Time-Varying Mortality Risk Suggests Increased Impact of Thrombosis in Hospitalized Covid-19 Patients

  1. Benjamin J. Lengerich
  2. Mark E. Nunnally
  3. Yin Aphinyanaphongs
  4. Rich Caruana

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Abstract

Treatment protocols, treatment availability, disease understanding, and viral characteristics have changed over the course of the Covid-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers have also changed. We add to the ongoing conversation regarding inflammation, hemostasis and vascular function in Covid-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for Covid-19 in a New York City hospital system from March 2020 to August 2021 to elucidate the changing impact of thrombosis, inflammation, and other risk factors on in-hospital mortality. We find that the predictive power of biomarkers of thrombosis risk have increased over time, suggesting an opportunity for improved care by identifying and targeting therapies for patients with elevated thrombophilic propensity.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.11.21267259: (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
    SentencesResources
    We use the GAM implemented in the Python InterpretML package [9] which is invariant to all monotonic feature transforms, and we estimate confidence intervals (CIs) by bootstrap resampling.
    Python
    suggested: (IPython, RRID:SCR_001658)
    InterpretML
    suggested: None
    We supplement these daily ORs with ORs from 3 logistic regression (LR) models each trained on approximately one third of the patients (LR1 was trained on patients hospitalized from day 1 to day 100, LR2 was trained on patients hospitalized from day 100 to 300, while LR3 was trained on patients hospitalized from day 300 to 527) and observe qualitatively similar patterns in the GAM and the LR models, although the GAM provides more statistical power and better temporal resolution.
    GAM
    suggested: None

    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 with all observational analyses, this study has limitations. Notably, without randomizing interventions, it is difficult to identify a singular cause driving these trends. Several hypotheses would be consistent with these data, including: (1) the SARS-CoV-2 Delta strain shifted the importance of intrinsic risk factors, (2) successful efforts for early detection and treatment of thrombosis risk [16] widened the difference in mortality risk between a serologically defined prothrombotic state and active thromboses, (3) potential interactions between anti-inflammation treatments and haemostasis, vascular function, and thrombophilic propensity [14,15], or (4) a lack of effective thromboprophylaxis treatments in Covid-19: there is little evidence for thromboprophylaxis from heparin in Covid-19 patients [12,13] which may be due to heparin’s reliance on endogenous Antithrombin (AT) [17] which can be reduced in Covid-19 patients [18] --- anticoagulants such as Argatroban [19] or Bivalirudin [20] which do not rely on AT may exert more powerful thromboprophylaxis than heparin in Covid-19 patients, or (5) an alternate process linked to thrombosis risk factors but also potentially implicating other aspects of endothelial and vascular dysfunction. All in all, our results suggest that it may be beneficial to focus more research on patients who have thrombosis or are at high thrombotic risk.

    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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  2. Version published to 10.1101/2021.12.11.21267259v1 on medRxiv