3.66</td><td>1.81</td><td>0.91–3.66</td><td>2.36</td><td>0.95–5.85</td></tr><tr><td>PSD</td><td>17.73</td><td>7.41–46.68</td><td>13.88</td><td>6.22–34.73</td><td>6.18</td><td>2.48–16.36</td></tr><tr><td>Female<sup>1</sup></td><td>4.15</td><td>1.72–10.61</td><td>1.71</td><td>0.91–3.23</td><td>0.91</td><td>0.34–2.37</td></tr><tr><td>Age</td><td>0.97</td><td>0.93–1.00</td><td>0.99</td><td>0.96–1.02</td><td>0.99</td><td>0.95–1.03</td></tr><tr><td>12 years education<sup>2</sup></td><td>0.84</td><td>0.30–2.29</td><td>0.96</td><td>0.40–2.24</td><td>1.49</td><td>0.39–5.45</td></tr><tr><td>&gt;12 years education<sup>2</sup></td><td>1.37</td><td>0.49–3.89</td><td>0.77</td><td>0.36–1.61</td><td>0.51</td><td>0.17–1.43</td></tr><tr><td>Right sided stroke<sup>3</sup></td><td>0.77</td><td>0.33–1.79</td><td>1.24</td><td>0.66–2.36</td><td>1.10</td><td>0.45–2.70</td></tr><tr><td>Other localization</td><td>0.00</td><td>NA-NA</td><td>1.04</td><td>0.39–2.63</td><td>NA</td><td>NA</td></tr><tr><td>Vascular risk factors</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>History of stroke</td><td>0.59</td><td>0.00–1.15</td><td>2.62</td><td>1.17–5.97</td><td>0.72</td><td>0.16–2.86</td></tr><tr><td>Hypertension</td><td>1.10</td><td>0.30–3.58</td><td>0.72</td><td>0.35–1.47</td><td>1.56</td><td>0.60–4.24</td></tr><tr><td>Diabetes mellitus</td><td>1.15</td><td>0.32–3.74</td><td>0.76</td><td>0.37–1.52</td><td>NA<sup>5</sup></td><td>NA<sup>5</sup></td></tr><tr><td>Smoking</td><td>0.93</td><td>0.27–2.89</td><td>1.28</td><td>0.62–2.65</td><td>0.54</td><td>0.15–1.69</td></tr><tr><td>Stroke aetiology<sup>4</sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Large artery</td><td>NA</td><td>NA</td><td>0.48</td><td>0.12–1.62</td><td>NA<sup>5</sup></td><td>NA<sup>5</sup></td></tr><tr><td>Small vessel</td><td>NA</td><td>NA</td><td>1.50</td><td>0.38–5.99</td><td>NA<sup>5</sup></td><td>NA<sup>5</sup></td></tr><tr><td>Other determined/ Undetermined</td><td>NA</td><td>NA</td><td>1.00</td><td>0.50–1.99</td><td>NA<sup>5</sup></td><td>NA<sup>5</sup></td></tr><tr><td>Severity (NIHSS)</td><td>NA</td><td>NA</td><td>1.04</td><td>0.98–1.10</td><td>0.79</td><td>0.52–1.09</td></tr></tbody></table>

such discrepancies. Interestingly, in comparison with CASPER and STROKDEM, the PROPOLIS cohort had nearly twice as many patients with PSA (39 %) and with PSCI (50 %) (which was assessed with a screening test) while the PSD rate (24 %) did not differ from the other cohorts (Table 2).

## 4.2. PSA demographical profile

In the total sample, anxious patients received less education than non-anxious patients, however the educational factor did not remain significant in the IPD analysis. Furthermore, in alignment with the findings of the meta-analysis by Menlove et al. (Menlove et al., 2015), we did not find association between age and PSA.

The question of biological sex is also of major interest (Hackett et al., 2014) (Kessler et al., 2005). Associations between PSA and female sex have been previously described, but not systematically (Morrison et al., 2005). In the present study, PSA affected 45 % of females and 28 % of males. The association between female sex and PSA was significant in CASPER cohort and in the total sample. In the second stage of the IPD analysis, the effect of sex was close to significance, with a wide confidence interval. The contribution of female sex to PSA needs further evidence (Hackett et al., 2014). It should be remembered that there exist sex differences in stroke and its consequences. Females tend to have more severe strokes at an older age than males, resulting in more severe impairments (Gall et al., 2010).

## 4.3. Clinical profile

Although anxious and non-anxious group differed in term of medical history or stroke characteristics, the logistic regressions did not support such associations.

To date, research findings have varied regarding the neural correlates of poststroke anxiety and emotional disorders. Some authors are in favour of a role for lateralization of the lesion in the emergence of PSA, however, there is no general agreement on this result as several studies have found no association between PSA and lesion location (Hackett et al., 2014; Barker-Collo, 2007). In the present study, lesion lateralization was not associated with PSA neither in the combined sample nor in the three individual cohorts. It is worth considering that neural pathway reorganization and in-depth analysis of neural networks may

provide more nuanced insights into the neural bases of poststroke emotional disorders (Vicentini et al., 2017).

## 4.4. Psychiatric profile

Although there are few studies on PSA, there is a general agreement on the clear link between PSA and PSD. Our results concur with the findings of previous studies by demonstrating a strong association between PSA and PSD in each of the three cohorts included. Ultimately, in the combined sample, the IPD analysis showed that PSD increases the odds of PSA. Anxiety and depression are the most common psychiatric disorders, share common mechanisms, and are highly comorbid in both the general population and post-stroke patients (Schöttke and Giabbiconi, 2015; Kessler et al., 2005). It is crucial to, at least, control for depression when studying anxiety, and reciprocally. For a better understanding of PSA and PSD respectively, it would be relevant to separate patients into groups according to the presence of PSA, PSD or both combined. Furthermore, it is likely that psychiatric history, which was not controlled in the present study, also contributes to the occurrence of PSA (Wright et al., 2017).

## 4.5. PSA is associated with global PSCI

Although it is widely recognized that anxiety and cognition are closely interrelated, their relations have been scarcely investigated in post-stroke patients.

### 4.5.1. Association between PSA and PSCI: limited evidence

While several studies demonstrated a clear association between PSD and cognitive impairment (Barker-Collo, 2007), there are fewer investigations into the link between PSA and PSCI, and limited evidence of their relationship. In the meta-analysis of Wright et al. (Wright et al., 2017), four studies reporting on anxiety and cognition were identified. Results are conflicting, as two of them found a significant association between PSA and PSCI (Barker-Collo, 2007; Fure et al., 2006), while the two others did not (Ayerbe et al., 2013; Leppävuori et al., 2003). It is important to note that studies of stroke survivors differ substantially from one study to another, in terms of sample characteristics, stroke-related factors and assessment tools. In particular, various methods have been used to assess cognitive functions, and a certain number of

<footnote n="1">¹ Reference to male.</footnote>

<footnote n="2">² Reference to <12 years of education.</footnote>

<footnote n="3">³ Reference to left stroke.</footnote>

<footnote n="4">⁴ Reference to cardioembolic aetiology.</footnote>

<footnote n="5">⁵ Too few occurrences to calculate odds ratio.</footnote>
studies have reported a global cognitive score obtained with screening tools such as MMSE and MoCA (Fure et al., 2006).

### 4.5.2. Criteria for global PSCI

After stroke, especially minor to mild, the choice of cognitive evaluation is a major concern, and can be challenging because of a lack of sensitivity to conventional tests. In the present studies, different criteria for global PSCI were implemented and adjusted as much as possible in regard of the reference population.

In PROPOLIS, the MoCA scores were provided. While the cutoff of <23 might appear very conservative, it was in fact chosen based on a specific study inside the Polish population, and the use of this criteria resulted in almost one in two patient diagnosed with global PSCI.

In CASPER and STROKDEM, MoCA and MMSE screening tools were available. However, these tests and associated cutoffs failed to provide a sensitive measure here, with <10 % of patients diagnosed with global PSCI. In contrast with PROPOLIS, these two studies included five domain-specific cognitive Z-scores. We have chosen not to use the mean of these five scores as a measure of global PSCI, because domain-specific impairments would have been masked by a satisfying global Z-score. We finally adopted the criterion of one or more cognitive domain impaired (<1.65), as defined in previous studies (Jokinen et al., 2015b). This criterion was aimed to be sensitive to mild cognitive impairment, which was reflected in a proportion of cognitively impaired patients that was much more in line with previous reports in the literature (Lo et al., 2020b).

### 4.5.3. Is PSCI an independent contributor to PSA?

In the recent study of Williams & Demeyere, 437 patients were assessed for PSA and PSD six months after stroke. Importantly, they incorporated both global and domain-specific cognitive evaluation (Williams and Demeyere, 2021). The results of a first model indicate that PSA is associated with global and spatial cognitive impairment. However, in contrast with previous studies, they controlled for co-occurring depression in a second model. In this second model, the association between PSA and PSCI did not remain significant, suggesting that the correlation between PSA and PSCI could be in fact attributable to comorbid PSD. Some of our results contrast with their observations. Importantly, the associations between anxiety and cognition failed to reach significance in the separated cohorts, but the 95%CI was wide (Fig. 2). However, through an IPD analysis that amalgamated data from these three distinct cohorts, we were able to establish an significant association between PSA and PSCI. It is noteworthy that this association was significant even though co-occurring PSD was included in all the models, suggesting that PSCI is an independent predictor of PSA.

Several methodological factors could explain the diversity of results. Unlike previous studies, we included three different cohorts. This leads to greater heterogeneity in assessment tools and diversity in patient's characteristics.

Nevertheless, by the combination of three cohorts of post-stroke patients and use of two-step IPD analysis, we demonstrated a modest but significant association between PSA and PSCI. Like PSD, PSA appears as an independent contributor for PSCI, even though the effect size is smaller (Barker-Collo, 2007). Importantly, anxiety and depression are highly comorbid, have an overlap of symptoms, and may have common mechanisms (Nutt, 2004). They could be differentially associated with PSCI depending on the cognitive domain evaluated and decision criteria (Barker-Collo, 2007) (Williams and Demeyere, 2021).

It is very likely that the relations between PSA and PSCI are bidirectional and that they could worsen each other. It is therefore crucial to conjointly study anxiety and cognition in stroke patients. It should also be pointed out that the relation between PSA and PSCI may be contingent on specific cognitive domains, as previously suggested (Barker-Collo, 2007). More studies are needed for a deeper understanding of PSA cognitive correlates and mechanisms. It would also be worth studying the long-term evolution of the relations between PSA and PSCI, since

these conditions are chronic.

### 4.6. Clinical implications

In our study, PSA was more prevalent in females and showed an association with PSD and PSCI. Identifying correlates and predictors of PSA is essential for targeting at-risk patients.

Anxiety assessment after stroke is crucial, however it is not systematically performed in clinical care and not reported in studies of post-stroke patients. In addition, more guidance is needed on the most appropriate way to assess PSA.

Careful cognitive and emotional evaluations after stroke could improve the patients' prognosis. Indeed, clinical practice might benefit from joint assessment of PSA and PSCI since they are two associated disorders that persist during the chronic phase of stroke. It is important to bear in mind that a significant proportion of post-stroke patients experience cognitive impairments despite provided care, leading to elevated rates of dementia (Pendlebury and Rothwell, 2009).

### 4.7. Strengths and limitations

#### 4.7.1. Strengths

A notable strength of our study is the number of participants (n = 584), which enhances our statistical power. Importantly, results were reported for the total sample and both sexes. We comprehensively examined various demographic and clinical domains, including the presence of cognitive impairment. Our approach provides some valuable insights by combining data from three different cohorts of the STROKOG consortium to demonstrate the association between PSA and PSCI through a 2-step IPD meta-analysis.

Studies of stroke survivors differ from one another in terms of the characteristics of the patients included. This may explain the heterogeneity of the results, and the fact that some effects are not replicated. We expect that the combination of distinct cohorts, as it has already been done in previous STROKOG studies (Lo et al., 2020b) allows us to represent more accurately the diversity of stroke survivors' profiles in comparison with mono-centric studies. Furthermore, the use of an effective and appropriate statistical method provided heterogeneity index which has proved to be low in our analyses.

#### 4.7.2. Limitations

It is important to highlight that many patients were not includable due to a lack of neuropsychological assessment (Fig. 1). Supplementary analyses suggest that non-assessed patients suffered more severe and disabling strokes (see supplementary material, Table 1). In the final sample, differences in population characteristics and the timing of assessments were also evident. Patients from PROPOLIS were older and had higher stroke severity score, which may partly explain the higher rates of both PSA and PSCI.

Another limitation in this study is our inability to control for participants' prior history of psychiatric disorder or medication usage. Additionally, our results could have been further refined by considering continuous scores, which was not feasible due to variations in testing methods across cohorts and need for harmonization. This was also the case for determining the presence of cognitive impairment. While CASPER and STROKDEM studies included a complete cognitive evaluation, resulting on 5 cognitive domain-specific Z scores, PROPOLIS patients were tested using MoCA. This screening test is widely used and validated. The MoCA also evaluates different cognitive domains, including an interesting portion of executive testing, which is determinant in the detection of poststroke PSCI (Chiti and Pantoni, 2014). Regarding psychiatric and cognitive assessment, we were attentive to using only cut-offs adjusted for the reference populations. However, it appears necessary to develop anxiety evaluation tool adapted to the post-stroke population, as is the case in Parkinson's disease for example (The Parkinson Anxiety Scale (PAS), 2024).
<figure><img src="image_4.png" /><figcaption>Fig. 1. Flow chart of included patients.</figcaption></figure>

We conducted our research on ischemic stroke survivors; therefore, our findings are not necessarily applicable to haemorrhagic stroke or TIA, which can be considered different pathological entities. Moreover, while we focused on identifying PSA correlates within the 3- to 6-month post-stroke timeframe, a longitudinal approach would be advantageous given the chronic nature of anxiety (Sagen-Vik et al., 2022). It is also important to note that our study can establish associations but cannot establish a causal pathway.

Finally, we were able to include only three cohorts from the STRO-KOG consortium. Notably, many studies did not assess anxiety levels, which underscores a lack of attention to the issue of PSA and emphasizes

the necessity for systematic assessment to enhance its management in clinical care.

## 5. Conclusion

The present study reveals a high prevalence of PSA, especially in females. Our findings support the association between PSA and PSD and provides new evidence on the connections between PSA and global PSCI.

Further investigation is warranted to gain a better understanding of the relationships between anxiety and domain-specific cognitive functions. In the future, the development and use of specific psychological
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Odds ratio per cohort and pooled odds ratio for a) cognitive impairment, b) post stroke depression, c) female sex, d) history of stroke at risk factors of post stroke anxiety and extracted from the two stage IPD analysis.</figcaption></figure>

tools for stroke patients would be beneficial. Additionally, adopting a longitudinal approach would be advantageous, given the chronic nature of anxiety.

## Fundings

The study was funded by the Hauts-de-France Regional Council.

## Disclosure

The authors report no disclosures relevant to the manuscript.

## CRediT authorship contribution statement

**Florine Ruthmann:** Methodology, Formal analysis, Conceptualization, Writing – review & editing, Writing – original draft. **Jessica W. Lo:** Validation, Methodology, Formal analysis, Conceptualization, Writing – review & editing. **Anne-Marie Mendyk-Bordet:** Validation, Data curation, Writing – review & editing. **Etienne Allart:** Validation, Writing – review & editing. **Sebastian Köhler:** Data curation, Conceptualization, Writing – review & editing. **Aleksandra Klimkowicz-Mrowiec:** Validation, Data curation, Writing – review & editing. **Julie Staals:** Validation, Data curation, Writing – review & editing. **Perminder S. Sachdev:** Funding acquisition, Data curation, Conceptualization, Writing – review & editing. **Régis Bordet:** Validation, Supervision, Methodology, Funding acquisition, Data curation, Conceptualization, Writing – review & editing. **Thibaut Dondaine:** Supervision, Formal analysis, Data curation, Conceptualization. **THI-BAUT DONDAINE:** Writing – review & editing.

## Declaration of competing interest

None of authors do not have any conflicts of interest to declare.

## Acknowledgments

None

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jad.2024.10.099.

## Data availability

Data may be shared with researchers on reasonable request, depending on the specific constraints of each cohort and research center.

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