Key Messages

• Post–COVID-19 condition is a growing health concern and has been associated with more than 200 possible symptoms. The diverse and varied ways the condition presents clinically creates challenges for developing standard diagnostic criteria, and for health systems aiming to provide effective treatment and management supports for people.
• To support health care, decision-makers and clinicians understand the different clinical presentations of the condition, we scanned the evidence base to examine early approaches being used to characterize and describe subtypes of post–COVID-19 condition. Subtypes can be developed with many different disease features and patient factors, but for this report we specifically reviewed potential subtypes based on symptoms and clinical presentation.
• We found that some of the early approaches used to develop subtypes are based on statistical methods that group together patterns of symptoms. These studies are beginning to reveal potential subtypes based on severity of symptoms, type and co-occurrence of symptoms, and symptoms affecting different organ systems.
• Many reported symptoms of post–COVID-19 condition are similar to previously characterized health conditions. In some cases, subtypes of post–COVID-19 may be manifestations of those other conditions. For example, certain subtypes may present with symptoms similar to myalgic encephalomyelitis/chronic fatigue syndrome or pulmonary fibrosis. It is uncertain whether those subtypes share the same or distinct pathophysiology and whether they may benefit from similar treatments.
• Early evidence comparing the variant of infection and its association with potential subtypes of post–COVID-19 condition is emerging, but the findings are currently mixed. Some studies suggest that variants such as Delta and Omicron may result in different clinical presentations, while other studies have not found significant differences. Further research assessing the association between variants and subtypes is likely needed.
• This review provides some implications and considerations for health systems should emerging research further characterize and validate proposed subtypes. These implications may be important for improving the diagnosis, treatment, and management of post–COVID-19 condition. With estimates in Canada suggesting that more than a million people could be affected by the condition, monitoring ongoing research on subtypes may help support the development of effective and tailored treatments, and guide health systems planning across the country.

Background

Post–COVID-19 condition is a growing health concern in Canada and across the world. The condition is defined as persisting or ongoing physical or psychological symptoms for more than 12 weeks or 3 months after acute COVID-19 illness.^1,2 Symptoms of post–COVID-19 condition can vary and may occur in people after either mild or severe acute illness of COVID-19.^3,4 Estimating the prevalence of post–COVID-19 condition is challenging due to an absence of a standard diagnostic criteria and varying definitions, leading to a wide range of reported prevalence (between 5% to 50%) of people with COVID-19.^5,6 As of October 2022, estimates from Canada suggest that 14.8% (or 1.4 million) of people had or were suspected of having experienced symptoms indicative of post–COVID-19 condition.⁷

Post–COVID-19 condition is a diverse condition and has varying effects to people’s health and well-being. While more than 200 symptoms have been associated with the condition across different studies, there are no standards for classifying or grouping the same symptoms, which can create challenges for characterizing the condition and for health systems planning.^5,8 Post–COVID-19 condition may be multiple syndromes or have different subtypes, with possibly different pathophysiologies.^2,9 Given the wide diversity of its clinical manifestation, people affected by the condition will have a range of treatment and management needs. Having a better understanding and characterization of possible post–COVID-19 condition subtypes may help identify its pathophysiology (or pathophysiologies) and could also help inform the development of multidisciplinary care tailored to people’s diverse needs.¹⁰

Objective

The objective of this review is to summarize and provide a narrative overview of the emerging evidence examining different subtypes of post–COVID-19 condition. We aimed to describe the emerging approaches used to propose or identify subtypes, including approaches based on statistical analyses or clinical groupings of disease features that could inform prognosis and treatments. We also aimed to describe the emerging evidence about whether different variants of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) are associated with different subtypes.

Owing to the complexity and rapidly evolving landscape of research on risk factors, we do not aim examine patient-level factors (e.g., demographics, sex, or age) that could affect people’s varying experiences with the long-term effects of COVID-19 or how post–COVID-19 condition may manifest differently among subgroups of people.^16,17 Rather, we aim to focus primarily on subtypes of clinical disease features.

Methods

Synthesis Approach

The findings from the literature search were summarized narratively and grouped by research question. For research question 1, full-text review was conducted for studies fulfilling the selection criteria. Studies that explicitly described characterizing subtypes or groupings of post–COVID-19 condition clinical presentation were initially used to develop broad categories and themes based on their approach. These themes were iteratively refined, with the remaining studies used to identify the common approaches for proposing or developing subtypes.

For research question 2, studies that fulfilled the selection criteria and specifically examined variants of SARS-CoV-2 or implied there could be variants (e.g., different waves) were examined separately from question 1. The findings were summarized collectively.

Table 1

Components of Literature Screening and Information Gathering.

Findings

A total of 1,081 citations were identified in the literature search. Following screening of titles and abstracts, 769 citations were excluded and 312 potentially relevant reports from the electronic search were retrieved for full-text review. Of these potentially relevant articles, 43 studies were considered relevant, and their findings were synthesized to develop this report. Studies were excluded for a variety of reasons, such as studies that did not examine or stratify different clinical presentations of post–COVID-19 condition, were smaller case reports, were more related to other conditions occurring in people who had acute COVID-19, or were those that examined the prevalence of post–COVID-19 condition symptoms but did not describe subtypes or clustering of symptoms.

Varied Approaches for Developing Subtypes

Symptoms Clustering

We identified that 27 of the included studies used or proposed some form of symptoms clustering to identify possible subtypes of post–COVID-19 condition and to suggest that the condition may manifest in specific patterns. These patterns may not necessarily be distinct and could have some overlap. Studies developed or proposed clusters based on symptoms severity, type and co-occurrence of symptoms, and symptoms affecting a common organ system; their findings have been summarized in Table 2, Table 3, and Table 4.

Symptoms Severity

We identified 4 studies that stratified clinical presentation based on the severity of people’s symptoms. These included a cross-sectional study from the UK¹⁸ that assessed people attending a COVID-19 rehabilitation centre, a prospective cohort study from Spain¹⁹ that examined people who had been discharged from a hospital with acute COVID-19, a cross-sectional study²⁰ that assessed people from multiple countries recruited from online groups, and a cross-sectional study from Germany²¹ that assessed people at least 4 months after a confirmed SARS-CoV-2 infection. All 4 studies used statistical-based clustering analyses to identify potential subtypes.

The UK study invited people from a rehabilitation centre to complete a standardized questionnaire (Covid‐19 Yorkshire Rehabilitation Scale) and had responses from 370 patients.¹⁸ Clustering analysis of 12 key most-reported symptoms indicated 3 distinct phenotypes of severe, moderate, and mild, based on mean symptoms scores.¹⁸ Severity of symptoms were correlated with lower perceived health and higher functional difficulty. Clustering analysis, however, did not identify consistent grouping of specific symptoms.¹⁸

The Spanish study identified 3 clusters (termed Cluster 0, Cluster 1, and Cluster 2) based on analyses of symptoms (including both symptoms at initial hospital admission and post–COVID-19 condition symptoms), clinical features, and hospital-based data of 1,969 patients.¹⁹ People within Cluster 0 (21.9% of all study participants) reported more severe functional impacts (e.g., effects on daily living, occupation, and leisure activities) and the highest number of symptoms, most prominently fatigue, anxiety and depression, and changes to sleep quality.¹⁹ People grouped into Cluster 1 reported a similar number of symptoms as Cluster 0 and the presence of comorbidities but reported a lesser impact on functional activities.¹⁹ People in Cluster 2 reported the least number of post–COVID-19 condition symptoms and functional impairments.¹⁹ Among people in Cluster 0 (the most severely affected group), 73.4% reported experiencing dyspnea or shortness of breath, which was significantly higher than people in Cluster 1 (18.8%) or Cluster 2 (1.3%).¹⁹ The authors of the study suggested that the number of symptoms experienced by people (symptoms load) and the development of dyspnea could be used in early clinical assessment to guide care and inform prognosis for people affected by post–COVID-19 condition.¹⁹

One cross-sectional study recruited 2,550 people from long COVID online groups (international representation) to complete a questionnaire about their symptoms and experiences after their initial infection.²⁰ The study used hierarchical clustering analysis to characterize ongoing symptoms among people along with several analyses looking at the association with demographic factors, baseline health, and other socioeconomic factors.²⁰ The study indicated that there were likely 2 clusters of symptoms among participants. The majority of people (88.8%) reported experiencing cardiopulmonary (e.g., shortness of breath, chest pain) and neuro-cognitive (e.g., brain fog, poor concentration) symptoms.²⁰ The second cluster represented a smaller proportion (11.2%) of study participants and was termed the multisystem ongoing cluster as it was associated with symptoms affecting the gastrointestinal, cardiopulmonary, neurologic, systemic, skin, and other systems of the body. People within this cluster reported more severe impacts to their functional abilities, greater time away from work, higher fatigue, and greater disruption to daily activities.²⁰ The authors suggested that the minority group with more multisystem effects would be more likely have complex needs and require multidisciplinary care.²⁰

Another cross-sectional study invited people from a county health department in Germany who had acquired COVID-19 to complete a Quality of Life (QoL) questionnaire (the Short Form 36) and to provide other information about their health status and sociodemographics.²¹ Clustering analysis showed that, based on severity of QoL outcomes, a majority of people with post–COVID-19 condition symptoms (70.6%) had QoL scores similar to the general population in Germany, while about one-third (29.4%) reported significantly reduced QoL.²¹ Despite people within the majority cluster reporting symptoms such as fatigue, pain, and sleep disturbance, their QoL outcomes were not substantially different than what would be expected for a healthy population.²¹ However, people with significantly reduced QoL were more likely to report a greater number of symptoms and other comorbidities.²¹

Type and Co-Occurrence of Symptoms

Another emerging approach to identify potential subtypes, used by at least 3 identified studies, is to employ analytical statistics to assess whether specific symptoms group together. These included a cohort study from Ireland that used multi-component analysis and identified 3 clusters among 232 study participants who had persisting symptoms beyond 4 weeks after their initial COVID-19 infection.²² Symptoms were recorded with clinical assessment, standardized questionnaires on health status and QoL, and biologic measurements (e.g., blood pressure, chest X-rays, electrocardiographs, and blood components). Analysis from these multiple components indicated that Cluster 1 (15.9%) had a higher proportion of people reporting musculoskeletal and pain symptoms, Cluster 2 (37.5%) had a higher proportion of cardiorespiratory symptoms, and Cluster 3 (46.6%) had the fewest reported symptoms.²² People within Cluster 1 and Cluster 2 reported more symptoms and functional impairments, including lower reported QoL and emotional health, and higher work absences following the initial acute phase.²² Authors of the study suggested that the distinction between musculoskeletal and cardiorespiratory symptoms highlights a need to understand possibly distinct disease mechanisms.²²

A retrospective cohort study from the US analyzed the electronic health records of more than 270,000 people who had been infected with SARS-CoV-2, and more than half (57.0%) of people reported clinical features that may be indicative of post–COVID-19 condition (i.e., long-term effects up to 180 days after COVID-19 infection).²³ The study used statistical approaches to assess the co-occurrence of symptoms and found that several symptoms were more likely to occur with each other among people with post–COVID-19 condition compared to a matched group of people with influenza. For example, abnormal breathing with chest pain, and abdominal symptoms with fatigue, were pairs of symptoms that had statistical associations of co-occurring.²³ Authors of the study also indicated that while the study was not powered or intended to identify subtypes, they did find associations between abnormal breathing, chest pain, fatigue, and anxiety and/or depression, suggesting these symptoms may be interconnected.²³

A Norwegian prospective cohort study examined people (n = 774) who had been infected with SARS-CoV-2 during the initial stages of the pandemic and who were assessed for 22 symptoms associated with long-terms effects.²⁴ This study used correlative analysis to assess pairs of symptoms.²⁴ The study reported a positive association between fatigue and poor memory and a negative association between chest pain and anxiety or depression. The Norwegian study also reported changes in the pattern of symptoms co-occurring between 2 different periods of the pandemic (March 2020 to April 2020 and September 2020 to January 2021), but did not assess the effects of variants.²⁴

Table 2

Summary of Primary Studies Using Symptoms Clustering to Identify Possible Subtypes or Clusters of Post–COVID-19 Condition.

Organ Systems

Grouping symptoms by organs or body systems is another emerging approach to understand how post–COVID-19 condition may manifest differently.¹⁰ We identified at least 5 studies and clinical reviews that classified the heterogenous symptoms of post–COVID-19 condition and mapped them to different organ systems (Table 3). These included a prospective study from Mexico that followed patients who were hospitalized with COVID-19 for up to 90 days after their discharge.²⁵ The study identified 45 symptoms in the cohort and categorized each into 1 of 8 body systems, which included neurologic; mood disorders; systemic; respiratory; musculoskeletal; symptoms affecting the ear, nose, and throat; dermatological; and gastrointestinal.²⁵ The most prominent symptoms clusters (> 30% of people) at 90 days were related to the neurologic, dermatological, and mood disorders systems.²⁵ Authors of the study indicated that some of the specific symptom clusters were moderately associated with health status, corticosteroid use, and other factors.

Organ systems clustering was used by a cross-sectional study²⁶ with 3,762 online participants that characterized 203 symptoms into 10 organ systems and 1 systematic review²⁷ that categorized symptoms into 7 body systems. The cross-sectional study also reported that it is possible that symptoms affecting different organ systems could co-occur with one another as distinct clusters (e.g., the study found clustering of neurologic and systemic symptoms).²⁶

Different review articles have also proposed examining manifestations of post–COVID-19 condition by organ systems. One review aimed to standardize varying symptoms reported by different studies according to the Human Phenotype Ontology²⁸ classification system and mapped the symptoms onto a limited number of terms and possible organ systems that may be implicated.⁸ The authors of a narrative review examining symptoms associated with post–COVID-19 condition and its implications for rehabilitation care reported that based on their assessment of the evidence, the condition could affect at least 7 organ and body systems.¹⁰ The overlap among studies and reviews describing the specific organ systems that are associated with post–COVID-19 condition are shown in Table 3. Note that some studies may have classified certain organ systems independently, such as cardiovascular and respiratory, while other studies may have described them as a single system (e.g., cardiorespiratory). In such cases, we denote the study as classifying both organ systems in the table.

Table 3

Studies Describe a Variety of Symptoms That Map on to Different Organ Systems and May be Associated With Post–COVID-19 Condition Subtypes.

We also identified at least 5 studies that have proposed that there may be a distinct post–COVID-19 neurologic syndrome or aim to characterize specific neurologic subtypes. For example, a cohort study from Switzerland suggested that in some people, neuroinflammation may reduce connectivity in the brain and could result in a specific neurologic post–COVID-19 syndrome associated with cognitive impairment.²⁹ An editorial review reported that because an estimated 20% of people with severe acute illness may develop neuropsychiatric symptoms and may have signs indicating neuroinflammation, there may be a unique post–COVID-19 neurologic syndrome or phenotype.³⁰

Other studies have reported that there may be different neurologic subtypes. A prospective cohort study from the US assessed neurologic symptoms among 100 participants with standardized questionnaires, cognitive tests, and brain scans.³¹ Early results from the study reported 2 clusters of neurocognitive impairment.³¹ One cluster had people with symptoms affecting all neurocognitive domains, whereas the other cluster had a milder impact limited to people’s attention.³¹ Brain imaging results also revealed that people who reported experiencing anosmia — the loss of sense of smell — had structural neurologic differences compared to those who did not report anosmia.³¹ Similarly, a prospective study from Italy assessed 109 people from a post–COVID-19 neurologic clinic.³² Clustering analysis of symptoms proposed 2 neurologic subtypes that primarily affect either the central nervous system (symptoms of head ache, anosmia, memory) or the peripheral nervous system (neuropathy).³² One cross-sectional study from Colombia assessed 100 people attending a post–COVID-19 clinic and reported 2 clusters based on autonomic nervous system symptoms assessed by a standard neurologic score (i.e., COMPASS 31).³³

Studies assessing symptoms related to the cardiovascular system are also emerging, but none that used clustering methods were identified.^34,35 Future research validating cardiovascular and neurologic biomarkers or functional impairment could complement approaches for assessing organ-specific subtypes, as well.^36-39

Association Between Different Variants and Subtypes of Post–COVID-19 Condition

Emerging research is beginning to assess whether different variants of SARS-CoV-2 or particular waves of the pandemic may be associated with post–COVID-19 condition and potentially different subtypes (Table 4). Studies have used either variant testing with polymerase chain reaction (PCR) or different time periods (i.e., pandemic waves) as a proxy for different variants.

One preprint prospective cohort study from Norway used data from a national registry to compare people (more than 35,000 individuals) with confirmed diagnoses (genomic testing) of the Delta and Omicron variants.⁵⁶ The study reported that people with either variant more commonly reported fatigue and shortness of breath compared to people who were not infected with COVID-19.⁵⁶ However, between both variants, the study did not find any significant differences in the type or frequency of common post–COVID-19 symptoms reported.⁵⁶

A case-control study using data from the Covid Symptoms Study⁵⁷ compared the odds of developing long COVID (defined as new or ongoing symptoms for at least 4 weeks) between 2 COVID-19 waves. Researchers selected 2 time periods where more than 70% of cases could be attributed to either the Delta or Omicron variant, respectively.⁵⁸ The study reported that people likely infected by the Omicron variant were less likely to experience long COVID compared to people infected by the Delta variant, with an odds ratio ranging from 0.24 to 0.50 (statistical P values not reported).⁵⁸ While the study did not assess whether the clinical features of people who developed long COVID differed between 2 variants, the study suggested that different variants may be associated with different risk profiles.

A subsequent preprint study from UK also using data from the COVID Symptoms Study and specifically aimed to examine whether variants were associated with different symptoms clustering patterns.⁵⁹ This study compared clinical manifestations of long COVID (symptoms lasting for more than 4 weeks) or post–COVID-19 (symptoms lasting for more than 12 weeks), which are the definitions used by the National Institute for Health and Care Excellence.² Clustering analysis revealed a differing number of symptoms clusters across the wild-type, Alpha, and Delta variants. These clusters affected a range of different organ systems and had varying severities.⁵⁹ However, among all variants examined, the 3 most common clusters were the central neurologic cluster (most prominent in Alpha and Delta variants), the cardiorespiratory cluster (most prominent in the wild-type variant), and the systemic/inflammatory cluster (prevalent across all variants but at a lower frequency).⁵⁹ A Spanish study examining hospital survivors who were infected with the same 3 variants similarly reported that people with the Delta variant were more likely to report neurologic symptoms (e.g., headache, anosmia, and ageusia).⁶⁰ Authors of the UK preprint study reported that this early evidence suggests that post–COVID-19 may have different subtypes that could, in part, be associated with the variant of infection; however, further research would need to assess the biologic mechanisms.⁵⁹

Table 4

Summary of Studies Examining Subtypes or Different Manifestations of Post–COVID-19 Condition Between Different Variants of Infection.

Limitations

This report provides a narrative overview of the emerging evidence examining different subtypes of post–COVID-19 condition, but did not quantify or qualify the strength of evidence about subtypes. We focused primarily on subtypes related to symptoms presentation and did not examine other disease features (e.g., biomarkers) or patient-level factors (e.g., demographics, sex, age, health status, vaccination status) that could affect people’s varying experiences with the long-term effects of COVID-19, or how post–COVID-19 condition may manifest differently among subgroups of people. Patient-level factors are an important aspect of understanding the condition, but given the complexity of analysis required, were outside the scope of this review.

Although effort was made to capture the expansive nature of the evidence, this report focused on the major approaches being used to propose or develop subtypes. It was not an exhaustive search of all subtypes, and other subtypes may not have been identified. The emerging approaches and subtypes presented in the report are not validated and would require further study to guide clinical care. Given the emerging nature of the evidence, and limited understanding of SARS-CoV-2 and its long-term effects, other approaches that examine the molecular underpinning of the virus may reveal different subtypes of post–COVID-19 condition. We found limited research from Canada or limited studies using data from people in Canada. As such, the findings may not be generalized to all settings or patients across Canadian health systems.

Regarding research question 2, there may be limitations in sampling approaches, access to testing at different time periods, and study designs that make assessing the relationship between variants and subtypes of post–COVID-19 condition complex. Preprint studies were also included to address this question due to limited published evidence. Overall, the report is a not a systematic review and it did not involve critical appraisal, assessment of the quality of evidence, or provide a detailed summary of study findings.

Conclusions and Implications for Health Systems

In our overview of the emerging evidence, we found studies using several different approaches to group together or characterize post–COVID-19 condition’s varied and diverse symptoms. Previous research has noted that beginning to group together symptoms and the diversity of clinical presentations of post–COVID-19 condition could help develop more tailored approaches for diagnosis and treatment.⁸ Our review explores the early approaches being considered to develop or propose subtypes based on clinical presentation of post–COVID-19 condition.

We identified at least 25 studies that are beginning to reveal that the condition may present in distinct patterns based on groups of symptoms, severity of symptoms, and certain organ systems. While specific symptoms groups range across different studies, there are common groupings related to the occurrence of some symptoms. For example, we identified at least 6 studies that proposed clusters of cardiorespiratory and neurologic symptoms.^{10,20,25,27,35,61} We also found 4 studies suggesting that a minority of people may experience symptoms that have more severe impacts to people’s health and well-being.^18-21 While the evidence seems to be in the early stages of exploring statistical associations, future work assessing and prospectively validating these potential subtypes could be helpful for designing and optimizing treatments, and tailoring models of care.

At the time of editing this report, a prospective cohort study that examined hospitalized patients 12 months after their initial diagnosis was identified.⁶² The study reported that symptoms clusters among their cohort also mapped to therapy clusters. For example, people within the symptoms cluster that reported shortness of breath and cognitive symptoms were more likely to receive physical and occupations therapy, while the cluster of those who reported higher anxiety and depression symptoms were more likely to receive anti-depressants, anti-anxiety, and psychological therapy interventions.⁶² It is likely that future studies will continue to identify other ways that assessing and identifying subtypes may guide health systems planning for delivering appropriate interventions.

Characterizing potential subtypes may help improve care pathways because having a better understanding of the condition’s varied clinical manifestations may help improve diagnostic criteria and refine existing tools used to assess people suspected of having post–COVID-19 condition. Identifying biomarkers or specific clinical features of subtypes may help reduce the challenges faced by some people in accessing appropriate care, including relevant specialists, and could inform prognosis.^36-39,42 Understanding the extent of symptoms affecting different organ systems may also provide health care decision-makers with foresight into the potential needs for specialists’ care.^10,27 Given that there may be a spectrum of needs across more than a million⁷ people who may be affected in Canada, health systems would need to ensure that the subset of people with the highest and most complex multisystem needs are provided enhanced treatment supports, while most people are provided with resources and access to their specific care needs. A growing body of evidence is also exploring whether some people may be developing previously established or recognized conditions, such as ME/CFS, pulmonary fibrosis, or post-intensive care syndrome.^40,43,46 People who develop those subtype conditions may be able to benefit from similar treatments known to be effective for those conditions.⁴⁰ Certain people who require hospitalization for acute illness may also need enhanced monitoring to ensure the presence of certain subtype conditions can be detected early and guide their care.⁴⁶ Moreover, determining appropriate classification criteria will be important as many proposed subtypes identified in the literature base show substantial overlap with each other and with other conditions.

Across the literature base, we found at least 4 studies examining whether different variants of infection may be associated with subtypes, or a differential risk of post–COVID-19 condition.^56,58-60 It is possible that, in part, particular symptoms patterns of post–COVID-19 condition may be associated different variants of infection; however, the evidence at this time remains uncertain. If these associations are validated with more rigorous study designs, it may further highlight the importance of continued testing of acute infections to help inform prognosis and long-term treatment approaches.

Although we did not explore the demographic, socioeconomic, and health-related factors of people with post–COVID-19 condition in this review, such factors could be associated with different subtypes.²⁰ Future research exploring the pathophysiology of the condition and its subtypes would need to consider these factors when providing accessible and equitable care. As many of the identified studies are at an exploratory stage, and as the evidence matures and proposed subtypes are validated, it will be important to continue to engage with patient partners and clinical experts to help inform care.

Abbreviations

ME/CFS

myalgic encephalomyelitis/chronic fatigue syndrome

MIS

multisystem inflammatory syndrome

PICS

post‐intensive care syndrome

POTS

postural orthostatic tachycardia syndrome

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

Acknowledgments: Sarah Garland, Robyn Haas

References

1.

Health Canada. Post COVID-19 condition (long COVID). 2022; https://www​.canada.ca​/en/public-health/services​/diseases/2019-novel-coronavirus-infection​/symptoms/post-covid-19-condition.html. Accessed 2022 Oct 17.

2.

National Institute for Health and Care Excellence. COVID-19 rapid guideline: managing the longterm effects of COVID-19. 2022; https://www​.nice.org​.uk/guidance/ng188/resources​/covid19-rapid-guideline-managing-the-longterm-effects-of-covid19-pdf-51035515742. Accessed 2022 Oct 17.

3.

Michelen M, Manoharan L, Elkheir N, et al. Characterising long COVID: a living systematic review. BMJ Glob Health. 2021;6(9). [PMC free article: PMC8478580] [PubMed: 34580069]

4.

Iqbal FM, Lam K, Sounderajah V, Clarke JM, Ashrafian H, Darzi A. Characteristics and predictors of acute and chronic post-COVID syndrome: a systematic review and meta-analysis. EClinicalMedicine. 2021;36:100899. [PMC free article: PMC8141371] [PubMed: 34036253]

5.

Ledford H. How common is long COVID? Why studies give different answers. Nature. 2022;606(7916):852-853. [PubMed: 35725828]

6.

Chen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B. Global prevalence of post COVID-19 condition or Long COVID: a meta-analysis and systematic review. J Infect Dis. 2022. [PMC free article: PMC9047189] [PubMed: 35429399]

7.

Statistics Canada. Long-term symptoms in Canadian adults who tested positive for COVID-19 or suspected an infection, January 2020 to August 2022. 2022; https://www150​.statcan​.gc.ca/n1/daily-quotidien​/221017/dq221017b-eng.htm. Accessed 2022 Oct 17.

8.

Deer RR, Rock MA, Vasilevsky N, et al. Characterizing long COVID: deep phenotype of a complex condition. EBioMedicine. 2021;74:103722. [PMC free article: PMC8613500] [PubMed: 34839263]

9.

Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601-615. [PMC free article: PMC8893149] [PubMed: 33753937]

10.

Yan Z, Yang M, Lai CL. Long COVID-19 syndrome: a comprehensive review of its effect on various organ systems and recommendation on rehabilitation plans. Biomedicines. 2021;9(8). [PMC free article: PMC8394513] [PubMed: 34440170]

11.

Schulz MA, Chapman-Rounds M, Verma M, Bzdok D, Georgatzis K. Inferring disease subtypes from clusters in explanation space. Sci Rep. 2020;10(1):12900. [PMC free article: PMC7393364] [PubMed: 32732917]

12.

Yin L, Duan JJ, Bian XW, Yu SC. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 2020;22(1):61. [PMC free article: PMC7285581] [PubMed: 32517735]

13.

Lopez-Leon S, Wegman-Ostrosky T, Perelman C, et al. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep. 2021;11(1):16144. [PMC free article: PMC8352980] [PubMed: 34373540]

14.

Salamanna F, Veronesi F, Martini L, Landini MP, Fini M. Post-COVID-19 syndrome: the persistent symptoms at the post-viral stage of the disease. a systematic review of the current data. Front Med (Lausanne). 2021;8:653516. [PMC free article: PMC8129035] [PubMed: 34017846]

15.

Dahl A, Zaitlen N. Genetic influences on disease subtypes. Annu Rev Genomics Hum Genet. 2020;21:413-435. [PubMed: 32873077]

16.

Thompson EJ, Williams DM, Walker AJ, et al. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records. Nat Commun. 2022;13(1):3528. [PMC free article: PMC9240035] [PubMed: 35764621]

17.

Razak F, Katz GM, Cheung AM, Herridge MS, Slutsky AS, Allen U. Understanding the post COVID-19 condition (long COVID) and the expected burden for Ontario. Science Briefs of the Ontario COVID-19 Science Advisory Table. 2021;2:44. 10.47326/ocsat.2021.02.44.1.0. Accessed 2022 Aug 12. [CrossRef]

18.

Sivan M, Parkin A, Makower S, Greenwood DC. Post-COVID syndrome symptoms, functional disability, and clinical severity phenotypes in hospitalized and nonhospitalized individuals: A cross-sectional evaluation from a community COVID rehabilitation service. J Med Virol. 2022;94(4):1419-1427. [PMC free article: PMC8661751] [PubMed: 34783052]

19.

Fernández-de-Las-Peñas C, Martín-Guerrero JD, Florencio LL, et al. Clustering analysis reveals different profiles associating long-term post-COVID symptoms, COVID-19 symptoms at hospital admission and previous medical co-morbidities in previously hospitalized COVID-19 survivors. Infection. 2022:1-9. [PMC free article: PMC9028890] [PubMed: 35451721]

20.

Ziauddeen N, Gurdasani D, O’Hara ME, et al. Characteristics and impact of Long Covid: findings from an online survey. PLoS One. 2022;17(3):e0264331. [PMC free article: PMC8903286] [PubMed: 35259179]

21.

Giszas B, Trommer S, Schusler N, et al. Post-COVID-19 condition is not only a question of persistent symptoms: structured screening including health-related quality of life reveals two separate clusters of post-COVID. Infection. 2022;22:22. [PMC free article: PMC9307219] [PubMed: 35869353]

22.

Kenny G, McCann K, O'Brien C, et al. Identification of distinct long COVID clinical phenotypes through cluster analysis of self-reported symptoms. Antivir Ther. 2021;26(1 SUPPL):10-11. [PMC free article: PMC8900926] [PubMed: 35265728]

23.

Taquet M, Dercon Q, Luciano S, Geddes JR, Husain M, Harrison PJ. Incidence, co-occurrence, and evolution of long-COVID features: a 6-month retrospective cohort study of 273,618 survivors of COVID-19. PLoS Med. 2021;18(9):e1003773. [PMC free article: PMC8478214] [PubMed: 34582441]

24.

Caspersen IH, Magnus P, Trogstad L. Excess risk and clusters of symptoms after COVID-19 in a large Norwegian cohort. Eur J Epidemiol. 2022. [PMC free article: PMC8872922] [PubMed: 35211871]

25.

Wong-Chew RM, Rodríguez Cabrera EX, Rodríguez Valdez CA, et al. Symptom cluster analysis of long COVID-19 in patients discharged from the Temporary COVID-19 Hospital in Mexico City. Ther. 2022;9:20499361211069264-20499361211069264. [PMC free article: PMC8764618] [PubMed: 35059196]

26.

Davis HE, Assaf GS, McCorkell L, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021;38. [PMC free article: PMC8280690] [PubMed: 34308300]

27.

Groff D, Sun A, Ssentongo AE, et al. Short-term and long-term rates of postacute sequelae of SARS-CoV-2 infection: a systematic review. JAMA Network Open. 2021;4(10):e2128568. [PMC free article: PMC8515212] [PubMed: 34643720]

28.

Kohler S, Gargano M, Matentzoglu N, et al. The human phenotype ontology in 2021. Nucleic Acids Res. 2021;49(D1):D1207-D1217. [PMC free article: PMC7778952] [PubMed: 33264411]

29.

Voruz P, Cionca A, Jacot de Alcantara I, et al. Functional connectivity underlying cognitive and psychiatric symptoms in post-COVID-19 syndrome: is anosognosia a key determinant? Brain Communications. 2022;4(2):fcac057. [PMC free article: PMC8956133] [PubMed: 35350554]

30.

Rubiano-Buitrago JD, Rahiran-Ramírez AF, Peña-Vargas DM, Paez-Rincon LA, Lozada-Martinez ID. Post-COVID 19 neurological syndrome: are we facing a neuropsychiatric phenotype? J Clin Neurosci. 2022;97:106-107. [PMC free article: PMC8783075] [PubMed: 35077946]

31.

Hack L, Brawer J, Zhang X, et al. Survivors of SARS-CoV-2 infection show neuropsychiatric sequelae measured by surveys, neurocognitive testing, and magnetic resonance imaging: Preliminary results. Neuropsychopharmacology. 2021;46:205-206.

32.

Grisanti SG, Garbarino S, Barisione E, et al. Neurological long-COVID in the outpatient clinic: two subtypes, two courses. J Neurol Sci. 2022;439:120315. [PMC free article: PMC9212262] [PubMed: 35717880]

33.

Anaya JM, Rojas M, Salinas ML, et al. Post-COVID syndrome. A case series and comprehensive review. Autoimmun Rev. 2021;20(11):102947. [PMC free article: PMC8428988] [PubMed: 34509649]

34.

Yim J, Tsang M, Venkataraman A, et al. Cardiac phenotyping of SARS-COV-2 in BC: a prospective ECHO study with strain imaging. Can J Cardiol. 2021;37(10 Supplement):S72-S73. [PMC free article: PMC10374389] [PubMed: 37488916]

35.

Mohanka MR, Joerns J, Lawrence A, et al. ECMO Long Haulers: a distinct phenotype of COVID-19-associated ARDS with implications for lung transplant candidacy. Transplantation. 2022;106(4):e202-e211. [PMC free article: PMC8942600] [PubMed: 35135970]

36.

Landers D, Jamal S, Turi Z, et al. Prospective analysis of vascular resistance in patients with persistent tachycardia and or dyspnea on exertion in the post acute sequela of covid-19 syndrome. (Conference abstract: American Heart Association's 2021 Scientific Sessions). Circulation. 2021;144(suppl 1):A11329.

37.

Fernandez-de-Las-Penas C, Ryan-Murua P, Rodriguez-Jimenez J, Palacios-Cena M, Arendt-Nielsen L, Torres-Macho J. Serological biomarkers at hospital admission are not related to long-term post-COVID fatigue and dyspnea in COVID-19 survivors. Respiration. 2022:1-8. [PMC free article: PMC9148886] [PubMed: 35381597]

38.

Buoite Stella A, Furlanis G, Frezza NA, Valentinotti R, Ajcevic M, Manganotti P. Autonomic dysfunction in post-COVID patients with and witfhout neurological symptoms: a prospective multidomain observational study. J Neurol. 2022;269(2):587-596. [PMC free article: PMC8359764] [PubMed: 34386903]

39.

Dennis A, Wamil M, Alberts J, et al. Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study. BMJ Open. 2021;11(3):e048391. [PMC free article: PMC8727683] [PubMed: 33785495]

40.

Yong SJ, Liu S. Proposed subtypes of post-COVID-19 syndrome (or long-COVID) and their respective potential therapies. Rev Med Virol. 2022;32(4):e2315. [PubMed: 34888989]

41.

Hama Amin BJ, Kakamad FH, Ahmed GS, et al. Post COVID-19 pulmonary fibrosis; a meta-analysis study. Ann Med Surg (Lond). 2022;77:103590. [PMC free article: PMC8983072] [PubMed: 35411216]

42.

Mirfazeli FS, Sarabi-Jamab A, Pereira-Sanchez V, et al. Chronic fatigue syndrome and cognitive deficit are associated with acute-phase neuropsychiatric manifestations of COVID-19: a 9-month follow-up study. Neurol Sci. 2022;43(4):2231-2239. [PMC free article: PMC8776380] [PubMed: 35059902]

43.

Wong TL, Weitzer DJ. Long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) - a systemic review and comparison of clinical presentation and symptomatology. Medicina. 2021;57(5):26. [PMC free article: PMC8145228] [PubMed: 33925784]

44.

Zhao S, Tran V. Postural orthostatic tachycardia syndrome. StatPearls. Treasure Island (FL): StatPearls Publishing; 2022: https://www​.ncbi.nlm​.nih.gov/books/NBK541074/. Accessed 2022 Oct 17. [PMC free article: PMC541074] [PubMed: 31082118]

45.

Raj SR, Arnold AC, Barboi A, et al. Long-COVID postural tachycardia syndrome: an American Autonomic Society statement. Clin Auton Res. 2021;31(3):365-368. [PMC free article: PMC7976723] [PubMed: 33740207]

46.

Hodgson CL, Higgins AM, Bailey MJ, et al. Comparison of 6-Month outcomes of survivors of COVID-19 versus non–COVID-19 critical illness. Am J Respir Crit Care Med. 2022;205(10):1159-1168. [PMC free article: PMC9872799] [PubMed: 35258437]

47.

Centers for Disease Control and Prevention (CDC). Multisystem inflammatory syndrome in children (MIS-C) and adults (MIS-A). 2022; https://www​.cdc.gov/mis/about.html. Accessed 2022 Oct 17.

48.

American Academy of Pediatrics. Multisystem inflammatory syndrome in children (MIS-C): interim guidance 2022; https://www​.aap.org/en​/pages/2019-novel-coronavirus-covid-19-infections​/clinical-guidance​/multisystem-inflammatory-syndrome-in-children-mis-c-interim-guidance/. Accessed 2022 Oct 17.

49.

Patel P, DeCuir J, Abrams J, Campbell AP, Godfred-Cato S, Belay ED. Clinical characteristics of multisystem inflammatory syndrome in adults: a systematic review. JAMA Network Open. 2021;4(9):e2126456-e2126456. [PMC free article: PMC8459192] [PubMed: 34550381]

50.

Sood M, Sharma S, Sood I, Sharma K, Kaushik A. Emerging evidence on multisystem inflammatory syndrome in children associated with SARS-CoV-2 infection: a systematic review with meta-analysis. SN Compr Clin Med. 2021;3(1):38-47. [PMC free article: PMC7788276] [PubMed: 33432304]

51.

Rhedin S, Lundholm C, Horne A, et al. Risk factors for multisystem inflammatory syndrome in children - a population-based cohort study of over 2 million children. Lancet Reg Health Eur. 2022;19:100443. [PMC free article: PMC9353212] [PubMed: 35945929]

52.

Son MBF, Friedman K. COVID-19: Multisystem inflammatory syndrome in children (MIS-C) management and outcome. In: Fulton DR, Kaplan SL, Sundel R, Randolph AG, TePas E, eds. UpToDate. UpToDate: Waltham (MA); 2022: https://www​.uptodate​.com/contents/covid-19-multisystem-inflammatory-syndrome-in-children-mis-c-management-and-outcome. Accessed 2022 Nov 15.

53.

Striha M, Edjoc R, Bresee N, et al. Multisystem inflammatory syndrome in children: rapid review of multisystem inflammatory syndrome in paediatrics: what we know one year later. Can Commun Dis Rep. 2021;47(11):466. [PMC free article: PMC8601103] [PubMed: 34880708]

54.

Pediatric post-acute COVID-19 syndrome (PACS) and multisystem inflammatory syndrome in children (MIS-C) – what we know so far. Toronto: Public Health Ontario; 2022 Jun: https://www​.publichealthontario​.ca/-/media​/documents/ncov/covid-wwksf​/2021/05/wwksf-children-long-term-sequelae​.pdf?sc_lang=en. Accessed 2022 Nov 15.

55.

National Institute for Health and Care Excellence. Myalgic encephalomyelitis (or encephalopathy)/chronic fatigue syndrome: diagnosis and management. (NICE Guidance NG206) 2021; https://www​.nice.org.uk/guidance/ng206. Accessed 2022 Nov 15.

56.

Magnusson K, Kristoffersen DT, Dell'Isola A, et al. Post-covid medical complaints after SARS-CoV-2 Omicron vs Delta variants - a prospective cohort study. medRxiv. 2022;25. [PMC free article: PMC9709355] [PubMed: 36450749]

57.

Varsavsky T, Graham MS, Canas LS, et al. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study. The Lancet Public Health. 2021;6(1):e21-e29. [PMC free article: PMC7785969] [PubMed: 33278917]

58.

Antonelli M, Pujol JC, Spector TD, Ourselin S, Steves CJ. Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet. 2022;399(10343):2263-2264. [PMC free article: PMC9212672] [PubMed: 35717982]

59.

Canas LS, Molteni E, Deng J, et al. Profiling post-COVID syndrome across different variants of SARS-CoV-2. medRxiv. 2022;31.

60.

Fernandez-de-Las-Penas C, Cancela-Cilleruelo I, Rodriguez-Jimenez J, et al. Associated-onset symptoms and post-COVID-19 symptoms in hospitalized COVID-19 survivors infected with Wuhan, Alpha or Delta SARS-CoV-2 variant. Pathogens. 2022;11(7):25. [PMC free article: PMC9320021] [PubMed: 35889971]

61.

Wildwing T, Holt N. The neurological symptoms of COVID-19: a systematic overview of systematic reviews, comparison with other neurological conditions and implications for healthcare services. Ther Adv Chronic Dis. 2021;12:2040622320976979. [PMC free article: PMC7970685] [PubMed: 33796241]

62.

Frontera JA, Thorpe LE, Simon NM, et al. Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: a prospective, observational study. PLoS One. 2022;17(9):e0275274. [PMC free article: PMC9521913] [PubMed: 36174032]

Disclaimer: The information in this document is intended to help Canadian health care decision-makers, health care professionals, health systems leaders, and policy-makers make well-informed decisions and thereby improve the quality of health care services. While patients and others may access this document, the document is made available for informational purposes only and no representations or warranties are made with respect to its fitness for any particular purpose. The information in this document should not be used as a substitute for professional medical advice or as a substitute for the application of clinical judgment in respect of the care of a particular patient or other professional judgment in any decision-making process. The Canadian Agency for Drugs and Technologies in Health (CADTH) does not endorse any information, drugs, therapies, treatments, products, processes, or services.

While care has been taken to ensure that the information prepared by CADTH in this document is accurate, complete, and up-to-date as at the applicable date the material was first published by CADTH, CADTH does not make any guarantees to that effect. CADTH does not guarantee and is not responsible for the quality, currency, propriety, accuracy, or reasonableness of any statements, information, or conclusions contained in any third-party materials used in preparing this document. The views and opinions of third parties published in this document do not necessarily state or reflect those of CADTH.

CADTH is not responsible for any errors, omissions, injury, loss, or damage arising from or relating to the use (or misuse) of any information, statements, or conclusions contained in or implied by the contents of this document or any of the source materials.

This document may contain links to third-party websites. CADTH does not have control over the content of such sites. Use of third-party sites is governed by the third-party website owners’ own terms and conditions set out for such sites. CADTH does not make any guarantee with respect to any information contained on such third-party sites and CADTH is not responsible for any injury, loss, or damage suffered as a result of using such third-party sites. CADTH has no responsibility for the collection, use, and disclosure of personal information by third-party sites.

Subject to the aforementioned limitations, the views expressed herein are those of CADTH and do not necessarily represent the views of Canada’s federal, provincial, or territorial governments or any third-party supplier of information.

This document is prepared and intended for use in the context of the Canadian health care system. The use of this document outside of Canada is done so at the user’s own risk.

This disclaimer and any questions or matters of any nature arising from or relating to the content or use (or misuse) of this document will be governed by and interpreted in accordance with the laws of the Province of Ontario and the laws of Canada applicable therein, and all proceedings shall be subject to the exclusive jurisdiction of the courts of the Province of Ontario, Canada.

The copyright and other intellectual property rights in this document are owned by CADTH and its licensors. These rights are protected by the Canadian Copyright Act and other national and international laws and agreements. Users are permitted to make copies of this document for non-commercial purposes only, provided it is not modified when reproduced and appropriate credit is given to CADTH and its licensors.

About CADTH: CADTH is an independent, not-for-profit organization responsible for providing Canada’s health care decision-makers with objective evidence to help make informed decisions about the optimal use of drugs, medical devices, diagnostics, and procedures in our health care system.

Funding: CADTH receives funding from Canada’s federal, provincial, and territorial governments, with the exception of Quebec.