Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae - Summary - MDSpire

Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae

  • By

  • Nusrat J Epsi

  • Josh G Chenoweth

  • Paul W Blair

  • David A Lindholm

  • Anuradha Ganesan

  • Tahaniyat Lalani

  • Alfred Smith

  • Rupal M Mody

  • Milissa U Jones

  • Rhonda E Colombo

  • Christopher J Colombo

  • Christina Schofield

  • Evan C Ewers

  • Derek T Larson

  • Catherine M Berjohn

  • Ryan C Maves

  • Anthony C Fries

  • David Chang

  • Andrew Wyatt

  • Ann I Scher

  • Celia Byrne

  • Jennifer Rusiecki

  • David L Saunders

  • Jeffrey Livezey

  • Allison Malloy

  • Samantha Bazan

  • Carlos Maldonado

  • Margaret Sanchez Edwards

  • Katrin Mende

  • Mark P Simons

  • Robert J O’Connell

  • David R Tribble

  • Brian K Agan

  • Timothy H Burgess

  • Simon D Pollett

  • Stephanie A Richard

  • June 25, 2024

  • 0 min

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Objective:

To improve the definition of post-COVID conditions (PCC) through a data-driven approach to phenotyping, specifically by utilizing advanced statistical methods to identify clinical predictors and inflammatory responses associated with chronic symptom phenotypes.

Key Findings:
  • Three symptom-based clusters identified: sensory, fatigue/difficulty thinking, and difficulty breathing/exercise intolerance.
  • Individuals in the sensory cluster were all outpatients during initial COVID-19 presentation.
  • The difficulty breathing cluster had higher rates of obesity and hospitalization compared to those with no/mild symptoms.
  • Early post-infection inflammatory markers were linked to specific symptom clusters.
Interpretation:

The study provides a framework for classifying PCC cases based on symptom phenotypes and early inflammatory predictors, which could enhance diagnosis and treatment strategies.

Limitations:
  • The study is limited to U.S. Military Health System beneficiaries, which may not represent the general population, potentially affecting the generalizability of the findings.
  • Reliance on self-reported data for symptom assessment and hospitalization status may introduce bias, as participants' perceptions and reporting accuracy can vary.
Conclusion:

Identifying distinct PCC phenotypes with specific clinical risk factors and inflammatory predictors may significantly enhance our understanding and management of post-COVID conditions, leading to improved patient outcomes.

Original Source(s)

Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae

  • by Nusrat J Epsi, Josh G Chenoweth, Paul W Blair, David A Lindholm, Anuradha Ganesan, Tahaniyat Lalani, Alfred Smith, Rupal M Mody, Milissa U Jones, Rhonda E Colombo, Christopher J Colombo, Christina Schofield, Evan C Ewers, Derek T Larson, Catherine M Berjohn, Ryan C Maves, Anthony C Fries, David Chang, Andrew Wyatt, Ann I Scher, Celia Byrne, Jennifer Rusiecki, David L Saunders, Jeffrey Livezey, Allison Malloy, Samantha Bazan, Carlos Maldonado, Margaret Sanchez Edwards, Katrin Mende, Mark P Simons, Robert J O’Connell, David R Tribble, Brian K Agan, Timothy H Burgess, Simon D Pollett, Stephanie A Richard

  • June 25, 2024

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