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[Preprint]. 2021 Mar 5:2021.03.03.21252086.
doi: 10.1101/2021.03.03.21252086.

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler: Looking for Clarity in the Haze of the Pandemic

Yong Huang  1 , Melissa D Pinto  2 , Jessica L Borelli  3 , Milad Asgari Mehrabadi  1 , Heather Abrihim  2 , Nikil Dutt  1 , Natalie Lambert  4 , Erika L Nurmi  5 , Rana Chakraborty  6 , Amir M Rahmani  1   2 , Charles A Downs  7
Affiliations

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler: Looking for Clarity in the Haze of the Pandemic

Yong Huang et al. medRxiv. .

Update in

Abstract

Emerging data suggest that the effects of infection with SARS-CoV-2 are far reaching extending beyond those with severe acute disease. Specifically, the presence of persistent symptoms after apparent resolution from COVID-19 have frequently been reported throughout the pandemic by individuals labeled as "long-haulers". The purpose of this study was to assess for symptoms at days 0-10 and 61+ among subjects with PCR-confirmed SARS-CoV-2 infection. The University of California COvid Research Data Set (UC CORDS) was used to identify 1407 records that met inclusion criteria. Symptoms attributable to COVID-19 were extracted from the electronic health record. Symptoms reported over the previous year prior to COVID-19 were excluded, using nonnegative matrix factorization (NMF) followed by graph lasso to assess relationships between symptoms. A model was developed predictive for becoming a long-hauler based on symptoms. 27% reported persistent symptoms after 60 days. Women were more likely to become long-haulers, and all age groups were represented with those aged 50 ± 20 years comprising 72% of cases. Presenting symptoms included palpitations, chronic rhinitis, dysgeusia, chills, insomnia, hyperhidrosis, anxiety, sore throat, and headache among others. We identified 5 symptom clusters at day 61+: chest pain-cough, dyspnea-cough, anxiety-tachycardia, abdominal pain-nausea, and low back pain-joint pain. Long-haulers represent a very significant public health concern, and there are no guidelines to address their diagnosis and management. Additional studies are urgently needed that focus on the physical, mental, and emotional impact of long-term COVID-19 survivors who become long-haulers.

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Figures

Figure 1.
Figure 1.. Demographics and symptoms prevalence among SARS-CoV-2 infected community dwellers at days 0–10
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=1345) at days 0–10. (B) Bar graph showing prevalence of symptoms reported at days 0–10. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 1.
Figure 1.. Demographics and symptoms prevalence among SARS-CoV-2 infected community dwellers at days 0–10
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=1345) at days 0–10. (B) Bar graph showing prevalence of symptoms reported at days 0–10. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 1.
Figure 1.. Demographics and symptoms prevalence among SARS-CoV-2 infected community dwellers at days 0–10
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=1345) at days 0–10. (B) Bar graph showing prevalence of symptoms reported at days 0–10. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 1.
Figure 1.. Demographics and symptoms prevalence among SARS-CoV-2 infected community dwellers at days 0–10
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=1345) at days 0–10. (B) Bar graph showing prevalence of symptoms reported at days 0–10. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 2.
Figure 2.. Demographics and symptom prevalence among SARS CoV-2 infected community dwellers at day 61+.
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=379) at days 61+. (B) Bar graph showing prevalence of symptoms reported at days 61+. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 2.
Figure 2.. Demographics and symptom prevalence among SARS CoV-2 infected community dwellers at day 61+.
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=379) at days 61+. (B) Bar graph showing prevalence of symptoms reported at days 61+. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 2.
Figure 2.. Demographics and symptom prevalence among SARS CoV-2 infected community dwellers at day 61+.
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=379) at days 61+. (B) Bar graph showing prevalence of symptoms reported at days 61+. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 2.
Figure 2.. Demographics and symptom prevalence among SARS CoV-2 infected community dwellers at day 61+.
(A) Bar and pie graphs showing distribution of age, ethnicity, and sex demographics (N=379) at days 61+. (B) Bar graph showing prevalence of symptoms reported at days 61+. (C) Graph of symptom clusters with (D) corresponding bar graphs demonstrating symptom ranking within each cluster, and (E) symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node and the darker the line connecting symptoms indicates a stronger relationship.
Figure 3.
Figure 3.. Key features during days 0–10 and their potential as indicators for developing prolonged COVID-19 symptoms or being a long-hauler.
Bar graph showing factors that positively or negatively affect the probability of developing persistent symptoms among COVID+ community dwellers.
Figure 4.
Figure 4.. Presence of key indicators at days 0–10 predict inclusion into specific symptom clusters reported at day 61+.
(A) NFM determined symptom clusters from SARS-CoV-2 infected community dwellers at day 61+; symptom clusters are named based upon the two most prevalent symptoms reported within each cluster. (B) Heat map demonstrating magnitude of association between key predictors from day 0–10 and assignment to a cluster with darker coloring indicating greater positive magnitude.
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