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Biometric covariates and outcome in COVID-19 patients: are we looking close enough?
Sharafutdinov, Konstantin; Fritsch, Sebastian Johannes; Marx, Gernot; Bickenbach, Johannes; Schuppert, Andreas.
  • Sharafutdinov K; Institute for Computational Biomedicine, RWTH Aachen University, Pauwelsstr. 19, 52074, Aachen, Germany.
  • Fritsch SJ; Joint Research Center for Computational Biomedicine, RWTH Aachen University, Pauwelsstr. 19, 52074, Aachen, Germany.
  • Marx G; Department of Intensive Care Medicine, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany. sfritsch@ukaachen.de.
  • Bickenbach J; Juelich Supercomputing Centre, Forschungszentrum Juelich, Wilhelm-Johnen-Straße, 52428, Jülich, Germany. sfritsch@ukaachen.de.
  • Schuppert A; Department of Intensive Care Medicine, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany.
BMC Infect Dis ; 21(1): 1136, 2021 Nov 04.
Article in English | MEDLINE | ID: covidwho-1504761
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ABSTRACT

BACKGROUND:

The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients.

METHODS:

We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step.

RESULTS:

We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group.

CONCLUSIONS:

The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article Affiliation country: S12879-021-06823-z

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article Affiliation country: S12879-021-06823-z