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. 2021 Jan 9;11(5):e02025. doi: 10.1002/brb3.2025

Central nervous system manifestations in COVID‐19 patients: A systematic review and meta‐analysis

Shahrzad Nazari 1, Amirhossein Azari Jafari 2, Seyyedmohammadsadeq Mirmoeeni 2, Saeid Sadeghian 3, Mohammad Eghbal Heidari 4, Siavash Sadeghian 5, Farhad Assarzadegan 6, Seyed Mahmoud Puormand 7, Hamid Ebadi 8, Davood Fathi 9,10,✉, Sahar Dalvand 11,✉
PMCID: PMC7994971  PMID: 33421351

Abstract

Background

At the end of December 2019, a novel respiratory infection, initially reported in China, known as COVID‐19 initially reported in China, and later known as COVID‐19, led to a global pandemic. Despite many studies reporting respiratory infections as the primary manifestations of this illness, an increasing number of investigations have focused on the central nervous system (CNS) manifestations in COVID‐19. In this study, we aimed to evaluate the CNS presentations in COVID‐19 patients in an attempt to identify the common CNS features and provide a better overview to tackle this new pandemic.

Methods

In this systematic review and meta‐analysis, we searched PubMed, Web of Science, Ovid, EMBASE, Scopus, and Google Scholar. Included studies were publications that reported the CNS features between 1 January 2020 and 20 April 2020. The data of selected studies were screened and extracted independently by four reviewers. Extracted data analyzed by using STATA statistical software. The study protocol registered with PROSPERO (CRD42020184456).

Results

Of 2,353 retrieved studies, we selected 64 studies with 11,687 patients after screening. Most of the studies were conducted in China (58 studies). The most common CNS symptom of COVID‐19 was headache (8.69%, 95%CI: 6.76%–10.82%), dizziness (5.94%, 95%CI: 3.66%–8.22%), and impaired consciousness (1.90%, 95%CI: 1.0%–2.79%).

Conclusions

The growing number of studies has reported COVID‐19, CNS presentations as remarkable manifestations that happen. Hence, understanding the CNS characteristics of COVID‐19 can help us for better diagnosis and ultimately prevention of worse outcomes.

Keywords: consciousness disorders, COVID‐19, dizziness, headache, nervous system diseases, SARS‐CoV‐2 infection

The most common CNS symptom of COVID‐19 was headache (8.69%, 95%CI: 6.76%–10.82%), dizziness (5.94%, 95%CI: 3.66%–8.22%), and impaired consciousness (1.90%, 95%CI: 1.0%–2.79%). The growing number of studies has reported COVID‐19, CNS presentations as remarkable manifestations that happen. Hence, understanding the CNS characteristics of COVID‐19 can help us for better diagnosis and ultimately prevention of worse outcomes.

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1. INTRODUCTION

At the end of December 2019, a novel respiratory syndrome, known as COVID‐19, was reported in Wuhan city, Hubei province, China. The first sign of this infection (2019‐nCoV, COVID‐19) was pneumonia (Adhikari et al., 2020; WHO, 2020; Shi, Qin, et al., 2020; Velavan & Meyer, 2020; Wang, Hu, et al., 2020; Wang, Wang, et al., 2020; Wu, Chen, et al., 2020). This new pandemic rapidly spread worldwide, and an increasing number of infected cases and deaths have been reported globally (Jiang et al., 2020; Sohrabi et al., 2020). Hence, the COVID‐19 outbreak was officially considered as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) Emergency Committee (Mackenzie & Smith, 2020; WHO, 2020). Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a zoonotic pathogen and can transmit from infected animals (such as bats and snakes) to humans eventually leading to epidemics and pandemics through human‐to‐human transmission (Hassan et al., 2020; Mackenzie & Smith, 2020). Most cases of COVID‐19 have shown respiratory symptoms ranging from cough to dyspnea and respiratory failure as well as the typical signs and symptoms of infection such as fever and fatigue (Cascella et al., 2020; Chen, Zhou, et al., 2020; Wang, Hu, et al., 2020; Young et al., 2020).

However, a growing number of COVID‐19 patients are presenting with different combinations of the central nervous system (CNS) manifestations (Asadi‐Pooya & Simani, 2020; Mao et al., 2020; Montalvan et al., 2020). Several case reports have indicated the presence of various CNS complications, including encephalitis, stroke, meningitis, and encephalopathy in COVID‐19 patients (Co et al., 2020; Filatov et al., 2020; Moriguchi et al., 2020; Zhou, Zhang, et al., 2020). Furthermore, a large observational study carried out by Mao et al. shows the prevalence of the CNS presentations such as dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizure (Mao et al., 2020). Therefore, awareness of the different aspects of the short‐ and long‐term effects of this virus on the central nervous system could decently guide scientists. In this systematic review and meta‐analysis, we assessed the CNS manifestations in COVID‐19 cases.

2. METHOD

2.1. Search strategy and selection criteria

We performed this systematic review and meta‐analysis based on Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines (Moher et al., 2009), and our study protocol is submitted to PROSPERO (ID: CRD42020184456). We systematically searched six databases including Google Scholar, Scopus, PubMed, Web of science, Ovid, and EMBASE for all published articles from 1 January 2020 until 20 April 2020 using the following Medical Subject Heading terms (MESH terms):

(“Wuhan coronavirus” OR “Wuhan seafood market pneumonia virus” OR “COVID19 virus” OR “COVID‐19 virus” OR “coronavirus disease 2019 virus” OR “SARS‐CoV‐2” OR “SARS2” OR “2019‐nCoV” OR “2019 novel coronavirus” OR “2019‐nCoV infection” OR “2019 novel coronavirus disease” OR “2019‐nCoV disease” OR “coronavirus disease‐19” OR “coronavirus disease 2019” OR “2019 novel coronavirus infection” OR “COVID19” OR “COVID‐19” OR “severe acute respiratory syndrome coronavirus 2” OR “coronavirus*”) AND (“Manifestation, Neurologic” OR “Neurological Manifestations” OR “Neurologic Manifestation” OR “Neurological Manifestation” OR “Neurologic Symptom” OR “CNS” OR “brain” OR “neuro*” OR “headache” OR “dizziness” OR “ataxia” OR “epilepsy” OR “seizure” OR “migraine*” OR “CSF” OR “Cerebrospinal Fluids” OR “Fluid, Cerebrospinal” OR “Fluids, Cerebrospinal” OR “Cerebro Spinal Fluid” OR “Cerebro Spinal Fluids” OR “Fluid, Cerebro Spinal” OR “Fluids, Cerebro Spinal” OR “Spinal Fluid, Cerebro” OR “Spinal Fluids, Cerebro” OR “stroke” OR “vertigo” OR “consciousness” OR “Impaired consciousness” OR “coma” OR “cerebrovascular disease” OR “acute cerebrovascular disease” OR “encephalitis”) alone or in combination with OR and AND operators.

After removing the duplicated records, articles were screened based on their titles and abstracts by two authors (S.S and M.H) independently. The full texts of eligible publications were examined for inclusion and exclusion criteria (A.AJ, S.M, S.S, and M.H). Observational studies reported at least one of the related CNS symptoms in COVID‐19 patients without any language, race, country, and gender limitations included for quantitative synthesis. The preprint studies, interventional studies, systematic reviews, case reports, conferences, commentaries, letters, editorial, author responses, correspondence articles, in vitro, animal studies, children population, articles without full text, or unreliable data were excluded. In addition, the reference list of the eligible studies was searched to prevent missing publication and include all related literature. The data were independently extracted (A.AJ. S.M, S.S, S.S, and M.H), and discrepancies were resolved with discussion and consensus by three independent researchers (SH.N, S.D, and F.A).

2.2. Data analysis and quality assessment

The desired data were recorded using an excel spreadsheet form that included the title, first author, year and month of publication, type of study, country, total sample size, the sample size of male and female, study design, demographic characteristics, exposure history, clinical manifestation, CNS symptoms, and any reported comorbidity.

We assessed the quality of included studies (A.AJ. S.M, S.S, S.S, and M.H), based on the NIH quality assessment tool for observational cohort and case series studies (NIH). This instrument assessed the quality of included studies based on the research question, study population, the participation rate of eligible persons, inclusion and exclusion criteria, sample size justification, analyses, reasonable timeframe, exposure, outcome measures, outcome assessors, and loss to follow‐up.

2.3. Meta‐analysis

Data from included studies were extracted for the number of events and total patients to perform a meta‐analysis (S.D). Cochrane's Q test and the I 2 index were used to assess heterogeneity among selected studies. Heterogeneity was categorized as low (below 25%), moderate (25%–75%), and high (above 75%) (Higgins & Thompson, 2002). Also, data adjusted by Freeman–Tukey double arcsine transformation and their 95% CIs were calculated by the Clopper–Pearson method (Clopper & Pearson, 1934). We calculate mean and standard deviations from median and quartiles by using Wan method (Wan et al., 2014). For continuous data, we estimate pooled results of means and their respective 95% CI by the inverse variance method. All analyses were performed using STATA statistical software, version 13 (StataCorp).

3. RESULTS

As illustrated in (Figure 1), a total of 2,353 studies were retrieved after a systematic search in the aforementioned databases. After removing duplicates, 1,760 studies remained. Then, we narrowed the studies to 203 articles by screening with titles and abstracts. In full‐text screening, 45 studies with no reliable or useful data, 24 review articles, 41 preprints, 6 case reports, 1 case controls, 4 reports, 4 papers with specific children population, one study with specific pregnant population, and 13 publications such as Commentary, editorial or Correspondence letters were excluded. Finally, 64 studies (Barrasa et al., 2020; Chen, Chen, et al., 2020; Chen, Qi, et al., 2020; Chen, Wu, et al., 2020; Chen, Yan, et al., 2020; Chen, Yang, et al., 2020; Chen, Zhou, et al., 2020; Cheng et al., 2020; Ding et al., 2020; Du, Liu, et al., 2020; Du, Tu, et al., 2020; Feng et al., 2020; Guan et al., 2020; Guo et al., 2020; Gupta et al., 2020; Han et al., 2020; Hsih et al., 2020; Huang et al., 2020; Jia et al., 2020; Jin et al., 2020; Kim et al., 2020; Kong et al., 2020; Lei, Huang, et al., 2020; Lei, Jiang, et al., 2020b; Li, Wang, et al., 2020; Liang et al., 2020; Ling et al., 2020; Liu, Yang, et al., 2020; Liu, Yang, et al., 2020; Liu, Yang, et al., 2020; Lo et al., 2020; Mao et al., 2020; Mi et al., 2020; Mo et al., 2020; Moein et al., 2020; Peng, Liu, et al., 2020; Peng, Meng, et al., 2020; Qian et al., 2020; Qin et al., 2020; Shao et al., 2020; Shi, Han, et al., 2020; Shi, Qin, et al., 2020; Song et al., 2020; Sun et al., 2020; Tan et al., 2020; Tian et al., 2020; Wan, Xiang, et al., 2020; Wan, Yi, et al., 2020; Wang, Hu, et al., 2020; Wang, He, et al., 2020; Wang, Fang, et al., 2020; Wu, Chen, et al., 2020; Wu, Wu, et al., 2020; Xu et al., 2020; Yang, Cao, et al., 2020; Yang, Yu, et al., 2020; Yu et al., 2020; Zhang, Cai, et al., 2020; Zhang, Wang, et al., 2020; Zhao et al., 2020; Zheng, Tang, et al., 2020; Zheng, Xu, et al., 2020; Zhong et al., 2020; Zhu et al., 2020) including 11,282 COVID‐19 patients, met our inclusion criteria, and were entered in meta‐analysis. The main characteristics of our included studies are presented in Table 1.

FIGURE 1.

FIGURE 1

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The process of surveying, screening, and selecting the articles for this systematic review and meta‐analysis based on PRISMA guideline

TABLE 1.

Demographics and baseline characteristics of included studies with COVID‐19‐infected patients presenting CNS symptoms

Author Month of publication Type of studies Country, City/Province Sample size No of positive cases (female/male) Quality assessment
Yang, Yu, et al. (2020) February 2020 Cohort China, Wuhan 52 17/35 Good
Yang, Cao, et al. (2020) February 2020 Cohort China, Wenzhou 149 68/81 Fair
Wang, Hu, et al. (2020) March 2020 Case series China, Wuhan 138 63/75 Good
Song et al. (2020) February 2020 Retrospective China, – 51 26/25 Good
Shi, Han, et al. (2020) March 2020 Cohort China, Wuhan 81 39/42 Good
Qian et al. (2020) March 2020 Case series China, Zhejiang province 91 54/37 Good
Mao et al. (2020) April 2020 Case series China, Wuhan 214 127/87 Good
Liu, Yang, et al. (2020) February 2020 Case series China, – 12 4/8 Fair
Liu, Yang, et al. (2020) February 2020 Retrospective China, Wuhan 137 76/61 Fair
Li, Fang, et al. (2020) March 2020 Cross‐Sectional China, ‐ 54 32/22 Fair
Du, Tu, et al. (2020) April 2020 Cohort China, Wuhan 85 23/62 Fair
Cheng et al. (2020) March 2020 Cross‐Sectional China, 1,079 505/573 Fair
Chen, Zhou, et al. (2020) January 2020 Retrospective China, Wuhan 99 32/67 Good
Zhu et al. (2020) March 2020 Retrospective China, Anhui province 32 17/15 Good
Wu, Chen, et al. (2020) March 2020 Cohort China, Wuhan 191 72/119 Good
Zhong et al. (2020) March 2020 Cohort China, Wuhan 49 42/7 Fair
Zheng, Xu, et al. (2020) April 2020 Case series China, Changsha 99 48/51 Fair
Zheng, Tang, et al. (2020) March 2020 Retrospective China, Changsha 161 81/80 Good
Zhao et al. (2020) March 2020 Retrospective Cohort China, Changsha 118 58/60 Good
Zhang, Cai, et al. (2020) March 2020 Retrospective China, – 573 278/295 Good
Zhang, Wang, et al. (2020) April 2020 Retrospective Cohort China, Wuhan 663 342/321 Good
Yu et al. (2020) March 2020 Prospective Cohort China, Beijing 76 38/38 Fair
Xu et al. (2020) February 2020 Retrospective China, Beijing 50 21/29 Fair
Wu, Wu, et al. (2020) February 2020 Cross‐Sectional China, Chongqing 80 38/42 Fair
Wang, Hu, et al. (2020) March 2020 Retrospective China, Wuhan 1,012 488/524 Good
Wang, He, et al. (2020) March 2020 Retrospective study China, Wuhan 339 173/166 Good
Wan, Yi, et al. (2020) March 2020 Cross sectional China, Chongqing 123 57/66 Good
Wan, Xiang, et al. (2020) 2020 Case series China, Chongqing 135 63/72 Good
Tian et al. (2020) February 2020 Retrospective observational China, Beijing 262 135/127 Fair
Tan et al. (2020) April 2020 Retrospective observational China, Changsha 27 16/11 Fair
Sun et al. (2020) April 2020 Retrospective observational China, Nanyang 150 83/67 Good
Shi, Qin, et al. (2020) March 2020 Retrospective observational China, Wuhan 416 211/205 Fair
Shao et al. (2020) April 2020 Retrospective observational China, Wuhan 136 46/90 Good
Qin et al. (2020) March 2020 Retrospective observational China, Wuhan 452 217/235 Good
Peng, Meng, et al. (2020) March 2020 Retrospective observational China, Wuhan 112 59/53 Fair
Peng, Liu, et al. (2020) April 2020 Cross‐sectional China, Shanghai 86 47/39 Fair
Moein et al. (2020) 2020 Retrospective observational Iran, Tehran 60 20/40 Fair
Mo et al. (2020) 2020 Retrospective China, Wuhan 155 69/86 Good
Mi et al. (2020) 2020 Retrospective China, Wuhan 10 8/2 Fair
Lo et al. (2020) March 2020 Retrospective China, Macau 10 7/3 Fair
Liu, He, et al. (2020) February 2020 Retrospective China, Wuhan 30 20/10 Fair
Ling et al. (2020) 2020 Retrospective China, Wuhan 8 4/4 Poor
Liang et al. (2020) March 2020 Retrospective China, Wuhan 88 37/51 Good
Lei, Jiang, et al. (2020) 2020 Retrospective China, Wuhan 34 20/14 Good
Lei, Huang, et al. (2020) 2020 Retrospective China, Guiyang 14 6/8 Good
Kong et al. (2020) February 2020 Case series South Korea, National survey 28 13/15 Poor
Kim et al. (2020) 2020 Retrospective Korea, National survey 28 13/15 Good
Jin et al. (2020) March 2020 Retrospective China, Zhejiang 651 320/331 Good
Jia et al. (2020) 2020 Retrospective China, Qingdao 44 29/15 Fair
Huang et al. (2020) January 2020 Retrospective China, Wuhan 41 11/30 Good
Hsih et al. (2020) 2020 Retrospective Taiwan, Taichung 43 26/13 Fair
Han et al. (2020) 2020 Retrospective China, Wuhan 108 70/38 Fair
Gupta et al. (2020) April 2020 Case series India, New Delhi 21 7/14 Fair
Guo et al. (2020) 2020 Retrospective China, Wuhan 174 98/76 Good
Guan et al. (2020) February 2020 cross China, National 1,099 459/640 Good
Feng et al. (2020) April 2020 Retrospective China, Wuhan, Shanghai and Anhui 476 205/271 Good
Du, Liu, et al. (2020) April 2020 Retrospective China, Wuhan 109 35/74 Good
Ding et al. (2020) March 2020 Case series China, Wuhan 5 3/2 Good
Chen, Chen, et al. (2020) April 2020 Retrospective China, Wuhan 42 27/15 Fair
Chen, Yang, et al. (2020) April 2020 Retrospective China, ‐ 104 52/52 Good
Chen, Wu, et al. (2020) March 2020 Case series China, Wuhan 274 103/171 Good
Chen, Qi, et al. (2020) March 2020 Retrospective China, Shanghai 249 123/126 Good
Chen, Yan, et al. (2020) March 2020 Retrospective China, Wuhan 150 66/84 Good
Barrasa et al. (2020) 2020 Case series Spain, Vitoria 48 21/27 Fair
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The total sample size of eligible studies was 11,687, including 5,568 females and 6,114 males. The mean age for noncritical patients was 48.557 (95% CI: 44.816%–52.299%) and for critical patients was 58.965 (95% CI: 55.792%–62.139%). As shown in Table 2, the proportion of patients with travel history to Wuhan, Wuhan‐related exposure, and Living in Wuhan was 51.15%, 78.52%, and 47.46%, respectively. In addition, the proportion of patients with travel history to other infected areas and contact with patients was 52.21% and 34.65%, respectively. Mortality was assessed in 25 studies with a pooled incidence rate of 10.47%. The incidence rate of positive females and males was 46.42% (95% CI: 43.01%–49.83%) and 49.50% (95% CI: 45.70%–53.31%), respectively. 36.17% (95% CI: 27.91%–44.84%) of infected patients were in the severe, critical, or intensive care unit condition. In addition, the incidence rate of mortality and survival was 10.47% (95% CI: 5.08%–17.33%) and 81.43% (95% CI: 65.75%–93.29%), respectively.

TABLE 2.

Positive PCR, severity, mortality, and exposure history of COVID‐19‐infected patients having CNS symptoms

Variables No of studies Total sample size No positive case Incidence rate (95% CI) Heterogeneity
I 2 (%) Q p‐Value
Positive female 60 11,425 5,363 0.4642 (0.4301–0.4983) 92.2 752.6 <.0001
Positive male 60 11,425 5,919 0.4950 (0.4570–0.5331) 93.8 957.8 <.0001
Severe or critical or ICU 40 9,821 2,611 0.3617 (0.2791–0.4484) 98.6 2,827.4 <.0001
Nonsevere or Noncritical or Non‐ICU 37 8,095 5,694 0.7061 (0.6229–0.7832) 98.3 2,154.0 <.0001
Mortality 25 7,087 556 0.1047 (0.0508–0.1733) 98.4 1,497.6 <.0001
Survival 18 3,174 2,585 0.8143 (0.6575–0.9329) 98.9 1,600.9 <.0001
Exposure history
Travel history to Wuhan 27 6,476 3,434 0.5115 (0.3295–0.6920) 99.5 5,434.9 <.0001
Wuhan‐related exposure 2 567 433 0.7852 (0.7501–0.8183) – – –
Living in Wuhan 2 1,151 535 0.4746 (0.4455–0.5037) – – –
Travel history to other infected areas 3 255 133 0.5221 (0.4609–0.5832) 0.0 1.1 .5664
Contact with patients 19 4,422 1504 0.3465 (0.2976–0.3953) 90.1 181.2 <.0001
Family clustering 6 1,182 254 0.2044 (0.1376–0.2712) 84.9 33.1 <.0001
Unknown exposure history 6 472 63 0.1244 (0.0446–0.2042) 91.3 57.7 <.0001
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Based on the results shown in Table 3 and Figure 2, the most common manifestations were fever 79.39% (95% CI: 73.94%–84.37%), cough 54.77% (95%CI: 49.10%–60.38%), fatigue 32.39% (95% CI: 26.78%–38.0%), dyspnea 28.47% (95% CI: 21.49%–35.99%), chest tightness 23.83% (95% CI: 17.84%–29.82%), and shortness of breath 20.42% (95% CI: 13.28%–28.85%). The highest incidence rate among CNS symptoms of COVID‐19 patients was for headache (8.69% with 95% CI: 6.76%–10.82%), followed by dizziness (5.94%, 95%CI: 3.66%–8.22%), and impaired consciousness (1.90% with 95% CI: 1.0%–2.79%).

TABLE 3.

Clinical Manifestations in COVID‐19‐infected patients presenting CNS symptoms

Variables No of studies Total sample size No of positive case Incidence rate (95% CI) Heterogeneity
I 2 (%) Q p‐Value
General symptoms
Fever 63 11,537 8,723 0.7939 (0.7394–0.8437) 97.7 2,689.5 <.0001
Fatigue 43 8,638 2,454 0.3239 (0.2678–0.3800) 97.8 1936.5 <.0001
Myalgia (muscle pain or muscle injury) 41 7,479 246 0.1395 (0.1169–0.1621) 88.2 338.4 <.0001
Nasal congestion 3 2,684 151 0.0554 (0.0428–0.0680) 51.2 4.1 .1290
Rhinorrhea 15 3,881 163 0.0447 (0.0258–0.0676) 83.9 87.2 <.0001
Dry cough or cough 62 11,507 6,047 0.5477 (0.4910–0.6038) 97.0 2054.7 <.0001
Arthralgia 3 204 8 0.0243 (0.000–0.0785) 63.6 5.5 .0642
Chill 11 3,878 512 0.1802 (0.0834–0.3021) 98.4 637.1 <.0001
GI symptoms 6 1,795 83 0.0501 (0.0148–0.0854) 87.7 40.8 <.0001
Nausea 11 1,934 115 0.0595 (0.0387–0.0803) 69.0 32.3 .0004
Vomiting 11 2,703 97 0.0322 (0.0255–0.0397) 23.6 13.1 .2183
Nausea and/or vomiting 13 3,160 181 0.0518 (0.0337–0.0700) 79.7 59.2 <.0001
Anorexia or inappetence 17 2,638 588 0.2052 (0.1393–0.2711) 96.8 508.7 <.0001
Diarrhea 45 8,270 909 0.1030 (0.0832–0.1227) 91.0 489.8 <.0001
Abdominal pain 14 3,132 112 0.0345 (0.0205–0.0485) 74.7 51.5 <.0001
Chest tightness 9 1,857 468 0.2383 (0.1784–0.2982) 88.3 68.6 <.0001
Shortness of breath 26 6,538 1,177 0.2042 (0.1328–0.2858) 98.1 1,329.0 <.0001
Dyspnea 32 4,793 1,255 0.2847 (0.2149–0.3599) 96.4 859.7 <.0001
Chest pain 13 2,490 68 0.0249 (0.0075–0.0490) 81.4 64.4 <.0001
Hemoptysis 13 3,518 71 0.0169 (0.0074–0.0289) 65.2 34.5 .0006
Heart palpitations 2 191 13 0.0671 (0.0316–0.1026) – – –
Pharyngodynia or Throat pain or Pharyngalgia or throat sore 41 9,021 888 0.0983 (0.0767–0.1219) 89.9 399.9 <.0001
Coryza or sputum production or expectoration 30 7,239 1,909 0.2517 (0.1852–0.3182) 98.4 1791.4 <.0001
CNS symptoms
Headache 48 9,782 897 0.0869 (0.0676–0.1082) 89.5 449.5 <.0001
Dizziness 10 2,296 139 0.0594 (0.0366–0.0822) 81.3 48.1 <.0001
Headache and/or Dizziness 5 558 58 0.0978 (0.0733–0.1224) 6.2 4.3 .3711
Impaired consciousness 2 877 26 0.0190 (0.0100–0.0279) – – –
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FIGURE 2.

FIGURE 2

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The incidence rate of CNS manifestations in COVID‐19 patients (this figure is Created with BioRender.com)

Table 4 shows comorbidities that were reported in 60 studies. The highest incidence rate in comorbidities was hypertension with 23.54% (95% CI: 19.14%–27.94%), diabetes mellitus (11.68% with 95% CI, 9.80%–13.57%), cardiovascular disease (11.66% with 95% CI: 8.97%–14.35%), and cerebrovascular diseases (3.47% with 95% CI: 2.29%–4.85%).

TABLE 4.

Comorbidities in COVID‐19‐infected patients with CNS symptoms

Variables No of studies Total sample size NO positive case Incidence rate (95% CI) Heterogeneity
I 2 (%) Q p‐Value
Any comorbidities 27 6,729 1,932 0.3247 (0.2729–0.3766) 95.8 617.9 <.0001
Cerebrovascular diseases 19 4,502 152 0.0347 (0.0229–0.0485) 76.3 76.1 <.0001
Cardiovascular diseases 35 8,394 743 0.1166 (0.0897–0.1435) 97.8 1577.8 <.0001
Cardiovascular disease and Cerebrovascular diseases 8 1,041 200 0.2028 (0.1194–0.2863) 93.5 108.6 <.0001
Malignancy/Cancer 32 6,986 197 0.0278 (0.0187–0.0383) 75.5 126.7 <.0001
Digestive system disease/GI disease 7 1,661 82 0.0504 (0.0267–0.0740) 82.2 33.7 <.0001
Immunity system‐related diseases
Immunosuppression 3 604 13 0.0172 (0.0069–0.0276) 28.5 2.8 .2467
Immunodeficiency 3 1,612 11 0.0101 (0.000–0.0227) 71.5 7.0 .0297
Autoimmune diseases 3 425 4 0.0083 (0.0000–0.0169) 0.0 0.5 .7860
Infectious diseases
Hepatitis B 5 1,801 40 0.0183 (0.0064–0.0303) 62.5 10.7 .0306
HIV 3 567 4 0.0058 (0.0000–0.0237) 64.6 5.6 .0590
Bacterial co‐infections/Bacteremia 2 675 8 0.0092 (0.0020–0.0164) – – –
Chronic renal disease 21 5,659 119 0.0204 (0.0143–0.0266) 60.1 50.1 .0002
Chronic liver disease 16 3,254 92 0.0218 (0.0136–0.0314) 49.1 29.5 .0140
Chronic Respiratory disease/Pulmonary disease 15 3,215 150 0.0428 (0.0270–0.0586) 82.7 80.7 <.0001
Endocrinology disorder 8 1,338 130 0.0897 (0.0744–0.1049) 43.3 12.3 .0896
Hyperlipidemia 2 70 3 0.0197 (0.0000–0.0519) – – –
Urinary system disease 2 781 23 0.0280 (0.0165–0.0396) – – –
Hypertension 40 8,106 1,697 0.2354 (0.1914–0.2794) 96.5 1,127.2 <.0001
Diabetes 40 8,045 840 0.1168 (0.0980–0.1357) 87.0 300.4 <.0001
COPD a 23 5,610 148 0.0262 (0.0185–0.0339) 82.9 129.1 <.0001
Smoking 19 4,407 371 0.0827 (0.0586–0.1069) 87.9 149.4 <.0001
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a

Chronic obstructive pulmonary disease.

4. DISCUSSION

Recently, the world has encountered an emergent outbreak posed by the novel coronavirus 2019, officially known as COVID‐19. This infection has become a global threat, endangering millions of lives worldwide. Hence, many experts, researchers, scientists, and clinicians are attempting to investigate various aspects of this new infection to find useful solutions for coping with COVID‐19. One of the various aspects of COVID‐19 is its impact on the CNS, as reported in a growing number of studies (Baig, 2020). In addition to the common symptoms in COVID‐19, several CNS symptoms such as headache and impaired consciousness have been observed in infected patients (Mao et al., 2020).

While most investigated the respiratory symptoms of COVID‐19, Mao et al. specifically examined the prevalence of neurological manifestations ranging from CNS to peripheral nervous system (PNS) and neuromuscular symptoms in an observational study on COVID‐19 patients. They demonstrated CNS presentations ranging from dizziness and headache to impaired consciousness, acute cerebrovascular disease, ataxia, and seizure (Mao et al., 2020). Based on the possible neuroinvasive potential of COVID‐19, in this systematic review and meta‐analysis, we analyzed those evidence indicating the involvement of CNS. We assessed 11,687 COVID‐19 adult patients from six countries. We reported that COVID‐19 patients commonly showed CNS symptoms, including headache, dizziness, and impaired consciousness. Headache (8.69%) was the most common CNS symptoms, followed by dizziness (5.94%) and impaired consciousness (1.9%). Exact reasons for headache, commonly seen in patients, remained unexplained. However, it can be due to COVID‐19‐related stress and anxiety (Garg, 2020). It is reported that the headache may also be related to the elevated level of inflammatory mediators and reduced cerebral blood flow in response to hypoxia (Jasti et al., 2020), but further studies are needed.

There are two main routes of CNS entry of COVID‐19 (hematogenous and peripheral nerves route) leading to CNS infection. In the hematogenous route, the virus infecting respiratory tracts can reach the CNS through the bloodstream via overcoming a strict obstacle known as the blood–brain barrier (BBB) (Desforges et al., 2014, 2020; Román et al., 2020; Sepehrinezhad et al., 2020; Swanson and McGavern, 2015). They also may enter the CNS through circumventricular organs, those CNS organs lacking the BBB (Chigr et al., 2020). The second route, a peripheral nerve, can provide the virus with a retrograde route in to access the CNS via an axonal transport machinery (Baig et al., 2020; Desforges et al., 2014, 2020; Román et al., 2020; Sepehrinezhad et al., 2020; Swanson and McGavern, 2015). In accordance with this finding, some previous studies on other types of coronaviruses indicate that coronaviruses can reach the brain via cranial nerves (e.g., olfactory, trigeminal nerve terminals in the nasal cavity) (Desforges et al., 2020; Li et al., 2016; Natoli et al., 2020; Netland et al., 2008).

Furthermore, SARS‐CoV‐2 can have indirect effects on the CNS (Zhou, Kang, et al., 2020). Cytokine storm as an immune system response during COVID‐19 infection could lead to the breakdown of the blood–brain barrier (BBB) (Liguori et al., 2020; Poyiadji et al., 2020). Infection of airway tissues by COVID‐19 in severe cases leads to impaired gas exchange, subsequently causing CNS hypoxia resulting in neural dysfunction (Abboud et al., 2020). More precisely, both cytokine storm and hypoxia which are frequently present in the severe condition of infection can contribute to making the BBB more permeable to the virus (Kaur & Ling, 2008; Zhou, Kang, et al., 2020).

There exists a wealth of evidence that supports the expression and distribution of the ACE2, the receptor for SARS‐CoV‐2, in the CNS (Jiang et al., 2013; Kawajiri et al., 2009; Li, Li, et al., 2020; Xia & Lazartigues, 2008; Xia et al., 2011; Xu et al., 2011; Zubair et al., 2020). Hence, ACE2 may be a potential target of COVID‐19 upon the entrance into the CNS, triggering its effects on CNS tissue (Baig et al., 2020). The presence of the virus in the central nervous system is also supported by some evidence reporting COVID‐19 in the CSF of the infected cases (Moriguchi et al., 2020; Zhou, Zhang, et al., 2020).

In our meta‐analysis, the mortality rate of COVID‐19 cases with at least one CNS symptom was 10.47%, which is much higher than the mortality rate of the general infected population (Borges do Nascimento et al., 2020). Such a mortality rate can indicate the importance of careful monitoring of CNS manifestations in COVID‐19 patients. This may be due to the effect of COVID‐19 on the brain stem and suppression of the cardiorespiratory control centers causing respiratory failure and death (Li, Bai, et al., 2020).

Moreover, recent studies have shown that COVID‐19 can accelerate the formation of the blood clot in the blood vessels, increasing the risk of cerebrovascular diseases in COVID‐19 patients (Choi et al., 2020; Hess et al., 2020). Hence, because the brain is nourished by a network of blood vessels, this could be indicative of the importance of cerebral vasculature investigations on the CNS symptoms in the COVID‐19 infection.

In a nutshell, attention to the CNS aspects of COVID‐19 infection has outstanding benefits for clinician's understanding of a very serious complication of this infection. At this point in time, researchers have mainly focused on finding medicinal treatments for respiratory symptoms of COVID‐19. However, it is necessary to investigate the various CNS manifestations of COVID‐19 since they are associated with increased severity and mortality (Mao et al., 2020). Not only respiratory system dysfunction, but also impairment of respiratory control centers in the CNS (brain stem) can induce acute respiratory failure (Carvalho et al., 2011; Li, Bai, et al., 2020). Therefore, considering all effective factors, it can provide clinicians to choose the best way in an attempt to manage this pandemic more efficiently.

5. LIMITATIONS

There are several limitations in our systematic review and meta‐analysis. Since in this ongoing pandemic, most of the investigations have conducted on typical signs and symptoms of COVID‐19. Thus, the number of studies on the atypical complications of COVID‐19, such as CNS presentations, is partially low. Moreover, there exist many COVID‐19 preprint papers that have not yet undergone peer review. Additionally, five studies included in our meta‐analysis reported headache and/or dizziness as one symptom in COVID‐19 cases. Because we were not sure that headache and/or dizziness is resulted from headache or is a consequence of the dizziness, it would be challenging to categorize headache and/or dizziness in the subgroup of dizziness or headache. Hence, in our meta‐analysis, it was not reported as a CNS manifestation and is implied as a separate symptom (Table 3).

6. CONCLUSION

COVID‐19 is a global problem that currently affects millions of people. This highly pathogenic virus can affect various parts of the human body. Although the respiratory tract has been mainly targeted by COVID‐19, the central nervous system can be affected significantly. In addition, patients with more severe illness showed more CNS symptoms, which may bring on worsen clinical conditions. This study achieved an important estimation for the incidence of neurological manifestations in patients with COVID‐19. The results of our survey may be helpful for clinicians for better diagnosis and management of CNS signs and symptoms in patients with COVID‐19.

7. AUTHOR CONTRIBUTORS

SH.N. and S.D. conceptualized and designed the study; A.A.J., S.M., S.S., S.S., and M.H. involved in acquisition of data; S.D. analyzed and interpreted the data; S.H.N., S.D., A.A.J. S.M., S.S., and S.M.P. drafted the manuscript; SH.N., S.D., A.A.J., F.A., H.E., and D.F. critically revised the article.

CONFLICT OF INTEREST

None.

Peer Review

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2025.

ACKNOWLEDGMENTS

Dr. Katayoun Alikhani for her supports and comments.

Nazari S, Azari Jafari A, Mirmoeeni S, et al. Central nervous system manifestations in COVID‐19 patients: A systematic review and meta‐analysis. Brain Behav. 2021;11:e02025. 10.1002/brb3.2025

Funding information

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Contributor Information

Davood Fathi, Email: dr.davoodfathi@gmail.com.

Sahar Dalvand, Email: dalvand-s@razi.tums.ac.ir.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available.

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Data Availability Statement

The data that support the findings of this study are openly available.

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