C-Reactive Protein Trajectories by Summary Metric Across the Coronavirus-2019 Period: A 16-Year Interrupted

Jeong Su Han1, Bo Kyeung Jung2, Jae-Sik Jeon1

  • 1Department of Biomedical Laboratory Science, College of Health Sciences, Dankook University, Cheonan-si 31116, Republic of Korea.

Insights

Summarizing C-reactive protein (CRP) trends with a single mean is unclear. Different CRP measures showed varied long-term patterns and post-2020 changes, suggesting shifts missed by simple mean surveillance.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Biostatistics

Background:

  • The clinical utility of summarizing long-term C-reactive protein (CRP) trends using a single mean is not well-established.
  • Understanding annual changes in CRP test volume and CRP level distributions is crucial, especially across significant public health events like the COVID-19 pandemic.

Purpose of the Study:

  • To systematically characterize annual changes in CRP test volume and CRP level distributions.
  • To evaluate long-term trends and temporal changes in CRP levels around the COVID-19 pandemic period using laboratory data.

Main Methods:

  • Analysis of 1,845,258 CRP values from Dankook University Hospital (2008-2023).
  • Calculation of annual arithmetic, harmonic, and geometric means.
  • Application of weighted least squares (WLS) regression and interrupted time-series (ITS) models to assess trends and pandemic-related shifts.

Main Results:

  • Annual CRP test volume fluctuated, with a notable drop in 2020.
  • The arithmetic mean showed no long-term trend, while harmonic and geometric means declined.
  • ITS models revealed no immediate significant shift in 2020, but post-2020 slope changes indicated a decline in the arithmetic mean and an attenuated decline in the harmonic mean.

Conclusions:

  • Different CRP summary measures exhibit distinct long-term patterns and post-2020 trend modifications.
  • No abrupt shift in CRP levels was observed in 2020, suggesting stratum-specific changes potentially missed by arithmetic mean surveillance.
  • Findings are institution-specific and require multicenter validation for broader implications.