Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Clustering and Statistical Analysis
3. Results
3.1. Population Study Characteristics
3.2. Clusters
4. Discussion
Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Overall Population N = 288 | Cluster-Mild N = 139 (48.26%) | Cluster-Moderate N = 106 (36.81%) | Cluster-Severe N = 43 (14.93%) | p-Value * | ||
---|---|---|---|---|---|---|
Sociodemographic Characteristics and Initial Severity Classification | Female N (%) | 170 (59%) | 73 (53%) | 66 (62%) | 31 (72%) | 0.053 |
Age (Years) | 43 ± 12 | 42 ± 12 | 43 ± 12 | 45 ± 14 | 0.360 | |
Body Mass Index (kg/m2) | 26.4 ± 5.5 | 25.8 ± 5.1 | 27.0 ± 5.8 | 26.7 ± 5.7 | 0.224 | |
Smoker N (%) | 45 (16%) | 16 (12%) | 15 (14%) | 14 (33%) | 0.027 | |
Moderate/severe illness N (%) | 95 (33%) | 34 (24%) | 41 (39%) | 20 (47%) | 0.015 | |
Comorbidities | At least one comorbidity N (%) | 40 (14%) | 12 (8.6%) | 16 (15%) | 12 (28%) | 0.007 |
Number of comorbidities Mean (SD) | 2.38 ± 0.33 | 2.37 ± 0.25 | 2.34 ± 0.16 | 2.48 ± 0.68 | 0.001 | |
Hypertension N (%) | 38 (13%) | 14 (10%) | 12 (11%) | 12 (28%) | 0.015 | |
Cardiac diseases N (%) | 11 (3.8%) | 3 (2.2%) | 6 (5.7%) | 2 (4.7%) | 0.311 | |
Asthma N (%) | 14 (4.9%) | 4 (2.9%) | 8 (7.5%) | 2 (4.7%) | 0.200 | |
Diabetes N (%) | 13 (4.5%) | 3 (2.2%) | 4 (3.8%) | 6 (14%) | 0.009 | |
Symptoms at inclusion N (%) | Fever | 98 (34%) | 45 (32%) | 36 (34%) | 17 (40%) | 0.688 |
Cough | 96 (33%) | 41 (29%) | 38 (36%) | 17 (40%) | 0.362 | |
Cough sputum | 27 (9.4%) | 11 (7.9%) | 9 (8.5%) | 7 (16%) | 0.279 | |
Sore throat | 50 (17%) | 17 (12%) | 24 (23%) | 9 (21%) | 0.076 | |
Rhinorrhea | 76 (26%) | 35 (25%) | 31 (29%) | 10 (23%) | 0.708 | |
Earache | 22 (7.6%) | 8 (5.8%) | 10 (9.4%) | 4 (9.3%) | 0.490 | |
Chest pain | 19 (6.6%) | 4 (2.9%) | 11 (10%) | 4 (9.3%) | 0.036 | |
Myalgia | 51 (18%) | 11 (7.9%) | 28 (26%) | 12 (28%) | <0.001 | |
Arthralgia | 25 (8.7%) | 4 (2.9%) | 14 (13%) | 7 (16%) | 0.001 | |
Fatigue | 136 (47%) | 47 (34%) | 60 (57%) | 29 (67%) | <0.001 | |
Dyspnea | 33 (11%) | 10 (7.2%) | 16 (15%) | 7 (16%) | 0.067 | |
Cephalea | 77 (27%) | 27 (19%) | 36 (34%) | 14 (33%) | 0.022 | |
Abdominal pain | 14 (4.9%) | 4 (2.9%) | 3 (2.8%) | 7 (16%) | 0.004 | |
Nausea | 13 (4.5%) | 5 (3.6%) | 4 (3.8%) | 4 (9.3%) | 0.289 | |
Diarrhea | 20 (6.9%) | 5 (3.6%) | 8 (7.5%) | 7 (16%) | 0.019 | |
Persisting symptoms at 12 months N (%) | Ear Nose Throat (ENT) symptoms | 110 (38%) | 24 (17%) | 65 (61%) | 21 (49%) | <0.001 |
Neurological symptoms | 188 (65%) | 51 (37%) | 101 (95%) | 36 (84%) | <0.001 | |
General symptoms | 229 (80%) | 80 (58%) | 106 (100%) | 43 (100%) | <0.001 | |
Cardiorespiratory symptoms | 159 (55%) | 33 (24%) | 87 (82%) | 39 (91%) | <0.001 | |
Gastrointestinal symptoms | 63 (22%) | 7 (5.0%) | 32 (30%) | 24 (56%) | <0.001 | |
Vascular symptoms | 76 (26%) | 10 (7.2%) | 29 (27%) | 37 (86%) | <0.001 | |
Urinary symptoms | 16 (5.6%) | 2 (1.4%) | 0 (0%) | 14 (33%) | <0.001 | |
Skin symptoms | 66 (23%) | 17 (12%) | 12 (11%) | 37 (86%) | <0.001 | |
Number of persisting symptoms at 12 months Mean (SD) | Total number of symptoms | 8 ± 8 | 2.89 ± 2.15 | 11.5 ± 5.7 | 18 ± 9 | <0.001 |
Number ENT symptoms | 0.70 ± 1.11 | 0.25 ± 0.63 | 1.12 ± 1.24 | 1.09 ± 1.44 | 0.079 | |
Number neurological symptoms | 2.12 ± 2.28 | 0.72 ± 1.27 | 3.27 ± 2.07 | 3.79 ± 2.63 | <0.001 | |
Number general symptoms | 3.02 ± 2.86 | 1.19 ± 1.48 | 4.04 ± 2.30 | 6.44 ± 3.13 | <0.001 | |
Number cardiorespiratory symptoms | 1.36 ± 1.72 | 0.42 ± 0.92 | 2.02 ± 1.65 | 2.81 ± 2.11 | 0.002 | |
Number gastrointestinal symptoms | 0.39 ± 0.87 | 0.079 ± 0.382 | 0.48 ± 0.86 | 1.19 ± 1.35 | 0.010 | |
Number vascular symptoms | 0.39 ± 0.75 | 0.09 ± 0.33 | 0.41 ± 0.73 | 1.35 ± 0.95 | 0.356 | |
Number urinary symptoms | 0.07 ± 0.32 | 0.01 ± 0.11 | 0.00 ± 0.00 | 0.44 ± 0.70 | 0.610 | |
Number skin symptoms | 0.27 ± 0.54 | 0.14 ± 0.38 | 0.13 ± 0.39 | 1.05 ± 0.62 | 0.570 | |
Quality of life N (%) | Could not envisage coping with symptoms long term | 45 (16%) | 11 (7.9%) | 24 (23%) | 10 (23%) | 0.002 |
Poor sleep # | 239 (83%) | 102 (73%) | 99 (93%) | 38 (88%) | <0.001 | |
Altered respiratory quality of life at 1 year | 81 (28%) | 8 (5.8%) | 51 (48%) | 22 (51%) | <0.001 |
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Fischer, A.; Badier, N.; Zhang, L.; Elbéji, A.; Wilmes, P.; Oustric, P.; Benoy, C.; Ollert, M.; Fagherazzi, G. Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study. Int. J. Environ. Res. Public Health 2022, 19, 16018. https://doi.org/10.3390/ijerph192316018
Fischer A, Badier N, Zhang L, Elbéji A, Wilmes P, Oustric P, Benoy C, Ollert M, Fagherazzi G. Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study. International Journal of Environmental Research and Public Health. 2022; 19(23):16018. https://doi.org/10.3390/ijerph192316018
Chicago/Turabian StyleFischer, Aurélie, Nolwenn Badier, Lu Zhang, Abir Elbéji, Paul Wilmes, Pauline Oustric, Charles Benoy, Markus Ollert, and Guy Fagherazzi. 2022. "Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study" International Journal of Environmental Research and Public Health 19, no. 23: 16018. https://doi.org/10.3390/ijerph192316018
APA StyleFischer, A., Badier, N., Zhang, L., Elbéji, A., Wilmes, P., Oustric, P., Benoy, C., Ollert, M., & Fagherazzi, G. (2022). Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study. International Journal of Environmental Research and Public Health, 19(23), 16018. https://doi.org/10.3390/ijerph192316018