Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae - Scorecard - 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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Clinical Scorecard: Targeted Symptom Profiling Reveals Early Clinical and Proteomic Indicators of Varied Post-COVID Conditions

At a Glance

CategoryDetail
ConditionPost-COVID conditions (PCC), also known as long COVID
Key MechanismsDistinct symptom clusters linked to early inflammatory markers including elevated D-dimer, IL-1RA, and ICAM-1
Target PopulationSARS-CoV-2 positive U.S. Military Health System adult beneficiaries
Care SettingOutpatient and inpatient settings within Military Health System

Key Highlights

  • Identification of three distinct PCC symptom clusters: sensory (loss of smell/taste), fatigue/difficulty thinking, and difficulty breathing/exercise intolerance.
  • Early post-infection inflammatory markers (D-dimer, IL-1RA, ICAM-1) predict specific PCC phenotypes.
  • Symptom clusters correlate with clinical risk factors such as obesity and COVID-19 hospitalization.

Guideline-Based Recommendations

Diagnosis

  • Use symptom-based phenotyping rather than broad nonspecific definitions to classify PCC.
  • Consider symptom clusters at 6 months post-infection for more precise diagnosis.

Management

  • Tailor management strategies based on identified symptom clusters and associated risk factors.
  • Monitor patients with obesity and prior COVID-19 hospitalization closely for respiratory-related PCC symptoms.

Monitoring & Follow-up

  • Longitudinal symptom surveys at 1, 3, 6, 9, and 12 months post-infection to track symptom evolution.
  • Assess inflammatory biomarkers early post-infection to identify patients at risk for specific PCC phenotypes.

Risks

  • Obesity and hospitalization during acute COVID-19 increase risk for respiratory symptom cluster PCC.
  • Sensory symptom cluster associated with outpatient initial COVID-19 presentation.

Patient & Prescribing Data

Adult SARS-CoV-2 positive individuals with moderate-to-severe symptoms at 6 months post-infection

Early identification of inflammatory markers may guide prognosis and targeted therapeutic interventions for distinct PCC phenotypes.

Clinical Best Practices

  • Employ machine learning-based symptom clustering to refine PCC diagnosis and classification.
  • Incorporate early inflammatory biomarker assessment (D-dimer, IL-1RA, ICAM-1) into clinical evaluation.
  • Recognize heterogeneity of PCC and avoid one-size-fits-all definitions or treatments.
  • Use detailed symptom surveys longitudinally to monitor PCC progression and response to interventions.

References

Original Source(s)

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