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Background <p>Although, COVID-19 has resulted in over 7 million deaths globally, many questions still remain about the risk factors for disease severity and the effects of variants and vaccinations over the course of the pandemic. To address this gap, we conducted a retrospective analysis of electronic health records from COVID-19 patients over 2.5 years of the COVID-19 pandemic to identify associated clinical features.</p> Methods <p>We analyze a retrospective cohort of 21,312 acute-care patients over a 2.5 year period and define six clinical trajectory groups (TGs) associated with demographics, diagnoses, vitals, labs, imaging, consultations, and medications.</p> Results <p>We show that the proportion of mild patients increased over time, particularly during Omicron waves. Additionally, while mild and fatal patients had differences in age, age did not distinguish patients with severe versus critical disease. Furthermore, we find that both male sex and Hispanic/Latino ethnicity are associated with more severe/critical TGs. More severe patients also have a higher rate of neuropsychiatric diagnoses and consultations, along with an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin is one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (e.g. Vitamin D, thiamine, B vitamins) or received vitamin supplementation.</p> Conclusions <p>Our findings expand on clinical risk factors in COVID-19, and highlight the interaction between severity, nutritional status, and neuropsychiatric complications in acute care patients to enable identification of patients at risk for severe disease.</p>

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Dissecting clinical features of COVID-19 in a cohort of 21,312 acute care patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Cole Maguire,
  • Elie Soloveichik,
  • Netta Blinchevsky,
  • Jaimie Miller,
  • Robert Morrison,
  • Johanna Busch,
  • W. Michael Brode,
  • Dennis Wylie,
  • Justin Rousseau,
  • Esther Melamed

摘要

Background

Although, COVID-19 has resulted in over 7 million deaths globally, many questions still remain about the risk factors for disease severity and the effects of variants and vaccinations over the course of the pandemic. To address this gap, we conducted a retrospective analysis of electronic health records from COVID-19 patients over 2.5 years of the COVID-19 pandemic to identify associated clinical features.

Methods

We analyze a retrospective cohort of 21,312 acute-care patients over a 2.5 year period and define six clinical trajectory groups (TGs) associated with demographics, diagnoses, vitals, labs, imaging, consultations, and medications.

Results

We show that the proportion of mild patients increased over time, particularly during Omicron waves. Additionally, while mild and fatal patients had differences in age, age did not distinguish patients with severe versus critical disease. Furthermore, we find that both male sex and Hispanic/Latino ethnicity are associated with more severe/critical TGs. More severe patients also have a higher rate of neuropsychiatric diagnoses and consultations, along with an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin is one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (e.g. Vitamin D, thiamine, B vitamins) or received vitamin supplementation.

Conclusions

Our findings expand on clinical risk factors in COVID-19, and highlight the interaction between severity, nutritional status, and neuropsychiatric complications in acute care patients to enable identification of patients at risk for severe disease.