Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

  1. M. C. Arjun
  2. Arvind Kumar Singh
  3. Debkumar Pal
  4. Kajal Das
  5. Alekhya G.
  6. Mahalingam Venkateshan
  7. Baijayantimala Mishra
  8. Binod Kumar Patro
  9. Prasanta Raghab Mohapatra
  10. Sonu Hangma Subba

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Abstract

Long COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients.

Methodology

We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID.

Results

We have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were—Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08).

Conclusion

A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

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  1. Version published to 10.1371/journal.pone.0278825
  2. ScreenIT

    SciScore for 10.1101/2022.01.04.21268536: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: After taking verbal consent, a detailed telephonic interview was conducted to record the socio-demographic details, past medical history including chronic disease and substance use, acute manifestations of COVID-19, and the treatment received.
    IRB: Ethical issues: The institutional ethics committee (IEC) of AIIMS Bhubaneswar granted ethical approval before starting the study (IEC Number: T/IM-NF/CM&FM/21/37).
    IACUC: The consent process was approved by the Institutional Ethics Committee.
    Sex as a biological variableIndividuals less than 18 years and pregnant women were excluded.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data was collected using EpiCollect5 and imported into Microsoft Excel for cleaning.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study also had limitations. The Long COVID symptoms were all self-reported, and thus objective assessment of symptoms like fatigue was not done. Telephonic interviews precluded us from collecting additional information like clinical and radiological examination for correlating with the findings. We assessed Long COVID after four weeks, and no further follow-up was done. The cause of death of fifty-two individuals who were not alive during the time of data collection was not enquired, and their death may have been related to Long covid complications. In developed countries, many large-scale cohort studies are undertaken to understand this phenomenon. (35,36) Similar studies on Long COVID are lacking in India, and our research community should bridge this gap. We need more research into Long COVID to objectively assess the symptoms, to monitor the symptoms for a longer duration, and to study the biological and radiological markers, which can lead to better treatment guidelines and comprehensive management of COVID-19 disease.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.01.04.21268536v1 on medRxiv