Vital signs and lab results at the time of hospital admission – not comorbidities and demographics – are the most accurate predictors of disease severity, found a study in Scientific Reports.
“Our models show that chronic conditions, comorbidities, sex, race and ethnicity are much less important in the hospital setting for early prediction of critical illness,” said Dr Sevda Molani, lead author.
Molani and team looked at risk factors based on two age groups of hospital patients, one being between 18 and 50 years old and the other being 50 or older, and found that risk factors that lead to severe cases and/or death differ with younger vs. older patients.
For example:
• Body mass index is a more important predictor of COVID-19 severity for younger patients than for older patients;
• Many comorbidities, such as malignancy, cardiomyopathy and COPD, have higher odds ratios for severe outcomes in younger patients than in older patients;
• For both older and younger patients, vital signs, early hospital laboratory tests and the need for supplemental oxygen are more useful for predicting severe outcomes than comorbidities and demographics.
The findings are meaningful in the clinical setting.
“Risk prediction in COVID-19 is complex as the disease course is highly variable between people, ranging from completely asymptomatic in some to critical illness or death in others. While age is known to be highly predictive of death, other risk factors within age strata are incompletely explored.
“This study challenges our dogma that comorbidities are the major drivers of severe outcomes like mechanical ventilation or death in patients with COVID-19. Instead, we find that other physiological features that can be measured within one hour of hospitalisation more strongly predict who will go on to severe outcomes,” said Dr Jason Goldman, an infectious disease specialist at Swedish Providence and a member of the study team.
“These findings remind the treating clinician to incorporate physiological parameters into risk stratification, and subsequently into decisions on treatment allocations.”
The retrospective study examined the electronic health records of more than 6,900 patients between 31 June and 15 November 2021. The vast majority of patients hospitalised with COVID-19 – 92% of the younger patients and 75% of the older patients – had not received COVID-19 vaccination.
Existing risk models for hospitalised patients were developed early on in the pandemic. This research addresses the need for updated models that reflect current standard of care for COVID-19, where fewer uncommon labs are used, and more therapeutic treatment options are available. Future investigations will benefit from finer granularity of subdivisions by age, BMI, and more detailed variables on conditions and drugs that affect individual immune response.
“Chronic medical conditions are still important risk factors for severe COVID-19. However, when a patient has just been admitted to hospital, their current status can be more helpful in predicting what level of care they are likely to need,” said Institute for Systems Biology assistant professor Dr Jennifer Hadlock, corresponding author of the study.
“As the standards of care for COVID-19 evolve, our risk models need to evolve with them.”
The collaborative study was conducted by researchers at Institute for Systems Biology, Swedish Providence, Onegevity and Mayo Clinic Jacksonville.
Study details
Risk factors for severe COVID-19 differ by age for hospitalized adults.
Sevda Molani, Patricia Hernandez, Ryan Roper, Venkata Duvvuri, Andrew Baumgartner, Jason Goldman, Nilüfer Ertekin-Taner, Cory Funk, Nathan Price, Noa Rappaport, Jennifer Hadlock.
Published in Scientific Reports on 28 April 2022.
Abstract
Risk stratification for hospitalised adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimised to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments.
This retrospective study analysed data from 6906 hospitalised adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalisation, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71–0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77–0.86).
Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalised patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.
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20% of hospitalised COVID-19 patients with diabetes died within 28 days — CORONADO
Tocilizumab reduces deaths in hospitalised COVID-19 patients — RECOVERY trial
Almost half of hospitalised COVID-19 patients show heart scan abnormalities — Global study
Anti-coagulants may improve survival in hospitalised COVID-19 patients