Long COVID: A Protocol for Systematic Review and Meta-analysis of Symptomatology and Treatment Approaches

Background: The burden of SAR COV-2 infection is not limited to the acute viraemia and its symptomatology but extends far beyond to include the long COVID, also known as post-COVID-19 syndrome,which may soon reach public health signicance. We set out to produce a protocol for reliable and accurate systematic review and meta-analysis of the symptomatology and treatment approaches of long COVID globally. Methods: We developed a search strategy using MeSH terms, text words and entry terms. Nine databases will be searched: PubMed, Embase, CINAHL, AJOL, Google Scholar, Web of Science, Cochrane Library, Researchgate and Scopus. Only observational studies retrievable in the English Language will be included. The primary measurable outcome is the pooled prevalence of the symptoms of long COVID. The secondary outcomes include the summary effect sizes of the treatment approaches to the long COVID; the geographic, race, gender and age variations in symptomatology, and the quality of life of patients with long COVID. Identied studies will be screened, deduplicated, selected and data items extracted using DistillerSR software. All studies will be assessed for methodological, clinical and statistical heterogeneity. Assessment of meta-bias in the selected studies will be performed using the NIH Quality assessment tool for observational studies. Publication bias will be assessed using the funnel plot and Egger’s regression intercept. The pooled prevalence will be expressed with SE and 95% CI. The strength of evidence from this analysis will be assessed using the NIH Quality Assessment for Systematic Reviews and Meta-analysis. analysis will map the exploring the race, It will the published in peer-reviewed


Background
The term "Long COVID" was coined by a patient; Elisa Perego, an archaeologist at the University College London in May 2020 as a hashtag on twitter. [1,2] Currently, there is no consensus on the terminologies for describing a conglomerate of long-term clinical signs and symptoms following COVID 19 infection. [3] This phenomenon has been given different terms including long COVID, chronic COVID syndrome (CCS), Post-COVID-19 syndrome and Long Hauler to mention a few. [2][3][4][5] This further underscores the confusing nature and complexity of the COVID 19 infection and its devastating effects on the patients' quality of life. Long COVID refers to chronic COVID 19 symptoms extending beyond 12 weeks while the term 'postacute COVID-19' is for symptoms extending beyond three weeks from the onset of rst symptoms to 12 weeks. [5][6][7][8] COVID 19 disease is a multi-systemic disorder capable of affecting virtually the entire human organ system. At the early days of the COVID pandemic, little did anyone know about the pending long term effect of the acute infection. Many patients who contracted SARS COV-2 infection recovered from the initial symptoms within a few weeks of on-set of the disease. But up to 20-80% in some series have been reported to experience symptoms long after the initial recovery. [9][10][11][12] These numbers appear to be increasing, almost reaching monumental public health signi cance. [13] Again, the persistence and pervasiveness of the long term symptomatology are devoid of the severity of the initial presentation. [14] The symptomatogy of COVID 19 are protean ranging from mild symptoms such as malaise to crippling fatigue, breathlessness, enduring tiredness, reduced muscle function, impaired ability to perform daily tasks, cough, chest pain and mental health problems such as post-traumatic stress disorder, anxiety, loss of taste and smell, di culty in sleeping, brain fog, gastrointestinal complaints and major depression. [4,15] Consequent on severe primary injury to the heart, brain and lungs, are multiple medical conditions such as Alzheimer, strokes, Parkinson disease and heart failure to mention a few; thus the need to summarise these symptoms. [16,17] Recently, complications such as poor glucose control in diabetic patients has been added to the list of the effects of long COVID. [18][19][20] The mechanism of these clinical manifestations is not clear, but suggested pathways include the permanent damage to involved organs such as the lungs and heart. Besides, other postulation includes post-intensive care syndrome, post viral fatigue, the persistence of the virus due to a weak immune system, reinfection with another strain of the virus, persistence of in ammatory markers, an overwhelming immune response to the infection and post-traumatic stress [4]. Other risk factors include age, excess weight, disease condition like asthma, and having more than ve symptoms in the rst week of COVID-19 infection. [4,8,11,[21][22][23] The occurrence of long COVID in paediatric age group infers that these symptoms are not agedependent. [24,25] However, symptoms appear to have gender variation in prevalence of the long COVID syndrome, likely in favour of the females compared with the males. [21] Everyone infected with COVID-19 is at the risk of long COVID. Therefore, the hallmark to halting and preventing long COVID 19 disease largely rests on primary prevention and adoption of strict infection prevention and control measures. [26][27][28][29] A recent report on over a hundred thousand people who were vaccinated from December 2020 to January 2021 showed protection from COVID 19 infection, thus they were immuned against Long COVID. [30] However, it is too early to conclude on the potency, safety, effectiveness and immunogenicity of vaccines. [31][32][33] The current approach to the treatment of long COVID is symptomatic rather than de nitive. Many patients may recover spontaneously. However, some will require some speci c therapeutic care in addition to rest and holistic support. The extent to which patients recover spontaneously and the need for monitoring of persistent sysmptoms are largely unknown. [34] We set out to produce a protocol for reliable and accurate systematic review and meta-analysis of the symptomatology and treatment approaches of long COVID syndrome globally.

Methods And Design
Objective: The speci c objectives of the study are: 1. To determine the pooled prevalence of the various symptoms of long COVID from primary studies.
2. To measure summary effect sizes of the treatment approaches to long COVID from primary studies.
3. To evaluate the in uence of geographic variation, race, gender and age on symptomatology, treatment approaches and the quality of life of patients with long COVID. The secondary outcome is the proportion of various treatment approaches to long COVID. The effect size is prevalence.

Information sources
The search will use sensitive topic-based strategies designed for each database. The search will be carried out in the following databases: PUBMED, EMBASE, CINAHL, RESEARCHGATE, AJOL, GOOGLE SCHOLAR, WEB OF SCIENCE, SCOPUS and COCHRANE LIBRARY. Only observational studies will be included, from 2019 to present time.

Search strategy
The search strategy includes text words and entry terms. Table 1 shows the search strategy for the long COVID as used in the Pubmed. The same search strategy will be used in other databases with slight modi cations.

Data Extraction and Management
Data Extraction Data will be managed in three main softwares: DistillerSR, CMA version 3 and Microsoft Excel.
a. Screening: Identi ed studies will be screened independently in pairs and blindly using the DistillerSR software at 6 different levels: i. Level 1 will involve screening of identi ed studies for the study design. Only observational studies would be accepted ii. Level 2 will involve screening of identi ed studies in the titles and abstracts using entry terms and keywords.
iii. Level 3 will involve further screening of the contents of articles by reading the full article using the same search strategy.
iv. Level 4 will involve snowballing of literature on references from eligible studies.
v. Level 5: Studies will be screened at outcome levels to select those that reported the primary outcome with or without secondary outcomes.
vi. Level 6 will involve grey literature that report primary outcome and or secondary outcomes.
Con icts during screening will be resolved by a third independent reviewer who serves as a tie breaker.
b. Selection Process: Screened studies will be selected based on study charateristics: study design, inclusion/exclusion criteria and agreement between two independent and blinded reviewers. Authors of included studies with missing data will be contacted via email and telephone. After selection, studies will be deduplicated. Data items will be extracted from selected studies into prede ned forms in the DistillerSR.

Data Items/Measurable Outcomes
The key data items linked to measureable outcomes are i) various symptoms of long COVID, ii) various treatment types to long COVID, ii) socio-demographic variables, and iv) Quality of life for long COVID patients.

Risk of bias
The risk of bias (methodological quality) in the included studies will be assessed for the individual article using the National Institute of Health (NIH) Quality assessment tool for observational cohort and crosssectional studies. The NIH Quality assessment tool has 14 questions. Studies that score 7 and above are considered good quality. This will be cross-checked with the Cochrane tool of risk of bias assessment (ROBINS-1). Publication bias in the selection of studies will be visually assessed using the funnel plot and associated variables such as trim and ll outcome, Egger's regression intercept, Begg and Mazumdar's rank correlation and Orwin's fail-safe N will be reported. Studies with extreme bias (NIH score less than 5) will be subjected to sensitivity testing using the include/exclude function in the CMA Software.

Assessment of Meta-bias
Meta-bias will be assessed as follows: i) Method of reporting long COVID at outcome level. It will consider the plurality of terms.
ii) Index of reporting outcomes in studies: Studies that were reported in different indices but similar in outcome and design will be converted to the primary effect size (prevalence) based on individual case evaluation.
iii) Heterogeneity will be assessed at the study level using the Q statistics, and its p-value, I², ² (Tau squared). As a rule of thumb, I² values of less than 40% will be considered low heterogeneity while values > 40 but < 75 % will be considered moderate and values > 75% are high.

Data synthesis
Extacted data items will be used for both narrative synthesis and quantitative analysis.
The following criteria will be applied for analysis: a. Studies that passed the methodological quality assessment using the NIH quality assessment tool will be cross-checked with the Cochrane Risk of Bias tool. The results will be presented in tabular format, indicating all the extractable data items as listed under data collection.
b. All studies with primary outcomes will be used for narrative synthesis.
c. All studies with good NIH quality scores that reported primary and or secondary outcomes will be used for quantitiative synthesis.
d. Further Analysis: Subgroup analysis will be performed using variables such as race, gender, socioenomic status, age and geographical location (country).
Meta-regression will be performed on quantitative variables such as age, proportions of treatment approaches and quality of life as explanatory variables e. Where heterogeneity is high, sensitivity testing using include/exclude functions in the CMA software will be performed.
f. The computational model for analysis is Random effect model since the several studies across the globe will be included.

Presentation and Reporting of Results
The study selection process will be summarised in a Prisma ow chart according to the PRISMA 2015 Statement and PRISMA-P Checklist. A table of the search strategy in various databases showing text words and entry terms will be included. A list of eligible studies will be summarized in a table.
Quantitative data such as prevalence of long COVID symptoms, 95 % CI, P values, and relative weights assigned to studies and heterogeneity tests will be reported in the forest plots. A table of quality scores and risk of bias of each eligible study will be included. Forest and regression plots to show sub-group analysis and meta-regression respectively will be included.

Discussion
This protocol will enable analysis to delineate the symptoms of long COVID and its correlates, exploring the in uences of geographic locations, race, age and gender, thereby enabling a severity index on a global scale. It will examine in detail the treatment approaches to long COVID and their impacts on the quality of life of patients. The evidence from this study will inform health policies toward the management of post-COVID-19 syndrome. The outcome of this study will be published in peer-reviewed scienti c journal.
GRADE: The quality of ndings from this study will be assessed using the NIH Quality Assessment for Systematic Reviews and Meta-analysis. The author(s) declared no potential con icts of interest with respect to the research, authorship, and/or publication of this article.

List Of Abbreviations
Funding: The Molecular Pathology Institute provides funding for this study.

Support:
The Molecular Pathology Institute provided the subscription for the DistillerSR and the funding.
Guarantor of the Review:

Dr. Emmanuel Nna
Ethical Approval/ Dissemination: The study will use published data, thus, no ethical approval is required. The results obtained from this study will provide important information on pooled prevalence of symptoms and various treatment approaches to long COVID. The study will be published in a peer-reviewed scienti c journal and made available to practitioners providing care to patients with long COVID.
Informed Consent: Not applicable Table   Table 1: Search strategy for long COVID in PubMed ((Long-COVID OR long-haul COVID OR long COVID OR chronic COVID syndrome OR post-acute COVID19 syndrome OR long hauler COVID OR long haul COVID OR post-acute COVID syndrome))