Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs
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Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
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SciScore for 10.1101/2022.05.24.22275398: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.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:Study limitations: While our study provides insight into the variability and natural history of long COVID, there are limitations that should be considered. While the U09.9 code provides a simple inclusion criterion, its application in health systems across the country is not uniform and may differ from one data partner to another. Also, …
SciScore for 10.1101/2022.05.24.22275398: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.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:Study limitations: While our study provides insight into the variability and natural history of long COVID, there are limitations that should be considered. While the U09.9 code provides a simple inclusion criterion, its application in health systems across the country is not uniform and may differ from one data partner to another. Also, since the use of the code began only recently, patients with long COVID that were diagnosed prior to the introduction of the code are not included, limiting our ability to compare the current clinical manifestations with those observed earlier in the pandemic before widespread vaccination and with different distributions of SARS-CoV2 strains and variants. However, in a pilot study in Denmark, coding with U09.9 was found to have a positive predictive value of 94% for long COVID.56 Our ability to capture clinical manifestations of long COVID is limited by the accessibility of clinical data in EHR systems. Of the 287 HPO terms we identified as being used in published cohort studies on long COVID,19 only 116 were identified in our data. The reasons for this presumably include unstructured data such as symptoms and radiological findings that are not well represented in the OMOP data that is the source of our data. Examples include Gaze-evoked nystagmus (HP:0000640), Pericardial effusion (HP:0001698), and Exercise intolerance (HP:0003546) that are typically diagnosed using specialist examinations or medical history that may not be easily coded in s...
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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