COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States
Abstract
:1. Introduction
- The use of reported symptoms to identify latent structures via categorical PCA and dimension reduction approaches, using data from the COVID-19 Delphi Facebook study.
- To scrutinize the previously created latent structures as potential COVID-19 phenotypes or phenotyping parameters via TSC and artificial intelligence-based classification.
2. Materials and Methods
2.1. Study Population
2.2. Study Design
2.3. Statistical Analysis
Pattern Recognition via Multiple Correspondence Analysis
2.4. Two-Step Clustering and Phenotype Extraction
2.5. Phenotype Validation: Cross-Sectional and Longitudinal Aspects
- (a)
- The weights extracted from August’s responders were applied to an MCA based on symptom data recorded for each subsequent and preceding month’s responders.
- (b)
- Object scores were calculated for each responder.
- (c)
- Object scores and symptom duration per month were used in TSC.
2.6. Crossectional Validation: Phenotypes vs. Controls
2.7. Longitudinal Validation: Phenotype Re-Emergence and Symptom Invariance
2.8. Post-Hoc Analyses
2.9. Determination of Data-Driven Diagnostic Rules via Decision Tree Analyses
3. Results
3.1. Study Population
3.2. Phenotype Extraction
- Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS).
- Febrile (100%) Multisymptomatic (FMS).
- Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS).
- Oligosymptomatic with additional self-described symptoms (100%; OSDS).
- Olfaction/Gustatory Impairment Predominant (100%; OGIP).
- (a)
- ANCOS and OSDS emerged in 10/10 months
- (b)
- MFS and ACOS emerged in 9/10 months
- (c)
- OGIP emerged in 4/10 months.
- (a)
- ANCOS was characterized by general malaise in the absence of fever and upper respiratory tract symptoms.
- (b)
- ACOS was characterized as a mainly afebrile upper respiratory tract viral infection.
- (c)
- FMS was a more typical, febrile syndrome covering respiratory and gastrointestinal (GI) manifestations.
- (d)
- OGIP, the most invariant syndrome, was characterized by the absence of fever and diarrhea.
- (e)
- OSDS did not typically include symptoms of pain or pressure on the chest, nor difficulty in breathing.
4. Discussion
Limitations and Strengths
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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April (n = 22,320) | May (n = 38,043) | June (n = 51,582) | July (n = 78,951) | August (n = 66,155) | September (n = 12,801) | October (n = 19,137) | November (n = 22,698) | December (n = 48,629) | ||
---|---|---|---|---|---|---|---|---|---|---|
COVID-19 | 4000 | 4955 | 6573 | 13,370 | 10,279 | 1773 | 5936 | 10,026 | 21,617 | |
Age Group | 18–24 | 1783 | 2973 | 4202 | 7047 | 5965 | 1194 | 1211 | 1606 | 3312 |
25–34 | 4856 | 7423 | 9837 | 15,559 | 12,158 | 2204 | 3353 | 4805 | 9002 | |
35–44 | 4794 | 7249 | 8919 | 14,450 | 11,502 | 2116 | 3886 | 4856 | 9529 | |
45–54 | 4281 | 7030 | 8886 | 13,660 | 11,146 | 2119 | 3519 | 4095 | 8809 | |
55–64 | 3220 | 6235 | 8661 | 12,385 | 10,775 | 2173 | 3320 | 3227 | 7440 | |
65–74 | 1447 | 3597 | 5655 | 7791 | 7218 | 1483 | 1808 | 1539 | 3907 | |
>75 | 312 | 907 | 1632 | 2317 | 2155 | 531 | 573 | 427 | 1181 | |
NA | 1627 | 2629 | 3790 | 5742 | 5246 | 981 | 1467 | 2143 | 5449 | |
Gender | M | 5001 | 9754 | 13,230 | 20,313 | 17,427 | 3434 | 4555 | 4752 | 10,684 |
F | 15,459 | 24,985 | 33,672 | 51,556 | 42,364 | 8170 | 12,732 | 15,360 | 31,612 | |
NB | 149 | 284 | 366 | 613 | 532 | 104 | 141 | 176 | 347 | |
SD | 102 | 219 | 252 | 357 | 297 | 72 | 119 | 144 | 248 | |
NA | 153 | 325 | 449 | 653 | 565 | 100 | 128 | 128 | 322 | |
N/A | 1456 | 2486 | 3613 | 5459 | 4980 | 921 | 1462 | 2138 | 5416 | |
Cancer | 1223 | 2338 | 3209 | 4353 | 3783 | 816 | 1082 | 1040 | 2289 | |
HD | 6556 | 7768 | 8450 | 13,352 | 10,792 | 2036 | 4738 | 6213 | 12,229 | |
HTN | 3952 | 4682 | 5129 | 8263 | 6486 | 1302 | 2979 | 3931 | 7785 | |
Asthma | 12,222 | 19,035 | 24,962 | 38,645 | 32,205 | 6031 | 11,461 | 14,309 | 28,597 | |
CLD | 9361 | 14,946 | 19,201 | 30,197 | 25,728 | 5141 | 9644 | 12,504 | 25,272 | |
KD | 8035 | 12,264 | 14,668 | 22,830 | 19,393 | 3907 | 8203 | 10,029 | 20,615 | |
AD | 8807 | 14,702 | 19,365 | 28,438 | 23,692 | 4587 | 8450 | 10,533 | 21,677 | |
Diabetes | T1D | 4817 | 8313 | 9950 | 17,181 | 12,822 | 2627 | 5102 | 5565 | 12,807 |
T2D | 4202 | 4397 | 4556 | 7698 | 5899 | 1110 | 2803 | 3933 | 7743 | |
IC | 3091 | 5150 | 5847 | 10,235 | 7325 | 14755 | 3519 | 4087 | 9238 |
N | AUC | p-Value | 95% CI | |
---|---|---|---|---|
ANCOS (1) | 2506 | <0.5 | NA | NA |
FMS (2) | 2266 | 0.963 | <0.001 | 0.961–0.965 |
ACOS (3) | 2060 | 0.737 | <0.001 | 0.729–0.746 |
OSDS (4) | 1013 | 0.777 | <0.001 | 0.762–0.792 |
OGIP (5) | 2434 | 0.983 | <0.001 | 0.982–0.984 |
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Vavougios, G.D.; Stavrou, V.T.; Konstantatos, C.; Sinigalias, P.-C.; Zarogiannis, S.G.; Kolomvatsos, K.; Stamoulis, G.; Gourgoulianis, K.I. COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States. Int. J. Environ. Res. Public Health 2022, 19, 4630. https://doi.org/10.3390/ijerph19084630
Vavougios GD, Stavrou VT, Konstantatos C, Sinigalias P-C, Zarogiannis SG, Kolomvatsos K, Stamoulis G, Gourgoulianis KI. COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States. International Journal of Environmental Research and Public Health. 2022; 19(8):4630. https://doi.org/10.3390/ijerph19084630
Chicago/Turabian StyleVavougios, George D., Vasileios T. Stavrou, Christoforos Konstantatos, Pavlos-Christoforos Sinigalias, Sotirios G. Zarogiannis, Konstantinos Kolomvatsos, George Stamoulis, and Konstantinos I. Gourgoulianis. 2022. "COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States" International Journal of Environmental Research and Public Health 19, no. 8: 4630. https://doi.org/10.3390/ijerph19084630
APA StyleVavougios, G. D., Stavrou, V. T., Konstantatos, C., Sinigalias, P. -C., Zarogiannis, S. G., Kolomvatsos, K., Stamoulis, G., & Gourgoulianis, K. I. (2022). COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States. International Journal of Environmental Research and Public Health, 19(8), 4630. https://doi.org/10.3390/ijerph19084630