Routine laboratory parameters, including complete blood count, predict COVID-19 in-hospital mortality in geriatric patients.
Mech Ageing Dev
; 204: 111674, 2022 06.
Article
in English
| MEDLINE | ID: covidwho-2015815
ABSTRACT
To reduce the mortality of COVID-19 older patients, clear criteria to predict in-hospital mortality are urgently needed. Here, we aimed to evaluate the performance of selected routine laboratory biomarkers in improving the prediction of in-hospital mortality in 641 consecutive COVID-19 geriatric patients (mean age 86.6 ± 6.8) who were hospitalized at the INRCA hospital (Ancona, Italy). Thirty-four percent of the enrolled patients were deceased during the in-hospital stay. The percentage of severely frail patients, assessed with the Clinical Frailty Scale, was significantly increased in deceased patients compared to the survived ones. The age-adjusted Charlson comorbidity index (CCI) score was not significantly associated with an increased risk of death. Among the routine parameters, neutrophilia, eosinopenia, lymphopenia, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein, procalcitonin, IL-6, and NT-proBNP showed the highest predictive values. The fully adjusted Cox regressions models confirmed that high neutrophil %, NLR, derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte count, eosinophil %, and lymphocyte-to-monocyte ratio (LMR) were the best predictors of in-hospital mortality, independently from age, gender, and other potential confounders. Overall, our results strongly support the use of routine parameters, including complete blood count, in geriatric patients to predict COVID-19 in-hospital mortality, independent from baseline comorbidities and frailty.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Frailty
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Aged
/
Humans
Language:
English
Journal:
Mech Ageing Dev
Year:
2022
Document Type:
Article
Affiliation country:
J.mad.2022.111674
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