Impact of Clinical and Genomic Factors on SARS-CoV2 Disease Severity
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Abstract
The SARS-CoV2 virus behind the COVID-19 pandemic is manifesting itself in different ways among infected people. While many are experiencing mild flue-like symptoms or are even remaining asymptomatic after infection, the virus has also led to serious complications, overloading ICUs while claiming more than 2.6 million lives world-wide. In this work, we apply AI methods to better understand factors that drive the severity of the disease. From the UK BioBank dataset we analyzed both clinical and genomic data of patients infected by this virus. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework for genomic feature extraction, we propose severity prediction algorithms with high F 1 score. Furthermore, we extracted insights on clinical and genomic factors driving the severity prediction. We also report on how these factors have evolved during the pandemic w.r.t. significant events such as the emergence of the B.1.1.7 SARS-CoV2 virus strain.
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SciScore for 10.1101/2021.03.15.21253549: (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
Software and Algorithms Sentences Resources All of the QC analysis was done using PLINK v1.911. PLINKsuggested: (PLINK, RRID:SCR_001757)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:As a simple use-case, we report the changes of impact of the top 25 biomarkers (instead of all 50 due to space limitation) for the first approved UK drug called ‘Dexamethazone’ with its approval date as June 15, 2020 in Figure 3. We …
SciScore for 10.1101/2021.03.15.21253549: (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
Software and Algorithms Sentences Resources All of the QC analysis was done using PLINK v1.911. PLINKsuggested: (PLINK, RRID:SCR_001757)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:As a simple use-case, we report the changes of impact of the top 25 biomarkers (instead of all 50 due to space limitation) for the first approved UK drug called ‘Dexamethazone’ with its approval date as June 15, 2020 in Figure 3. We can observe that most of the 25 significant factors from the whole dataset still had similar impact after the drug being used, but only 5 features had significant impact before the drug was used. Similarly, we also wanted to assess how the impacts of clinico-genomic features change over different strains of SARS-CoV-2. In the UK, a reportedly more contagious strain named B.1.1.729 started surfacing from early September 2020 and it is estimated that by December 2020, over 60% of new COVID-19 patients had the newer strain. The UKBB also did not collect the virus genome prospectively. So the only way to assess the impact of clinico-genomic biomarkers is through the date when a patient was diagnosed with COVID-19. We divided the whole cohort into three parts: those diagnosed before Aug 31, 2020 (older strain), diagnosed between September 1st, 2020 and December 15, 2020 (both strains) and after December 15, 2020 (newer strain). Figure 4 shows how the effect of the top clinico-genomic features obtained by the global model change over these three cohorts. We can observe that some factors like age, prior DM condition and number of medication are consistent across both strains, while the smoking history (current and present) had larger impact on older stra...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
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.
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