Abstract
Background
Cognitive deficits have been increasingly reported as possible long-term manifestations after SARS-CoV-2 infection.
Aims
In this study we aimed at evaluating the factors associated with cognitive deficits 6 months after hospitalization for Coronavirus Disease 2019 (COVID-19).
Methods
One hundred and six patients, discharged from a pneumology COVID-19 unit between March 1 and May 30 2020, accepted to be evaluated at 6 months according to an extensive neurological protocol, including the Montreal Cognitive Assessment (MoCA).
Results
Abnormal MoCA scores at 6 months follow-up were associated with higher pre-hospitalization National Health System (NHS) score (Duca et al. in Emerg Med Pract 22:1–2, 2020) (OR 1.27; 95% CI 1.05–1.6; p = 0.029) and more severe pulmonary disease expressed by the Brescia-COVID Respiratory Severity Scale (Duca et al. in Emerg Med Pract 22:1–2, 2020) (BCRSS > 1OR 4.73; 95% CI 1.53–14.63; p = 0.003) during the acute phase of the disease.
Discussion
This longitudinal study showed that the severity of COVID-19, indicated by BCRSS, and a complex score given by age and premorbid medical conditions, expressed by NHS, play a major role in modulating the long-term cognitive consequences of COVID-19 disease.
Conclusions
These findings indicate that the association of age and premorbid factors might identify people at risk for long-term neurological consequences of COVID-19 disease, thus deserving longer and proper follow-up.
Keywords: COVID-19, Cognitive functions, Long-COVID, Neurology
Introduction
Since the global pandemic of COVID-19 began on March 1st, 2020, neurological complications of patients during the acute phases have been largely described in single series and multi-center studies [1, 2].
With the growing number of patients affected by COVID-19, several reports claimed for possible neurological long-term consequences of COVID-19. Persistent neurological complaints include mild symptoms, like headaches, loss of smell and taste, tingling sensations, dizziness, and severe fatigue; more specifically, cognitive impairment, memory, and attention deficits have been increasingly reported as possible short- and long-term manifestations after SARS-CoV-2 infection [3] It is still unclear whether the effects of SARS-CoV-2 on the brain are indirect (mediated by oxygen starvation of the brain and/or the body’s extreme inflammatory response in severely affected patients) or direct (mediated by virus invasion in the brain), or both [4–6].
In this longitudinal study, we aim at evaluating the factors associated with cognitive deficits 6 months after hospitalization for COVID-19. We specifically hypothesize that premorbid vulnerability and older age play a major role in predicting long-term cognitive sequalae of SARS-Cov-2 infection.
Materials and methods
This observational cohort study included all adult patients (≥ 18 years old) admitted at the Pneumology Ward, ASST Spedali Civili Hospital, Brescia (Italy), for respiratory complications of SARS-CoV2 infection, from March 1 to May 31. Out of 201 patients hospitalized for COVID-19, a sample of 106 patients accepted to be evaluated at 6 months from discharge through a full clinical and neurological examination, including the Montreal Cognitive Assessment (MoCA). Patients with anamnestic premorbid cognitive impairment (n = 5) or dementia (n = 3) were excluded. An abnormal MoCA score was considered for values under 2 standard deviations from the mean score obtained in a similar age and education sample.
Epidemiological, demographical, and clinical data were collected during the interview and extracted from medical records using standardized anonymized data collection forms. Premorbid comorbidities and general health conditions were assessed by the Cumulative Illness Rating Scale (CIRS) [7] and by the National Health Service (NHS) Covid-19 Decision Support Tool [8], a complex score obtained by the sum of 3 different domains, naming age, clinical frailty scale, and comorbidity. Hospitalization data included the severity of COVID-19 disease, classified according to the quick Sequential Organ Failure Assessment (qSOFA) and the Brescia-COVID Respiratory Severity Scale (BCRSS), an algorithm using patient examination features along with the need for escalating levels of respiratory support (low-flow/high-flow oxygen therapy, intubation) to suggest treatment recommendations [9]. More specifically, the patients included in this study were stratified according the BCRSS in mild and moderate, with a score of 0 and > 1, respectively. Furthermore, the follow-up evaluations were performed by neurologists and all data were imputed and checked by four physicians (VC, AP, SCP, and NZ). Patients with signs and symptoms occurred during SARS-CoV2 infection and lasted for more than 12 weeks, not explained by an alternative diagnosis, were identified as affected by “Long-COVID” syndrome, according to NICE guidelines (2020).
The Neuro-COVID Next study was approved by the local ethics committee of ASST Spedali Civili di Brescia Hospital (NP 4067, approved 08.05.2020).
Statistical analysis
Continuous and categorical variables are reported as median with interquartile range and n (%), respectively. Differences between patients with and without cognitive difficulties were compared by Wilcoxon–Mann–Whitney test or Fisher’s exact test where appropriate. To explore the risk factors associated with cognitive impairment, univariable logistic regression model was implemented, including the following predictors: age, sex, premorbid CIRS, premorbid NHS, O2 therapy needed, and COVID-19 severity. The MoCA scores were adjusted for the effect of age and educational levels [10]. A two-sided p < 0.05 was considered statistically significant. Data analyses were carried out using SPSS software (version 24.0).
Results
From a sample of 168 consecutive patients, 106 (mean age 64.9 years, 26.7% female) were evaluated at 6 months according to an extensive medical and neurological protocol, including the Montreal Cognitive Assessment (MoCA). Table 1 highlights the factors associated with abnormal age- and education-adjusted MoCA scores (n = 18, 18.2%) [10]. Patients with abnormal scores exhibited higher premorbid vulnerability NHS score (OR 1.27; 95% CI 1.05–1.6; p = 0.029) and more severe pulmonary disease (BCRSS > 1OR 4.73; 95% CI 1.53–14.63; p = 0.003), with higher prevalence of patients requiring O2 treatment (100% vs. 77.1%; p = 0.024). Conversely, the two groups did not differ for age (70.3 vs 64.0 p = 0.085) and sex distribution (F% 44.4% vs. 22.9%, p = 0.061); more specifically, as considered the gender, the borderline p value is most likely an effect of the small size sample, thereby lack of statistical significance. The severity of COVID-19 and premorbid NHS score were confirmed as the only predictors of long-term cognitive deficits surviving in age- and sex-adjusted logistic regression model (Exp(B) = 1.27, p = 0.04 and Exp(B) = 4.73, p = 0.007, respectively). The predictive value for both variables was confirmed in models including and excluding age/ CIRS (R2 = 0.19 and R2 = 18, respectively), highlighting the importance of complex effect due to interaction between age and comorbidity, as expressed by NHS. Specifically, patients with both high premorbid vulnerability (NHS > 8) and moderate to severe COVID-19 disease (BCRSS > 1) exhibited a 5.84 increased risk of cognitive deficits (95% CI 1.87–18.27) compared to patients with mild respiratory disease (BCRSS < 1) and a lower vulnerability (NHS < 8); the logistic exhibited similar.
Table 1.
Demographic and clinical factors associated with abnormal MoCA score
Normal MoCA (n = 83) | Abnormal MoCA (n = 18) | p value | OR | |
---|---|---|---|---|
Demographic and clinical characteristics | ||||
Age, years | 64.0 (53.9–71.6) | 70.3 (63.3–77.2) | 0. 085 | |
Sex, female | 19 (22.9%) | 8 (44.4%) | 0.061 | 2.69 (CI 0.93–7.79) |
mRS pre | 0.00 (0.0–1.0) | 1.00 (0.0–1.0) | 0.123 | |
CIRS severity index pre-hospitalization | 1.31 (1.15–1.46) | 1.46 (1.21–1.61) | 0.188 | |
NHS at admission (median value) | 6.0 (4.0–7.0) | 8.0 (5.0–8.25) | 0.029 | |
NHS at admission > 8 (%) | 18 (21.7%) | 10 (35.7%) | 0.007 | 1.27 (CI 1.005–1.6) |
qSOFA at admission | 0.00 (0.0–1.0) | 0.00 (0.0–1.0) | 0.135 | |
BCRSS at admission (median value) | 0.00 (0.0–1.0) | 1.00 (1.0–1.0) | 0.003 | |
BCRSS at admission > 1 (%) | 39 (47.0%) | 15 (83.3%) | 0.008 | 4.73 (CI 1.53–14.63) |
BCRSS at discharge | 0.00 (0.0–0.0) | 0.00 (0.0–0.0) | 0.481 | |
Low-flow oxygen treatment | 64 (77.1%) | 18 (100%) | 0.024 | 1.28 (CI 1.14–1.44) |
Non-invasive ventilation | 15 (83.3%) | 3 (16.7%) | 0.704 | 1.31 (CI 0.32–5.27) |
Total days of O2 therapy | 4.00 (1.0–10-0) | 7.00 (2.0–11.25) | 0.179 | |
NHS > 8 + BCRSS > 1 | 10 (12.0%) | 8 (44.4%) | 0.003 | 5.84 (CI 1.87–18.27) |
BCRSS Brescia-COVID Respiratory Severity Scale, CIRS cumulative illness rating scale; mRS modified ranking scale; qSOFA quick Sequential Organ Failure Assessment
*p values were calculated by Mann–Whitney test or Fisher’s exact test, as appropriate
Discussion and conclusions
This longitudinal study indicates that premorbid conditions play a major role in modulating the long-term consequences of COVID-19 disease. Recent data coming from larger surveys indicating age as key predictor of long-COVID [11–13]; of interest, age alone was not a good predictor of long-term cognitive deficits in our sample. This might indicate that older subjects are at higher risk of long-COVID only when premorbid vulnerability interacts with the severity of COVID-19 infection. Although the mechanisms underlying long-term central nervous system involvement are still unclear, these preliminary findings suggest that long-term cognitive deficits might represent part of the spectrum of long-COVID. Of interest, we included only subjects who did not reported any previous neurological disease or syndromes (including encephalopathies or encephalitis) during the acute phases of the disease. Conversely, we found that 18.2% of non-neurological patients exhibited abnormal age- and education-adjusted MoCA score––a percentage definitively higher compared with the expected value in the Italian general population, identified with 11%, considering both borderline and pathological performances [10]. For this, larger and longer clinical follow-up are definitively needed in order to better characterize the nature and progression of cognitive decline in patients with COVID-19, as we only observed subtle to mild deficits with no impact in activities of daily living. The main limitation of this study was the unavailability of premorbid cognitive assessment, although we accurately excluded subjects with cognitive impairment before hospitalization. Other important limitations were the small sample size and the lack data of asymptomatic and more severe COVID-19 patients that definitely need to be evaluated in larger on-going studies.
Limitations notwithstanding, this study highlighted for the first time that long-term cognitive deficits are predicted by the complex interaction between premorbid vulnerability and COVID-19 severity in hospitalized patients.
Acknowledgements
The authors thank all the patients for their active participation. The Neuro Covid Next Study group: Ilenia Libri, Marcello Giunta, Matteo Cortinovis, Martina Locatelli, Barbara Risi, Francesca Schiano di Cola, Nicola Zoppi.
Author contributions
VC, AP, and AP contributed to conception and design of the study. VC, AP, SCP, GB, SG, MB, ML, and AP were involved in acquisition and analysis of data. VC, AP, and AP drafted the manuscript and figures.
Funding
This study was not financial supported.
Declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in these studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the local ethics committee of ASST Spedali Civili di Brescia Hospital (NP 4067, approved 08.05.2020).
Consent to participate
Informed consent was obtained from all individual participants included in this study.
Consent for publication
The authors affirm that human research participants provided informed consent for publication of the article.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Viviana Cristillo, Email: viviana.cristillo@gmail.com.
the Neuro Covid Next Study group:
Ilenia Libri, Marcello Giunta, Matteo Cortinovis, Martina Locatelli, Barbara Risi, Francesca Schiano di Cola, and Nicola Zoppi
References
- 1.Ellul MA, Benjamin L, Singh B, et al. Neurological associations of COVID-19. Lancet Neurol. 2020;19:767–783. doi: 10.1016/S1474-4422(20)30221-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Benussi A, Pilotto A, Premi E, et al. Clinical characteristics and outcomes of inpatients with neurologic disease and COVID-19 in Brescia, Lombardy Italy. Neurology. 2020;95:e910–e920. doi: 10.1212/WNL.0000000000009848. [DOI] [PubMed] [Google Scholar]
- 3.Cristillo V, Pilotto A, Piccinelli SC, Neuro Covid Next Study g et al. Age and subtle cognitive impairment are associated with long-term olfactory dysfunction after COVID-19 infection. J Am Geriatr Soc. 2021 doi: 10.1111/jgs.17296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lucchese G, Floel A. Molecular mimicry between SARS-CoV-2 and respiratory pacemaker neurons. Autoimmun Rev. 2020;19:102556. doi: 10.1016/j.autrev.2020.102556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Pezzini A, Padovani A. Lifting the mask on neurological manifestations of COVID-19. Nat Rev Neurol. 2020;16:636–644. doi: 10.1038/s41582-020-0398-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Zhou Z, Kang H, Li S, et al. Understanding the neurotropic characteristics of SARS-CoV-2: from neurological manifestations of COVID-19 to potential neurotropic mechanisms. J Neurol. 2020;267:2179–2184. doi: 10.1007/s00415-020-09929-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Parmelee PA, Thuras PD, Katz IR, et al. Validation of the cumulative Illness Rating Scale in a geriatric residential population. J Am Geriatr Soc. 1995;43:130–137. doi: 10.1111/j.1532-5415.1995.tb06377.x. [DOI] [PubMed] [Google Scholar]
- 8.Peter Foster BS, Rovnick N. (2020) NHS ‘score’ tool to decide which patients receive critical care. Financial Times
- 9.Duca A, Piva S, Foca E, et al. Calculated decisions: brescia-COVID respiratory severity scale (BCRSS)/algorithm. Emerg Med Pract. 2020;22:1–2. [PubMed] [Google Scholar]
- 10.Santangelo G, Siciliano M, Pedone R, et al. Normative data for the Montreal Cognitive Assessment in an Italian population sample. Neurol Sci. 2015;36:585–591. doi: 10.1007/s10072-014-1995-y. [DOI] [PubMed] [Google Scholar]
- 11.Huang C, Huang L, Wang Y, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397:220–232. doi: 10.1016/S0140-6736(20)32656-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sudre CH, Murray B, Varsavsky T, et al. Attributes and predictors of long COVID. Nat Med. 2021 doi: 10.1038/s41591-021-01292-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Writing Committee for the CSG. Morin L, Savale L, Pham T, et al. Four-month clinical status of a cohort of patients after hospitalization for COVID-19. JAMA. 2021;325:1525–1534. doi: 10.1001/jama.2021.3331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.National Institute for Health and Care Excellence (2020) COVID-19 rapid guideline: managing the long-term effects of COVID-19. National Institute for Health and Care Excellence: Clinical Guidelines. London, UK [PubMed]