COVID 19 - Clinical Picture in the Elderly Population: A Qualitative Systematic Review

The SARS-CoV-2 tendency to affect the older individuals more severely, raises the need for a concise summary isolating this age population. Analysis of clinical features in light of most recently published data allows for improved understanding, and better clinical judgement. A thorough search was performed to collect all articles published from 1st of January to 1st of June 2020, using the keywords COVID-19 and SARS-CoV-2 followed by the generic terms elderly, older adults or older individuals. The quality assessment of studies and findings was performed by an adaptation of the STROBE statement and CERQual approach. Excluding duplicates, a total of 1598 articles were screened, of which 20 studies were included in the final analysis, pertaining to 4965 older COVID-19 patients (≥60 years old). Variety in symptoms was observed, with fever, cough, dyspnea, fatigue, or sputum production being the most common. Prominent changes in laboratory findings consistently indicated lymphopenia and inflammation and in some cases organ damage. Radiological examination reveals ground glass opacities with occasional consolidations, bilaterally, with a possible peripheral tendency. An evident fraction of the elderly population (25.7%) developed renal injury or impairment as a complication. Roughly 71.4% of the older adults require supplementary oxygen, while invasive mechanical ventilation was required in almost a third of the reported hospitalized older individuals. In this review, death occurred in 20.0% of total patients with a recorded outcome (907/4531). Variability in confidence of findings is documented. Variety in symptom presentation is to be expected, and abnormalities in laboratory findings are present. Risk for mortality is evident, and attention to the need for supplementary oxygen and possible mechanical ventilation is advised. Further data is required isolating this age population. Presented literature may allow for the construction of better predictive models of COVID-19 in older populations.