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Is a high chest CT severity score a risk factor for an increased incidence of long-term neuroimaging findings after COVID-19?

      Highlights

      • The most common neuroimaging finding was ASIS [47/272 (17.3%)].
      • There was insignificant relationship between NIP and ICU admission and mortality.
      • Significant temporal changes of CT-SS in both the patients with NIP and NIN.
      • The temporal change in was not only associated with NIP.

      Abstract

      Objectives

      We aimed to determine the incidences of neuroimaging findings (NIF) and investigate the relationship between the course of pneumonia severity and neuroimaging findings.

      Materials and methods

      Our study was a retrospective analysis of 272 (>18 years) COVID-19 patients who were admitted between “March 11, 2021, and September 26, 2022". All patients underwent both chest CT and neuroimaging. The patient's chest CTs were evaluated for pneumonia severity using a severity score system (CT-SS). The incidence of NIF was calculated. NIF were categorized into two groups; neuroimaging positive (NIP) and neuroimaging negative (NIN). Consecutive CT-SS changes in positive and negative NIF patients were analyzed.

      Results

      The median age of total patients was 71; IQR, 57-80. Of all patients, 56/272 (20.6%) were NIP. There was no significant relationship between NIP and mortality (p = 0.815) and ICU admission (p = 0.187). The incidences of NIF in our patients were as follows: Acute-subacute ischemic stroke: 47/272 (17.3%); Acute spontaneous intracranial hemorrhage: 13/272 (4.8%); Cerebral microhemorrhages: 10/272 (3.7%) and Cerebral venous sinus thrombosis: 3/25 (10.7%). Temporal change of CT-SSs, there was a statistically significant increase in the second and third CT-SSs compared to the first CT-SS in both patients with NIP and NIN.

      Conclusion

      Our results showed that since neurological damage can be seen in the late period and neurological damage may develop regardless of pneumonia severity.

      Keywords

      Introduction

      From the outbreak of the Coronavirus disease 2019 (COVID-19) pandemic on 17 November 2019 in Wuhan to 2 November 2022, the total number of cases was reported as 617,879,854 and the total number of deaths was reported as 6,546,448 worldwide.
      Johns Hopkins Coronavirus Resource Center
      COVID-19 Map - Johns Hopkins Coronavirus Resource Center.
      In Turkey, the first case was reported on March 11, 2020. Until November 2, 2022, the total number of cases was 16,873,793 and the total number of deaths was 101,139.
      Johns Hopkins Coronavirus Resource Center
      COVID-19 Map - Johns Hopkins Coronavirus Resource Center.
      Today, there is a significant decrease in the number of cases and deaths as a result of vaccination and mutations in the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often causes severe respiratory distress. In addition, a wide range of neurological symptoms has been reported during the disease (28.2%) and in the post-COVID-19 period (55%), from mental status changes to acute cerebrovascular disease.
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      Acute-subacute ischemic stroke (ASIS) is the most common cause of neurological deficit detected in brain computed tomography (CT) and magnetic resonance imaging (MRI)  in the post-COVID-19 period. Other neuroimaging findings are cerebral venous sinus thrombosis (CVST), acute-subacute spontaneous intracranial hemorrhage (ICH), and cerebral microhemorrhages (CMH).
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      Post-COVID-19 neurological damage can occur as a result of direct and/or indirect pathways.

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      While these studies investigated neurological complications in inpatients, post-discharge patients were not investigated. The relationship between the course of pneumonia severity and neurological damage has not been investigated.
      Therefore, we aimed to determine the incidences of neuroimaging findings (NIF) in the post-COVID-19 period and investigate the relationship between the temporal changes in pneumonia severity in consecutive chest CTs and NIF.

      Methods

      Study population

      This is a single-center, retrospective analysis of patients admitted to our hospital between “March 11, 2021, and September 26, 2022”. This study was approved by the Ethical Committee of Amasya University Faculty of Medicine and was conducted according to the Declaration of Helsinki and Good Clinical Practice (02 December 2021, number: 12/155). Patient information was obtained from electronic records and censored. Since the study was retrospective, the ethics committee did not find it necessary to obtain written informed consent from the patients.

      Data collection

      In our study, we obtained electronic medical records of individuals who applied to other associated clinics in addition to the “COVID-19 clinic and/or post-COVID-19 follow-up clinic”. The first electronic data search resulted in a list of a total of 21,878 case records. Patients with positive RT-PCR were evaluated if they had chest CT scans in addition to neuroimaging (with or without contrast) scans. In patients with more than one COVID-19 positive, the date of the first RT-PCR positive was accepted. Patients with a time interval of more than 10 days between RT-PCR and first chest CT were excluded from the study. The first positive neuroimaging was enrolled in the study in patients with more than one neuroimaging. If sequential neuroimaging is negative, the last neuroimaging was enrolled in the study. As a result, 272 patients were included in the study (Fig. 1).

      Inclusion criteria

      Patients over 18 years of age, with positive RT-PCR test, and with neuroimaging (brain CT and/or MRI) and chest CT were included in the study.

      Exclusion criteria

      Patients with chronic-stage infarcts and hemorrhages confirmed by neuroimaging before the study time interval were excluded.

      Clinical and laboratory data

      Demographic information of the patients, comorbidities, history of hospitalization or intensive care unit (ICU) admission, and survival were recorded from electronic medical records.

      Imaging protocols

      Chest and brain imaging were performed in the routine protocols of our hospital. The chest and brain CT scans were performed using the multidetector CT (MDCT) scanner 128-slice GE Healthcare Revolution EVO CT (GE Medical Systems; Milwaukee, WI). All brain MRI examinations were performed on a 1.5 Tesla scanner (Avanto, Siemens Healthcare).

      Image analysis

      Two radiologists (ATK, BA) with 9 and 15 years of experience in general radiology evaluated chest and neuroimaging together. Firstly, neuroimaging findings were categorized into two groups; neuroimaging positive (NIP) and neuroimaging negative (NIN). After, NIP was categorized into four subgroups; 1) Acute-subacute ischemic stroke (ASIS) (Fig. 2); 2) Acute spontaneous intracranial hemorrhage (ICH); 3) Cerebral microhemorrhages (CMH); 4) Cerebral venous sinus thrombosis (CVST) (Fig. 3).
      Fig 2
      Fig. 2(A) 53-year-old male patient has a positive RT-PCR test for COVID-19. He was discharged after being treated in Non-ICU in our hospital. 464 days after the RT-PCR positive date, brain CT and DWI were performed in our hospital due to neurological complaints. Focal ground glass opacities were present in both lung peripheries. Chest CT-SS:6. (B) Axial DWI image showed hyperintense cerebellum in the right paramedian area, (C)ADC images had hypointense acute ischemic infarct with diffusion restriction (Open arrow).
      Fig 3
      Fig. 3A 48-year-old female patient had a positive RT-PCR test for COVID-19 (First CT-SS= 14; Second CT-SS= 15; Third CT-SS= 10). She was discharged after her treatment in the Non-ICU in our hospital. Brain CEMRI and MRV were performed in our hospital due to neurological complaints 24 days after the RT-PCR positive date. In the axial FLAIR (a) and coronal T2W (b) images, there were hyperintense edematous gray matter changes (GMCs) in both parietal lobe gyri (white arrows), and there was an increase in signal due to a filling defect in the superior sagittal sinus (SSS) in the T2W coronal image (magnified area). c) Coronal post-contrast T1W image showed hypointense edematous GMCs (white arrows) in both parietal lobe gyri, dilated veins due to venous congestion (open arrows), and no filling with contrast in the SSS lumen (magnified area). d-e) MRI was repeated 14 days later. Axial unenhanced T1W (d) and T2* GRE (e) images show a hyperintense hemorrhage area with a hypointense rim in the left parietal lobe gyrus. There were also millimetric hemorrhage areas in both lobes on the same images. f) Sagittal 2D TOF MRI shows no flow-related hyperintense in the SSS localization and dilatation due to congestion in the adjacent vein (open arrow). g) 647 days later, the control sagittal 2D TOF MRI shows contrast filling in the SSS localization (SS: straight sinus).
      In addition, the NIP group was divided into two groups “white matter changes” (WMC) and “grey matter changes (GMC)” according to the localization of the changes. Radiologists evaluated the patients' first and, if available, second and/or third chest CTs for pneumonia severity. They used the extent of parenchymal involvement per lobe using a computed tomography severity score (CT-SS) on a total 25-point scale (0 = 0.1% = 1% - 5.2% = 6% - 25%, 3 = 26% - 50%, 4 = 51%-75% and 5 => 75%).
      • Chang YC
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      Statistical analysis

      Statistical analyzes were performed by using IBM SPSS Statistics for Windows, Version 25.0 (IBM, Armonk, New York, USA). The normal distribution of the variables was examined using Kolmogorov-Smirnov. In the descriptive analysis, continuous and categorical variables were compared according to neuroimaging groups and subgroups. Pearson Chi-square or Fisher tests were used to comparing categorical variables. The Mann-Whitney U test was used to compare the neuroimaging groups. Median and interquartile ranges (IQR) were used for the results. p < 0.05 was considered statistically significant. The statistical significance of the temporal changes of CT-SSs according to neuroimaging groups was examined with the Friedman test. Pairwise comparisons were made using the Wilcoxon test. After Bonferroni's correction, p < 0.017 was considered statistically significant.

      Results

      Demographic results and frequencies of neuroimaging findings

      272 patients (median age= 71; IQR, 57-80) were included in our study. Of these patients, 56/272 (20.6%) were neuroimaging positive (NIP). 144/272 (52.9%) of our patients were male and there was no significant relationship between patients with NIP and gender (p = 0.621). 60/272 (22.1%) of our patients died, and there was no significant relationship between patients with NIP and mortality (p = 0.815). 60/272 (22.1%) of our patients were admitted to the ICU. There was no significant relationship between patients with NIP and ICU admission (p = 0.187). The incidences of NIF in our patients were as follows: ASIS, 47/272 (17.3%); ICH, 13/272 (4.8%); CMH, 10/272 (3.7%) and CVST, 3/25 (10.7%). CVST was present in all 3/272 (1.1%) patients who underwent MRV. There was no significant relationship between patients with NIP and inpatient or ICU admission (p = 0.535; p = 0.187). In the patients with NIP, chronic cardiovascular diseases 48/56 (85.7%) and chronic neurological diseases 47/56 (83.9%) were significantly more common (p = 0.017; p < 0.001) (Tables 1 and 2). Of the 272 patients included in our study, 138 had second a chest CT and 45 had a third chest CT.
      Table 1Frequencies of demographic data and neuroimaging findings.
      FrequencyPercent
      GenderFemale12847.1
      Male14452.9
      Inpatients?Outpatients7226.5
      Inpatients20073.5
      ICU?Non-ICU21277.9
      ICU6022.1
      SurvivalAlive21277.9
      Death6022.1
      NeuroimagingNegative21679.4
      Positive5620.6
      Neuroimaging localizations
      WMCNegative23084.6
      Positive4215.4
      GMCNegative23486.0
      Positive3814.0
      Neuroimaging findings
      ASISNegative22582.7
      Positive4717.3
      ICHNegative25995.2
      Positive134.8
      CMHNegative26296.3
      Positive103.7
      CVSTNegative259.289.3
      Valid Percent
      Positive31.110.7
      Valid Percent
      Not performed
      MRV or Contrast-enhanced neuroimaging wasn't performed ICU: Intensive care unit; WMC: White matter changes; GMC: Grey matter changes; Acute-subacute ischemic stroke (ASIS); ICH: Acute – subacute spontaneous intracranial hemorrhage; CMH: Cerebral microhemorrhages; CVST: Cerebral venous sinus thrombosis; CECT: Contrast enhanced CT; CEMRI: Contrast enhanced MRI; DWI: Diffusion-weighted imaging; MRV: Magnetic Resonance Venography
      24489.7
      Neuroimaging methods
      Brain CTNot performed41.5
      Performed26898.5
      CECTNot performed25593.8
      Performed176.3
      Brain MRINot performed21478.7
      Performed5821.3
      CEMRINot performed25794.5
      Performed155.5
      Brain DWINot performed8732
      Performed18568
      MRVNot performed26998.9
      Performed31.1
      low asterisk Valid Percent
      low asterisklow asterisk MRV or Contrast-enhanced neuroimaging wasn't performedICU: Intensive care unit; WMC: White matter changes; GMC: Grey matter changes; Acute-subacute ischemic stroke (ASIS); ICH: Acute – subacute spontaneous intracranial hemorrhage; CMH: Cerebral microhemorrhages; CVST: Cerebral venous sinus thrombosis; CECT: Contrast enhanced CT; CEMRI: Contrast enhanced MRI; DWI: Diffusion-weighted imaging; MRV: Magnetic Resonance Venography
      Table 2Comparison of positive neuroimaging with demographic data and comorbidities.
      Neuroimaging
      NegativePositivep value
      n(%)n(%)
      GenderFemale10046.328500.621
      Male11653.72850
      Total21656
      Inpatients or outpatientsOutpatients5927.31323.20.535
      Inpatients15772.74376.8
      Total21656
      ICUNon-ICU17279.64071.40.187
      ICU4420.41628.6
      Total21656
      SurvivalAlive16978.24376.80.815
      Death4721.81323.2
      Total21656
      Pulmonary diseasesAbsent17781.94376.80.382
      Present3918.11323.2
      Total21656
      Cardiovascular diseaseAbsent6530.1814.30.017
      Present15169.94885.7
      Total21656
      Neurological diseasesAbsent15169.9916.1<0.001
      Present6530.14783.9
      Total21656
      Diabetes mellitusAbsent17078.74682.10.571
      Present4621.31017.9
      Total21656
      Kidney diseasesAbsent20494.44987.50.069
      Present125.6712.5
      Total21656
      Liver diseases*Absent21599.5561000.999
      Present10.500
      Total21656
      Chi-square or (*) Fisher tests were used to compare categorical variables according to neuroimaging groups

      The relationship between ICU admission and NIF

      The most common NIF was ASIS 13/47 (21.7%) in the ICU admission group, which was not statistically significant (p = 0.887). The rate of WMC in the ICU group compared to the non-ICU group was [25% (15/60) versus 12.7% (27/212)], which was statistically significantly higher (p = 0.02). There was a statistically insignificant increase in NIF (excluding CMH) rates in the ICU group compared to the non-ICU group (p > 0.05) (Table 3).
      Table 3Comparison of neuroimaging findings with ICU admission and survival.
      ICUSurvival
      Non-ICUICUAliveDeath
      n(%)n(%)p valuen(%)n(%)p value
      WMCNegative18587.345750.0218285.848800.268
      Positive2712.715253014.21220
      Total2126021260
      GMCNegative18587.34981.70.2718285.85286.70.872
      Positive2712.71118.33014.2813.3
      Total2126021260
      ASISNegative178844778.30.30917582.55083.30.887
      Positive34161321.73717.51016.7
      Total2126021260
      ICH*Negative20395.85693.30.49220094.35998.30.309
      Positive94.246.7125.711.7
      Total2126021260
      CMH*Negative20496.25896.70.99920496.25896.70.999
      Positive83.823.383.823.3
      Total2126021260
      CVST*Negative2191.34800.4592388.521000.999
      Positive28.7120311.500
      Total235262
      WMC: White matter changes; GMC: Grey matter changes; ASIS: Acute-subacute ischemic stroke; ICH: Acute – subacute spontaneous intracranial hemorrhage; CMH: Cerebral microhemorrhages; CVST: Cerebral venous sinus thrombosis.
      Chi-square or (*) Fisher tests were used to compare categorical variables according to ICU admission and survival.

      Relationship between mortality and NIF

      There was no significant relationship between NIF and mortality (p > 0.05). In the ex-patients, the highest incidence of NIF was ASIS [10/50 (16.7%)]. But it was not statistically significant (p = 0.887). There was a statistically insignificant decrease in NIF rates in the ex-patients compared to the alive patients (p > 0.05) (Table 3).

      Relationship between NIF and CT-SS

      The median age of patients with NIP was 71.5 (IQR; 63.25 – 82.75), which was not statistically significant compared to the negative group (p = 0.098). There was no significant relationship between patients with NIP, and CT-SS values of the first, second, and third chest CT scans (p = 0.247; p = 0.832; p = 0.978). The time between RT-PCR and the first chest CT was 1.24±2.33 (0-9) days. The time interval between RT-PCR and neuroimaging was 94.62±172.37 (0-692) days. The time interval between the first chest CT and neuroimaging was 93.38±172.55 (0-692) days. There was no significant relationship between the patients with NIP and the time interval between RT-PCR and the first CT (p = 0.257). In the patients with NIP, the median time interval (days) between RT-PCR and neuroimaging was 22.24 (IQR, 4.44-141.23); the median time interval (days) between chest CT and neuroimaging was 20.81 (IQR, 2.37-141.23). These time intervals were statistically higher than in patients without NIP (p = 0.023; p = 0.01) (Table 4).
      Table 4Comparison of positive neuroimaging with CT-SS's and time intervals.
      NeuroimagingNMeanSDMin.Max.Median25th75thp value
      Mann-Whitney U test was used CT-SS: CT Severity Score SD: Standart Deviation; Min: Minimum; Max: Maximum.
      Age
       Negative21666.6816.20229471.0056.0079.000.098
       Positive5671.2013.72329471.5063.2582.75
       Total27267.6115.80229471.0057.0080.00
      First CT-SS
       Negative2166.936.960255.000.0010.000.247
       Positive567.957.150256.002.0012.75
       Total2727.147.000255.000.0011.00
      Second CT-SS
       Negative10911.627.5902512.005.0017.000.832
       Positive2911.246.1802512.007.0015.00
       Total13811.547.3002512.006.0016.25
      Third CT-SS
       Negative3512.177.0802513.008.0016.000.978
       Positive1011.905.0441913.008.5015.25
       Total4512.116.6302513.008.0015.50
      Time from RT-PCR test to First Chest CT (days)
       Negative2161.322.36090.000.002.000.257
       Positive560.942.18090.000.000.75
       Total2721.242.33090.000.001.13
      Time from RT-PCR test to Neuroimaging (days)
       Negative21687.39164.7906817.671.8073.000.023
       Positive56122.52198.07069222.244.44141.23
       Total27294.62172.3706929.392.0079.00
      Time from First Chest CT to Neuroimaging (days)
       Negative21686.07164.9806755.000.0673.000.01
       Positive56121.58198.22069220.812.37141.23
       Total27293.38172.5506927.950.4179.00
      low asterisk Mann-Whitney U test was usedCT-SS: CT Severity Score SD: Standart Deviation; Min: Minimum; Max: Maximum.

      Relationship between follow-up chest CT-SS and NIF

      We compared the course of consecutive CT-SS values according to neuroimaging groups. There was a statistically significant increase between the first CT-SS and second CT-SS (p < 0.001; p < 0.001) and first CT-SS and third CT-SS (p=0.001; p=0.014) values ​​in both patients with NIP and patients with NIN, respectively (Fig. 4) (Table 5).
      Fig 4
      Fig. 4Graph of temporal change of chest CT-SSs according to neuroimaging results.
      Table 5Temporal change of consecutive CT-SSs in patients with positive and negative neuroimaging.
      NMean RankSum of Ranksp value
      Wilcoxon Signed Ranks Test was used. p < 0.017 was considered statistically significant.
      Neuroimaging Negative
      Second CT-SS- First CT-SSNegative Ranks20
      Second CT-SS < First CT-SS
      25.78515.50<0.001
      Positive Ranks75
      Second CT-SS > First CT-SS
      53.934044.50
      Ties14
      Second CT-SS = First CT-SS
      Total109
      Third CT-SS- First CT-SSNegative Ranks9
      Third CT-SS < First CT-SS
      12.28110.500.001
      Positive Ranks25
      Third CT-SS > First CT-SS
      19.38484.50
      Ties1
      Third CT-SS = First CT-SS
      Total35
      Third CT-SS- Second CT-SSNegative Ranks20
      Third CT-SS < Second CT-SS
      13.33266.500.963
      Positive Ranks12
      Third CT-SS > Second CT-SS
      21.79261.50
      Ties3
      Third CT-SS = Second CT-SS
      Total35
      Neuroimaging Positive
      Second CT-SS- First CT-SSNegative Ranks2
      Second CT-SS < First CT-SS
      5.2510.50<0.001
      Positive Ranks25
      Second CT-SS > First CT-SS
      14.70367.50
      Ties2
      Second CT-SS = First CT-SS
      Total29
      Third CT-SS- First CT-SSNegative Ranks1
      Third CT-SS < First CT-SS
      3.503.500.014
      Positive Ranks9
      Third CT-SS > First CT-SS
      5.7251.50
      Ties0
      Third CT-SS = First CT-SS
      Total10
      Third CT-SS- Second CT-SSNegative Ranks6
      Third CT-SS < Second CT-SS
      3.7522.500.607
      Positive Ranks4
      Third CT-SS > Second CT-SS
      8.1332.50
      Ties0
      Third CT-SS = Second CT-SS
      Total10
      low asterisklow asterisk Wilcoxon Signed Ranks Test was used. p < 0.017 was considered statistically significant.
      a Second CT-SS < First CT-SS
      b Second CT-SS > First CT-SS
      c Second CT-SS = First CT-SS
      d Third CT-SS < First CT-SS
      e Third CT-SS > First CT-SS
      f Third CT-SS = First CT-SS
      g Third CT-SS < Second CT-SS
      h Third CT-SS > Second CT-SS
      i Third CT-SS = Second CT-SS

      Discussion

      In our study, we investigated the incidences of post-COVID-19 neuroimaging findings (NIF) and the relationship between the temporal changes in pneumonia severity in consecutive chest CTs and acute-subacute neurological pathologies. The incidence of patients with neuroimaging positive (NIP) was 20.6% (56/272). In the patients with NIP, the highest incidence was acute-subacute ischemic stroke (ASIS) [47/272 (17.3%)], while the lowest incidence was cerebral microhemorrhages (CMH) [10/272 (3.7%)] in the subgroup analysis. ASIS had the highest incidence of NIF in patients who were admitted to ICU [13/60 (21.7%)] and in the ex-patients group [10/60 (16.7%)]. There was no significant relationship between patients with NIP and CT-SS values. When we analyzed the temporal change, there was a statistically significant increase in the second and third CT-SSs compared to the first chest CT in both patients with NIP and NIN.
      SARS-CoV-2 can cause brain damage through both direct and indirect pathways. Four different pathways are thought to be effective in direct damage. First, SARS-CoV-2, which reaches the brain tissue by a hematogenous route, may attach to the ACE-2 receptor, causing endothelial damage, slowing blood flow, and disrupting the blood-brain barrier.
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      Secondly, it may be due to inflammatory damage of the virus that reaches the brain retrogradely from peripheral nerves.

      Wu Y, Xu X, Chen Z, et al. Nervous System Involvement After Infection with COVID-19 and Other Coronaviruses. Elsevier.

      Third, the virus can enter via the neuronal pathway between the respiratory tract and the brain stem.
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      Fourth, some authors claim that the virus enters the intestinal epithelial cells via ACE-2 receptors, which are abundant there, and reaches the brain by the neuronal spread.
      • Esposito G
      • Pesce M
      • Seguella L
      • Sanseverino W
      • Lu J
      • Sarnelli G.
      Can the enteric nervous system be an alternative entrance door in SARS-CoV2 neuroinvasion?.
      The indirect pathway can be divided into brain damage secondary to hypoxia, especially in critical COVID-19 patients, and severe inflammatory response syndrome (SIRS) due to an excessive immune response to the viruses. Increased IL-6 in the CSF samples is important evidence for cytokine storms.

      Wu Y, Xu X, Chen Z, et al. Nervous System Involvement After Infection with COVID-19 and Other Coronaviruses. Elsevier.

      In addition, thromboembolism may be seen due to familial hypercoagulation disorder, multi-organ dysfunction secondary to SIRS, antiphospholipid antibody syndrome, viral myocarditis, and triggered atrial fibrillation.
      • Zhang L
      • Yan X
      • Fan Q
      • et al.
      D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19.
      • Varga Z
      • Flammer AJ
      • Steiger P
      • et al.
      Endothelial cell infection and endotheliitis in COVID-19.
      • Zhang Y
      • Xiao M
      • Zhang S
      • et al.
      Coagulopathy and antiphospholipid antibodies in patients with Covid-19.
      • Deng Q
      • Hu B
      • Zhang Y
      • et al.
      Suspected myocardial injury in patients with COVID-19: evidence from front-line clinical observation in Wuhan, China.
      Some studies hold hypertension and empirical anticoagulation therapy responsible for the development of ICH and CMH.
      • Kvernland A
      • Kumar A
      • Yaghi S
      • et al.
      Anticoagulation use and hemorrhagic stroke in SARS-CoV-2 patients treated at a New York healthcare system.
      • Lioutas VA
      • Goyal N
      • Katsanos AH
      • et al.
      Clinical outcomes and neuroimaging profiles in non-disabled patients with anticoagulant-related intracerebral hemorrhage.
      • Tsivgoulis G
      • Wilson D
      • Katsanos AH
      • et al.
      Neuroimaging and clinical outcomes of oral anticoagulant–associated intracerebral hemorrhage.
      They also reported that severe coagulopathy is effective in the pathogenesis of CMH.
      • Maas MB.
      Critical medical illness and the nervous system.
      Neurological complications were reported by approximately 37% in studies and reviews conducted in the early period of the pandemic, and this rate has been reduced in the late period of the pandemic with the development of vaccination and appropriate treatments.
      • Mao L
      • Jin H
      • Wang M
      • et al.
      Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China.
      For example, Ladopoulos et al. reported that the main factor in the etiology of ASIS is large vessel occlusion.
      • Ladopoulos T
      • Zand R
      • Shahjouei S
      • et al.
      COVID-19: neuroimaging features of a pandemic.
      This may be due to the lack of experience with the infection in the early period of the pandemic and inadequate anticoagulant therapy.
      • Ladopoulos T
      • Zand R
      • Shahjouei S
      • et al.
      COVID-19: neuroimaging features of a pandemic.
      In the studies that included in the early period of the pandemic, the incidence ranges were reported as ASIS: 1.76%-59.9%; ICH: 5.4%-69.2%; CMH: 0.8%-58.7% and CVST: 0.08%-5.5%, respectively.
      • Mogensen MA
      • Wangaryattawanich P
      • Hartman J
      • et al.
      Special report of the RSNA COVID-19 task force: systematic review of outcomes associated with COVID-19 neuroimaging findings in hospitalized patients.
      • Ladopoulos T
      • Zand R
      • Shahjouei S
      • et al.
      COVID-19: neuroimaging features of a pandemic.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      • Lu Y
      • Zhao J-ji
      • Ye M-fan
      • et al.
      The relationship between COVID-19’s severity and ischemic stroke: a systematic review and meta-analysis.
      • Baldini T
      • Asioli GM
      • Romoli M
      • et al.
      Cerebral venous thrombosis and severe acute respiratory syndrome coronavirus-2 infection: a systematic review and meta-analysis.
      In our study, NIF incidences were ASIS: 17.3%; ICH: 4.8%; CMH: 3.7%, and CVST: 10.7%, respectively. In a study of hospitalized patients in New York, the incidence of ASIS was reported as 0.9%.
      • Yaghi S
      • Ishida K
      • Torres J
      • et al.
      SARS2-CoV-2 and stroke in a New York healthcare system.
      Yaghi et al. and Tan et al. reported that the reasons for the different results in neuroimaging incidence studies were severe patients who were intubated and sedated, incomplete imaging due to difficulty in mobilization due to isolation, and long MRI scans time.
      • Yaghi S
      • Ishida K
      • Torres J
      • et al.
      SARS2-CoV-2 and stroke in a New York healthcare system.
      ,
      • Tan YK
      • Goh C
      • Leow AST
      • et al.
      COVID-19 and ischemic stroke: a systematic review and meta-summary of the literature.
      As a result of delays due to these reasons, ASIS may have a falsely low incidence because hemorrhagic transformation developing after ASIS is interpreted as ICH.
      • Yaghi S
      • Ishida K
      • Torres J
      • et al.
      SARS2-CoV-2 and stroke in a New York healthcare system.
      ,
      • Tan YK
      • Goh C
      • Leow AST
      • et al.
      COVID-19 and ischemic stroke: a systematic review and meta-summary of the literature.
      Due to the small number of participants in reviews of CVST-positive COVID-19 patients, case reports are generally included rather than clinical trials.
      • Ladopoulos T
      • Zand R
      • Shahjouei S
      • et al.
      COVID-19: neuroimaging features of a pandemic.
      ,
      • Baldini T
      • Asioli GM
      • Romoli M
      • et al.
      Cerebral venous thrombosis and severe acute respiratory syndrome coronavirus-2 infection: a systematic review and meta-analysis.
      This reduces the reliability of the reported incidence of CVST. In our study, only 3 patients underwent MRV and all had CVST. In addition, since sinuses and veins can be evaluated in brain CECT and CEMRI, a total of 28 patients were analyzed in terms of CVST. MRV or any contrast-enhanced neuroimaging modality was not performed on 244 patients. Therefore, we calculated the valid percent as 2/28 (10.7%) while CVST was positive in 3/272 (1.1%). This was the reason for our higher prevalence compared to other studies. In another review, Choi et al. reported that the prevalence may vary depending on the difference in imaging modalities used in the studies.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      Choi et al.divided studies into using MR only and using CT and/or MRI. The incidences of NIF in these two groups were reported as ASIS: 20.0% [7.9–32.2] and 27.1% [16.4–37.7]; ICH: 3.9% [0.6–7.3] and 6.1% [3.3–8.9]; CMH: 13.8% [10.5–17.2] and 3.1% [1.0–5.2], respectively.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      As seen in this study, a higher prevalence was reported when CT and/or MRI was performed in ASIS and ICH compared to patients who only underwent MRI, while it was reported to be lower in CMH.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      Kim et al. reported that, unlike this study, there was no statistically significant difference in the rates of COVID-19 patients with NIP between the studies that used and did not use MRI.
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      The long time interval in our study also included the vaccination program that started in the first half of 2021 in our country. Although complications especially ASIS and CVST have been reported post-COVID-19 vaccination, Rahming et al. reported in their review that there was no significant increase in the overall incidence of stroke in the population of individuals administered COVID-19 vaccines.
      • Rahmig J
      • Altarsha E
      • Siepmann T
      • Barlinn K.
      Acute ischemic stroke in the context of SARS-CoV-2 vaccination: a systematic review.
      According to our results, we thought that the main cause of positive neuroimaging in our patients who were COVID-19 positive before vaccination and who were vaccinated afterward was the infection itself.
      In our study, the incidence of NIP in patients admitted to the ICU [26.7% (16/60) vs 18.9% (40/212)] showed a statistically insignificant increase compared to the non-ICU group (p = 0.187). Like our study, Choi et al. also reported that the incidence of NIP in patients admitted to ICU (11.8 % vs. 3.2%) was higher compared to the non-ICU group.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      Kim et al. compared the incidence of NIP in studies that included critically ill patients with other studies. The incidence of NIP was 9.1% in studies that included critically ill patients, which was higher than in other studies (1.6%)
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      . In three different reviews including critically or ICU admitted patients, the incidence ranges of NIF were reported as ASIS: 3.37%-17.2%; ICH: 6.2%-11.3%; CMH: 8.8%-14.8% and CVST: 1.8%-15.6%, respectively.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      • Lu Y
      • Zhao J-ji
      • Ye M-fan
      • et al.
      The relationship between COVID-19’s severity and ischemic stroke: a systematic review and meta-analysis.
      The fact that most of the patients admitted to the ICU were intubated suggests that they may have a history of hypoxia. The increase in the incidence of NIP in this group may be due to this reason in our study and Choi et al.’s study.
      • Choi Y
      • Lee MK.
      Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis.
      Since this group of patients has a low level of consciousness or is under sedation, neurological deficits of the patients may be hidden and examination may be difficult. Therefore, the need for neuroimaging should be kept in mind in clinically critical patients and patients admitted to the ICU. In our study, the incidence of NIF in patients admitted to ICU was similar to the literature, and ASIS: 21.7%; ICH 6.7%; CMH: 3.3%, and CVST: 20%. There was no statistically significant difference in NIF between the ICU and non-ICU groups. Kim et al. compared NIF with other studies in critically ill patients and reported that there was no significant difference between the two groups, similar to our study.
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      In particular, they argued that the development of ASIS was due to an increased risk of thrombosis due to hypercoagulability, not due to the systemic inflammatory response secondary to acute respiratory distress syndrome (ARDS).
      • Kim PH
      • Kim M
      • Suh CH
      • et al.
      Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis.
      This may explain the non-significant ASIS increase in the ICU admission group in our study.
      Mogensen et al. reported the highest rate of neuroimaging findings in patients who died, as ICH (49.7%). The incidence of ASIS was 30% in their study.
      • Mogensen MA
      • Wangaryattawanich P
      • Hartman J
      • et al.
      Special report of the RSNA COVID-19 task force: systematic review of outcomes associated with COVID-19 neuroimaging findings in hospitalized patients.
      In our study, the most common incidence of NIF in patients who died was ASIS: 16.7%; CMH: 3.3% and ICH: 1.7%; respectively. The low incidence in our study may be due to the increase in knowledge in diagnosis and treatment because the first case was seen late compared to other countries. Lang et al. reported a statistically insignificant increase in the mortality rate in the patients with NIP (21%) compared to patients with NIN (17%) in their study (p = 0 .945).
      • Lang M
      • Li MD
      • Jiang KZ
      • et al.
      Severity of chest imaging is correlated with risk of acute neuroimaging findings among patients with COVID-19.
      Similarly, in our study, there was a statistically insignificant increase in the mortality rate in the patients with NIP (23.2%) compared to patients with NIN (21.8%) (p = 0.815).
      In our study, we investigated the effect of increased pneumonia severity on the incidence of NIP. We analyzed the temporal change of CT-SSs in three consecutive chest CTs. There was no significant increase in CT-SS in the patients with NIP compared to the patients with NIN. In addition, there was a statistically significant increase in the second and third CT-SS compared to the first CT-SS in both patients with NIP and NIN. This showed us that the increase in CT-SS was not only associated with NIP. Mahammedi et al. also investigated the effect of CT-SS on patients with NIP.
      • Mahammedi A
      • Ramos A
      • Bargalló N
      • et al.
      Brain and lung imaging correlation in patients with COVID-19: could the severity of lung disease reflect the prevalence of acute abnormalities on neuroimaging? A global multicenter observational study.
      While our study included inpatients, outpatients, and discharged patients, other studies included only inpatients. They analyzed the highest CT-SS value in patients with more than one chest CT and reported significantly higher CT-SS in the group with NIP, unlike our study.
      • Mahammedi A
      • Ramos A
      • Bargalló N
      • et al.
      Brain and lung imaging correlation in patients with COVID-19: could the severity of lung disease reflect the prevalence of acute abnormalities on neuroimaging? A global multicenter observational study.
      They reported a higher incidence of neurological symptoms in patients with severe respiratory disease.
      • Mahammedi A
      • Ramos A
      • Bargalló N
      • et al.
      Brain and lung imaging correlation in patients with COVID-19: could the severity of lung disease reflect the prevalence of acute abnormalities on neuroimaging? A global multicenter observational study.
      Lang et al. also reported that the CT-SS value was high in the patients with NIP, but there was no significant predictor of acute NIP in multivariate analysis.
      • Lang M
      • Li MD
      • Jiang KZ
      • et al.
      Severity of chest imaging is correlated with risk of acute neuroimaging findings among patients with COVID-19.
      The difference in results in our study is consistent with the literature. It has been reported that not only high CT-SS but also silent hypoxia, metabolic disorder, intubation history, advanced age, history of ICU admission, cardiovascular diseases, autoimmune diseases, angiopathies, and proinflammatory cytokines are effective in brain damage.
      • Raabe A
      • Wissing H
      • Zwissler B.
      Brain cell and S-100B increase after acute lung injury.
      • Mascia L.
      Acute lung injury in patients with severe brain injury: a double hit model.
      • Wenting A
      • Gruters A
      • van Os Y
      • et al.
      COVID-19 neurological manifestations and underlying mechanisms: a scoping review.
      In addition, patients with NIP at low CT-SS have been reported, and the cause of neurological damage in these patients can be explained by direct damage to the SARS-CoV-2 virus, which reaches the brain in a retrograde way from peripheral nerves.
      • Conde-Cardona G
      • Quintana-Pájaro LD
      • Quintero-Marzola ID
      • Ramos-Villegas Y
      • Moscote-Salazar LR
      Neurotropism of SARS-CoV 2: mechanisms and manifestations.
      Although the interval between positive neuroimaging after SARS-CoV-2 infection is not clear in the literature, Li et al. reported it as about 12 days.
      • Li Y
      • Li M
      • Wang M
      • et al.
      Acute cerebrovascular disease following COVID-19: a single center, retrospective, observational study.
      In our study, the median time between the first positive RT-PCR and the first positive neuroimaging was 22.24 days (IQR: 4.44-141.23, p=0.023). As seen in Table 4, the time interval are 0-692 days, and unlike the existing studies, our results were obtained over a much longer period.
      To our knowledge, our study was the first to investigate the incidence of ischemic and hemorrhagic strokes post-COVID-19 with the longest time interval of more than two years. It was also the first study to evaluate the effect of temporal variation of CT severity of pneumonia on neuroimaging findings.
      Our study had some limitations. First, it was a retrospective single-center study. Secondly, no analysis was performed to show the virus in the cerebrospinal fluid during the NIP period of the patients. Third, a histopathological examination of the brain tissue after mortality was not performed. Finally, it is difficult to definitively associate new neurological findings post-COVID-19 with this disease.
      In conclusion, our results showed that since neurological damage can be seen in the late period, careful follow-up should be done in risky groups and neurological damage may develop regardless of the severity of the disease.

      Ethics Committee Approval

      This retrospective and the single-center study was approved by the Ethical Committee of Amasya University Sabuncuoğlu Şerefeddin Education and Research Hospital (02 December 2021, number: 12/155). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

      Informed Consent

      The study is retrospective, patient information was obtained from electronic records and censored. Since the study was retrospective, the ethics committee did not find it necessary to obtain written informed consent from the patients.

      Data Availability Statement

      The data that support the findings of this study are available on request from the corresponding author, [ATK].

      Authors’ contribution statements

      All of the authors declare that they have all participated in the design, execution, and analysis of the paper and that they have approved the final version

      CRediT authorship contribution statement

      Ahmet Turan Kaya: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Supervision. Burcu Akman: Methodology, Writing – original draft, Writing – review & editing, Supervision.

      Declaration of Competing Interest

      The authors declare they have no conflicts of interest

      Funding

      No funding was received to assist with the preparation of this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

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