Symptom-based clusters in people with post-COVID-19 condition (PCC)

Symptom-based clusters in people with post-COVID-19 condition (PCC) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptom-based clusters in people with post-COVID-19 condition (PCC) Charlotte Christine Kuczyk, Christoph Herrmann-Lingen, Maike Stolz, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8764088/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background. Identifying symptom clusters in post-COVID-19 condition (PCC) is crucial for developing targeted therapeutic interventions and gaining a better understanding of the underlying pathophysiological mechanisms. Therefore, the aim of this study was to identify symptom clusters based on 14 specific PCC symptoms, accounting for both symptom presence and impairment. The identified clusters were then compared with respect to sociodemographic, clinical, and psychological factors. Methods. A clinical sample of individuals with a PCC diagnosis lasting at least one year was included (final n = 1673). A two-step cluster analysis was performed to identify symptom clusters. Subsequent comparisons between clusters were performed using Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables. Results. A total of four clusters were identified: two symptom burden clusters (“ Systemic ” and “ Few Symptoms ”) and two symptom-specific clusters (“ Neurocognitive ” and “ Pain ”). Patients in the “ Systemic ” cluster reported greater psychological distress and more chronic comorbidities. Compared to the “ Pain ” cluster, the “ Neurocognitive ” cluster included more younger women. Conclusion. In PCC, different symptom clusters can be identified that differ in terms of sociodemographic, clinical, and psychological factors. Future research should then investigate biologically defined subgroups to better understand the underlying pathophysiological mechanisms. Covid-19 Post Covid-19 condition Symptoms Clusters Figures Figure 1 Introduction Although most patients recover within a few weeks after contracting COVID-19, some experience long-term consequences of COVID-19 infection, also known as post-COVID-19 condition (PCC). The definition of PCC encompasses a variety of symptoms and signs reported by patients after an initial SARS-CoV-2 infection [ 1 ]. PCC can cause a wide range of symptoms that can affect various organ systems, including the respiratory, cardiovascular, gastrointestinal, and nervous systems. In this context, more than 200 different types of symptoms have been reported [ 2 ], with the most common symptoms including fatigue, post-exertional malaise (PEM), cognitive impairment, dyspnea, sleep disturbances, and muscle and joint pain [ 3 , 4 , 5 ]. The variety and heterogeneity of symptoms pose a considerable challenge when it comes to treating PCC. Over the past few years, a number of studies have been conducted with the aim of better understanding the risk factors and pathophysiology of PCC in order to derive appropriate treatment strategies. To date, however, the pathophysiological mechanisms of PCC have not been clearly elucidated. So far, it has only been hypothesized that dysregulation of the immune system, inflammatory reactions, viral persistence, reactivation of pathogens in connection with the host microbiome, autoimmune processes, and activation of coagulation could contribute to the etiology of PCC [ 6 , 7 ]. Accordingly, no specific pharmacological treatment or other intervention has yet been established for the treatment of PCC, and the treatment approach is exclusively symptomatic. Against the backdrop of an exclusively symptom-oriented treatment approach, there are already a few studies that have investigated the extent to which certain distinguishable symptom clusters/symptom phenotypes can be identified, taking into account the multitude of possible symptoms associated with PCC. A symptom cluster is defined as several symptoms that occur together, are associated with each other, and differ from other clusters. Some studies provide evidence of individual recurring symptom clusters [ 8 , 9 ], although depending on the study, different sets of symptoms were examined over varying periods of time since infection and clustering was performed using different methods, which means that no uniform set of symptom clusters has yet been identified in the context of previous research. Most previous studies, which also aimed to identify symptom clusters, mostly considered the presence or absence of various symptoms in their analyses, while the impairment caused by the respective symptoms was addressed to a lesser extent. However, in clinical practice, it is important to consider not only the presence of symptoms but also the degree of impairment each symptom causes. Against this background, the present study aims to investigate the extent to which specific symptom clusters can be identified using a large sample of patients who are still affected by PCC in the longer term, and taking into account a broad spectrum of symptoms. These clusters are formed not only on the basis of the presence of symptoms, but also on the degree of impairment caused by the respective symptoms. In addition, the extent to which the identified clusters are associated with sociodemographic, clinical, and psychological factors is analyzed. The identification of possible symptom patterns or symptom phenotypes offers a way to divide the multitude of symptoms into structured patterns. This could create a framework for assessing symptom burden, setting treatment priorities, and developing targeted treatment approaches. In this way, there could be a shift from treating isolated symptoms to a more precise, targeted approach that focuses on the treatment of specific symptom phenotypes. Methods and Materials Individuals diagnosed with PCC were identified using health insurance data from the largest statutory health insurer in Lower Saxony, AOK Niedersachsen, which provides coverage for approximately 3 million people, which is almost 40% of the population in this region. Patient identification followed the procedures described in the VePoKaP (Care for post-COVID-19 condition in Germany from the perspectives of patients, informal caregivers and general practitioners) study protocol [ 10 ]. Individuals insured with AOK Niedersachsen who met the following inclusion criteria were invited by mail to participate in an online survey: (1) age 18 years or older with a confirmed PCC diagnosis, indicated by the ICD code U09.9! in their claims data in 2022 (outpatient billing records or a 2022 certificate of incapacity for work); 2) residence in Lower Saxony; and 3) continuous insurance coverage with AOK Niedersachsen since 2019. Patients under legal guardianship and AOK employees were excluded. A total of N = 26,438 individuals met the eligibility criteria. For budgetary reasons, N = 20,163 were randomly selected and contacted by mail. Of those invited, N = 2,159 (10.7%) provided informed consent and completed the survey. Of the 2,159 respondents, individuals who reported ongoing post-COVID symptoms were included, resulting in a final sample of N = 1,673 participants for the subsequent statistical analyses. Ethical approval was obtained from the ethics committee of Hannover Medical School (reference number 11077_BO_K_2023). All participants provided their informed consent electronically regarding study participation and use of their health insurance data. Further, an approval for the use of health insurance data according to § 75 SGB X (Social Security Code Book 10) was given by the competent supervisory authority. Measurement Symptom Impairment Patients were asked about the presence of 14 symptoms [ 11 , 12 ] to determine whether each symptom was currently present. The symptoms assessed were fatigue, long recovery phase after light exertion (PEM), brain fog, difficulty concentrating, memory difficulties, chest pain, joint pain, muscle pain/cramps, headache, heart palpitations, shortness of breath, cough, sleep disorder, loss of smell/taste. Responses were dichotomized, resulting in 14 binary variables indicating whether each symptom was currently present (1) or not (0). For all symptoms reported as currently present, the degree of impairment was assessed using the question: “How severely do the following currently existing symptoms impair you?” Responses were given on a five-point Likert scale (not at all, slightly, moderately, severely, very severely). For the subsequent main cluster analysis, 14 binary impairment variables were created by combining the impairment levels “not at all” and “slightly” into one category, and the impairment levels “moderately,” “severely,” and “very severely” into another. Other Measurement Instruments: Depression and Anxiety. Depression and anxiety were assessed using the four-item Patient Health Questionnaire (PHQ-4) [ 13 ]. The PHQ-4 is an ultra-brief self-report screening instrument comprising a two-item depression scale (PHQ-2) and a two-item anxiety scale (GAD-2). Responses are rated on a four-point Likert scale from 0 (not at all) to 3 (nearly every day), with total scores ranging from 0 to 12. Cut-offs of 6 or greater for the total scale and 3 or greater for each subscale have been recommended as cut-offs for clinically relevant symptoms. Perception of Social Participation and Social Support . Social participation was assessed using the Short Scale Measuring Perceived Social Participation (KsT-5), a five-point scale (rated on a four-point scale; 1 = disagree, 4 = agree) [ 14 ]. The responses were averaged; higher values indicate a higher level of perceived social participation. For normative values stratified by gender and age, see Berger et al. (2020) [ 14 ]. Perceived social support was assessed using the 6-item Brief Social Support Scale (BS-6). The items are rated on a 4-point Likert scale from 0 (“never”) to 3 (“always”). The responses were summed to a total score (range: 0–18), with scores ≤ 11 indicating a lack of social support. [ 15 ]. Chronic Fatigue and Post-Exertional Malaise (PEM). Chronic Fatigue was measured using the Chalder Fatigue Scale [ 16 , 17 , 18 ], which includes 11 items rated on a four-point ordinal scale (0 = less than usual, 3 = much more than usual). In the present study, a bimodal scoring procedure was applied, whereby responses coded 0 or 1 were scored as 0, and responses coded 2 or 3 were scored as 1. The resulting total score ranged from 0 to 11. A total score of 4 or more indicates severe fatigue. PEM was assessed using the validated German version of the DSQ-PEM [ 19 , 20 ]. The DSQ-PEM includes five core items specifically aimed at measuring the frequency and severity of PEM. For each of the five items, a frequency score and a severity score were summed using a 5-point Likert scale (0 = none of the time/not present, 4 = all of the time/very severe), resulting in a score ranging from 0 to 8 for each of the five items. The DSQ-PEM incorporates additional items assessing recovery time, symptom exacerbation following physical exertion, and the duration of post-exertional malaise (PEM), with a duration of ≥ 14 hours being a key diagnostic criterion. A diagnosis of PEM requires the fulfillment of multiple criteria, including this prolonged recovery phase. For a comprehensive overview, see Cotler et al., 2018 and Kuczyk et al., 2025 [ 19 , 20 ]. Medical and Sociodemographic Data . The data were derived from the participants’ questionnaires and from routine AOK data. Statistical Analyses All statistical analyses were conducted using IBM® SPSS® Statistics Version 29. Descriptive statistics were calculated using means, standard deviations (SD), and ranges for continuous variables, and frequencies and percentages for categorical variables. Two-step cluster analysis was selected for cluster formation because the algorithm is well-suited to large datasets and has the additional advantage of automatically determining the optimal number of clusters. In two-step cluster analysis, the first step involves pre-clustering observations into small subclusters, followed by a hierarchical clustering procedure that groups these subclusters into the final clusters [ 21 , 22 ]. Comparative studies have identified two-step cluster analysis as one of the most reliable methods in terms of the number of subgroups identified, the accuracy of individual classification, and the reproducibility of findings across clinical and other types of datasets [ 21 , 22 , 23 ]. A two-step cluster analysis was performed using the 14 generated symptom impairment variables to identify groups of patients with distinct symptom patterns. Subsequently, the resulting clusters were compared regarding clinical, psychological, and sociodemographic characteristics. Kruskal–Wallis tests were used to compare continuous variables across clusters, and chi-square tests were used to compare categorical variables. As a supplementary analysis, the same analyses were then performed including the symptom presence variables. Results Of the total sample of N = 1,673 patients who reported ongoing PCC symptoms, 71.6% were female. The mean age was 51.3 years (SD = 12.45). Before the infection that presumably caused the PCC, 22.5% (n = 361) of the participants stated that they had not yet received a SARS-CoV-2 vaccination, 77.5% (n = 1246) had received at least one vaccination, 26.9% (n = 432) reported having been vaccinated twice, and 43.4% (n = 698) reported having received more than 2 vaccinations (data from 1607 participants available). Additional sociodemographic and clinical characteristics are presented in Table 1 . The number and percentages of patients with different degrees of impairment of the 14 symptoms are summarized in Tables 2 and 3 . Using the symptom impairment variable based on the 14 PCC symptoms, the two-step cluster analysis identified four distinct symptom clusters (Fig. 1 , Table 4 ). Cluster 1 (“ Systemic ”) comprised 461 individuals (27.6%) and was characterized by a high prevalence of nearly all assessed symptoms. Cluster 2 (“ Neurocognitive ”) included 489 individuals (29.2%) and was characterized primarily by cognitive impairment, including difficulty concentrating, memory difficulties, and brain fog. Cluster 3 (“ Pain ”) , consisting of 419 individuals (25.0%), was distinguished by an elevated occurrence of muscle and joint pain. Cluster 4 (“ Few Symptoms ”) , with 304 individuals (18.2%), exhibited a low prevalence of most symptoms, although fatigue (20.4%) and shortness of breath (30.9%) were the most frequently reported symptoms within this group. Overall, fatigue was the most prevalent symptom and was distributed across clusters. Table 1 Sociodemographic and clinical characteristics of the total sample (N = 1673). Variables N = 1673 Range Gender, female, n (%) 1198 (71.6) Age, years, M (SD) 51.26 (12.45) 21–93 Unemployed, yes, n (%) 397 (25.7) School, ≥ 12 years, n (%) 415 (28.0) Partnership, yes, n (%) 1201 (81.0) Other chronic diseases, yes, n (%) 725 (48.8) Time since infection, months, M (SD) 22.09 (8.48) PHQ-4, M (SD) 5.00 (3.28) 0–12 PHQ-4 (cut-off score ≥ 6), n (%) 271 (16.9) PHQ-2, M (SD) 2.63 (1.75) 0–6 PHQ-2 (cut-off score ≥ 3), n (%) 274 (17.0) GAD-2, M (SD) 2.37 (1.78) 0–6 GAD-2 (cut-off score ≥ 3), n (%) 232 (14.4) KsT-5, M (SD) 2.58 (0.67) 1–4 BS-6, M (SD) 17.48 (5.26) 6–24 CFS (total), M (SD) 8.01 (3.22) 0–11 CFS ≥ 4, n (%) 1448 (87.6) DSQ-PEM, M (SD) 2.91 (1.99) 0–5 PEM, cut-off ≥ 14 hours, n, (%) 337 (20.6) Number of specialists consulted , M (SD) 2.76 (1.60) 0–8 Number of different therapies for PCC, M (SD) 2.27 (2.74) 0–23 Use of psychological therapy, n, (%) 479 (30.2) Use of active therapy, n, (%) 407 (25.7) Sick days due to PCC, M (SD) 122.76 (209.59) 0-1377 ICU due to infection n, (%) 51 (3.05) Improvement in PCC symptoms over time, yes, n, (%) 990 (61.5) Had received at least 1 vaccination prior to infection, n, (%) 1246 (77.5) Had received at least 1 vaccination at the time of the survey, n, (%) 1628 (97.7) Notes. PHQ-4: Patient Health Questionnaire-4. A total score of ≥ 6 is considered the cut-off for clinically relevant symptomatology.PHQ-2: Patient Health Questionnaire-2, and GAD-2: Generalized Anxiety Disorder-2. A total score of ≥ 3 is considered the cut-off for clinically relevant psychological distress. KsT-5: Short Scale for Assessing Perceived Social Participation. BS-6: Brief Social Support Scale (BS6). CFS: Chalder Fatigue Scale. DSQ-PEM: DePaul Symptom Questionnaire – Post-Exertional Malaise. Psychological therapy includes relaxation therapy, psychological counseling, and psychotherapy. Active therapy includes rehabilitative sports and functional training/sports therapy. Table 2 Number and percent of participants with different degrees of symptom impairment of the 14 PCC symptoms Degree of Impairment N = 1673 Symptom not present Not at all Slight Medium Severe Very Severe Fatigue 382 (22.8) 2 (0.1) 27 (1.6) 360 (21.5) 550 (32.9) 352 (21.0) PEM 714 (42.7) 1 (0.1) 21 (1.3) 241 (14.4) 414 (24.7) 282 (16.9) Brain fog 999 (59.7) 4 (0.2) 30 (1.8) 249 (14.9) 247 (14.8) 144 (8.6) Difficulty concentrating 678 (40.5) - 37 (2.2) 348 (20.8) 396 (23.7) 214 (12.8) Memory difficulties 775 (46.3) 1 (0.1) 54 (3.2) 301 (18.0) 357 (21.3) 185 (11.1) Chest Pain 1329 (79.4) 4 (0.2) 39 (2.3) 168 (10.0) 101 (6.0) 32 (1.9) Joint pain 876 (52.4) 3 (0.2) 38 (2.3) 241 (14.4) 310 (18.5) 205 (12.3) Muscle pain/cramps 940 (56.2) 1 (0.1) 43 (2.6) 221 (13.2) 268 (16.0) 200 (12.0) Headache 1074 (64.2) 41 (2.5) 204 (12.2) 226 (13.5) 128 (7.7) Heart palpitations 1141 (68.2) 5 (0.3) 62 (3.7) 236 (14.1) 165 (9.9) 64 (3.8) Shortness of breath 692 (41.4) 3 (0.2) 54 (3.2) 341 (20.4) 383 (22.9) 200 (12.0) Cough 1170 (69.9) 1 (0.1) 58 (3.5) 203 (12.1) 167 (10.0) 74 (4.4) Sleep disorder 768 (45.9) - 26 (1.6) 236 (14.1) 363 (21.7) 280 (16.7) Loss of smell/taste 1420 (84.9) 2 (0.1) 31 (1.9) 74 (4.4) 68 (4.1) 78 (4.7) Notes . PEM = post-exertional malaise Table 3 Percent of participants with moderate, severe, and very severe impairment based on the 14 PCC symptoms and mean impairment rating (1–5) (N = 1673). N = 1673 Percent Mean impairment Fatigue 77.2 3.95 PEM 57.3 4.00 Brain fog 40.3 3.74 Difficulty concentrating 59.5 3.79 Memory difficulties 53.7 3.75 Chest Pain 20.6 3.34 Joint pain 47.6 3.85 Muscle pain/cramps 43.8 3.85 Headache 35.8 3.74 Heart palpitations 31.8 3.42 Shortness of breath 58.6 3.74 Cough 30.1 3.51 Sleep disorder 54.1 3.99 Loss of smell/taste 15.1 3.75 Note : For all symptoms reported as currently present, the degree of impairment was assessed using the question: “How severely do the following currently existing symptoms impair you?” Responses were given on a five-point Likert scale (not at all, slightly, moderately, severely, very severely) Table 4 Four clusters resulting from the two-step cluster analysis based on the symptom-impairment variables, with the frequency n (%) of the 14 symptoms in the total sample and within each cluster. Symptoms are sorted in descending order of importance for cluster formation. Cluster Symptom Variables n (%) Total n = 1673 Systemic n = 461 Neuro-cognitive n = 489 Pain n = 419 Few Symptoms n = 304 Difficulty concentrating 958 (57.3) 434 (94.1) 461 (94.3) 50 (11.9) 13 (4.3) Memory difficulties 843 (50.4) 403 (87.4) 402 (82.2) 21 (5.0) 17 (5.6) Muscle pain/cramps 689 (41.2) 437 (94.8) 43 (8.8) 205 (48.9) 4 (1.3) Joint Pain 756 (45.2) 434 (94.1) 65 (13.3) 244 (58.2) 13 (4.3) Brain fog 640 (38.3) 300 (65.1) 314 (64.2) 20 (4.8) 6 (2.0) Fatigue 1262 (75.4) 445 (96.5) 410 (83.8) 345 (82.3) 62 (20.4) PEM 937 (56.0) 389 (84.4) 275 (56.2) 251 (59.9) 22 (7.2) Sleep disorder 879 (52.5) 358 (77.7) 254 (51.9) 231 (55.1) 36 (11.8) Headache 558 (33.4) 269 (58.4) 156 (31.9) 117 (27.9) 16 (5.3) Chest Pain 301 (18.0) 175 (38.0) 38 (7.8) 79 (18.9) 9 (3.0) Shortness of breath 924 (55.2) 335 (72.7) 233 (47.6) 262 (62.5) 94 (30.9) Heart Palpitations 465 (27.8) 212 (46.0) 101 (20.7) 121 (28.9) 31 (10.2) Cough 444 (26.5) 182 (39.5) 94 (19.2) 131 (31.3) 37 (12.2) Loss of smell/taste 220 (13.2) 96 (20.8) 52 (10.6) 38 (9.1) 34 (11.2) The FIT indices, the importance of the individual symptoms, and the cluster quality (which was fair) are depicted in Additional file 1: Supplementary Table 1 and Supplementary Figs. 1 and 2. Using the presence or absence of the 14 PCC symptoms, the cluster structure closely mirrored that of the symptom impairment analysis, yielding: Cluster 1 (“Systemic”) (n = 426; 25.5%), Cluster 2 (“Neurocognitive”) (n = 447; 26.7%), Cluster 3 (“Pain”) (n = 369; 22.1%), and Cluster 4 (“Few Symptoms”) (n = 431; 25.8%) ( Additional file 1: supplementary Tables 2–5 and supplementary Figs. 3–5 ). Comparison of Clusters on Sociodemographic, Psychological, and Clinical Variables Participants in the “Systemic” cluster reported the most pathological values in basically all patient-reported outcome measures (PHQ-4, BS-6, KsT-5, CFS, PEM) compared to the other 3 clusters, and had consulted more specialists and had more sick days due to PCC than the participants in the other clusters. Patients in the “ Systemic ” cluster were more often unemployed, reported more comorbid chronic health conditions and were less likely to report an improvement of their symptoms over time (43.3%). A higher percentage of participants in this cluster reported not having been vaccinated before the infection that presumably caused the PCC (25.9%). For detailed statistical results, see Tables 5 and 6 and Additional file 1: Supplementary Table 6 . Table 5 Comparison of the symptom-impairment clusters regarding sociodemographic, clinical, and psychological continuous variables. Variables Cluster Kruskal-Wallis-Text df = 3 M (SD) Systemic n = 461 Neurocognitive n = 489 Pain n = 419 Few Symptoms n = 304 Age (years) 52.59 (10.66) a 49.15 (12.63) b 52.73 (12.60) a 50.62 (13.91) ab H = 21.48 p < .001 PHQ-4 6.62 (3.22) a 5.88 (3.04) b 4.00 (2.65) c 2.38 (2.41) d H = 380.53 p < .001 PHQ-2 3.48 (1.71) a 3.08 (1.61) b 2.18 (1.45) c 1.19 (1.27) d H = 382.05 p < .001 GAD-2 3.14 (1.75) a 2.81 (1.72) a 1.82 (1.48) b 1.19 (1.43) c H = 291.71 p < .001 BS-6 16.83 (5.09) a 17.36 (5.11) ab 18.00 (5.17) b 18.03 (5.77) b H = 18.22 p < .001 KsT-5 11.68 (3.32) a 12.57 (3.28) b 13.52 (3.06) c 14.61 (3.10) d H = 133.42 p < .001 CFS (total) 10.18 (1.58) a 9.28 (2.08) b 7.04 (2.68) c 3.94 (3.04) d H = 765.09 p < .001 CFS (physical) 17.17 (2.95) a 15.07 (3.30) b 13.78 (3.40) c 9.40 (3.44) d H = 619.51 p < .001 CFS (psychological) 8.98 (2.03) a 8.53 (2.11) b 5.38 (2.09) c 4.66 (2.04) d H = 753.79 p < .001 DSQ-PEM 4.29 (1.29) a 3.22 (1.81) b 2.51 (1.80) c 0.76 (1.31) d H = 588.33 p < .001 Number of different therapies for PCC 3.10 (3.12) a 2.32 (2.59) b 2.04 (2.60) b 1.21 (2.04) c H = 86.24 p < .001 Number of specialists consulted 3.35 (1.73) a 2.86 (1.57) b 2.56 (1.46) c 1.92 (1.15) d H = 139.10 p < .001 Time since infection, months 23.29 (9.06) a 22.40 (8.62) ab 21.14 (7.87) b 21.06 (7.90) b H = 18.94 p < .001 Sick days due to PCC 201.88 (263.93) a 133.94 (223.74) b 94.78 (163.52) b 35.12 (76.30) c H = 133.34 p < .001 Notes. Post-hoc test: values with different superscripts are significantly different. PHQ-4: Patient Health Questionnaire-4; PHQ-2: Patient Health Questionnaire-2; GAD-2: Generalized Anxiety Disorder-2; BS-6: Brief Social Support Scale. KsT-5: Short Scale Measuring Perceived Social Participation; CFS: Chalder Fatigue-Scale; DSQ-PEM: DePaul Symptom Questionnaire – Post-Exertional Malaise. Table 6 Comparison of the symptom-impairment clusters regarding sociodemographic, clinical, and psychological categorical variables. Variables Cluster Chi-square test, df = 3 n (%) Systemic n = 461 Neurocognitive n = 489 Pain n = 419 Few Symptoms n = 304 Gender, female 342 (74.2) 358 (73.2) 286 (68.3) 212 (69.7) χ 2 = 4.96 p = 0.175 Unemployed 143 (33.4) 102 (22.2) 89 (22.8) 63 (23.5) χ 2 = 23.46 p < .001 School ≥ 12 years. 99 (24.0) 137 (31.1) 94 (25.3) 85 (33.5) χ 2 = 10.51 p = 0.015 Partnership 330 (79.5) 350 (79.2) 317 (85.2) 204 (80.3) χ 2 = 5.91 p = 0.116 Other chronic diseases 244 (58.8) 199 (45.0) 184 (49.2) 98 (38.4) χ 2 = 30.11 p < .001 PHQ- 4 ≥ 6 134 (49.4) 103 (38.0) 27 (10.0) 7 (2.6) χ 2 = 233.67 p < .001 DSQ-PEM: Dead, heavy feeling after starting to exercise 428 (94.1) 351 (72.4) 279 (68.0) 65 (22.8) χ 2 = 418.31 p < .001 DSQ-PEM: Next day soreness or fatigue after non-strenuous, everyday activities 388 (85.3) 274 (56.6) 195 (47.6) 30 (10.5) χ 2 = 404.51 p < .001 DSQ-PEM: Mentally tired after the slightest effort 375 (82.4) 346 (71.3) 132 (32.2) 33 (11.6) χ 2 = 491.86 p < .001 DSQ-PEM: Minimum exercise makes you physically tired 385 (84.6) 296 (61.0) 208 (50.7) 46 (16.1) χ 2 = 345.84 p < .001 DSQ-PEM: Physically drained or sick after mild activity 377 (82.9) 297 (61.2) 217 (52.9) 43 (15.1) χ 2 = 334.94 p < .001 PEM recovery time ≥ 14 hours 168 (49.9) 89 (26.4) 64 (19.0) 16 (4.7) χ 2 = 121.00 p < .001 CFS ≥ 4 452 (31.2) 474 (32.7) 372 (25.7) 150 (10.4) χ 2 = 462.71 p < .001 Use of psychological therapy 180 (40.9) 160 (34.2) 98 (24.6) 41 (14.7) χ 2 = 64.95 p < .001 Use of active therapy 145 (33.0) 127 (27.1) 92 (23.1) 43 (15.5) χ 2 = 29.35 p < .001 Inpatient admission due to post-COVID 48 (10.7) 46 (9.6) 38 (9.4) 15 (5.3) χ 2 = 6.65 p = .084 ICU due to infection 19 (4.1) 11 (2.2) 12 (2.9) 9 (3.0) χ 2 = 5.50 p = .139 Improvement in PCC symptoms over time, yes 192 (43.3) 259 (64.0) 310 (65.0) 229 (80.6) χ 2 = 109.12 p < .001 Had received at least 1 SARS-CoV-2 vaccination prior to infection 326 (74.1) 328 (81.0) 356 (75.9) 236 (80.5) χ 2 = 8.01 p = .046 Had received at least 1 SARS-CoV-2 vaccination at the time of the survey 448 (97.8) 410 (97.9) 474 (97.3) 296 (98.0) χ 2 = 0.50 p = .919 Notes. CFS: Chalder-Fatigue-Scale; DSQ-PEM: DePaul Symptom Questionnaire – Post-Exertional Malaise. Psychological therapy includes relaxation therapy, psychological counseling, and psychotherapy. Active therapy includes rehabilitative sports and functional training/sports therapy. Participants in the “Few Symptoms” cluster were less likely to report other chronic conditions, had the fewest sick days due to PCC, and showed the lowest levels of psychological distress among all clusters. 80.6% (N = 192) of patients in the “ Few Symptoms ” cluster reported that their PCC symptoms had already improved over time, The results of the participants in the “Pain” and “Neurocognitive” clusters lay somewhere in between; however, the participants in the “ Neurocognitive” cluster were younger and reported more distress compared to the participants in the “ Pain ” cluster. Discussion This study aimed to identify symptom clusters in the long-term course after SARS-CoV-2 infection using a large sample of patients with PCC. Data were obtained from the largest health insurance providers in Lower Saxony, ensuring that all patients included had a medically confirmed PCC diagnosis. The identified clusters were subsequently compared with regard to sociodemographic, clinical, and psychological characteristics. The following four symptom impairment clusters emerged: Systemic (Cluster 1), Neurocognitive (Cluster 2), Pain (Cluster 3), and Few Symptoms (Cluster 4). Clusters 1 and 4 differed primarily in overall symptom severity, with this distinction between high- and low-symptom subgroups corresponding to previous findings from symptom-based cluster analyses in ME/CFS [ 24 , 25 , 26 , 27 ]. Participants in the “Few Symptom” cluster had fewer comorbidities overall and showed lower psychological distress. Beyond these general severity clusters, two more specific symptom clusters emerged. Participants in Cluster 2 , which was the largest cluster, primarily exhibited difficulty concentrating, brain fog, and memory difficulties as their leading symptoms, which is why this cluster was described as a “ Neurocognitive” cluster. Similar neurocognitive subgroups have been identified in earlier studies, e.g., by Grafström et al. (2025) [ 9 ] six months after infection or by Moniz et al. (2024) [ 28 ] at 9- and 12-month post-infection. In our study, the mean time since infection was 22 months, suggesting that this cluster represents a stable, long-term pattern. Cluster 3 was defined by the predominance of muscle pain/cramps and joint pain, and was therefore defined as a “Pain” cluster, consistent with previous reports of pain-focused subgroups in ME/CFS and PCC [ 25 , 26 , 29 ]. As expected, substantial symptom overlap occurred between the clusters. Fatigue and PEM - central symptoms of ME/CFS - were present in all clusters, although only 5 to 50% in each cluster reported that post-exertional malaise lasted more than 14 hours after activities. The analysis incorporating symptom presence variables yielded essentially the same clusters as described earlier, but with a different distribution/varying group size. In particular, the low-symptom cluster was significantly larger in the symptom presence cluster analysis. A supplementary exploratory examination of individuals whose cluster membership differed between the two cluster analyses revealed the following pattern: Individuals with a low overall symptom burden were more frequently assigned to the low-symptom cluster when the presence variables were taken into account. However, if these individuals reported at least moderate to severe impairment in a single symptom of a symptom area (pain, neurocognitive), they were more likely to be assigned to a symptom-specific cluster when the impairment variables were included. Compared with the “ Pain ” cluster, the “ Neurocognitive ” cluster tended to include younger women with higher levels of psychological distress. These observations are consistent with findings from Matias-Guiu et al. (2023) [ 30 ], who reported more frequent cognitive impairments among younger individuals with PCC. It must be noted that the survey referred to an early stage of the COVID-19 pandemic, when vaccinations may not yet have been widely available or available for only a short period. Thus, the non-vaccination rate in our sample was still high, with 22.5% not having received any vaccination prior to the infection that presumably caused the PCC. Participants in the “ Systemic ” cluster had the highest non-vaccination rate (25.9%). In comparison, among participants who reported no longer having any PCC symptoms, only 9.7% (N = 45) had not been vaccinated against SARS-CoV-2 prior to the infection. These results suggest that vaccination may protect against the development or persistence of PCC symptoms. Several cohort studies and meta-analyses have reported a lower incidence of PCC among individuals who received at least one dose of a COVID-19 vaccine [ 31 , 32 ]. One study attempted to define clusters based on biological changes rather than subjective symptoms. Asprusten et al. (2021) [ 33 ] identified clusters in adolescents with ME/CFS using biomarkers across five domains (endocrine, inflammatory, cardiovascular, pressure pain threshold, and cognitive). However, substantial overlap between clusters limited the identification of distinct pathophysiological subtypes. While biomarker-based clustering in ME/CFS has proven challenging, convergent evidence from mechanistic studies in PCC —including viral persistence, autoimmunity, and neurodegeneration — now suggests pathway-specific biological explanations for symptom-based clusters. We aimed to match our clusters with recent findings from basic research to shed light on possible explanations: Importantly, this study was not designed to investigate the underlying biological mechanisms of PCC, and the following considerations remain speculative. However, integrating our symptom-based findings with current mechanistic research may help generate testable hypotheses regarding the pathophysiology of different PCC phenotypes. Cluster 1 (“ Systemic ”) might represent a state of persistent viral antigen-driven pathology. The multi-organ involvement and severe symptom burden observed in this cluster align with evidence of ongoing systemic immune dysregulation: sustained activation of JAK-STAT and IL-6 pathways, coupled with immunothrombotic cascades that persist months post-infection [ 34 , 35 ]. Critically, this chronic inflammation does not require active viral replication. We recently demonstrated in animal models that intravenous administration of the SARS-CoV-2 Spike (S1) protein alone induces widespread neuroinflammation, glial activation, and alpha-synuclein accumulation [ 36 ], supporting a “protein-as-pathogen” mechanism where circulating viral components drive multisystem injury. The profound fatigue characterizing this cluster might stem from metabolic dysregulation, including mitochondrial dysfunction and immune exhaustion marked by persistent lymphopenia [ 37 , 38 ]. Cluster 2 (“ Neurocognitive ”) presents a phenotype that mirrors early neurodegenerative disease. Structurally, patients in this cluster might exhibit hippocampal iron deposition, cortical thinning, and astrocytic damage on neuroimaging [ 39 ]. The preclinical work of some co-authors of this manuscript provides a cellular mechanism: SARS-CoV-2 infection initiates persistent accumulation of alpha-synuclein and Tau in hippocampal and cortical regions, establishing a proteinopathy that outlasts viral clearance [ 40 ]. The specific symptom of “brain fog” may be mechanistically linked to dysfunction of parvalbumin-positive (PV+) interneurons, which generate the gamma oscillations critical for cognitive focus. These interneurons are significantly altered in post-COVID models [ 41 ], potentially explaining the attentional deficits that define this cluster. The female predominance observed in this cluster aligns with clinical evidence of heightened inflammatory T-cell responses in women [ 42 ] and is directly paralleled in animal models, where females exhibit more severe biphasic neuroinflammatory responses, greater alpha-synuclein burden, and more pronounced PV+ interneuron dysfunction than males [ 36 , 41 ]. Cluster 3 (“ Pain ”) appears mechanistically distinct from systemic inflammation, possibly driven instead by targeted autoimmune and peripheral nervous system pathology. Passive transfer experiments provide causal evidence: IgG from patients with Long COVID pain induces small-fiber neuropathy and mechanical hypersensitivity in mice by directly binding to sensory neurons [ 43 , 44 ]. Additionally, viral peptides can directly activate spinal TLR4 signaling pathways, triggering central sensitization and sustained nociception [ 45 ]. This dual mechanism, autoantibody-mediated peripheral neuropathy combined with central sensitization, provides a biological framework for the chronic pain phenotype. Cluster 4 (“ Few Symptoms ”) may represent successful immune resolution. This favorable outcome may be mediated by tolerogenic immune profiles, particularly the emergence of IgG4 antibodies, which prevent the chronic inflammatory and autoimmune cascades observed in more severe clusters [ 46 , 47 ]. These findings suggest that the four clusters might represent biologically distinct PCC phenotypes driven by separable mechanisms: systemic viral antigen persistence and metabolic dysfunction ( Cluster 1 ), neurodegeneration and circuit dysfunction with sex-specific vulnerability ( Cluster 2 ), peripheral autoimmunity and central sensitization ( Cluster 3 ), and successful immune regulation ( Cluster 4 ). This translation from clinical phenotyping to molecular mechanisms supports the development of biologically defined subgroups and provides a framework for precision medicine approaches, where cluster assignment could guide targeted immunomodulatory, neuroprotective, or pain-specific interventions. Strengths and Limitations The present study offers several strengths. First, its large sample size provides greater robustness than previous studies with smaller sample sizes. Second, patient selection was based on physician confirmed PCC diagnoses. Third, the assessment of both symptom prevalence and impairment facilitated a more realistic and nuanced characterization of symptom burden and, in turn, of cluster differentiation. It is important to note that our study has certain limitations that must be considered. We had a 10.7% response rate of those who were mailed the questionnaire, which might have introduced any kind of bias. Even though our “ Few Symptoms” cluster was the smallest, individuals with a very high neurocognitive symptom burden might have been less likely to participate. Conversely, people whose symptoms had subsided in the meantime may also have been less willing to participate. Overall, therefore, selection bias in both directions can be assumed, suggesting that predominantly patients with moderate symptoms were included, although no definitive statement can be made on this. Also, similar to earlier studies, the participants predominantly consisted of females (71%), although it has been recognized that the prevalence of PCC is higher in women compared to men. The cluster quality was fair, and there was a high overlap of symptom prevalence and symptom impairment between clusters. We lack pre-infection data on symptom burden, and we could not provide longitudinal data. We did not correct for multiple testing due to the explorative nature of this research; however, the differences between clusters were big, with p-values well below the threshold of significance. Overall, this study’s findings extend the current literature on symptom-based cluster analyses in PCC by demonstrating the presence of both general clusters of symptom burden and specific subgroups, particularly pain and neurocognitive clusters. Fatigue and PEM in patients occurred as central symptoms across nearly all clusters, underscoring the significant overlap between PCC and ME/CFS symptomatology. Classifying PCC patients by severity and characteristic symptom patterns could help develop more targeted, individually tailored therapeutic approaches. Future research should aim to define biologically defined subgroups with greater precision to clarify the underlying pathophysiological mechanisms of different symptom clusters. Declarations Ethics approval and consent to participate Ethics approval was obtained from the Ethics Committee of Hannover Medical School (reference number 11077_BO_K_2023). Further, an approval for the use of health insurance data according to § 75 SGB X (Social Security Code Book 10) was given by the competent supervisory authority. The study was conducted in accordance with the principles of the Declaration of Helsinki. All potential participants are informed that their participation in the study is voluntary and they have the right to refuse or withdraw at any time without any disadvantage. Participation in the study was based on electronic informed consent. Consent for publication All authors approved the final draft of the manuscript and are aware that this paper is submitting to this journal Competing interests The authors declare no competing interests Funding The present study is supported by the COVID-19-Research Network Lower Saxony (COFONI), through funding from the Ministry of Science and Culture of Lower Saxony in Germany (14-76403-184). The funder played no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript. Authors' contributions MS, CKr, BB, JTS, CHL and MdZ designed the VEPOKAP study and are responsible for funding acquisition. CKu and MdZ were responsible for data analysis and drafted the manuscript. MN, AD, CKä and FR contributed substantially to writing the manuscript. All authors critically reviewed and revised the manuscript and approved the final version. Acknowledgements No acknowledgements Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Katz GM, Bach K, Bobos P, Cheung A, Décary S, Goulding S, Herridge MS, McNaughton CD, Palmer KS, Razak FA, Zhang B, Quinn KL. Understanding How Post-COVID-19 Condition Affects Adults and Health Care Systems. JAMA Health Forum. 2023;4(7):e231933. 10.1001/jamahealthforum.2023.1933 . Davis HE, McCorkell L, Vogel JM, Topol EJ, Long COVID. major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133–146. 10.1038/s41579-022-00846-2 . Erratum in: Nat Rev Microbiol. 2023;21(6):408. doi: 10.1038/s41579-023-00896-0. Boaventura P, Macedo S, Ribeiro F, Jaconiano S, Soares P. Post-COVID-19 Condition: Where Are We Now? Life (Basel). 2022;12(4):517. 10.3390/life12040517 Alghamdi SA, Alfares MA, Alsulami RA, Alghamdi AF, Almalawi AM, Alghamdi MS, Hazazi HA. Post-COVID-19 Syndrome: Incidence, Risk Factor, and the Most Common Persisting Symptoms. Cureus. 2022;14(11):e32058. 10.7759/cureus.32058 . Domingo FR, Waddell LA, Cheung AM, Cooper CL, Belcourt VJ, Zuckermann AM, Corrin T, Ahman R, Boland L, Laprise C, Idzerda L, Khan A, Morissette K, Garcia AJ. Prevalence of long-term effects in individuals diagnosed with COVID-19: an updated living systematic review. MedRxiv, 2021–06. 10.1101/2021.06.03.21258317 Castanares-Zapatero D, Chalon P, Kohn L, Dauvrin M, Detollenaere J, Maertens de Noordhout C, Primus-de Jong C, Cleemput I, Van den Heede K. Pathophysiology and mechanism of long COVID: a comprehensive review. Ann Med. 2022;54(1):1473–87. 10.1080/07853890.2022.2076901 . Batiha GE, Al-Kuraishy HM, Al-Gareeb AI, Welson NN. Pathophysiology of Post-COVID syndromes: a new perspective. Virol J. 2022;19(1):158. 10.1186/s12985-022-01891-2 . Kuodi P, Gorelik Y, Gausi B, Bernstine T, Edelstein M. Characterization of post-COVID syndromes by symptom cluster and time period up to 12 months post-infection: A systematic review and meta-analysis. Int J Infect Dis. 2023;134:1–7. 10.1016/j.ijid.2023.05.003 . Grafström T, Barros GWF, Persson IL, Sundh J, Forsell MNE, Ahlm C, Månsson E, Tevell S, Lind A, Normark J, Cajander S. Post COVID-19 condition phenotypes: A prospective cohort study identifying four symptom clusters and their impact on long-term outcomes. J Infect Public Health. 2025;18(12):102994. 10.1016/j.jiph.2025.102994 . Brinkmann M, Stolz M, Herr A, Herrmann-Lingen C, Koch I, Müller C, Müller F, Sekanina U, Stahmeyer JT, de Zwaan M, Krauth C, Schneider N. Care for post-COVID-19 condition in Germany from the perspectives of patients, informal caregivers and general practitioners: Study protocol for a mixed methods study. PLoS ONE. 2024;19(12):e0316335. 10.1371/journal.pone.0316335 . Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV, WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22(4):e102–7. 10.1016/S1473-3099(21)00703-9 . World Health Organization (WHO) clinical case definition working group on post COVID-19 condition. A clinical case definition of post COVID-19 condition by a Delphi consensus. World Health Organization 2021. WHO/2019-nCoV/ Post_COVID-19_condition/Clinical_case_definition/2021.1 Löwe B, Wahl I, Rose M, Spitzer C, Glaesmer H, Wingenfeld K, Schneider A, Brähler E. A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord. 2010;122(1–2):86–95. 10.1016/j.jad.2009.06.019 . Berger U, Kirschner H, Muehleck J, Gläser A, Werner B, Kurz M, Schwager S, Wick K, Strauß B. Kurz-Skala zur Erfassung wahrgenommener sozialer Teilhabe (KsT-5): faktorielle Struktur, interne Konsistenz, inhaltliche und konvergente Validität sowie Normwerte in einer repräsentativen Stichprobe [Short Scale Measuring Perceived Social Participation: Factorial Structure, Internal Consistency, Content Validity, Convergent Validity and Standard Values in a Representative German Sample]. Psychother Psychosom Med Psychol. 2020;70(9–10):396–404. 10.1055/a-1088-1354 . German. Beutel ME, Brähler E, Wiltink J, Michal M, Klein EM, Jünger C, Wild PS, Münzel T, Blettner M, Lackner K, Nickels S, Tibubos AN. Emotional and tangible social support in a German population-based sample: Development and validation of the Brief Social Support Scale (BS6). PLoS ONE. 2017;12(10):e0186516. 10.1371/journal.pone.0186516 . Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace EP. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147–53. 10.1016/0022-3999(93)90081-p . Martin A, Staufenbiel T, Gaab J, Rief W, Brähler E. Messung chronischer Erschöpfung–Teststatistische Prüfung der Fatigue Skala (FS). Z für klinische Psychologie und Psychother. 2010;9(1):33–44. 10.1026/1616-3443/a000010 . Krakau L, Wicke F, Häuser W, Beutel ME, Brähler E, Hettich-Damm N. Chronic fatigue: psychometric properties and updated norm values of the Chalder fatigue scale in a cross-sectional sample representative of the German population. Ann Med. 2025;57(1):2524087. 10.1080/07853890.2025.2524087 . Cotler J, Holtzman C, Dudun C, Jason LA. A Brief Questionnaire to Assess Post-Exertional Malaise. Diagnostics (Basel). 2018;8(3):66. 10.3390/diagnostics8030066 . Kuczyk C, Nöhre M, Herrmann-Lingen C, Stolz M, Krauth C, Brähler E, Jason LA, de Zwaan M. Reliability and validity of the German version of the DePaul Symptom Questionnaire Post-Exertional Malaise (DSQ-PEM). Front Psychiatry. 2025;16:1647040. 10.3389/fpsyt.2025.1647040 . Gelbard R, Goldman O, Spiegler I. Investigating diversity of clustering methods: an empirical comparison. Data Knowl Eng. 2007;63(1):155–66. doi: 10 1016/j.datak 2007 01 002. Kent P, Jensen RK, Kongsted A. A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold, and SNOB. BMC Med Res Methodol. 2014;14(1):113. doi:101186/1471-2288-14-113. Bacher J, Wenzig K, Vogler M. (2004). SPSS TwoStep Cluster - a first evaluation. (2., corr. ed.) (Arbeits- und Diskussionspapiere / Universität Erlangen-Nürnberg, Sozialwissenschaftliches Institut, Lehrstuhl für Soziologie, 2004-2). Nürnberg: Universität Erlangen-Nürnberg, Wirtschafts- und Sozialwissenschaftliche Fakultät, Sozialwissenschaftliches Institut Lehrstuhl für Soziologie. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-327153 Hickie I, Lloyd A, Hadzi-Pavlovic D, Parker G, Bird K, Wakefield D. Can the chronic fatigue syndrome be defined by distinct clinical features? Psychol Med. 1995;25(5):925–35. 10.1017/s0033291700037417 . Collin SM, Nikolaus S, Heron J, Knoop H, White PD, Crawley E. Chronic fatigue syndrome (CFS) symptom-based phenotypes in two clinical cohorts of adult patients in the UK and the Netherlands. J Psychosom Res. 2016;81:14–23. 10.1016/j.jpsychores.2015.12.006 . Erratum in: J Psychosom Res. 2023;168:111324. doi: 10.1016/j.jpsychores.2023.111324. Collin SM, Heron J, Nikolaus S, Knoop H, Crawley E. Chronic fatigue syndrome (CFS/ME) symptom-based phenotypes and 1-year treatment outcomes in two clinical cohorts of adult patients in the UK and the Netherlands. J Psychosom Res. 2018;104:29–34. 10.1016/j.jpsychores.2017.11.007 . Vaes AW, Van Herck M, Deng Q, Delbressine JM, Jason LA, Spruit MA. Symptom-based clusters in people with ME/CFS: an illustration of clinical variety in a cross-sectional cohort. J Transl Med. 2023;21(1):112. 10.1186/s12967-023-03946-6 . Moniz M, Ruivinho C, Goes AR, Soares P, Leite A. Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test. BMC Infect Dis. 2024;24(1):1001. 10.1186/s12879-024-09896-8 . Erratum in: BMC Infect Dis. 2024;24(1):1178. doi: 10.1186/s12879-024-10086-9. Mfouth Kemajou P, Besse-Hammer T, Lebouc C, Coppieters Y. Cluster analysis identifies long COVID subtypes in Belgian patients. Biol Methods Protoc. 2024;9(1):bpae076. 10.1093/biomethods/bpae076 . Matias-Guiu JA, Herrera E, González-Nosti M, Krishnan K, Delgado-Alonso C, Díez-Cirarda M, Yus M, Martínez-Petit Á, Pagán J, Matías-Guiu J, Ayala JL, Busch R, Hermann BP. Development of criteria for cognitive dysfunction in post-COVID syndrome: the IC-CoDi-COVID approach. Psychiatry Res. 2023;319:115006. 10.1016/j.psychres.2022.115006 . European Centre for Disease Prevention and Control. Does COVID-19 vaccination reduce the risk and duration of post-COVID-19 condition? Rapid systematic literature review. Stockholm: ECDC; 2025. 10.2900/5489226 . Guimarães GN, Brunetti NS, De Lima DG, Proenca-Modena JL, Farias AS. Vaccination and COVID-19: impact on long-COVID. Front Immunol. 2025;16:1686572. 10.3389/fimmu.2025.1686572 . Asprusten TT, Sletner L, Wyller VBB. Are there subgroups of chronic fatigue syndrome? An exploratory cluster analysis of biological markers. J Transl Med. 2021;19(1):48. 10.1186/s12967-021-02713-9 . Aid M, Boero-Teyssier V, McMahan K, Dong R, Doyle M, Belabbaci N, Borducchi E, Collier AY, Mullington J, Barouch DH. Long COVID involves activation of proinflammatory and immune exhaustion pathways. Nat Immunol. 2026;27(1):61–71. 10.1038/s41590-025-02353-x . Whitcomb LA, Berry K, LaVergne SM, Natter N, Baxter BA, Rao S, Tipton M, Gritsenko MA, Weitz KK, Gerbasi V, Bramer LM, Piehowski PD, Webb TL, Henao-Tamayo M, Chicco AJ, Dunn J, Dutt TS, Ryan EP. Blood pro-thrombotic analytes and platelet activation are associated with post-acute sequelae of COVID-19. BMC Infect Dis. 2025 Dec;10. 10.1186/s12879-025-11824-3 . Schreiber CS, Navarro Ramil L, Bieligk J, Meineke R, Rimmelzwaan G, Käufer C, Richter F. Intravenous SARS-CoV-2 Spike protein induces neuroinflammation and alpha-Synuclein accumulation in brain regions relevant to Parkinson’s disease. Brain Behav Immun. 2025;129:102–23. 10.1016/j.bbi.2025.05.021 . Jiang Z, Shan T, Li Y, Han F, Feng B, Zhen X, Ni H, Peng J, Xu M. Persistent Attenuation of Lymphocyte Subsets After Mass SARS-CoV-2 Infection. Int J Infect Dis. 2025;108287. 10.1016/j.ijid.2025.108287 . Shankar V, Wilhelmy J, Curtis EJ, Michael B, Cervantes L, Mallajosyula V, Davis RW, Snyder M, Younis S, Robinson WH, Shankar S, Mischel PS, Bonilla H, Davis MM. Oxidative stress is a shared characteristic of ME/CFS and Long COVID. Proc Natl Acad Sci U S A. 2025;122(28):e2426564122. 10.1073/pnas.2426564122 . Seo D, Choi Y, Jeong E, Bang S, Lee JS, Jang IH, Choi L, Kim JH, Shin W, Seo BR, Kim S, Jung HJ, Kim JY, Kim H, Lim YM, Kwon JS, Chang E, Lee J, Kam TI, Park SH, Lee EJ, Kim SH. Distinct brain alterations and neurodegenerative processes in cognitive impairment associated with post-acute sequelae of COVID-19. Nat Commun. 2025;16(1):10552. 10.1038/s41467-025-65597-z . Käufer C, Schreiber CS, Hartke AS, Denden I, Stanelle-Bertram S, Beck S, Kouassi NM, Beythien G, Becker K, Schreiner T, Schaumburg B, Beineke A, Baumgärtner W, Gabriel G, Richter F. Microgliosis and neuronal proteinopathy in brain persist beyond viral clearance in SARS-CoV-2 hamster model. EBioMedicine. 2022;79:103999. 10.1016/j.ebiom.2022.103999 . Schreiber CS, Wiesweg I, Stanelle-Bertram S, Beck S, Kouassi NM, Schaumburg B, Gabriel G, Richter F, Käufer C. Sex-specific biphasic alpha-synuclein response and alterations of interneurons in a COVID-19 hamster model. EBioMedicine. 2024;105:105191. 10.1016/j.ebiom.2024.105191 . Shahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW, Elahi S. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med. 2025;6(11):102449. 10.1016/j.xcrm.2025.102449 . Bevan, S., Javed, H., Israel, M., Primicheru, L., Mumu, M., Sun, H., Maurer, M., Oey,O., Fedele, L., Ribiero, A., Serra, J., Lang, L., Birklein, F., Dresel, C., Chalabi,J., Seibert, F., Westhoff, T., Wiemers, L., Babel, N., … Anft, M. (2025). Autoantibodies mediate pain and sensory dysfunction in post-COVID syndrome [Preprint]. Research Square:doi: 10.21203/rs.3.rs-7989936/v1. Mignolet M, Deroux C, Florkin T, Bielarz V, Swert KD, Halloin N, Sprimont L, Ladang A, George F, Gilloteaux J, Abeloos L, Weyenbergh JV, Jamoulle M, Diederich C, Gillet NA, Bulpa P, Nicaise C. (2025). Pathogenic IgG from long COVID patients with neurological sequelae triggers sensitive but not cognitive impairments upon transfer into mice [Preprint]. BioRxiv: 10.1101/2025.11.20.689423 Silva BE, Santos RS, dos, Veras FP, Cilli EM, Olivier DdaS, Belo MA, de Charlie-Silva A, Caixeta I, Barchuk ES, Nunes-Silva AR, Romero A, T. R. L., Galdino G. (2025). SARS-CoV-2 Spike Peptides Trigger Nociceptive Responses Through Spinal TLR4 Pathways [Preprint]. BioRxiv: 10.1101/2025.12.01.691535 Sano K, Kimura Y, Hirahata K, Kato H, Hasegawa H, Akutsu H, Ryo A, Goto A, Miyakawa K. SARS-CoV-2 spike-specific IgG4 class switching associates with clinical recovery in Long COVID. J Infect. 2025;91(5):106641. 10.1016/j.jinf.2025.106641 . Kwissa M, Mathayan M, Salunkhe SS, Bakthavachalam V, Ye Z, Sanborn MA, Condo S, Upadhye A, Nemakal A, Richner JM, Basu S, Novak RM, Jacobson JR, Ganesh BB, Cerda M, Utz PJ, Krishnan JA, Prabhakar BS, Rehman J. (2025). Persistent Immune Dysregulation during Post-Acute Sequelae of COVID-19 is Manifested in Antibodies Targeting Envelope and Nucleocapsid Proteins [Preprint]. BioRxiv: 10.1101/2025.08.18.670908 Supplementary Files AdditionalFile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 04 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8764088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590567128,"identity":"ea2f84e3-d1be-4fab-b71b-87d8609e2994","order_by":0,"name":"Charlotte Christine 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Center Göttingen: Universitatsmedizin Gottingen","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Herrmann-Lingen","suffix":""},{"id":590567130,"identity":"e4e458ba-170a-42e9-ba70-b3f1b09a2105","order_by":2,"name":"Maike Stolz","email":"","orcid":"","institution":"Hannover Medical School: Medizinische Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Maike","middleName":"","lastName":"Stolz","suffix":""},{"id":590567131,"identity":"785b9b23-863e-48cd-9857-b381dd17d0ea","order_by":3,"name":"Christian Krauth","email":"","orcid":"","institution":"Hannover Medical School: Medizinische Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Krauth","suffix":""},{"id":590567132,"identity":"d6ab1326-9799-4e5c-80eb-6838623e35e0","order_by":4,"name":"Birte Burger","email":"","orcid":"","institution":"AOK Bundesverband","correspondingAuthor":false,"prefix":"","firstName":"Birte","middleName":"","lastName":"Burger","suffix":""},{"id":590567133,"identity":"bcb1e7b9-7c6a-4b12-b6da-661309a46b5e","order_by":5,"name":"Jona Theodor Stahmeyer","email":"","orcid":"","institution":"AOK Bundesverband","correspondingAuthor":false,"prefix":"","firstName":"Jona","middleName":"Theodor","lastName":"Stahmeyer","suffix":""},{"id":590567134,"identity":"28f96e47-7204-4625-872f-73be16f778e8","order_by":6,"name":"Mariel Nöhre","email":"","orcid":"","institution":"Hannover Medical School: Medizinische Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Mariel","middleName":"","lastName":"Nöhre","suffix":""},{"id":590567135,"identity":"22cd9077-0078-45f4-a5c4-1a505972d2d7","order_by":7,"name":"Aline Debener","email":"","orcid":"","institution":"Hannover Medical School: Medizinische Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Aline","middleName":"","lastName":"Debener","suffix":""},{"id":590567136,"identity":"0df43916-52ae-4299-8a82-f5e7148be3ac","order_by":8,"name":"Christopher Käufer","email":"","orcid":"","institution":"University of Veterinary Medicine Hannover: Tierarztliche Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Käufer","suffix":""},{"id":590567137,"identity":"9fcff95b-0aac-4d05-a915-70dc2d47f3bb","order_by":9,"name":"Franziska Richter","email":"","orcid":"","institution":"University of Veterinary Medicine Hannover: Tierarztliche Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Franziska","middleName":"","lastName":"Richter","suffix":""},{"id":590567138,"identity":"47531468-49ca-44fa-96fa-5311e665ce9b","order_by":10,"name":"Martina de Zwaan","email":"","orcid":"","institution":"Hannover Medical School: Medizinische Hochschule Hannover","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"de Zwaan","suffix":""}],"badges":[],"createdAt":"2026-02-02 11:04:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8764088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8764088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102940284,"identity":"8b472cd9-7133-49ec-ab3c-7b4203f95a91","added_by":"auto","created_at":"2026-02-18 17:05:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104558,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the two-step cluster analysis, including the \u003cem\u003esymptom-impairment\u003c/em\u003e variables: prevalence of the 14 symptoms across clusters. Cluster 1 - \u003cem\u003eSystemic\u003c/em\u003e: N = 461, Cluster 2 - \u003cem\u003eNeurocognitive\u003c/em\u003e: N = 489, Cluster 3 - \u003cem\u003ePain\u003c/em\u003e: N = 419, Cluster 4 - \u003cem\u003eFew-Symptoms\u003c/em\u003e: N = 304. PEM = post-exertional malaise\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8764088/v1/62efe8cd8cdb7617026b096d.png"},{"id":102964573,"identity":"60cfbbf1-6fd4-48d3-beb9-a9d481e3ef0f","added_by":"auto","created_at":"2026-02-19 04:22:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329889,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8764088/v1/6de4f666-c149-4621-b258-454ef9355f18.pdf"},{"id":102940285,"identity":"78c608be-155e-4602-8361-3d41cdc185ee","added_by":"auto","created_at":"2026-02-18 17:05:13","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":361787,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8764088/v1/b838b40d486b737b1bba8bd7.docx"}],"financialInterests":"","formattedTitle":"Symptom-based clusters in people with post-COVID-19 condition (PCC)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlthough most patients recover within a few weeks after contracting COVID-19, some experience long-term consequences of COVID-19 infection, also known as post-COVID-19 condition (PCC). The definition of PCC encompasses a variety of symptoms and signs reported by patients after an initial SARS-CoV-2 infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PCC can cause a wide range of symptoms that can affect various organ systems, including the respiratory, cardiovascular, gastrointestinal, and nervous systems.\u003c/p\u003e \u003cp\u003eIn this context, more than 200 different types of symptoms have been reported [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with the most common symptoms including fatigue, post-exertional malaise (PEM), cognitive impairment, dyspnea, sleep disturbances, and muscle and joint pain [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The variety and heterogeneity of symptoms pose a considerable challenge when it comes to treating PCC. Over the past few years, a number of studies have been conducted with the aim of better understanding the risk factors and pathophysiology of PCC in order to derive appropriate treatment strategies. To date, however, the pathophysiological mechanisms of PCC have not been clearly elucidated. So far, it has only been hypothesized that dysregulation of the immune system, inflammatory reactions, viral persistence, reactivation of pathogens in connection with the host microbiome, autoimmune processes, and activation of coagulation could contribute to the etiology of PCC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Accordingly, no specific pharmacological treatment or other intervention has yet been established for the treatment of PCC, and the treatment approach is exclusively symptomatic. Against the backdrop of an exclusively symptom-oriented treatment approach, there are already a few studies that have investigated the extent to which certain distinguishable symptom clusters/symptom phenotypes can be identified, taking into account the multitude of possible symptoms associated with PCC. A symptom cluster is defined as several symptoms that occur together, are associated with each other, and differ from other clusters. Some studies provide evidence of individual recurring symptom clusters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], although depending on the study, different sets of symptoms were examined over varying periods of time since infection and clustering was performed using different methods, which means that no uniform set of symptom clusters has yet been identified in the context of previous research.\u003c/p\u003e \u003cp\u003eMost previous studies, which also aimed to identify symptom clusters, mostly considered the presence or absence of various symptoms in their analyses, while the impairment caused by the respective symptoms was addressed to a lesser extent. However, in clinical practice, it is important to consider not only the presence of symptoms but also the degree of impairment each symptom causes. Against this background, the present study aims to investigate the extent to which specific symptom clusters can be identified using a large sample of patients who are still affected by PCC in the longer term, and taking into account a broad spectrum of symptoms. These clusters are formed not only on the basis of the presence of symptoms, but also on the degree of impairment caused by the respective symptoms. In addition, the extent to which the identified clusters are associated with sociodemographic, clinical, and psychological factors is analyzed.\u003c/p\u003e \u003cp\u003eThe identification of possible symptom patterns or symptom phenotypes offers a way to divide the multitude of symptoms into structured patterns. This could create a framework for assessing symptom burden, setting treatment priorities, and developing targeted treatment approaches. In this way, there could be a shift from treating isolated symptoms to a more precise, targeted approach that focuses on the treatment of specific symptom phenotypes.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003eIndividuals diagnosed with PCC were identified using health insurance data from the largest statutory health insurer in Lower Saxony, AOK Niedersachsen, which provides coverage for approximately 3\u0026nbsp;million people, which is almost 40% of the population in this region. Patient identification followed the procedures described in the VePoKaP (Care for post-COVID-19 condition in Germany from the perspectives of patients, informal caregivers and general practitioners) study protocol [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Individuals insured with AOK Niedersachsen who met the following inclusion criteria were invited by mail to participate in an online survey: (1) age 18 years or older with a confirmed PCC diagnosis, indicated by the ICD code U09.9! in their claims data in 2022 (outpatient billing records or a 2022 certificate of incapacity for work); 2) residence in Lower Saxony; and 3) continuous insurance coverage with AOK Niedersachsen since 2019. Patients under legal guardianship and AOK employees were excluded. A total of N\u0026thinsp;=\u0026thinsp;26,438 individuals met the eligibility criteria. For budgetary reasons, N\u0026thinsp;=\u0026thinsp;20,163 were randomly selected and contacted by mail. Of those invited, N\u0026thinsp;=\u0026thinsp;2,159 (10.7%) provided informed consent and completed the survey. Of the 2,159 respondents, individuals who reported ongoing post-COVID symptoms were included, resulting in a final sample of N\u0026thinsp;=\u0026thinsp;1,673 participants for the subsequent statistical analyses. Ethical approval was obtained from the ethics committee of Hannover Medical School (reference number 11077_BO_K_2023). All participants provided their informed consent electronically regarding study participation and use of their health insurance data. Further, an approval for the use of health insurance data according to \u0026sect;\u0026nbsp;75 SGB X (Social Security Code Book 10) was given by the competent supervisory authority.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSymptom Impairment\u003c/h2\u003e \u003cp\u003ePatients were asked about the presence of 14 symptoms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to determine whether each symptom was currently present. The symptoms assessed were fatigue, long recovery phase after light exertion (PEM), brain fog, difficulty concentrating, memory difficulties, chest pain, joint pain, muscle pain/cramps, headache, heart palpitations, shortness of breath, cough, sleep disorder, loss of smell/taste. Responses were dichotomized, resulting in 14 binary variables indicating whether each symptom was currently present (1) or not (0).\u003c/p\u003e \u003cp\u003eFor all symptoms reported as currently present, the degree of impairment was assessed using the question: \u003cem\u003e\u0026ldquo;How severely do the following currently existing symptoms impair you?\u0026rdquo;\u003c/em\u003e Responses were given on a five-point Likert scale (not at all, slightly, moderately, severely, very severely). For the subsequent main cluster analysis, 14 binary impairment variables were created by combining the impairment levels \u0026ldquo;not at all\u0026rdquo; and \u0026ldquo;slightly\u0026rdquo; into one category, and the impairment levels \u0026ldquo;moderately,\u0026rdquo; \u0026ldquo;severely,\u0026rdquo; and \u0026ldquo;very severely\u0026rdquo; into another.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eOther Measurement Instruments:\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eDepression and Anxiety.\u003c/em\u003e Depression and anxiety were assessed using the four-item Patient Health Questionnaire (PHQ-4) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The PHQ-4 is an ultra-brief self-report screening instrument comprising a two-item depression scale (PHQ-2) and a two-item anxiety scale (GAD-2). Responses are rated on a four-point Likert scale from 0 (not at all) to 3 (nearly every day), with total scores ranging from 0 to 12. Cut-offs of 6 or greater for the total scale and 3 or greater for each subscale have been recommended as cut-offs for clinically relevant symptoms.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePerception of Social Participation and Social Support\u003c/em\u003e. Social participation was assessed using the Short Scale Measuring Perceived Social Participation (KsT-5), a five-point scale (rated on a four-point scale; 1\u0026thinsp;=\u0026thinsp;disagree, 4\u0026thinsp;=\u0026thinsp;agree) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The responses were averaged; higher values indicate a higher level of perceived social participation. For normative values stratified by gender and age, see Berger et al. (2020) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Perceived social support was assessed using the 6-item Brief Social Support Scale (BS-6). The items are rated on a 4-point Likert scale from 0 (\u0026ldquo;never\u0026rdquo;) to 3 (\u0026ldquo;always\u0026rdquo;). The responses were summed to a total score (range: 0\u0026ndash;18), with scores\u0026thinsp;\u0026le;\u0026thinsp;11 indicating a lack of social support. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eChronic Fatigue and Post-Exertional Malaise (PEM).\u003c/em\u003e Chronic Fatigue was measured using the Chalder Fatigue Scale [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which includes 11 items rated on a four-point ordinal scale (0\u0026thinsp;=\u0026thinsp;less than usual, 3\u0026thinsp;=\u0026thinsp;much more than usual). In the present study, a bimodal scoring procedure was applied, whereby responses coded 0 or 1 were scored as 0, and responses coded 2 or 3 were scored as 1. The resulting total score ranged from 0 to 11. A total score of 4 or more indicates severe fatigue.\u003c/p\u003e \u003cp\u003ePEM was assessed using the validated German version of the DSQ-PEM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The DSQ-PEM includes five core items specifically aimed at measuring the frequency and severity of PEM. For each of the five items, a frequency score and a severity score were summed using a 5-point Likert scale (0\u0026thinsp;=\u0026thinsp;none of the time/not present, 4\u0026thinsp;=\u0026thinsp;all of the time/very severe), resulting in a score ranging from 0 to 8 for each of the five items. The DSQ-PEM incorporates additional items assessing recovery time, symptom exacerbation following physical exertion, and the duration of post-exertional malaise (PEM), with a duration of \u0026ge;\u0026thinsp;14 hours being a key diagnostic criterion. A diagnosis of PEM requires the fulfillment of multiple criteria, including this prolonged recovery phase. For a comprehensive overview, see Cotler et al., 2018 and Kuczyk et al., 2025 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eMedical and Sociodemographic Data\u003c/em\u003e. The data were derived from the participants\u0026rsquo; questionnaires and from routine AOK data.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using IBM\u0026reg; SPSS\u0026reg; Statistics Version 29. Descriptive statistics were calculated using means, standard deviations (SD), and ranges for continuous variables, and frequencies and percentages for categorical variables.\u003c/p\u003e \u003cp\u003eTwo-step cluster analysis was selected for cluster formation because the algorithm is well-suited to large datasets and has the additional advantage of automatically determining the optimal number of clusters. In two-step cluster analysis, the first step involves pre-clustering observations into small subclusters, followed by a hierarchical clustering procedure that groups these subclusters into the final clusters [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparative studies have identified two-step cluster analysis as one of the most reliable methods in terms of the number of subgroups identified, the accuracy of individual classification, and the reproducibility of findings across clinical and other types of datasets [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA two-step cluster analysis was performed using the 14 generated symptom impairment variables to identify groups of patients with distinct symptom patterns. Subsequently, the resulting clusters were compared regarding clinical, psychological, and sociodemographic characteristics. Kruskal\u0026ndash;Wallis tests were used to compare continuous variables across clusters, and chi-square tests were used to compare categorical variables. As a supplementary analysis, the same analyses were then performed including the symptom presence variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the total sample of N\u0026thinsp;=\u0026thinsp;1,673 patients who reported ongoing PCC symptoms, 71.6% were female. The mean age was 51.3 years (SD\u0026thinsp;=\u0026thinsp;12.45). Before the infection that presumably caused the PCC, 22.5% (n\u0026thinsp;=\u0026thinsp;361) of the participants stated that they had not yet received a SARS-CoV-2 vaccination, 77.5% (n\u0026thinsp;=\u0026thinsp;1246) had received at least one vaccination, 26.9% (n\u0026thinsp;=\u0026thinsp;432) reported having been vaccinated twice, and 43.4% (n\u0026thinsp;=\u0026thinsp;698) reported having received more than 2 vaccinations (data from 1607 participants available). Additional sociodemographic and clinical characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The number and percentages of patients with different degrees of impairment of the 14 symptoms are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Using the \u003cem\u003esymptom impairment\u003c/em\u003e variable based on the 14 PCC symptoms, the two-step cluster analysis identified four distinct symptom clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Cluster 1 \u003cem\u003e(\u0026ldquo;\u003c/em\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSystemic\u003c/span\u003e\u003cem\u003e\u0026rdquo;)\u003c/em\u003e comprised 461 individuals (27.6%) and was characterized by a high prevalence of nearly all assessed symptoms. Cluster 2 \u003cem\u003e(\u0026ldquo;\u003c/em\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eNeurocognitive\u003c/span\u003e\u003cem\u003e\u0026rdquo;)\u003c/em\u003e included 489 individuals (29.2%) and was characterized primarily by cognitive impairment, including difficulty concentrating, memory difficulties, and brain fog. Cluster 3 \u003cem\u003e(\u0026ldquo;\u003c/em\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePain\u003c/span\u003e\u003cem\u003e\u0026rdquo;)\u003c/em\u003e, consisting of 419 individuals (25.0%), was distinguished by an elevated occurrence of muscle and joint pain. \u003cem\u003eCluster 4 (\u0026ldquo;\u003c/em\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eFew Symptoms\u003c/span\u003e\u003cem\u003e\u0026rdquo;)\u003c/em\u003e, with 304 individuals (18.2%), exhibited a low prevalence of most symptoms, although fatigue (20.4%) and shortness of breath (30.9%) were the most frequently reported symptoms within this group. Overall, fatigue was the most prevalent symptom and was distributed across clusters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and clinical characteristics of the total sample (N\u0026thinsp;=\u0026thinsp;1673).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1673\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1198 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.26 (12.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u0026ndash;93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e397 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool, \u0026ge; 12 years, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnership, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1201 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther chronic diseases, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e725 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since infection, months, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.09 (8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-4, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-4 (cut-off score\u0026thinsp;\u0026ge;\u0026thinsp;6), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-2, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.63 (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-2 (cut-off score\u0026thinsp;\u0026ge;\u0026thinsp;3), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-2, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-2 (cut-off score\u0026thinsp;\u0026ge;\u0026thinsp;3), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKsT-5, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.58 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS-6, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.48 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS (total), M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.01 (3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS\u0026thinsp;\u0026ge;\u0026thinsp;4, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1448 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.91 (1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM, cut-off \u0026ge;\u0026thinsp;14 hours, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e337 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of specialists consulted\u003c/b\u003e, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76 (1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of different therapies for PCC, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.27 (2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of psychological therapy, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e479 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of active therapy, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e407 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSick days due to PCC, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.76 (209.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-1377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU due to infection n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImprovement in PCC symptoms over time, yes, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e990 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad received at least 1 vaccination prior to infection, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1246 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad received at least 1 vaccination at the time of the survey, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1628 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes.\u003c/em\u003e PHQ-4: Patient Health Questionnaire-4. A total score of \u0026ge;\u0026thinsp;6 is considered the cut-off for clinically relevant symptomatology.PHQ-2: Patient Health Questionnaire-2, and GAD-2: Generalized Anxiety Disorder-2. A total score of \u0026ge;\u0026thinsp;3 is considered the cut-off for clinically relevant psychological distress. KsT-5: Short Scale for Assessing Perceived Social Participation. BS-6: Brief Social Support Scale (BS6). CFS: Chalder Fatigue Scale. DSQ-PEM: DePaul Symptom Questionnaire \u0026ndash; Post-Exertional Malaise. Psychological therapy includes relaxation therapy, psychological counseling, and psychotherapy. Active therapy includes rehabilitative sports and functional training/sports therapy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber and percent of participants with different degrees of \u003cem\u003esymptom impairment\u003c/em\u003e of the 14 PCC symptoms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDegree of Impairment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSymptom\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003enot present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNot at all\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSlight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMedium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSevere\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eVery Severe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e550 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e352 (21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e714 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e414 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e282 (16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain fog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e999 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e247 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e144 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulty concentrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e678 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e396 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e214 (12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e775 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e185 (11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1329 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e876 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e205 (12.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle pain/cramps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e940 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1074 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e204 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart palpitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1141 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e692 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e383 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1170 (69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e768 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e363 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e280 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of smell/taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1420 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e. PEM\u0026thinsp;=\u0026thinsp;post-exertional malaise\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercent of participants with moderate, severe, and very severe impairment based on the 14 PCC symptoms and mean impairment rating (1\u0026ndash;5) (N\u0026thinsp;=\u0026thinsp;1673).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1673\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean impairment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain fog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulty concentrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle pain/cramps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart palpitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of smell/taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: For all symptoms reported as currently present, the degree of impairment was assessed using the question: \u003cem\u003e\u0026ldquo;How severely do the following currently existing symptoms impair you?\u0026rdquo;\u003c/em\u003e Responses were given on a five-point Likert scale (not at all, slightly, moderately, severely, very severely)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFour clusters resulting from the two-step cluster analysis based on the \u003cem\u003esymptom-impairment\u003c/em\u003e variables, with the frequency n (%) of the 14 symptoms in the total sample and within each cluster. Symptoms are sorted in descending order of importance for cluster formation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom\u003c/p\u003e \u003cp\u003eVariables n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSystemic\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeuro-cognitive\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFew Symptoms\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulty concentrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e958 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e843 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403 (87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle pain/cramps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e437 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e756 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain fog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e640 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1262 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e345 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937 (56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e251 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e879 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e558 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest Pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e924 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94 (30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Palpitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of smell/taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe FIT indices, the importance of the individual symptoms, and the cluster quality (which was fair) are depicted in Additional file 1: Supplementary Table\u0026nbsp;1 and Supplementary Figs.\u0026nbsp;1 and 2.\u003c/p\u003e \u003cp\u003eUsing the presence or absence of the 14 PCC symptoms, the cluster structure closely mirrored that of the \u003cem\u003esymptom impairment\u003c/em\u003e analysis, yielding: Cluster 1 \u003cem\u003e(\u0026ldquo;Systemic\u0026rdquo;)\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;426; 25.5%), Cluster 2 \u003cem\u003e(\u0026ldquo;Neurocognitive\u0026rdquo;)\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;447; 26.7%), Cluster 3 \u003cem\u003e(\u0026ldquo;Pain\u0026rdquo;)\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;369; 22.1%), and Cluster 4 \u003cem\u003e(\u0026ldquo;Few Symptoms\u0026rdquo;)\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;431; 25.8%) (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdditional file 1: supplementary Tables\u0026nbsp;2\u0026ndash;5 and supplementary Figs.\u0026nbsp;3\u0026ndash;5\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Clusters on Sociodemographic, Psychological, and Clinical Variables\u003c/h2\u003e \u003cp\u003eParticipants in the \u003cem\u003e\u0026ldquo;Systemic\u0026rdquo;\u003c/em\u003e cluster reported the most pathological values in basically all patient-reported outcome measures (PHQ-4, BS-6, KsT-5, CFS, PEM) compared to the other 3 clusters, and had consulted more specialists and had more sick days due to PCC than the participants in the other clusters. Patients in the \u0026ldquo;\u003cem\u003eSystemic\u003c/em\u003e\u0026rdquo; cluster were more often unemployed, reported more comorbid chronic health conditions and were less likely to report an improvement of their symptoms over time (43.3%). A higher percentage of participants in this cluster reported not having been vaccinated before the infection that presumably caused the PCC (25.9%). For detailed statistical results, see Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand Additional file 1: Supplementary Table\u0026nbsp;6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the \u003cem\u003esymptom-impairment\u003c/em\u003e clusters regarding sociodemographic, clinical, and psychological continuous variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKruskal-Wallis-Text df\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystemic\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeurocognitive\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFew Symptoms\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.59 (10.66) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.15 (12.63) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.73 (12.60) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.62 (13.91) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;21.48\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.62 (3.22) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.88 (3.04) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (2.65) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38 (2.41) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;380.53\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.48 (1.71) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.08 (1.61) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18 (1.45) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.27) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;382.05\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14 (1.75) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.81 (1.72) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.48) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.43) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;291.71\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.83 (5.09) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.36 (5.11) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00 (5.17) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.03 (5.77) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;18.22\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKsT-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.68 (3.32) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.57 (3.28) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.52 (3.06) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.61 (3.10) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;133.42\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS (total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.18 (1.58) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.28 (2.08) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.04 (2.68) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.94 (3.04) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;765.09\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS (physical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.17 (2.95) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.07 (3.30) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.78 (3.40) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.40 (3.44) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;619.51\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS (psychological)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.98 (2.03) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.53 (2.11) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.38 (2.09) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.66 (2.04) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;753.79\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.29 (1.29) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22 (1.81) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51 (1.80) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (1.31) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;588.33\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of different therapies for PCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 (3.12) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32 (2.59) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04 (2.60) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 (2.04) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;86.24\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of specialists consulted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35 (1.73) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86 (1.57) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56 (1.46) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92 (1.15) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;139.10\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since infection, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.29 (9.06) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.40 (8.62) \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.14 (7.87) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.06 (7.90) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;18.94\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSick days due to PCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201.88 (263.93)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.94 (223.74)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.78 (163.52)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.12 (76.30)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;133.34\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Post-hoc test: values with different superscripts are significantly different. PHQ-4: Patient Health Questionnaire-4; PHQ-2: Patient Health Questionnaire-2; GAD-2: Generalized Anxiety Disorder-2; BS-6: Brief Social Support Scale. KsT-5: Short Scale Measuring Perceived Social Participation; CFS: Chalder Fatigue-Scale; DSQ-PEM: DePaul Symptom Questionnaire \u0026ndash; Post-Exertional Malaise.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the \u003cem\u003esymptom-impairment\u003c/em\u003e clusters regarding sociodemographic, clinical, and psychological categorical variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-square test, df\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystemic\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeurocognitive\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFew Symptoms\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.96\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;23.46\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool\u0026thinsp;\u0026ge;\u0026thinsp;12 years.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.51\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e204 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.91\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;30.11\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ- 4\u0026thinsp;\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;233.67\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM: Dead, heavy feeling after starting to exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e279 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;418.31\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM: Next day soreness or fatigue after non-strenuous, everyday activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e388 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;404.51\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM: Mentally tired after the slightest effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;491.86\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM: Minimum exercise makes you physically tired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296 (61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;345.84\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSQ-PEM: Physically drained or sick after mild activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;334.94\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM recovery time\u0026thinsp;\u0026ge;\u0026thinsp;14 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;121.00\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFS\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;462.71\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of psychological therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;64.95\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of active therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;29.35\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient admission due to post-COVID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.65\u003c/p\u003e \u003cp\u003ep = .084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU due to infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.50\u003c/p\u003e \u003cp\u003ep = .139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImprovement in PCC symptoms over time, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;109.12\u003c/p\u003e \u003cp\u003ep \u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad received at least 1 SARS-CoV-2 vaccination prior to infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.01\u003c/p\u003e \u003cp\u003ep = .046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad received at least 1 SARS-CoV-2 vaccination at the time of the survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e410 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e \u003cp\u003ep = .919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes.\u003c/em\u003e CFS: Chalder-Fatigue-Scale; DSQ-PEM: DePaul Symptom Questionnaire \u0026ndash; Post-Exertional Malaise. Psychological therapy includes relaxation therapy, psychological counseling, and psychotherapy. Active therapy includes rehabilitative sports and functional training/sports therapy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants in the \u003cem\u003e\u0026ldquo;Few Symptoms\u0026rdquo;\u003c/em\u003e cluster were less likely to report other chronic conditions, had the fewest sick days due to PCC, and showed the lowest levels of psychological distress among all clusters. 80.6% (N\u0026thinsp;=\u0026thinsp;192) of patients in the \u0026ldquo;\u003cem\u003eFew Symptoms\u003c/em\u003e\u0026rdquo; cluster reported that their PCC symptoms had already improved over time,\u003c/p\u003e \u003cp\u003eThe results of the participants in the \u003cem\u003e\u0026ldquo;Pain\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;Neurocognitive\u0026rdquo;\u003c/em\u003e clusters lay somewhere in between; however, the participants in the \u0026ldquo;\u003cem\u003eNeurocognitive\u0026rdquo;\u003c/em\u003e cluster were younger and reported more distress compared to the participants in the \u0026ldquo;\u003cem\u003ePain\u003c/em\u003e\u0026rdquo; cluster.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify symptom clusters in the long-term course after SARS-CoV-2 infection using a large sample of patients with PCC. Data were obtained from the largest health insurance providers in Lower Saxony, ensuring that all patients included had a medically confirmed PCC diagnosis. The identified clusters were subsequently compared with regard to sociodemographic, clinical, and psychological characteristics.\u003c/p\u003e \u003cp\u003eThe following four \u003cem\u003esymptom impairment\u003c/em\u003e clusters emerged: \u003cem\u003eSystemic (Cluster 1), Neurocognitive (Cluster 2), Pain (Cluster 3), and Few Symptoms (Cluster 4). Clusters 1 and 4\u003c/em\u003e differed primarily in overall symptom severity, with this distinction between high- and low-symptom subgroups corresponding to previous findings from symptom-based cluster analyses in ME/CFS [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Participants in the \u003cem\u003e\u0026ldquo;Few Symptom\u0026rdquo;\u003c/em\u003e cluster had fewer comorbidities overall and showed lower psychological distress.\u003c/p\u003e \u003cp\u003eBeyond these general severity clusters, two more specific symptom clusters emerged. Participants in \u003cem\u003eCluster 2\u003c/em\u003e, which was the largest cluster, primarily exhibited difficulty concentrating, brain fog, and memory difficulties as their leading symptoms, which is why this cluster was described as a \u0026ldquo;\u003cem\u003eNeurocognitive\u0026rdquo;\u003c/em\u003e cluster. Similar neurocognitive subgroups have been identified in earlier studies, e.g., by Grafstr\u0026ouml;m et al. (2025) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] six months after infection or by Moniz et al. (2024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] at 9- and 12-month post-infection. In our study, the mean time since infection was 22 months, suggesting that this cluster represents a stable, long-term pattern.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 3\u003c/em\u003e was defined by the predominance of muscle pain/cramps and joint pain, and was therefore defined as a \u003cem\u003e\u0026ldquo;Pain\u0026rdquo;\u003c/em\u003e cluster, consistent with previous reports of pain-focused subgroups in ME/CFS and PCC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs expected, substantial symptom overlap occurred between the clusters. Fatigue and PEM - central symptoms of ME/CFS - were present in all clusters, although only 5 to 50% in each cluster reported that post-exertional malaise lasted more than 14 hours after activities.\u003c/p\u003e \u003cp\u003eThe analysis incorporating \u003cem\u003esymptom presence\u003c/em\u003e variables yielded essentially the same clusters as described earlier, but with a different distribution/varying group size. In particular, the low-symptom cluster was significantly larger in the \u003cem\u003esymptom presence\u003c/em\u003e cluster analysis. A supplementary exploratory examination of individuals whose cluster membership differed between the two cluster analyses revealed the following pattern: Individuals with a low overall symptom burden were more frequently assigned to the low-symptom cluster when the presence variables were taken into account. However, if these individuals reported at least moderate to severe impairment in a single symptom of a symptom area (pain, neurocognitive), they were more likely to be assigned to a symptom-specific cluster when the impairment variables were included.\u003c/p\u003e \u003cp\u003eCompared with the \u0026ldquo;\u003cem\u003ePain\u003c/em\u003e\u0026rdquo; cluster, the \u0026ldquo;\u003cem\u003eNeurocognitive\u003c/em\u003e\u0026rdquo; cluster tended to include younger women with higher levels of psychological distress. These observations are consistent with findings from Matias-Guiu et al. (2023) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], who reported more frequent cognitive impairments among younger individuals with PCC.\u003c/p\u003e \u003cp\u003eIt must be noted that the survey referred to an early stage of the COVID-19 pandemic, when vaccinations may not yet have been widely available or available for only a short period. Thus, the non-vaccination rate in our sample was still high, with 22.5% not having received any vaccination prior to the infection that presumably caused the PCC. Participants in the \u0026ldquo;\u003cem\u003eSystemic\u003c/em\u003e\u0026rdquo; cluster had the highest non-vaccination rate (25.9%). In comparison, among participants who reported no longer having any PCC symptoms, only 9.7% (N\u0026thinsp;=\u0026thinsp;45) had not been vaccinated against SARS-CoV-2 prior to the infection. These results suggest that vaccination may protect against the development or persistence of PCC symptoms. Several cohort studies and meta-analyses have reported a lower incidence of PCC among individuals who received at least one dose of a COVID-19 vaccine [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne study attempted to define clusters based on biological changes rather than subjective symptoms. Asprusten et al. (2021) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] identified clusters in adolescents with ME/CFS using biomarkers across five domains (endocrine, inflammatory, cardiovascular, pressure pain threshold, and cognitive). However, substantial overlap between clusters limited the identification of distinct pathophysiological subtypes. While biomarker-based clustering in ME/CFS has proven challenging, convergent evidence from mechanistic studies in PCC \u0026mdash;including viral persistence, autoimmunity, and neurodegeneration \u0026mdash; now suggests pathway-specific biological explanations for symptom-based clusters. We aimed to match our clusters with recent findings from basic research to shed light on possible explanations:\u003c/p\u003e \u003cp\u003eImportantly, this study was not designed to investigate the underlying biological mechanisms of PCC, and the following considerations remain speculative. However, integrating our symptom-based findings with current mechanistic research may help generate testable hypotheses regarding the pathophysiology of different PCC phenotypes.\u003c/p\u003e \u003cp\u003eCluster 1 (\u0026ldquo;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSystemic\u003c/span\u003e\u0026rdquo;) might represent a state of persistent viral antigen-driven pathology. The multi-organ involvement and severe symptom burden observed in this cluster align with evidence of ongoing systemic immune dysregulation: sustained activation of JAK-STAT and IL-6 pathways, coupled with immunothrombotic cascades that persist months post-infection [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Critically, this chronic inflammation does not require active viral replication. We recently demonstrated in animal models that intravenous administration of the SARS-CoV-2 Spike (S1) protein alone induces widespread neuroinflammation, glial activation, and alpha-synuclein accumulation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], supporting a \u0026ldquo;protein-as-pathogen\u0026rdquo; mechanism where circulating viral components drive multisystem injury. The profound fatigue characterizing this cluster might stem from metabolic dysregulation, including mitochondrial dysfunction and immune exhaustion marked by persistent lymphopenia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCluster 2 (\u0026ldquo;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eNeurocognitive\u003c/span\u003e\u0026rdquo;) presents a phenotype that mirrors early neurodegenerative disease. Structurally, patients in this cluster might exhibit hippocampal iron deposition, cortical thinning, and astrocytic damage on neuroimaging [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The preclinical work of some co-authors of this manuscript provides a cellular mechanism: SARS-CoV-2 infection initiates persistent accumulation of alpha-synuclein and Tau in hippocampal and cortical regions, establishing a proteinopathy that outlasts viral clearance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The specific symptom of \u0026ldquo;brain fog\u0026rdquo; may be mechanistically linked to dysfunction of parvalbumin-positive (PV+) interneurons, which generate the gamma oscillations critical for cognitive focus. These interneurons are significantly altered in post-COVID models [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], potentially explaining the attentional deficits that define this cluster. The female predominance observed in this cluster aligns with clinical evidence of heightened inflammatory T-cell responses in women [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and is directly paralleled in animal models, where females exhibit more severe biphasic neuroinflammatory responses, greater alpha-synuclein burden, and more pronounced PV+ interneuron dysfunction than males [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCluster 3 (\u0026ldquo;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePain\u003c/span\u003e\u0026rdquo;) appears mechanistically distinct from systemic inflammation, possibly driven instead by targeted autoimmune and peripheral nervous system pathology. Passive transfer experiments provide causal evidence: IgG from patients with Long COVID pain induces small-fiber neuropathy and mechanical hypersensitivity in mice by directly binding to sensory neurons [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, viral peptides can directly activate spinal TLR4 signaling pathways, triggering central sensitization and sustained nociception [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This dual mechanism, autoantibody-mediated peripheral neuropathy combined with central sensitization, provides a biological framework for the chronic pain phenotype.\u003c/p\u003e \u003cp\u003eCluster 4 (\u0026ldquo;\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eFew Symptoms\u003c/span\u003e\u0026rdquo;) may represent successful immune resolution. This favorable outcome may be mediated by tolerogenic immune profiles, particularly the emergence of IgG4 antibodies, which prevent the chronic inflammatory and autoimmune cascades observed in more severe clusters [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings suggest that the four clusters might represent biologically distinct PCC phenotypes driven by separable mechanisms: systemic viral antigen persistence and metabolic dysfunction (\u003cem\u003eCluster 1\u003c/em\u003e), neurodegeneration and circuit dysfunction with sex-specific vulnerability (\u003cem\u003eCluster 2\u003c/em\u003e), peripheral autoimmunity and central sensitization (\u003cem\u003eCluster 3\u003c/em\u003e), and successful immune regulation (\u003cem\u003eCluster 4\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThis translation from clinical phenotyping to molecular mechanisms supports the development of biologically defined subgroups and provides a framework for precision medicine approaches, where cluster assignment could guide targeted immunomodulatory, neuroprotective, or pain-specific interventions.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations\u003c/h3\u003e\n\u003cp\u003eThe present study offers several strengths. First, its large sample size provides greater robustness than previous studies with smaller sample sizes. Second, patient selection was based on physician confirmed PCC diagnoses. Third, the assessment of both symptom prevalence and impairment facilitated a more realistic and nuanced characterization of symptom burden and, in turn, of cluster differentiation.\u003c/p\u003e \u003cp\u003eIt is important to note that our study has certain limitations that must be considered. We had a 10.7% response rate of those who were mailed the questionnaire, which might have introduced any kind of bias. Even though our \u0026ldquo;\u003cem\u003eFew Symptoms\u0026rdquo;\u003c/em\u003e cluster was the smallest, individuals with a very high neurocognitive symptom burden might have been less likely to participate. Conversely, people whose symptoms had subsided in the meantime may also have been less willing to participate. Overall, therefore, selection bias in both directions can be assumed, suggesting that predominantly patients with moderate symptoms were included, although no definitive statement can be made on this. Also, similar to earlier studies, the participants predominantly consisted of females (71%), although it has been recognized that the prevalence of PCC is higher in women compared to men. The cluster quality was fair, and there was a high overlap of symptom prevalence and symptom impairment between clusters. We lack pre-infection data on symptom burden, and we could not provide longitudinal data. We did not correct for multiple testing due to the explorative nature of this research; however, the differences between clusters were big, with p-values well below the threshold of significance.\u003c/p\u003e \u003cp\u003eOverall, this study\u0026rsquo;s findings extend the current literature on symptom-based cluster analyses in PCC by demonstrating the presence of both general clusters of symptom burden and specific subgroups, particularly pain and neurocognitive clusters. Fatigue and PEM in patients occurred as central symptoms across nearly all clusters, underscoring the significant overlap between PCC and ME/CFS symptomatology. Classifying PCC patients by severity and characteristic symptom patterns could help develop more targeted, individually tailored therapeutic approaches. Future research should aim to define biologically defined subgroups with greater precision to clarify the underlying pathophysiological mechanisms of different symptom clusters.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Ethics approval was obtained from the Ethics Committee of Hannover Medical School (reference number 11077_BO_K_2023). Further, an approval for the use of health insurance data according to \u0026sect;\u0026nbsp;75 SGB X (Social Security Code Book 10) was given by the competent supervisory authority. The study was conducted in accordance with the principles of the Declaration of Helsinki. All potential participants are informed that their participation in the study is voluntary and they have the right to refuse or withdraw at any time without any disadvantage. Participation in the study was based on electronic informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e All authors approved the final draft of the manuscript and are aware that this paper is submitting to this journal\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe present study is supported by the COVID-19-Research Network Lower Saxony (COFONI), through funding from the Ministry of Science and Culture of Lower Saxony in Germany (14-76403-184). The funder played no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eMS, CKr, BB, JTS, CHL and MdZ designed the VEPOKAP study and are responsible for funding acquisition. CKu and MdZ were responsible for data analysis and drafted the manuscript. MN, AD, CK\u0026auml; and FR contributed substantially to writing the manuscript. All authors critically reviewed and revised the manuscript and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNo acknowledgements\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKatz GM, Bach K, Bobos P, Cheung A, D\u0026eacute;cary S, Goulding S, Herridge MS, McNaughton CD, Palmer KS, Razak FA, Zhang B, Quinn KL. Understanding How Post-COVID-19 Condition Affects Adults and Health Care Systems. JAMA Health Forum. 2023;4(7):e231933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamahealthforum.2023.1933\u003c/span\u003e\u003cspan address=\"10.1001/jamahealthforum.2023.1933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis HE, McCorkell L, Vogel JM, Topol EJ, Long COVID. major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-022-00846-2\u003c/span\u003e\u003cspan address=\"10.1038/s41579-022-00846-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Erratum in: Nat Rev Microbiol. 2023;21(6):408. doi: 10.1038/s41579-023-00896-0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoaventura P, Macedo S, Ribeiro F, Jaconiano S, Soares P. Post-COVID-19 Condition: Where Are We Now? Life (Basel). 2022;12(4):517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/life12040517\u003c/span\u003e\u003cspan address=\"10.3390/life12040517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlghamdi SA, Alfares MA, Alsulami RA, Alghamdi AF, Almalawi AM, Alghamdi MS, Hazazi HA. Post-COVID-19 Syndrome: Incidence, Risk Factor, and the Most Common Persisting Symptoms. Cureus. 2022;14(11):e32058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.32058\u003c/span\u003e\u003cspan address=\"10.7759/cureus.32058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomingo FR, Waddell LA, Cheung AM, Cooper CL, Belcourt VJ, Zuckermann AM, Corrin T, Ahman R, Boland L, Laprise C, Idzerda L, Khan A, Morissette K, Garcia AJ. Prevalence of long-term effects in individuals diagnosed with COVID-19: an updated living systematic review. MedRxiv, 2021\u0026ndash;06. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2021.06.03.21258317\u003c/span\u003e\u003cspan address=\"10.1101/2021.06.03.21258317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastanares-Zapatero D, Chalon P, Kohn L, Dauvrin M, Detollenaere J, Maertens de Noordhout C, Primus-de Jong C, Cleemput I, Van den Heede K. Pathophysiology and mechanism of long COVID: a comprehensive review. Ann Med. 2022;54(1):1473\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07853890.2022.2076901\u003c/span\u003e\u003cspan address=\"10.1080/07853890.2022.2076901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatiha GE, Al-Kuraishy HM, Al-Gareeb AI, Welson NN. Pathophysiology of Post-COVID syndromes: a new perspective. Virol J. 2022;19(1):158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12985-022-01891-2\u003c/span\u003e\u003cspan address=\"10.1186/s12985-022-01891-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuodi P, Gorelik Y, Gausi B, Bernstine T, Edelstein M. Characterization of post-COVID syndromes by symptom cluster and time period up to 12 months post-infection: A systematic review and meta-analysis. Int J Infect Dis. 2023;134:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2023.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2023.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrafstr\u0026ouml;m T, Barros GWF, Persson IL, Sundh J, Forsell MNE, Ahlm C, M\u0026aring;nsson E, Tevell S, Lind A, Normark J, Cajander S. Post COVID-19 condition phenotypes: A prospective cohort study identifying four symptom clusters and their impact on long-term outcomes. J Infect Public Health. 2025;18(12):102994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jiph.2025.102994\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2025.102994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrinkmann M, Stolz M, Herr A, Herrmann-Lingen C, Koch I, M\u0026uuml;ller C, M\u0026uuml;ller F, Sekanina U, Stahmeyer JT, de Zwaan M, Krauth C, Schneider N. Care for post-COVID-19 condition in Germany from the perspectives of patients, informal caregivers and general practitioners: Study protocol for a mixed methods study. PLoS ONE. 2024;19(12):e0316335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0316335\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0316335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoriano JB, Murthy S, Marshall JC, Relan P, Diaz JV, WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22(4):e102\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1473-3099(21)00703-9\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(21)00703-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO) clinical case definition working group on post COVID-19 condition. A clinical case definition of post COVID-19 condition by a Delphi consensus. World Health Organization 2021. WHO/2019-nCoV/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ePost_COVID-19_condition/Clinical_case_definition/2021.1\u003c/span\u003e\u003cspan address=\"http://Post_COVID-19_condition/Clinical_case_definition/2021.1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;we B, Wahl I, Rose M, Spitzer C, Glaesmer H, Wingenfeld K, Schneider A, Br\u0026auml;hler E. A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord. 2010;122(1\u0026ndash;2):86\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2009.06.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2009.06.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerger U, Kirschner H, Muehleck J, Gl\u0026auml;ser A, Werner B, Kurz M, Schwager S, Wick K, Strau\u0026szlig; B. Kurz-Skala zur Erfassung wahrgenommener sozialer Teilhabe (KsT-5): faktorielle Struktur, interne Konsistenz, inhaltliche und konvergente Validit\u0026auml;t sowie Normwerte in einer repr\u0026auml;sentativen Stichprobe [Short Scale Measuring Perceived Social Participation: Factorial Structure, Internal Consistency, Content Validity, Convergent Validity and Standard Values in a Representative German Sample]. Psychother Psychosom Med Psychol. 2020;70(9\u0026ndash;10):396\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/a-1088-1354\u003c/span\u003e\u003cspan address=\"10.1055/a-1088-1354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. German.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeutel ME, Br\u0026auml;hler E, Wiltink J, Michal M, Klein EM, J\u0026uuml;nger C, Wild PS, M\u0026uuml;nzel T, Blettner M, Lackner K, Nickels S, Tibubos AN. Emotional and tangible social support in a German population-based sample: Development and validation of the Brief Social Support Scale (BS6). PLoS ONE. 2017;12(10):e0186516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0186516\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0186516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace EP. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0022-3999(93)90081-p\u003c/span\u003e\u003cspan address=\"10.1016/0022-3999(93)90081-p\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin A, Staufenbiel T, Gaab J, Rief W, Br\u0026auml;hler E. Messung chronischer Ersch\u0026ouml;pfung\u0026ndash;Teststatistische Pr\u0026uuml;fung der Fatigue Skala (FS). Z f\u0026uuml;r klinische Psychologie und Psychother. 2010;9(1):33\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1026/1616-3443/a000010\u003c/span\u003e\u003cspan address=\"10.1026/1616-3443/a000010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakau L, Wicke F, H\u0026auml;user W, Beutel ME, Br\u0026auml;hler E, Hettich-Damm N. Chronic fatigue: psychometric properties and updated norm values of the Chalder fatigue scale in a cross-sectional sample representative of the German population. Ann Med. 2025;57(1):2524087. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07853890.2025.2524087\u003c/span\u003e\u003cspan address=\"10.1080/07853890.2025.2524087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotler J, Holtzman C, Dudun C, Jason LA. A Brief Questionnaire to Assess Post-Exertional Malaise. Diagnostics (Basel). 2018;8(3):66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/diagnostics8030066\u003c/span\u003e\u003cspan address=\"10.3390/diagnostics8030066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuczyk C, N\u0026ouml;hre M, Herrmann-Lingen C, Stolz M, Krauth C, Br\u0026auml;hler E, Jason LA, de Zwaan M. Reliability and validity of the German version of the DePaul Symptom Questionnaire Post-Exertional Malaise (DSQ-PEM). Front Psychiatry. 2025;16:1647040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2025.1647040\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2025.1647040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelbard R, Goldman O, Spiegler I. Investigating diversity of clustering methods: an empirical comparison. Data Knowl Eng. 2007;63(1):155\u0026ndash;66. doi: 10 1016/j.datak 2007 01 002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKent P, Jensen RK, Kongsted A. A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold, and SNOB. BMC Med Res Methodol. 2014;14(1):113. doi:101186/1471-2288-14-113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBacher J, Wenzig K, Vogler M. (2004). SPSS TwoStep Cluster - a first evaluation. (2., corr. ed.) (Arbeits- und Diskussionspapiere / Universit\u0026auml;t Erlangen-N\u0026uuml;rnberg, Sozialwissenschaftliches Institut, Lehrstuhl f\u0026uuml;r Soziologie, 2004-2). N\u0026uuml;rnberg: Universit\u0026auml;t Erlangen-N\u0026uuml;rnberg, Wirtschafts- und Sozialwissenschaftliche Fakult\u0026auml;t, Sozialwissenschaftliches Institut Lehrstuhl f\u0026uuml;r Soziologie. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nbn-resolving.org/urn:nbn:de:0168-ssoar-327153\u003c/span\u003e\u003cspan address=\"https://nbn-resolving.org/urn:nbn:de:0168-ssoar-327153\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHickie I, Lloyd A, Hadzi-Pavlovic D, Parker G, Bird K, Wakefield D. Can the chronic fatigue syndrome be defined by distinct clinical features? Psychol Med. 1995;25(5):925\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/s0033291700037417\u003c/span\u003e\u003cspan address=\"10.1017/s0033291700037417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollin SM, Nikolaus S, Heron J, Knoop H, White PD, Crawley E. Chronic fatigue syndrome (CFS) symptom-based phenotypes in two clinical cohorts of adult patients in the UK and the Netherlands. J Psychosom Res. 2016;81:14\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpsychores.2015.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychores.2015.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Erratum in: J Psychosom Res. 2023;168:111324. doi: 10.1016/j.jpsychores.2023.111324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollin SM, Heron J, Nikolaus S, Knoop H, Crawley E. Chronic fatigue syndrome (CFS/ME) symptom-based phenotypes and 1-year treatment outcomes in two clinical cohorts of adult patients in the UK and the Netherlands. J Psychosom Res. 2018;104:29\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpsychores.2017.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychores.2017.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaes AW, Van Herck M, Deng Q, Delbressine JM, Jason LA, Spruit MA. Symptom-based clusters in people with ME/CFS: an illustration of clinical variety in a cross-sectional cohort. J Transl Med. 2023;21(1):112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-023-03946-6\u003c/span\u003e\u003cspan address=\"10.1186/s12967-023-03946-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoniz M, Ruivinho C, Goes AR, Soares P, Leite A. Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test. BMC Infect Dis. 2024;24(1):1001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-024-09896-8\u003c/span\u003e\u003cspan address=\"10.1186/s12879-024-09896-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Erratum in: BMC Infect Dis. 2024;24(1):1178. doi: 10.1186/s12879-024-10086-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMfouth Kemajou P, Besse-Hammer T, Lebouc C, Coppieters Y. Cluster analysis identifies long COVID subtypes in Belgian patients. Biol Methods Protoc. 2024;9(1):bpae076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/biomethods/bpae076\u003c/span\u003e\u003cspan address=\"10.1093/biomethods/bpae076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatias-Guiu JA, Herrera E, Gonz\u0026aacute;lez-Nosti M, Krishnan K, Delgado-Alonso C, D\u0026iacute;ez-Cirarda M, Yus M, Mart\u0026iacute;nez-Petit \u0026Aacute;, Pag\u0026aacute;n J, Mat\u0026iacute;as-Guiu J, Ayala JL, Busch R, Hermann BP. Development of criteria for cognitive dysfunction in post-COVID syndrome: the IC-CoDi-COVID approach. Psychiatry Res. 2023;319:115006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2022.115006\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2022.115006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Centre for Disease Prevention and Control. Does COVID-19 vaccination reduce the risk and duration of post-COVID-19 condition? Rapid systematic literature review. Stockholm: ECDC; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2900/5489226\u003c/span\u003e\u003cspan address=\"10.2900/5489226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuimar\u0026atilde;es GN, Brunetti NS, De Lima DG, Proenca-Modena JL, Farias AS. Vaccination and COVID-19: impact on long-COVID. Front Immunol. 2025;16:1686572. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1686572\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1686572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsprusten TT, Sletner L, Wyller VBB. Are there subgroups of chronic fatigue syndrome? An exploratory cluster analysis of biological markers. J Transl Med. 2021;19(1):48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-021-02713-9\u003c/span\u003e\u003cspan address=\"10.1186/s12967-021-02713-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAid M, Boero-Teyssier V, McMahan K, Dong R, Doyle M, Belabbaci N, Borducchi E, Collier AY, Mullington J, Barouch DH. Long COVID involves activation of proinflammatory and immune exhaustion pathways. Nat Immunol. 2026;27(1):61\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-025-02353-x\u003c/span\u003e\u003cspan address=\"10.1038/s41590-025-02353-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitcomb LA, Berry K, LaVergne SM, Natter N, Baxter BA, Rao S, Tipton M, Gritsenko MA, Weitz KK, Gerbasi V, Bramer LM, Piehowski PD, Webb TL, Henao-Tamayo M, Chicco AJ, Dunn J, Dutt TS, Ryan EP. Blood pro-thrombotic analytes and platelet activation are associated with post-acute sequelae of COVID-19. BMC Infect Dis. 2025 Dec;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-025-11824-3\u003c/span\u003e\u003cspan address=\"10.1186/s12879-025-11824-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreiber CS, Navarro Ramil L, Bieligk J, Meineke R, Rimmelzwaan G, K\u0026auml;ufer C, Richter F. Intravenous SARS-CoV-2 Spike protein induces neuroinflammation and alpha-Synuclein accumulation in brain regions relevant to Parkinson\u0026rsquo;s disease. Brain Behav Immun. 2025;129:102\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbi.2025.05.021\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2025.05.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Z, Shan T, Li Y, Han F, Feng B, Zhen X, Ni H, Peng J, Xu M. Persistent Attenuation of Lymphocyte Subsets After Mass SARS-CoV-2 Infection. Int J Infect Dis. 2025;108287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2025.108287\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2025.108287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankar V, Wilhelmy J, Curtis EJ, Michael B, Cervantes L, Mallajosyula V, Davis RW, Snyder M, Younis S, Robinson WH, Shankar S, Mischel PS, Bonilla H, Davis MM. Oxidative stress is a shared characteristic of ME/CFS and Long COVID. Proc Natl Acad Sci U S A. 2025;122(28):e2426564122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2426564122\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2426564122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo D, Choi Y, Jeong E, Bang S, Lee JS, Jang IH, Choi L, Kim JH, Shin W, Seo BR, Kim S, Jung HJ, Kim JY, Kim H, Lim YM, Kwon JS, Chang E, Lee J, Kam TI, Park SH, Lee EJ, Kim SH. Distinct brain alterations and neurodegenerative processes in cognitive impairment associated with post-acute sequelae of COVID-19. Nat Commun. 2025;16(1):10552. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-025-65597-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-65597-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026auml;ufer C, Schreiber CS, Hartke AS, Denden I, Stanelle-Bertram S, Beck S, Kouassi NM, Beythien G, Becker K, Schreiner T, Schaumburg B, Beineke A, Baumg\u0026auml;rtner W, Gabriel G, Richter F. Microgliosis and neuronal proteinopathy in brain persist beyond viral clearance in SARS-CoV-2 hamster model. EBioMedicine. 2022;79:103999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2022.103999\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2022.103999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreiber CS, Wiesweg I, Stanelle-Bertram S, Beck S, Kouassi NM, Schaumburg B, Gabriel G, Richter F, K\u0026auml;ufer C. Sex-specific biphasic alpha-synuclein response and alterations of interneurons in a COVID-19 hamster model. EBioMedicine. 2024;105:105191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2024.105191\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2024.105191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW, Elahi S. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med. 2025;6(11):102449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xcrm.2025.102449\u003c/span\u003e\u003cspan address=\"10.1016/j.xcrm.2025.102449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBevan, S., Javed, H., Israel, M., Primicheru, L., Mumu, M., Sun, H., Maurer, M., Oey,O., Fedele, L., Ribiero, A., Serra, J., Lang, L., Birklein, F., Dresel, C., Chalabi,J., Seibert, F., Westhoff, T., Wiemers, L., Babel, N., \u0026hellip; Anft, M. (2025). Autoantibodies mediate pain and sensory dysfunction in post-COVID syndrome [Preprint]. Research Square:doi: 10.21203/rs.3.rs-7989936/v1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMignolet M, Deroux C, Florkin T, Bielarz V, Swert KD, Halloin N, Sprimont L, Ladang A, George F, Gilloteaux J, Abeloos L, Weyenbergh JV, Jamoulle M, Diederich C, Gillet NA, Bulpa P, Nicaise C. (2025). Pathogenic IgG from long COVID patients with neurological sequelae triggers sensitive but not cognitive impairments upon transfer into mice [Preprint]. BioRxiv: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2025.11.20.689423\u003c/span\u003e\u003cspan address=\"10.1101/2025.11.20.689423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva BE, Santos RS, dos, Veras FP, Cilli EM, Olivier DdaS, Belo MA, de Charlie-Silva A, Caixeta I, Barchuk ES, Nunes-Silva AR, Romero A, T. R. L., Galdino G. (2025). SARS-CoV-2 Spike Peptides Trigger Nociceptive Responses Through Spinal TLR4 Pathways [Preprint]. BioRxiv: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2025.12.01.691535\u003c/span\u003e\u003cspan address=\"10.1101/2025.12.01.691535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSano K, Kimura Y, Hirahata K, Kato H, Hasegawa H, Akutsu H, Ryo A, Goto A, Miyakawa K. SARS-CoV-2 spike-specific IgG4 class switching associates with clinical recovery in Long COVID. J Infect. 2025;91(5):106641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jinf.2025.106641\u003c/span\u003e\u003cspan address=\"10.1016/j.jinf.2025.106641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwissa M, Mathayan M, Salunkhe SS, Bakthavachalam V, Ye Z, Sanborn MA, Condo S, Upadhye A, Nemakal A, Richner JM, Basu S, Novak RM, Jacobson JR, Ganesh BB, Cerda M, Utz PJ, Krishnan JA, Prabhakar BS, Rehman J. (2025). Persistent Immune Dysregulation during Post-Acute Sequelae of COVID-19 is Manifested in Antibodies Targeting Envelope and Nucleocapsid Proteins [Preprint]. BioRxiv: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2025.08.18.670908\u003c/span\u003e\u003cspan address=\"10.1101/2025.08.18.670908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Covid-19, Post Covid-19 condition, Symptoms, Clusters","lastPublishedDoi":"10.21203/rs.3.rs-8764088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8764088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eIdentifying symptom clusters in post-COVID-19 condition (PCC) is crucial for developing targeted therapeutic interventions and gaining a better understanding of the underlying pathophysiological mechanisms. Therefore, the aim of this study was to identify symptom clusters based on 14 specific PCC symptoms, accounting for both symptom presence and impairment. The identified clusters were then compared with respect to sociodemographic, clinical, and psychological factors.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eA clinical sample of individuals with a PCC diagnosis lasting at least one year was included (final n\u0026thinsp;=\u0026thinsp;1673). A two-step cluster analysis was performed to identify symptom clusters. Subsequent comparisons between clusters were performed using Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eA total of four clusters were identified: two symptom burden clusters (\u0026ldquo;\u003cem\u003eSystemic\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eFew Symptoms\u003c/em\u003e\u0026rdquo;) and two symptom-specific clusters (\u0026ldquo;\u003cem\u003eNeurocognitive\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003ePain\u003c/em\u003e\u0026rdquo;). Patients in the \u0026ldquo;\u003cem\u003eSystemic\u003c/em\u003e\u0026rdquo; cluster reported greater psychological distress and more chronic comorbidities. Compared to the \u0026ldquo;\u003cem\u003ePain\u003c/em\u003e\u0026rdquo; cluster, the \u0026ldquo;\u003cem\u003eNeurocognitive\u003c/em\u003e\u0026rdquo; cluster included more younger women.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eIn PCC, different symptom clusters can be identified that differ in terms of sociodemographic, clinical, and psychological factors. Future research should then investigate biologically defined subgroups to better understand the underlying pathophysiological mechanisms.\u003c/p\u003e","manuscriptTitle":"Symptom-based clusters in people with post-COVID-19 condition (PCC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:05:09","doi":"10.21203/rs.3.rs-8764088/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-13T02:12:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T01:21:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T03:04:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-02-03T04:01:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff89ef58-3228-4a3d-8cfe-1bb36461e190","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T11:07:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 17:05:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8764088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8764088","identity":"rs-8764088","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}