Evolving phenotypes of non-hospitalized patients that indicate long COVID
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Abstract
Background
For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection.
Methods
In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized.
Results
We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65.
Conclusions
The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.
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SciScore for 10.1101/2021.04.25.21255923: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Use of clinical data in this study was approved by the MGB Institutional Review Board (IRB) with a waiver of informed consent.
Consent: Use of clinical data in this study was approved by the MGB Institutional Review Board (IRB) with a waiver of informed consent.Sex as a biological variable This resulted in the following strata: 1) all patients, 2) 65 and older, 3) under 65, 4) 65 and older female, 5) 65 and older male, 6) under 65 female, and 7) under 65 male. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to …
SciScore for 10.1101/2021.04.25.21255923: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Use of clinical data in this study was approved by the MGB Institutional Review Board (IRB) with a waiver of informed consent.
Consent: Use of clinical data in this study was approved by the MGB Institutional Review Board (IRB) with a waiver of informed consent.Sex as a biological variable This resulted in the following strata: 1) all patients, 2) 65 and older, 3) under 65, 4) 65 and older female, 5) 65 and older male, 6) under 65 female, and 7) under 65 male. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
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:We acknowledge that this study’s findings may present limitations due to the use of only diagnosis codes, which can result in missing signs and symptoms that are in clinical notes and laboratory results. In addition, given the intensity of the pandemic and spread of misinformation, EHR data may represent confirmatory bias between providers and patients. Finally, we have excluded hospitalized COVID-19 patients. On the one hand, it would be difficult to match hospitalized Coronavirus patients during the COVID era with non-COVID hospitalized patients. On the other hand, the post-COVID syndrome can still be observed in patients who were never hospitalized.12,43–47 Regardless, future PASC studies should include hospitalized patients. Our understanding of COVID-19 and its chronic sequelae is evolving, and new risks are unknown. We do not know who might develop post-COVID syndrome, how long symptoms last, and whether COVID-19 prompts the presentation of chronic diseases. There is a unique opportunity today to understand the post-acute effects that can follow SARS-CoV-2 infection. The ever-increasing adoption and magnitude of clinical data stored in EHR repositories over the past decade provide exceptional opportunities for instrumenting healthcare systems to study evolving pandemic byproducts. Our approach avoids a flood of false positive discoveries, while offering a more probabilistic flexible criterion than the standard phenome-wide association study (PheWAS).
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.
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