*Article RP11-362K2.2:RP11-767I20.1* **Genetic Variation Is Associated with Post-Reperfusion Therapy Parenchymal Hematoma. A GWAS Meta-Analysis**

**Elena Muiño 1,†, Jara Cárcel-Márquez 1,†, Caty Carrera 2, Laia Llucià-Carol 1, Cristina Gallego-Fabrega 1,3, Natalia Cullell 1,4, Miquel Lledós 1, José Castillo 5, Tomás Sobrino 5, Francisco Campos 5, Emilio Rodríguez-Castro 6, Mònica Millán 7, Lucía Muñoz-Narbona 7, Alejandro Bustamante 7, Elena López-Cancio 8, Marc Ribó 9, José Álvarez-Sabín 10, Jordi Jiménez-Conde 11, Jaume Roquer 11, Eva Giralt-Steinhauer 11, Carolina Soriano-Tárraga 11, Cristófol Vives-Bauza 12, Rosa Díaz Navarro 13, Silvia Tur 13, Victor Obach 14, Juan F. Arenillas 15, Tomás Segura 16, Gemma Serrano-Heras 17, Joan Martí-Fàbregas 3, Raquel Delgado-Mederos 3, Pol Camps-Renom 3, Luis Prats-Sánchez 3, Daniel Guisado 3, Marina Guasch 3, Rebeca Marin 3, Alejandro Martínez-Domeño 3, Maria del Mar Freijo-Guerrero 18, Francisco Moniche 19, Juan Antonio Cabezas 19, Mar Castellanos 20, Jerzy Krupinsky 21,22, Daniel Strbian 23, Turgut Tatlisumak 24,25, Vincent Thijs 26,27, Robin Lemmens 28, Agnieszka Slowik 29, Joanna Pera 29, Laura Heitsch 30,31, Laura Ibañez 32, Carlos Cruchaga 32, Rajat Dhar 31, Jin-Moo Lee 31, Joan Montaner 19, Israel Fernández-Cadenas 1,\*, on behalf of International Stroke Genetic Consortium and the Spanish Stroke Genetic Consortium**

**Citation:** Muiño, E.; Cárcel-Márquez, J.; Carrera, C.; Llucià-Carol, L.; Gallego-Fabrega, C.; Cullell, N.; Lledós, M.; Castillo, J.; Sobrino, T.; Campos, F.; et al. *RP11-362K2.2:RP11-767I20.1* Genetic Variation Is Associated with Post-Reperfusion Therapy Parenchymal Hematoma. A GWAS Meta-Analysis. *J. Clin. Med.* **2021**, *10*, 3137. https://doi.org/10.3390/ jcm10143137

Academic Editor: Hyo Suk Nam

Received: 7 June 2021 Accepted: 14 July 2021 Published: 16 July 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

	- <sup>11</sup> Department of Neurology, Neurovascular Research Group, Instituto de Investigaciones Médicas Hospital del Mar-Hospital del Mar, 08025 Barcelona, Spain; jjimenez@imim.es (J.J.-C.); jroquer@hospitaldelmar.cat (J.R.); egiralt@imim.es (E.G.-S.); csoriano@imim.es (C.S.-T.)
	- <sup>12</sup> Neurobiology Laboratory, Instituto de Investigación Sanitaria de Palma, 07120 Mallorca, Spain; cristofol.vives@ssib.es
	- <sup>13</sup> Department of Neurology, Hospital Universitari Son Espases, 07120 Mallorca, Spain; rosam.diaz@ssib.es (R.D.N.); silvia.tur@ssib.es (S.T.)
	- <sup>14</sup> Department of Neurology, Hospital Clínic i Provincial de Barcelona, 08025 Barcelona, Spain; VOBACH@clinic.cat
	- <sup>15</sup> Department of Neurology, Hospital Clínico Universitario, University of Valladolid, 47003 Valladolid, Spain; juanfarenillas@gmail.com
	- <sup>16</sup> Department of Neurology, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain; tseguram@gmail.com

**Abstract:** Stroke is one of the most common causes of death and disability. Reperfusion therapies are the only treatment available during the acute phase of stroke. Due to recent clinical trials, these therapies may increase their frequency of use by extending the time-window administration, which may lead to an increase in complications such as hemorrhagic transformation, with parenchymal hematoma (PH) being the more severe subtype, associated with higher mortality and disability rates. Our aim was to find genetic risk factors associated with PH, as that could provide molecular targets/pathways for their prevention/treatment and study its genetic correlations to find traits sharing genetic background. We performed a GWAS and meta-analysis, following standard quality controls and association analysis (fastGWAS), adjusting age, NIHSS, and principal components. FUMA was used to annotate, prioritize, visualize, and interpret the meta-analysis results. The total number of patients in the meta-analysis was 2034 (216 cases and 1818 controls). We found rs79770152 having a genome-wide significant association (beta 0.09, *<sup>p</sup>*-value 3.90 <sup>×</sup> <sup>10</sup>−8) located in the *RP11-362K2.2:RP11-767I20.1* gene and a suggestive variant (rs13297983: beta 0.07, *p*-value 6.10 <sup>×</sup> <sup>10</sup><sup>−</sup>8) located in *PCSK5* associated with PH occurrence. The genetic correlation showed a shared genetic background of PH with Alzheimer's disease and white matter hyperintensities. In addition, genes containing the ten most significant associations have been related to aggregated amyloid-β, tau protein, white matter microstructure, inflammation, and matrix metalloproteinases.

**Keywords:** hemorrhagic transformation; parenchymal hematoma; GWAS; single nucleotide variants

#### **1. Introduction**

Stroke is the second most common cause of death worldwide, and the third most common cause of disability [1]. For ischemic strokes, the only treatments available during the acute phase are the reperfusion therapies such as thrombolysis and mechanical thrombectomy.

Ischemic strokes may present hemorrhagic transformation (HT). This may be early, associated with reperfusion of the occluded vessel; or late, which is thought to be related to increased permeability and blood flow [2].

HT is a well-recognized complication following reperfusion therapies. HT could be classified, according to the European Cooperative Acute Stroke Study (ECASS) criteria, into petechial infarction without space-occupying effect (HI) and hematoma/coagulum with mass effect (PH) [2].

HT may result in neurological deterioration [3], and the presence of a PH independently predicts early and late mortality, with a hazard ratio of late mortality of 7.9, with a 95% confidence interval (CI) of 2.9–21.4 [4]. Nevertheless, petechial changes may indicate that reperfusion occurred when the ischemic tissue was still at least partially viable.

Patients exhibiting an early HI did not have a higher risk of neurological deterioration compared with patients without hemorrhagic transformation. Among patients treated with rtPA, HI was even loosely associated with early improvement. Overall, three-month mortality and disability were also not influenced by HI [2].

The percentage of HT in studies of stroke patients varies from 6.4% to 43% [3], and the use of reperfusion therapies has favored the increase in this incidence. Moreover, clinical trials such as WAKE-UP [5], DAWN [6], or DEFUSE 3 [7] will allow a major use of these therapies, extending the time-window administration, which may lead to an increase in HT. It is therefore of utmost importance to identify those patients at higher risk of suffering a PH, as this is the subtype of HT that causes the highest morbidity and mortality [2,4].

There is a genetic predisposition for HTs following intravenous thrombolysis (IVT). This genetic contribution has been explored through candidate genes [8,9] or more recently through a Genome Wide Association Study (GWAS), carried out by our own group [10]. In this last study, we found that single nucleotide variants (SNVs) in the *ZBTB46* gene were associated with PH in patients who underwent IVT [10]. For this purpose, we studied the extreme phenotype, patients with PH vs. patients without HT, excluding patients with petechial infarction (HI) subtype.

We decided to carry out a new analysis by including in the control group those patients who had a HI, to ensure that the findings achieved are exclusively attributed to the PH subtype due to reperfusion therapies, including patients that underwent mechanical thrombectomy or intra-arterial fibrinolysis, increasing our sample size, and with it, our statistical power.

Currently, articles using GWAS to understand different diseases are complemented by the study of genetic correlations with other traits to find common genetic architecture [11]. Knowing which traits share a genetic correlation allows a better understanding of diseases and the realization of further studies to find variants associated with them by increasing its statistical power, such as multitrait analysis of GWAS (MTAG). As example, the article performing a MTAG of small vessel occlusion strokes and intracerebral hemorrhage, due to these traits sharing a genetic background, allows us to find new loci associated with these diseases [12].

In the article we mentioned above, published by our group, we found that PH shared a genetic background with deep intracerebral hemorrhage (ICH), lobar ICH, and white matter hyperintensities (WMH) [10]. After Bonferroni correction, only lobar ICH remained significantly correlated.

Therefore, the aim of our study was to find genetic risk factors associated exclusively with PH, including patients with different reperfusion treatments. PH occurrence is still an important problem in the reperfusion strategy for stroke patients. Hence the importance of finding molecules that could be used as biomarkers to guide the therapeutic decision or potential therapeutic targets to prevent the appearance of this life-threatening complication. We also wanted to assess whether the same genetic correlations found in our previous paper were still found and whether we could find any new ones.

In this work we found a genome-wide significant locus associated with PH, regardless of the reperfusion treatment performed. Moreover, we found that there is a genetic

correlation of PH with Alzheimer's disease and white matter hyperintensities (WMH). In fact, the study of nominally significant genomic loci in the meta-analysis has shown that pathways related to aggregated amyloid-β, tau protein, and inflammatory pathways could be related to PH occurrence.

#### **2. Materials and Methods**

This is an observational case-control study, conducted in a discovery and replication cohort, with subsequent meta-analysis of both results, in order to find SNVs associated with PH.

#### *2.1. Subjects*

#### 2.1.1. Discovery Cohort

The participants included in the discovery cohort were part of the Genetic Study in Ischemic Stroke Patients treated with recombinant tissue plasminogen activator (r-tPA) (GenoTPA) [9], Genetic contribution to Functional Outcome and Disability after Stroke (GODS) [13], the Genotyping Recurrence Risk of Stroke (GRECOS) [14], and Genetics of Early Neurological Instability After Ischemic Stroke (GENISIS) [15] studies. These studies have, in common, the recruitment of patients with ischemic stroke between 2002 and 2020.

From these four studies, (*n* = 4667), 161 cases (patients with PH after reperfusion therapy) and 1236 controls (patients without PH after reperfusion therapy) fulfilled the inclusion and exclusion criteria, incorporated in a total of 8 batches (Table 1). All of the subjects of the discovery cohort had a Spanish origin.


**Table 1.** Discovery cohort.

GenoTPA: Genetic Study in Ischemic Stroke Patients treated with recombinant tissue plasminogen activator (r-tPA); GODS: Genetic contribution to Functional Outcome and Disability after Stroke; GRECOS: Genotyping Recurrence Risk of Stroke study; GENISIS: Genetics of Early Neurological Instability After Ischemic Stroke.

#### 2.1.2. Replication Cohort

The participants included in the replication cohort were part of the Genetic Study in Ischemic Stroke Patients treated with tPA (GenoTPA) [9], BAse de Datos de ICtus del hospital del MAR (BASICMAR) (Stroke database of the Hospital del Mar) [16], Leuven Stroke Genetics Study (LSGS) [17], Helsinki 2000 Ischemic Stroke Genetics Study, and Genetics of Early Neurological Instability After Ischemic Stroke (GENISIS) [15] studies.

From these five studies, the imputed genotype was available from a total of 1064 patients, 112 cases and 913 controls, incorporated in a total of 7 batches (Table 2).

For a detailed description of the different studies included in the discovery and replication cohorts see Supplemental Methods.

#### 2.1.3. Variables

Detailed clinical-epidemiological data was collected from each patient, including age, sex, vascular risk factors such as hypertension, diabetes mellitus (DM), dyslipidemia (DLP), smoking habits, history of atrial fibrillation (AF), physical examination including stroke severity assessed with the National Institutes of Health Stroke Scale (NIHSS) at initial evaluation and the modified Rankin Score (mRS) prior to stroke, systolic (SBP) and diastolic blood pressure (DBP), initial glycaemia, TOAST classification, or treatment decisions. In Supplemental Methods, there is detailed information about variable definition.

CT scans were obtained prior to reperfusion procedure (baseline), and 24 h after, or whenever a neurological deterioration detected by the clinician was observed, to assess the presence of HT and its degree. All brain images were reviewed by a radiologist or neuro-radiologist.



GenoTPA: Genetic Study in Ischemic Stroke Patients treated with recombinant tissue plasminogen activator (r-tPA); BASICMAR: BAse de Datos de ICtus del hospital del MAR; LSGS: Leuven Stroke Genetics Study; HELSINKI2000: Helsinki 2000 Ischemic Stroke Genetics Study; and Genetics of Early Neurological Instability After Ischemic Stroke studies.

> HT was classified, according to the ECASS criteria, into petechial infarction without space-occupying effect (HI) with two subtypes, HI1 (small petechiae) and HI2 (more confluent petechiae); and hematoma/coagulum with mass effect (PH) divided into PH1 when affecting ≤30% of the infarct bed with mild mass effect and PH2, when affecting >30% of the infarct bed with significant mass effect or remote hemorrhage [2].

> As the aim of our study was to find SNV associated with the risk of PH (PH1 and PH2) after reperfusion treatment, patients without HT or with HI (HI1 and HI2) were chosen as controls, and patients with PH were chosen as cases. Remote hemorrhages were excluded from the study, as their etiology has not yet been clarified and the biological mechanisms underlying remote hemorrhages are probably different compared to the other HTs [18].

#### 2.1.4. Eligibility Criteria

For the association study, patients >18 years of age with an ischemic stroke that underwent reperfusion therapy (ITV, including mechanical thrombectomy or intra-arterial fibrinolysis as second intention), who presented with PH, were considered as cases. Controls were selected as patients >18 years with ischemic stroke that underwent reperfusion therapy, who did not present HT or who presented with HI.

Exclusion criteria: patients not receiving reperfusion therapy, who suffered a remote PH or unknown HT phenotype.

#### 2.1.5. Standard Protocol Approvals and Patient Consent

This study was approved by the local ethics committee of each participant and an informed consent document was signed by every patient or their relatives.

#### *2.2. Genotyping*

DNA samples were genotyped on commercial arrays from Illumina (San Diego, CA, USA) (Tables 1 and 2).

#### 2.2.1. Quality Control

For detailed quality controls performed see Supplemental Methods.

Briefly, SNV missing in a large proportion of the subjects, non-biallelic SNV, ambiguous, monomorphic or duplicated SNV, or SNV that violates the Hardy–Weinberg (dis)equilibrium (HWE) law were deleted.

Individuals with high rates of genotype missingness, sex discrepancy or unknown sex, family members or duplicated samples, non-European individuals, and patients with outlier heterozygosity rates (*n* = 814) were removed.

After all these QCs, the total number of patients was 141 cases and 1003 controls in the discovery cohort. To ensure that there were no duplicate samples between the discovery and replication cohorts, patients with a pihat > 0.8 were removed from replication cohort. The number of patients with information for the covariates introduced in the analysis were 1139, 140 cases and 999 controls.

Finally, 895 patients (76 cases and 819 controls) passed the QC and had information for the covariates in the analysis, constituting the replication cohort.

Studies genotyped on the same platforms were combined in the discovery cohort. For the replication cohorts data were already imputed [10].

#### 2.2.2. Genome Build

All genomic coordinates are given in NCBI Build 37/UCSC hg19.

#### *2.3. Imputation*

Imputation was performed with the Michigan Imputation Server Pipeline using Minimac4, following their instructions (https://imputationserver.readthedocs.io/en/latest (accessed on 1 May 2021)). HRC r1.1 2016 (GRCh37/hg19) was the reference panel used, with European population and, for phasing, Eagle v2.4 was used.

After imputation, QC were performed. We removed SNV with r<sup>2</sup> < 0.6 and MAF < 0.1%. After merging all cohorts, SNVs that were not present in at least 90% of the individuals were removed.

#### *2.4. Genome-Wide Association Analysis and Meta-Analysis*

We performed a linear regression-based association analysis using fastGWAS [19]. Those SNV with minor allele count (MAC) < 6 were subsequently removed. For the discovery cohort, we adjusted for the first two principal components (PC) (Figure 1), age and the variables remaining significant in the multivariable logistic regression (*p*-value < 0.05) and that we had information on the replication cohort: NIHSS. For the replication cohort, the analysis was adjusted for the three first PC (Figure 1), and the same clinical variables as in the discovery analysis: age and NIHSS.

Due to the small sample size of the discovery cohort, in order to increase statistical power, we carried out a meta-analysis of the results of the discovery and replication cohort with the metal software (http://csg.sph.umich.edu/abecasis/metal (accessed on 5 May 2021)), weighted by the number of individuals contributing to each result [20]. Genomic control correction was applied to both input files and then to the meta-analysis results.

<sup>A</sup> *<sup>p</sup>*-value < 5 × <sup>10</sup>−<sup>8</sup> was considered genome-wide significant and a *<sup>p</sup>*-value < 1 × <sup>10</sup>−<sup>5</sup> a nominal genome-wide association.

#### *2.5. Functional Annotation of Associated Variants*

FUMA (Functional Mapping and Annotation of Genome-Wide Association Studies) was used to annotate, prioritize, visualize, and interpret the meta-analysis results (https: //fuma.ctglab.nl (accessed on 6 May 2021)) [21]. This platform also permits the realization of an ANNOVAR enrichment test; MAGMA gene analysis, gene-set analysis and geneproperty analysis; identification of expression quantitative trait loci (eQTL), chromatin interaction data, and mapping. It also provides information about the RegulomeBD score. This score, that provides information on the probability of affect binding and expression of target gene, goes from 1 (most likely) to 7 (least likely). As a reference panel, we used UKB release2b 10k European population.

To search for traits to which the genes closest to the most significant SNVs have been related, we used the GWAS Catalog (https://www.ebi.ac.uk/gwas (accessed on 6 May 2021)).

For finding gene ontology (GO) terms of the genes of interest, we performed a search in Ensembl (https://www.ensembl.org/index.html (accessed on 6 May 2021)).

**Figure 1.** Principal component analysis (PCA) representation for discovery and replication cohorts. EV: eigenvector.

#### *2.6. Estimation of Genetic Correlations*

We used GNOVA (GeNetic cOVariance Analyzer) to estimate genetic covariance and correlation between traits. For this estimation, GNOVA only requires the genetic information available in the summary statistics of the traits of interest.

We tested genetic correlation for traits that have been related with HT: any ischemic strokes (AIS, *n* = 440,328), large artery atherosclerosis strokes (LAS, *n* = 301,663), cardieombolic strokes (CES, *n* = 362,661), and small vessel occlusion strokes (SVO, *n* = 348,946) using MEGASTROKE European data [22], deep intracerebral hemorrhage (*n* = 2075) [23], lobar intracerebral hemorrhage (*n* = 1148) [23], white matter hyperintensities (WMH, *n* = 11,226) [24], Alzheimer's disease (AD, *n* = 63,926) [11], total cholesterol (*n* = 94,595) [25], LDL (*n* = 94,595) [25], HDL (*n* = 94,595) [25], triglycerides (*n* = 94,595) [25], sistolic blood pressure (SBP, *n* = 757,601) [26], diastolic blood pressure (DBP, *n* = 757,601) [26], and diabetes mellitus 2 (DM2) (*n* = 69,033) [27].

#### *2.7. Statistical Analyses*

R version 3.6.3 and Bioconductor packages were used to perform the statistical analysis. To study whether there were significant differences (*p*-value < 0.05) between cases and controls in the discovery and replication cohorts, for quantitative variables with a normal distribution, we used *t*-test and a Mann–Whitney U for non-normal quantitative or ordinal variables. The Chi-square test was used for categorical variables.

Multivariable logistic regression was conducted following a forward stepwise approach to select clinical variables as covariates for the association study. First, univariable

logistic regression was performed to study the association between the available variables and the occurrence of PH. Then, they were added to the multivariable logistic regression model according to their *p*-value, from the most significant to the least.

Variables with more than 10% missing values (less than 1030 observations) were not taken into account for the multivariate model (DLP, smoking habits, mRS, SBP, DBP, intraarterial fibrinolysis, and mechanical thrombectomy), as the results of subsequent statistical analyses might be biased [28] and the analysis underpowered.

#### *2.8. Data Availability*

The data that supports the findings of this study is available from the corresponding author upon reasonable request.

#### **3. Results**

#### *3.1. Descriptive Analysis of the Cohorts*

#### 3.1.1. Discovery

A total of 1144 patients with an ischemic stroke, and who were treated with reperfusion treatment, met the inclusion criteria and passed the QC; a total of 1139, with 140 cases and 999 controls, had information for the covariates of the analysis. A total of 10,058,599 SNP passed QC and were evaluated.

There was a total of 141 cases with PH (12%) and 1003 controls (88%). Of these controls, 840 had no hemorrhagic transformation (84%) and 163 had HI (16%). Cases were 77 ± 12 years old (median ± interquartile range -IQR-), 52% were males, 13% (11/88) received intra-arterial fibrinolysis, and none received mechanical thrombectomy. Controls were 75 ± 16 years old (median ± IQR), 55% were males, 5% (28/620) received intra-arterial fibrinolysis, and 6% (17/286) mechanical thrombectomy. In the univariable analysis, the variables significantly associated with PH were a higher NIHSS, higher mean mRS (0.83 vs. 0.46 in controls), higher percentage of intra-arterial fibrinolysis, and lower percentage of strokes of atherothrombotic etiology. The detailed descriptive analysis can be found in Table 3.

The final sample for the analysis with information for all the covariates included in the association test was 1139 patients, with 140 cases and 999 controls.

In the multivariate analysis with age and the first two PCs, only NIHSS remains significant (*p*-value 5.36 × <sup>10</sup>−3). Variables with a miss rate >10% or those that were not collected in the replication cohort were excluded from this analysis.

#### 3.1.2. Replication

A total of 895 patients with an ischemic stroke undergoing reperfusion treatment, met the inclusion criteria and passed the QC. A total of 7,224,265 SNP after QCs were evaluated.

There was a total of 76 cases with PH (8%) and 819 controls (92%). Cases were 76 ± 11 years old (median ± IQR) and 53% were males. Controls were 72 ± 17 years old (median ± IQR) and 52% were males. In the univariable analysis, the variables significantly associated with PH were a higher age, a higher proportion of AF and CES, and a higher NIHSS. The detailed descriptive analysis can be found in Table 4.

The final sample for the analysis with covariates was 895 patients, 76 cases and 819 controls.

#### *3.2. GWAS*

We did not observe any SNV that reached the GWAS significance threshold (*p*-value < 5 × <sup>10</sup><sup>−</sup>8) in the discovery analysis.

The Manhattan and quantile-quantile (QQ) plots, obtained from the discovery and replication cohorts association study, can be visualized in the supplementary Figures S1 and S2, respectively. We did not observe an overall inflation of *p*-values; genomic inflation factor λ was 1.007 in the discovery cohort and 0.999 in the replication.


**Table 3.** Descriptive analysis of discovery cohort.

OR (95% IC): odds ratio (95% confidence interval -CI-). HTN: hypertension, DLP: dyslipidemia, AF: atrial fibrillation, SH: smoking habits, NIHSS: National Institutes of Health Stroke Scale, mRS: modified Rankin Score, Gly: initial glycaemia, SBP: systolic blood pressure, DBP: diastolic blood pressure; IA: intra-arterial fibrinolysis, TM: mechanical thrombectomy, CES: cardioembolic stroke, LAS: large artery atherosclerosis stroke, SVO: small vessel occlusion stroke. For quantitative variables, information is expressed as median ± interquartile range. For categorical variables in frequency (%). Variables significantly associated with PH (*p*-value < 0.05) are highlighted in bold.

**Table 4.** Descriptive analysis of the replication cohort.


OR (95% IC): odds ratio (95% confidence interval). AF: atrial fibrillation, NIHSS: National Institutes of Health Stroke Scale, Gly: initial glycaemia, CES: cardioembolic stroke. For quantitative variables, information is expressed as median ± interquartile range. For categorical variables, in frequency (%).

#### *3.3. Meta-Analysis*

With the meta-analysis, we found a genomic locus with a significant genome-wide association (*p*-value <5 × <sup>10</sup><sup>−</sup>8). This genomic locus is constituted by 57 SNV in our metaanalysis (Supplementary Table S1). Its leading SNV is 12:59127963:A:G (rs79770152) and it is an intronic variant located in the RP11-362K2.2:RP11-767I20.1 gene, with a *p*-value of 3.90 × <sup>10</sup>−<sup>8</sup> (MAF: 0.09; Beta coefficient: 0.09, standard error (SE): 0.015).

In addition, a total of 28 genomic loci with nominal SNV were found (*p*-value < 1.00 × <sup>10</sup><sup>−</sup>5) (Supplementary Table S2). One of these loci contains a leading SNV that almost reaches statistical significance at genome-wide level, 9:78563802:G:T (rs13297983). It is an intronic variant located in the gene PCSK5 with a *<sup>p</sup>*-value of 6.10 × <sup>10</sup>−<sup>8</sup> (MAF: 0.07; Beta coefficient: 0.097, SE: 0.017).

None of these two SNVs are eQTL or present chormatin interactions regarding the databases available in FUMA. Table 5 shows the description of the top ten genomic loci with the most significant SNV and Figure 2 the Manhattan plot.

One of the SNV belonging to one of this top ten genomic loci (17:72393744:A:G, rs4348170, *<sup>p</sup>*-value 1.60 × <sup>10</sup>−6) has been associated in another GWAS with interleukin levels [28]. If we perform a GWAS Catalog search for the genes closest to the leading SNVs of these genomic loci, we find that variants of PCSK5 have been associated with diffuse plaques of aggregated amyloid-β peptide in the brain, measurement of tau protein in the form of paired helical filaments, apolipoproteina B, or LDL levels regarding the consumption of alcohol. KLF5 with neutrophil and monocyte count or lymphocyte percentage of leukocytes. TGFBR3 with multiple sclerosis and pulse pressure measurement. C15orf48 with urinary albumin to creatinine ratio, glomerular filtration rate, and albuminuria. RNA5SP448 with LDL and interleukin 12 measurement. SEMA3A with white matter microstructure measurement, cortical thickness, major depression, and alcohol dependence or DNA methylation. EIF3H with neurofibrillary tangles.

Gene-based analysis performed with FUMA took into account a total of 18317 protein coding genes. Therefore, the significant *p*-value corrected for multiple comparisons was 2.73 × <sup>10</sup>−6. None of the genes reached statistical significance. The most significant associations were SLC30A4 (*p*-value 1.82 × <sup>10</sup><sup>−</sup>5) and C15orf48 (*p*-value 4.58 × <sup>10</sup><sup>−</sup>5), both in chromosome 15 (Figure 3).

#### *3.4. MAGMA Analysis and GO Terms*

FUMA platform performs MAGMA gene-set analysis for curated gene sets and gene ontology (GO) terms obtained from MsigDB. The only significant association after adjusting for the Bonferroni method was the GO term (molecular function) myosin V binding (adjusted *<sup>p</sup>*-value 2.04 × <sup>10</sup>−3), which definition is the interaction selectively and noncovalently with a class V myosin. Supplementary Table S3 shows the top ten of the most significant curated gene sets and GO terms.

The most relevant GO terms could be visualized on Table 5.

#### *3.5. Genetic Correlations*

Genetic correlation analysis detected a shared genetic background among PH presence and Alzheimer' Disease and white matter hyperintensities (WMH) with a raw *p*-value < 0.05 (Table 6). None of the traits reached a significant *p*-value adjusted for multiple comparisons (*p*-value adjusted with Bonferroni method: 4.16 × <sup>10</sup><sup>−</sup>3).



*J. Clin. Med.* **2021**, *10*, 3137


consequence of the SNV on the gene obtained from ANNOVAR; RDB: RegulomeDB score which is the categorical score (from 1a to 7), 1a is the highest score that the SNV has the most biological evidence to be regulatory element; eQTL: expression quantitative trait loci, here appears the gene which expression the SNV modifies; GO terms: the most relevant gene ontology terms. +: positive effect of the β coefficient; -: negative effect of the β coefficient; ?: the SNV was not evaluated; the first symbol corresponds to discovery and the second to replication cohorts.

**Figure 2.** Manhattan and QQ plot of the meta-analysis. (**A**) Manhattan plot. SNVs were represented by dots and plotted based on their genome-wide association study *p*-values. Red line shows genome-wide significance (*p*-value < 5 × 10−8). (**B**) QQ plot of the *p*-values obtained after the association testing. The x-axis represents the expected −log10—*p*-value under the null hypothesis and lambda is the median of the resulting chi-squared test statistics divided by the expected median of the chi-squared distribution under the null hypothesis.

**Figure 3.** Manhattan and QQ plot of the gene-based meta-analysis. (**A**) Manhattan plot. Genes were represented by dots and plotted based on their *p*-values. Red line shows the considered significant *p*-value (*<sup>p</sup>* < 5 × 10−8). (**B**) QQ plot of the *p*-values obtained after the association testing. The x-axis represents the expected −log10—*p*-value under the null hypothesis and lambdaisthemedianoftheresultingchi-squaredteststatisticsdividedbytheexpectedmedianofthechi-squareddistributionunderthenullhypothesis.


**Table 6.** Results of the genetic correlation (GNOVA).

Rho: the genetic covariance estimate; rho SE: standard error of the estimate of rho; Corr: the genetic correlation estimate. ICH: intracerebral hemorrhage; SVO: small vessel occlusion stroke; SBP: systolic blood pressure; CES: cardioembolic stroke; AIS: any ischemic stroke; DBP: diastolic blood pressure; LAS: large artery atherosclerosis stroke; AS: all strokes. Traits with *p*-values < 0.05 are highlighted in bold.

#### **4. Discussion**

This is an observational case-control study in order to find genetic risk factors and biological mechanisms associated with brain parenchymal hemorrhagic transformation after reperfusion treatment in ischemic stroke.

In a previous work by our group, we explored which SNVs were associated with hemorrhagic transformation through a GWAS, analyzing extreme phenotypes: PH vs. non hemorrhagic transformation in patients undergoing only IVT [10]. This led to the finding that rs7648433, located in *ZBTB46* gene, was associated with this phenotype and it has been implicated in mechanisms such as shear stress and atherosclerosis in other studies.

In the current study, we analyzed patients undergoing IVT and including, additionally, patients with intra-arterial fibrinolysis or mechanical thrombectomy. We wanted to obtain more generalized results, as these therapies are widely used and their window time administration has recently been increased [5–7]. This longer time-window administration may lead to an increase of hemorrhagic complications, one of the major problems of these treperfusion therapies. Understanding why a patient may develop PH including patients underwent any type of reperfusion treatment may be of great interest, as this subtype is the one with the highest rates of morbi-mortality [2,4].

In addition, we have added other HT subtypes different from PH to the group of controls (HI). This strategy is interesting to find genetic risk factors associated exclusively to PH in contrast to our previous work [10], as we are avoiding any possible genetic risk factor that could be associated to both, HI and PH.

Including HI patients and all reperfusion therapies, we could increase the number of cases respect to previous studies, increasing our statistical power and analyzing the major genetic study performed in this field. In our previous work, we analyzed 1904 patients and in our present study, we were able to analyze 2034 patients.

The differences in these sample sizes are due to the slight increase in the number of cohorts introduced, the generalization of the study to patients who had undergone intraarterial fibrinolysis or mechanical thrombectomy as a second intention, and the different QC carried out.

Although we did not find statistically significant SNVs after adjusting for multiple comparisons in our discovery cohort, the meta-analysis did allow us to detect rs79770152 with a *<sup>p</sup>*-value 3.90 × <sup>10</sup>−8*,* an intronic variant located in the *RP11-362K2.2:RP11-767I20.1* genes, which are uncharacterized genes. We found that the lncRNAs are supposed to likely exert their functions in other genomic locations (trans-regulation) [29].

Another SNV very close to be genome-wide significant was rs13297983 with a *p*-value 6.10 × <sup>10</sup>−8*,* an intronic variant located in the gene *PCSK5*.

From these leading SNVs of the first ten loci, we can point out that there is one with the most biological evidence to be a regulatory element: rs6686126, an intronic variant located in TGFBR3. In addition, some of these SNVs are eQTL which regulate the expression of different genes in tissues such as the brain, arteries, and peripheral nerves. None of these two SNVs most significant are eQTL or present chromatin interactions regarding the databases available in FUMA.

All the leading SNVs that constituted the top ten most significant variants, followed the same direction of effect in the discovery and replication cohorts. Except rs4348170, which was not present in the discovery cohort. Furthermore, some of the GO terms were related with angiogenesis or neuronal development. This is noteworthy, since the blood vessel is of relevance in the PH and neuronal apoptosis in the prognosis.

Interestingly, several of the genes from the genes included in these loci have been associated in other GWAS studies to aggregated amyloid-β peptide and tau protein such as *PCSK5* or *EIF3H* [30]. *SEMA3A* has been associated with cortical thickness and white matter microstructure measurement [31], parameters related to cognitive impairment. *SEMA3A* gene was also found in the GWAS performed previously by our group (*p*value: 7.85 × <sup>10</sup><sup>−</sup>8) [10].

We have also found that Alzheimer's disease, the leading cause of dementia characterized by amyloid-β and tau aggregates, shares a genetic background with a predisposition to PH in patients undergoing reperfusion treatment (raw *p*-value < 0.05). Moreover, we found that WMH also share a genetic background with PH. In previous results from our group, we also observed this genetic correlation with WMH and also with ICH that has not been observed in the current work [10]. We could hypothesize that the lack of this association could be due to the fact that it shares genetic background with HT but not so much with PH, or simply due to a lack of statistical power.

The effect of IVT on overall HT in patients with dementia is controversial in the literature [32]. Some authors conclude that ITV did not increase the risk of HT in the patients with dementia compared to the controls without dementia, that underwent IVT [32].

Our results suggest that dementia might play a role in the development of PH due to Alzheimer's disease and WMH share a genetic background with PH, although these associations did not remain significant after adjusting for multiple comparisons. Besides, we found SNVs (from the genes *PCSK5*, *EIF3H,* and *SEMA3A)* related to amyloid-β, tau protein, cortical thickness, or WMH. Moreover, the occurrence and localization of cerebral microbleeds (CMBs) associated with IVT-related hemorrhagic complications could indicate an underlying cerebral amyloid angiopathy [33]. This pathology is characterized by the presence of amyloid-β aggregated in the vascular walls of the brain, leading to dementia and a predisposition to ICH. That could indicate that patients who may develop amyloid angiopathy in the future may have an increased risk of HT. However, we did not find a genetic correlation between ICH or ICH subtypes with PH occurrence in our study.

*PCSK5* [34] and *RNA5SP448* [35] has been found to be associated with LDL levels, a molecule that has been shown to promote inflammation [36]. Actually, it has been found that lower LDL cholesterol levels had been associated with HT [3]. *KLF5* has been associated with neutrophil and monocyte count or lymphocyte percentage of leukocytes [37], and *RNA5SP448* with interleukin 12 [38]. Both interleukins and the neutrophil-to-lymphocyte ratio (NLR) have been shown to be a marker associated with inflammation; a high NLR can predict HT [39]. This suggests that inflammation may play an important role in the development of PH. Actually, it has been observed that r-tPA mobilizes immune cells that exacerbate hemorrhagic transformation in stroke [40].

*TGFBR3* has been associated with pulse pressure measurement. Besides, the SNV found with nominal significance: 1:92310874:A:G, an intronic variant located in TGFBR3, has a RegulomeBD score of 2b. In addition, blood pressure variability was found to be correlated with HT [41]. Nevertheless, we failed to find a genetic correlation between SBP and DBP with PH.

It is also worth noting that myosin V binding was the GO term significantly associated with PH. Myosin V is primarily found in the central nervous system serving as neuronal marker [42] and has been linked to recycling endosomes and exocytosis of secretory MMP2 and MMP9 which have been widely associated with TH [43–45].

Regarding limitations, one of the most important is the small sample size of both the discovery and replication cohorts, even though it is one of the largest made in this topic. This is probably the root cause of not finding significant SNVs in the discovery cohort. For this reason, to increase our statistical power, we performed the meta-analysis that showed a genome-wide significant SNV and another that was almost significant. Another limitation is the lack of replication in an independent cohort. However, the same direction of effect observed for the most significant SNVs in the discovery and replication cohorts indicates that the results are consistent.

Another limitation is the Spanish origin of all the patients from the discovery cohort, this might make it difficult to generalize the results to other populations. To overcome this limitation, the replication cohort included patients from Poland and Finland. Likewise, the lack of values for the variable of the time elapsed between the onset of symptoms and the administration of treatment may limit our results. Furthermore, the fact that we did not have any patient with mechanical thrombectomy who presented PH limits the generalization of our results to this subgroup of patients. Therefore, studies with a larger sample size, incorporating more variables, and more patients subjected to mechanical thrombectomy will be necessary to establish more robust conclusions.

#### **5. Conclusions**

With this meta-analysis, we have found a new locus significantly associated with the risk of PH in patients treated with the different types of reperfusion therapies used in the clinical practice. Correlation analysis has shown us shared background genetics between PH and Alzheimer's disease and WMH. Moreover, the analysis of the most significant genomic loci supports this relationship, as the nearest genes associated with the leading SNVs have been related to aggregated amyloid-β, tau protein, or white matter microstructure. However, also of great interest is that other traits related to these SNVs pointed to the importance that inflammation may play in the risk of developing PH. Further studies are needed to test these hypotheses.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/jcm10143137/s1, Figure S1: Manhattan and QQ plot of the discovery cohort; Figure S2: Manhattan and QQ plot of the discovery cohort; Table S1: SNVs belonging to the genomic locus with the leading SNP being significant at GWAS level; Table S2: Description of the GWAS significant locus and the 28 nominal significant loci; and Table S3: Top ten of the most significant curated gene sets and gene ontology terms obtained from MsigDB.

**Author Contributions:** Conception and design of the work and writing—original draft preparation: E.M., J.C.-M. and I.F.-C.; Writing—review and editing: All authors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by grants from the Instituto de Salud Carlos III (PI 11/0176), Generación Project, Maestro Project (PI18/01338), INVICTUS+ network, Epigenesis Project (Marató de TV3), FEDER funds. E. Muiño is supported by a Río Hortega Contract (CM18/00198) from the Instituto de Salud Carlos III. J. Cárcel-Márquez is supported by an AGAUR Contract (agència de gestió d'ajuts universitaris i de recerca; FI\_DGR 2020, grant number 2020FI\_B1 00157) co-financed with Fons Social Europeu (FSE). C. Gallego-Fabrega is supported by a Sara Borrell Contract (CD20/00043) from Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional (ISCIII-FEDER). M. Lledós is supported by a PFIS Contract (Contratos Predoctorales de Formación en Investigación en Salud) from the Instituto de Salud Carlos III. I (FI19/00309). Fernández-Cadenas (CP12/03298), Tomás Sobrino (CPII17/00027), and Francisco Campos (CPII19/00020) are supported by a research contract from Miguel Servet Program from the Instituto de Salud Carlos III.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the local Ethics Committee of every hospital participant.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** We are grateful to Lucía Muñoz (Hospital Germans Trias i Pujol), Anna Penalba (Vall d'Hebron Research Institute), Uxue Lascano (IMIM-Hospital del Mar), Carmen Jimenez (Hospital Universitari Son Espases), Elisa Cortijo (Hospital Clínico Universitario), Esther Sarasola Diez (Hospital de Basurto), Carmen Gubern (Josep Trueta University Hospital), Aki Havulinna (Institute for Molecular Medicine Finland), Veikko Salomaa (Institute for Molecular Medicine Finland), and Antoni Ferens (Jagiellonian University) for their contribution to patient recruitment; and to Agustin Ruiz and Oscar Sotolongo (Fundació ACE) for their technical support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Reperfusion Therapies for Acute Ischemic Stroke in COVID-19 Patients: A Nationwide Multi-Center Study**

**Justina Jurkeviˇciene˙ 1,\*, Mantas Vaišvilas 1, Rytis Masiliunas ¯ 2, Vaidas Matijošaitis 3, Antanas Vaitkus 3, Dovile Geštautait ˙ e˙ 3, Saulius Taroza 4, Paulius Puzinas 5, Erika Galvanauskaite˙ 6, Dalius Jatužis <sup>2</sup> and Aleksandras Vilionskis <sup>7</sup>**


**Abstract:** (1) Background: Acute ischemic stroke (AIS) is a possible complication of the coronavirus disease 2019 (COVID-19). Safety and efficacy data on reperfusion therapies (RT)—intravenous thrombolysis and endovascular treatment (EVT)—in stroke patients with COVID-19 is lacking. (2) Methods: We performed a retrospective nationwide multi-center pair-matched analysis of COVID-19 patients with AIS who underwent RT. We included adult COVID-19 patients with AIS who were treated with RT between 16 March 2020 and 30 June 2021. All subjects were paired with non-infected controls, matched for age, sex, stroke arterial vascular territory, and RT modality. The primary outcome measure was a favorable functional outcome defined by the modified Rankin scale (mRS 0–2). (3) Results: Thirty-one subjects and thirty-one matched controls were included. The median baseline National Institutes of Health Stroke Scale (NIHSS) score was higher in the COVID-19 group (16 vs. 12, *p* = 0.028). Rates of ischemic changes and symptomatic intracerebral hemorrhages did not differ significantly between the two groups at 24 h after RT. The median NIHSS 24 h after reperfusion remained significantly higher in the COVID-19 group (16 vs. 5, *p* = 0.003). MRS 0–2 at discharge was significantly less common in COVID-19 patients (22.6% vs. 51.8%, *p* = 0.018). Three-month mortality was 54.8% in the COVID-19 group versus 12.9% in controls (*p* = 0.001). (4) Conclusion: Reperfusion therapies on AIS in COVID-19 patients appear to be safe; however, functional outcomes are significantly worse, and 3-month mortality is higher.

**Keywords:** COVID-19; ischemic stroke; thrombolysis; thrombectomy; Lithuania; reperfusion therapies; outcomes; safety

#### **1. Introduction**

In December 2019, a cluster of patients with pneumonia caused by a novel severe acute respiratory coronavirus 2 (SARS-CoV-2) was first described in Wuhan, China [1]. Due to the vast spread of the virus across the globe, a pandemic was declared in March 2020. Ever since, a growing number of publications regarding extrapulmonary manifestations of coronavirus disease (COVID-19) arose. Neurologic manifestations of both the

**Citation:** Jurkeviˇciene, J.; Vaišvilas, ˙ M.; Masiliunas, R.; Matijošaitis, V.; ¯ Vaitkus, A.; Geštautaite, D.; Taroza, ˙ S.; Puzinas, P.; Galvanauskaite, E.; ˙ Jatužis, D.; et al. Reperfusion Therapies for Acute Ischemic Stroke in COVID-19 Patients: A Nationwide Multi-Center Study. *J. Clin. Med.* **2022**, *11*, 3004. https://doi.org/ 10.3390/jcm11113004

Academic Editors: Hyo Suk Nam and Byung Moon Kim

Received: 18 April 2022 Accepted: 23 May 2022 Published: 26 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

central and the peripheral nervous system described included COVID-19 encephalitis, acute disseminated encephalomyelitis, epileptic seizures, neuromuscular symptoms, acute demyelinating polyneuropathies, and their variants, as well as acute cerebrovascular syndromes [2–8]. It has been postulated that COVID-19 patients are at an increased risk for stroke, although the true causality is yet uncertain [9].

The first COVID-19 case in Lithuania was confirmed in late February 2020, followed shortly by the introduction of a strict nationwide lockdown. Despite thousands of daily new confirmed cases and the need for allocation of specific healthcare resources, emergency stroke services were operating in all major stroke centers across the country throughout the pandemic at full capacity [10,11]. Both intravenous thrombolysis (IVT) and endovascular treatment (EVT) were used continuously for acute ischemic stroke (AIS) in COVID-19 patients. However, data on the safety of reperfusion therapies (RT) in the COVID-19 population is scarce, and potential adverse effects of RTs could be life-threatening. Therefore, we sought to evaluate the safety and outcomes of reperfusion therapies in COVID-19 patients with AIS in a nationwide pair-matched retrospective study.

#### **2. Materials and Methods**

We conducted a multi-center retrospective pair-matched analysis of reperfusion therapy in COVID-19 patients with AIS across all six comprehensive stroke centers (CSCs) in Lithuania [12].

**Data collection.** The data were extracted retrospectively from electronic health records. We collected demographic data (age, gender), cardiovascular risk factors (hypertension, dyslipidemia, smoking, diabetes, atrial fibrillation, presence of symptomatic internal carotid artery (ICA) >70% or intracranial artery stenosis > 70% on computed tomography angiography), clinical (hypoxemia, body temperature, blood pressure on admission) and laboratory test data (white blood cell (WBC) and lymphocyte count, C reactive protein (CRP) and D-dimer levels on admission), head computed tomography (CT) findings (Alberta Stroke Programme Early CT Score (ASPECTS) on admission, ischemic changes on CT scan 24 h after RT), median timeliness metrics (onset-to-door (OTD), door-to-needle (DTN) and door-to-puncture (DTP) times), National Institute of Health Stroke Scale (NIHSS) on admission, at 24 h after reperfusion therapy, and on day 7 after stroke or at discharge (whichever occurred first) and reperfusion therapy data (treatment modality, Thrombolysis in Cerebral Infarction (TICI) score). Neurologic (symptomatic intracerebral hemorrhage (sICH), cerebral edema), COVID-19-related, and other complications (urinary tract infection, pulmonary embolism, myocardial infarction, acute heart failure, pulmonary edema, other organ dysfunction, or major bleeding) were collected. Patient functional outcomes corresponding to modified Rankin Scale (mRS) score at discharge, as well as in-hospital and 3-month mortality rates, were retrieved.

**Patient selection.** We included adult (18 years old or older) AIS patients with diagnosed acute COVID-19 infection prior to or on admission to a CSC, treated with reperfusion therapy (IVT, EVT, or both) between 16 March 2020 and 30 June 2021. Our patients had not received full vaccination doses. COVID-19 status was confirmed by a nasopharyngeal swab SARS-CoV-2 real-time polymerase chain reaction (RT-PCR). Patients who recovered from COVID-19 according to the epidemiological criteria at the time of index AIS were excluded from the analysis despite having a positive SARS-CoV2 RT-PCR test result.

**Control group.** Each patient from the subject group was weighted against a control. All control patients were treated in one of the 6 Lithuanian CSCs during the study period and were not concomitant with a COVID-19 infection. In addition, control subjects were matched for age (±5 years), gender, stroke arterial vascular territory, and type of reperfusion therapy (IVT, EVT, or both). To avoid selection bias, cases for this group were collected by independent stroke physicians, who were not part of this study, and were only informed about matching criteria.

**Outcomes.** The primary outcome measure was a favorable functional outcome, defined as the mRS score of 0–2 on the day of discharge.

Secondary outcome measures included: early neurological improvement, defined as reduction of NIHSS score by 4 points or more or score 0–1 at 24 h after reperfusion therapy; change in NIHSS score 24 h after reperfusion therapy; change in NIHSS score 7 days after stroke onset or on discharge (whichever occurred first); neurological complications of reperfusion therapy: sICH was classified using the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) classification (parenchymal hemorrhage type 2, 22–36 h after treatment leading to neurologic deterioration 4 points or more on NIHSS from baseline or lowest NIHSS or leading to death as previously reported) [13], and cerebral edema; in-hospital mortality rate; mortality rate 3 months after stroke.

To investigate the effects of clinical and laboratory factors (evaluated on admission) on the likelihood of favorable functional outcome (mRS 0–2) on the day of discharge and of 3-month mortality after stroke and reperfusion therapies, multivariate logistic regression models were built.

**Statistical analysis.** Statistical analysis was performed using the IBM SPSS Statistics for Windows, Version 26 (IBM SPSS Statistics for Windows, IBM Corporation, Armonk, NY, USA). The Kolmogorov–Smirnov test was used to verify the normality of the distribution of continuous variables. The qualitative variables were expressed as absolute frequencies and percentages. For continuous data, the mean and standard deviation (SD) or median and interquartile range (IQR) were reported, as appropriate. The Student's *t* test (for normally distributed data) or the Mann–Whitney U test (for not normally distributed data) was used for the continuous variables and the Chi-square test for the categorical variables. *p* < 0.05 was considered to be statistically significant. The significant predictors (using a significance level of <0.1) in the univariate analysis were included in the multivariate analysis, and the entered method was applied for the logistic regression model to determine the predictors for a favorable functional outcome (mRS 0–2) on discharge and 3-month mortality after stroke. The odds ratio (OR) and 95% confidence interval (95% CI) were calculated.

#### **3. Results**

#### *3.1. Demographic, Clinical, and Stroke-Related Data*

Thirty-one pairs of subjects and matched controls were included in the study. The mean age was 74.0 years in COVID-19-positive AIS patients and 73.7 years in controls. Forty females (64.5%) comprised the entire cohort. Prevalence of stroke risk factors did not differ statistically significantly between the two groups. Fourteen (22.5%) patients underwent IVT, thirty (48.4%) patients were treated with EVT, and eighteen (29.1%) patients received bridging therapy. Fifty-six (90.3%) patients in the entire cohort were diagnosed with anterior circulation stroke. The detailed demographic data and stroke risk factors are displayed in Table 1.

The median NIHSS score on admission was significantly higher in the COVID-19 patient group compared to controls (16 [10–19] vs. 12.5 (5–15), *p* = 0.028). The timeliness metrics (OTD, DTN, and DTP times) did not differ significantly between the two groups. Albeit not significant, the OTD time was longer for COVID-19 patients as compared to controls (126 (83–218) vs. 95 (66–205) minutes, respectively). The ASPECTS score on admission also did not differ significantly.

As expected, the baseline body temperature was statistically significantly higher in COVID-19 patients compared to controls (*p* = 0.025), while the rate of hypoxemia and arterial blood pressure on admission did not differ significantly (Table 2). A significantly lower lymphocyte count (*p* = 0.013) and higher CRP values (*p* < 0.001) were observed in the COVID-19 group compared to controls, while total WBC count and D-dimer concentration on admission did not differ.


**Table 1.** Patient demographic data and stroke characteristics.

SD—standard deviation, ICA—internal carotid artery, IV—Intravenous thrombolysis, EVT—endovascular treatment, mRS—modified Rankin Scale, IQR—interquartile range, NIHSS—National Institutes of Health Stroke Scale, ASPECTS—Alberta Stroke Programme Early CT Score. § Sample size differs for both subjects (*n* = 30), and control group (*n* = 27) due to missing data. Bold values denote statistical significance at the *p* < 0.05 level.

**Table 2.** Baseline clinical and laboratory data.


IQR—interquartile range, SD—standard deviation, WBC—white blood cells, CRP—C-reactive protein. † Defined as SpO2 < 93%. Bold values denote statistical significance at the *p* < 0.05 level.

#### *3.2. Primary and Secondary Outcomes*

Only 22.6% of COVID-19 patients with AIS in the subject cohort achieved favorable functional outcomes (mRS 0–2) on discharge as compared to 51.6% in the control group (*p* = 0.018) (Table 3).


**Table 3.** Patient treatment outcomes and complications.

TICI—thrombolysis in cerebral infarction, NIHSS—National Institutes of Health Stroke Scale, IQR—interquartile range, mRS—modified Rankin Scale, ICH—intracerebral hemorrhage, ICU—intensive care unit. † Only patients who had undergone mechanical thrombectomy (*n* = 46, data of 2 patients was missing). ‡ Whichever occurred first. § Defined as reduction of NIHSS score by 4 points or more or score 0–1 at 24 h after reperfusion therapy. || Sample size differs for both subjects (*n* = 26) and control group (*n* = 29) due to missing data. ¥ Sample size differs for both subjects (*n* = 31) and control group (*n* = 25) due to missing data. ¥¥ Sample size differs for both subjects (*n* = 31) and control group (*n* = 18) due to missing data. ¶ Including urinary tract infection, pulmonary embolism, myocardial infarction, acute heart failure, pulmonary oedema, other organ dysfunction, major bleeding (excluding pneumonia and respiratory failure). Bold values denote statistical significance at the *p* < 0.05 level.

Significantly higher NIHSS scores 24 h after reperfusion therapy (16 (5–24) vs. 5 (2–13), *p =* 0.003) and on day 7 or discharge (15 (5–21) vs. 4 (1–10), *p <* 0.001) were evident in the COVID-19 group as compared to matched controls. The detail outcome data are shown in Table 3. Rate of cerebral edema after the reperfusion treatment did not differ between the two groups, and no sICHs were observed. Both in-hospital and 3 month mortality rates were significantly higher in the COVID-19 group compared to controls (29% and 54.8% vs. 6.5% and 12.9%, *p* = 0.043 and *p* = 0.001, respectively).

The analysis of in-hospital mortality patients in both groups showed severe stroke from onset (baseline NIHSS > 15). COVID-19-positive stroke patients who died in hospital: 5/9 (55.6%) underwent MTE and 4/9 (44.4%) underwent bridging therapy, 2/9 (22.2%) had unsuccessful MTE (TICI 1 and 2a), 7/9 (77.8%) had acute ischemic changes on CT scan 24 h after RT, 2/9 (22.2%) experienced reperfusion complications (small scattered petechiae and subarachnoid hemorrhage, confluent petechiae), 5/9 (55.6%) had various degree cerebral edema, 8/9 (88.9%) had pneumonia and respiratory failure, 2/9 (22.2%) had other somatic complications (sepsis, acute kidney failure and urinary tract infection), 2/2 (100%) control group stroke patients who died in hospital underwent MTE, and reperfusion therapy was successful (TICI 3) in both cases, Both patients had acute ischemic changes on CT scan 24 h after RT, both experienced reperfusion complications (hematoma within infarcted tissue, occupying <30%, intraventricular hemorrhage), both had cerebral edema, and both had pneumonia and respiratory failure and no other somatic complications.

#### *3.3. COVID-19 Associated Complications*

Severe respiratory failure was observed in 64.5% of COVID-19 patients during any time point of inpatient treatment, and it was significantly more common compared to controls, where only 22% of patients were in respiratory compromise (*p =* 0.007). Importantly, on admission, rates of respiratory failure did not differ between the two groups (hypoxemia rate 5 (16.1%) in COVID-19 group vs. 3 (9.7%) in controls, *p* = 0.712). Pneumonia complicated the disease course of 67.7% of COVID-19 patients as compared to 8% of controls *(p <* 0.001). Prolonged stay in ICU was observed in 38.7% of COVID-19 patients compared to 19.4% in control group (*p* = 0.093).

#### *3.4. Multivariate Analysis*

The accuracy of a favorable functional outcome prediction was 83.6%. The significant variables in the univariate analysis included age (*p* = 0.028), baseline NIHSS (*p* < 0.001), and COVID-19 infection (*p* = 0.011). In the multivariable model, only baseline NIHSS retained significance (OR 0.790; 95% CI 0.691–0.902) (Table 4).

**Table 4.** Logistic regression model on the likelihood of favorable functional outcome (mRS 0–2) on discharge (*n* = 61).


OR—odds ratio, CI—confidence interval, NIHSS—National Institutes of Health Stroke Scale. Bold values denote statistical significance at the *p* < 0.05 level in multivariate analysis.

The accuracy of 3-month mortality after stroke and reperfusion therapy was 78.8%. The significant variables included age (*p* = 0.022), hypoxemia (*p* = 0.079), baseline NIHSS (*p* = 0.001), COVID-19 infection (*p* = 0.001), total WBC count (*p* = 0.079), and CRP concentration (*p* = 0.093). Increasing age and higher baseline NIHSS on admission were associated with a higher likelihood of 3-month mortality after stroke and reperfusion therapy. COVID-19 infection increased the likelihood of death 3 months after stroke and reperfusion therapy seven times (OR 6.696; 95% CI 1.029–43.584), while hypoxemia, total WBC count, and CRP concentration were not significant predictors (Table 5).

**Table 5.** Logistic regression model on the likelihood of 3-month mortality after stroke and reperfusion therapy (*n* = 52).


OR—odds ratio, CI—confidence interval, NIHSS—National Institutes of Health Stroke Scale, WBC—white blood cells, CRP—C-reactive protein. Bold values denote statistical significance at the *p* < 0.05 level in multivariate analysis.

#### **4. Discussion**

This is the first Lithuanian nationwide pair-matched multicenter study evaluating outcomes of COVID-19-positive AIS patients treated with reperfusion therapies. We demonstrated that COVID-19 stroke patients present with a significantly higher neurologic burden than non-infected controls. We also found that reperfusion therapies appear safe for COVID-19 stroke patients in relation to reperfusion-associated complications (symptomatic ICH

and cerebral edema). Despite successful reperfusion, the COVID-19 stroke patients had significantly worse outcomes and a high 3-month mortality rate as compared to control patients. We additionally report 3-month mortality of COVID-19-positive patients with AIS representing distant sequalae of AIS. Hypoxia had a major role in our COVID-19 cohort and may have contributed to the high in-hospital and 3-month mortality rate.

Outcomes of COVID-19 patients with AIS seem to be universally unfavorable despite successful reperfusion. Although COVID-19 patients with mild stroke presentations seemed to have more favorable outcomes, in general, COVID-19 patients with AIS were more severely disabled, with a median NIHSS of 15 at discharge as compared to controls. This is in line with other studies reporting in-hospital mortality rates ranging from 31% to 60% [14–16]. The European multicenter EVT study provided data on 30-day mortality of 27% [17]. In contrast, we report insights on 3-month mortality even higher than previously reported [18].

In our study, the absolute majority of COVID-19 stroke patients had a more severe stroke despite no differences in ASPECTS scores between study groups on admission. These results are comparable to previous reports [18]. However, the true size of ischemic territory in COVID-19 patients may be larger than initially anticipated. Significantly lower ASPECTS scores and higher infarct volumes were observed for COVID-19 patients with AIS on MRI despite early imaging in a previous study [19]. In contrast, we used CT as our main screening modality. Although discordances between MRI and CT median ASPECTS scores in non-COVID-19 AIS have been documented, no impact to overall outcomes was observed [20]. Therefore, COVID-19-specific endothelial dysfunction may have a role in infarct core size expansion and contribute to poor outcomes.

Moreover, in our study, we demonstrated that COVID-19 stroke patients eligible for reperfusion therapies had prolonged onset-to-door times. Prolonged ODT in COVID-19 patients might be explained by human factors: first, the lack of available paramedical teams on-call could have delayed arrival to the hospital. Second, both stroke admission rates and prolonged ODT were previously reported owing to the reluctance of stroke patients to seek medical care, especially during the start of the pandemic when vaccination was not yet available [21]. However, the impact of prolonged ODT on stroke severity is debatable. Prolonged ODT might also be explained in part by the expanded intervention window for EVT according to the DAWN trial, demonstrating the undeniable benefits of EVT beyond 6 h for rigorously selected patients [22]. However, this approach was not validated for COVID-19 patients, but despite the lack of evidence, the DAWN criteria were applied according to best clinical practice and consensus statements valid at the time of therapy [23,24]. Second, data regarding the efficacy of EVT beyond 6 h in COVID-19 stroke patients are conflicting, since there are no studies specifically addressing this issue in the COVID-19 population. Studies specifically addressing reperfusion beyond 6 h are required to assess their safety and efficacy profile and more importantly, assess the impact of COVID-19 in these patients, especially in cases with respiratory compromise.

In our study, DTN and DTP times did not differ significantly between patients infected with COVID-19 and controls. Every stroke center was pre-notified about COVID-19 positivity in cases when information was available to the paramedical team and when stroke teams made safety preparations in advance. However, in most cases, COVID-19 status was unknown. Treatment of stroke and reperfusion therapy was considered a priority and did not cause delays in logistics in the emergency departments in either of the stroke centers.

Another aspect to consider is early neurological improvement after reperfusion therapy. In our cohort, successful reperfusion (TICI 2b or TICI 3) was observed in 79.2% of COVID-19 patients with AIS who underwent EVT, and in all but one patient (95.5%) in the control group. In addition, the rate of ischemic changes on CT scan 24 h after RT did not differ between COVID-19 and control groups. Despite successful and timely reperfusion, COVID-19 stroke patients did not improve neurologically 24 h after reperfusion. We acknowledge the possibility that some patients may have exhibited a higher neurological burden due to their severe general state and the need for intensive care due to COVID-19. We did not calculate the ICU severity scores to represent the general state of these patients. However, NIHSS scores were evaluated either at 7 days or on discharge for every patient. At these time points, the absolute majority of patients were discharged from the ICU. Therefore, we believe that evaluation of NIHSS later in the disease course more accurately reflects the true neurologic burden. Moreover, a lack of early neurological improvement was observed in other studies owing to several factors. Early consecutive ischemic strokes or re-occlusions of the same vessel after successful or complete recanalization were observed at a higher than expected rate of 8% in a systematic study [25]. In our cohort, we have no data regarding early re-occlusions in COVID-19 stroke patients, since this was a retrospective study and we do not routinely perform CTA after successful reperfusion according to national guidelines, unless there is a high clinical suspicion of re-occlusion.

Another proposed explanation for no neurological improvement is the difference in clot composition in COVID-19 and non-COVID-19 patients. Wang et al. described several patients with excessive clot fragmentation and distal migration during thrombectomy. Moreover, once evaluated with thromboelastography, the thrombi showed features of high clot consolidation and reduced time of clot formation consistent with a severe procoagulant state [26]. Several other studies reported a hypercoagulable state in COVID-19 patients as compared to controls, which may attribute to both the devastating multivessel occlusions, clot fragmentation, consecutive ischemic strokes, or early re-occlusions of blood vessels that might contribute to poor outcomes [27]. Although we cannot confirm the different clot features for COVID-19 stroke patients in our study, other aspects of these patients are worth considering.

Hypoxia is a major contributing factor to poor outcomes in AIS patients. In our cohort, 64.5% of COVID-19 stroke patients suffered from respiratory failure. Almost one-third of COVID-19 patients with AIS required prolonged intubation due to severe respiratory system compromise. In a subgroup analysis of the former group (unpublished data), patients in whom the respiratory function was severely affected were those who showed no neurologic improvement 24 h after reperfusion. Most of these patients presented with LVOs and required EVT for reperfusion. Due to a relatively small sample size in our cohort, we could not perform a subgroup analysis with optimal statistical power, but a tendency toward more severe strokes in patients with severe respiratory compromise was observed. This is in line with previous reports. Two meta-analyses showed that severe COVID-19 disease is more often complicated by severe ischemic strokes [16,28]. It is proposed that patients with severe respiratory compromise can be deemed as high risk for poor outcomes and in-hospital mortality [15]. A stroke center in New York reported good early neurological improvement in COVID-19 stroke patients who underwent endovascular treatment. None of the COVID-19 stroke patients who dramatically improved showed signs of respiratory distress [29]. Respiratory function, although analyzed in AIS with COVID-19 cohorts, has not been widely addressed in the subpopulation of patients undergoing reperfusion therapies for AIS. In our study, we emphasize the importance of respiratory complications for AIS patients undergoing specialized treatment. Respiratory failure could be an important factor for early neurological deterioration or lack of improvement despite successful reperfusion. Novel strategies involving optimal management of respiratory compromise should be exploited to improve the outcomes for stroke patients undergoing reperfusion therapy.

Although available safety evidence is scarce, reperfusion in cases of AIS was recommended by an international panel of experts [23,24]. For IVT, various studies report sICH rates from 2.8% to 10% in COVID-19 stroke patients [30–33]. As for EVT, a European multicenter retrospective study of 93 COVID-19 stroke patients reported a rate of sICH of 5.4% [17]. In contrast, results from the largest to date EVT trial MR CLEAN reports sICH rates of 7.7%, although differences between the two studies' sample sizes have to be taken into account [34]. Results from our study are comparable to the aforementioned studies and provide additional insights into the safety of reperfusion therapies for COVID-19 stroke patients. All ICHs were asymptomatic in the COVID-19 group and did not differ

statistically from controls. As given the information provided, reperfusion therapies appear to be safe and beneficial for some patients, but large prospective trials evaluating both the safety and efficacy of these treatments are warranted.

Risk factors associated with high dependency and mortality in COVID-19 AIS patients include older age, COVID-19 infection, and stroke severity on admission. The logistic regression model in our study showed only higher baseline NIHSS to be associated with worse functional outcomes. As for 3-month mortality, age, higher baseline NIHSS and COVID-19 infection were significant predictors in the logistic regression model. COVID-19 infection increased the likelihood of death 3 months after stroke and reperfusion therapy seven times. We acknowledge that the regression analysis model in our study may not reflect the true predictors of poor outcomes in COVID-19 AIS patients undergoing RT due to the retrospective nature of the study, data shortages, and a small sample size. Furthermore, we included to our univariate and multivariate logistic regression only patient history data and clinical and laboratory data evaluated on admission. Earlier, we argued that hypoxia is an important factor for the expansion of infarcted brain tissue and may be associated with poor outcomes given the high rates of severe respiratory failure in our study. This might explain the higher rates of in-hospital mortality. However, for the survivors, the causes of 3-month mortality rates remain to be validated.

**Strengths.** The strength of our study lies within a couple of points. First, the study was conducted across all Lithuanian stroke centers. Second, we added valuable insights to the available safety data of reperfusion therapies in AIS with COVID-19 demonstrating relative safety of all treatment modalities. We have performed one of the few studies reporting COVID-19 patients with AIS mortality at 3 months. As a result, it was possible to compare COVID-19 patients with AIS with controls demonstrating clear differences in mortality and functional outcomes, raising COVID-19 as a potential risk factor predicting poor outcomes in AIS patients.

**Limitations.** The major weaknesses of our study are the retrospective nature and a relatively small sample size, restricting subgroup analysis of reperfusion modalities and evaluation of outcomes within. Another weakness is the chosen pair-matched analysis method, which might not accurately represent the true demographic and stroke-specific data of the control patients. We could not perform a subgroup analysis of different treatment modalities that would have added additional safety and outcome data. The regression analysis model, albeit significant for some factors, we believe, does not reflect all predictors of poor outcomes in COVID-19 patients. Heterogeneity between different centers concerning treatment management of patients with AIS should be considered. Although we reported 3-month mortality rates, we could not compare functional outcomes of surviving COVID-19 stroke patients to the control group, which would provide additional information on distant effects of COVID-19 on AIS survivors.

#### **5. Conclusions**

In conclusion, reperfusion therapies on AIS in COVID-19 patients appear to be safe and should be used. COVID-19-positive AIS patients seem to have more debilitating strokes from onset. Despite successful and timely reperfusion, they tend to have poor functional outcomes with high in-hospital and 3-month mortality rates. For the surviving patients, studies to compare functional outcomes in the post-acute COVID phase between COVID-19 patients with AIS and non-infected stroke survivors are needed.

**Author Contributions:** Conceptualization, A.V. (Aleksandras Vilionskis), M.V. and J.J.; methodology, A.V. (Aleksandras Vilionskis), M.V., J.J., S.T. and V.M.; software, J.J.; validation, A.V. (Antanas Vaitkus), R.M. and D.J.; formal analysis, J.J.; investigation, J.J., M.V., A.V. (Aleksandras Vilionskis), S.T., V.M., E.G., P.P. and D.G.; data curation: J.J., M.V., A.V. (Aleksandras Vilionskis), D.G., V.M., S.T., E.G. and P.P.; writing—original draft preparation, J.J. and M.V.; writing—review and editing, A.V. (Aleksandras Vilionskis), R.M., D.J., S.T. and V.M.; supervision, D.J. and A.V. (Antanas Vaitkus). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the LITHUANIAN BIOETHICS COMMITTEE (protocol code Nr. L-21-06, 15 September 2021).

**Informed Consent Statement:** Patient consent was waived due to the retrospective nature of the study and the impossibility to obtain written consent from patients, who were discharged before the study had been started.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This publication is based upon work from IRENE COST Action—Implementation Research Network in Stroke Care Quality (CA18118), supported by COST (European Cooperation in Science and Technology; www.cost.eu, accessed on 22 April 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

