PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes

  1. Liqin Wang
  2. Dinah Foer
  3. Erin MacPhaul
  4. Ying-Chih Lo
  5. David W. Bates
  6. Li Zhou

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Abstract

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  1. Version published to 10.1016/j.jbi.2021.103951
  2. ScreenIT

    SciScore for 10.1101/2021.07.29.21261260: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved by the institutional review board of MGB with waiver of informed consent from study participants for secondary use of EHR data.
    Consent: This study was approved by the institutional review board of MGB with waiver of informed consent from study participants for secondary use of EHR data.
    Sex as a biological variablenot detected.
    RandomizationWith each lexicon revision, we evaluated the performance of NLP for its ability to identify symptoms from a random set of clinical notes in the development dataset.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: The symptom lexicon was developed using data from a multi-institution, U.S.-based health care system using a single EHR. Documentation patterns, preferred terminology, and local dialect may differ by geographic location, health system, and EHR vendor. This might impact symptom prevalence or lead to missed symptoms common in other populations. However, the large cohort sample size, and similarities of our findings to those from meta-analyses of prospective and retrospective studies in clinically and geographically distinct populations, supports the external validity and generalizability of this work. Future studies might be needed to replicate the symptom lexicon development pipeline in other EHR systems. Lexicon development was further enriched by manual review to minimize false positive and false negative symptom terminology in the EHR context and to enhance clinical relevance by term consolidation. Several limitations are inherent in use of EHR data to study post-acute COVID-19 symptoms. First, our study is limited to patients with clinical follow-up in the healthcare system. Patients may have sought out-of-system care post-COVID-19 infection; these patients’ symptoms would not be captured in our lexicon. However, travel advisories during COVID-19 may have limited the ability of patients to seek out of system (or out of state) care, improving our ability to capture encounters in the post-COVID-19 period. Second, the EHR reflects routine care, and therefore, sym...

    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

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  3. Version published to 10.1101/2021.07.29.21261260v1 on medRxiv