Background: The older population is often disproportionately and adversely affected during humanitarian emergencies, as has also been seen during the COVID-19 pandemic. Data regarding COVID-19 in older adults is usually over-generalised and does not delve into details of the clinical characteristics in them. This study was conducted to analyse clinical and laboratory characteristics, risk factors, and complications of COVID-19 between older adults who survived and those who did not.

Methods: We conducted a case-control study among older adults(age > 60 years) admitted to the Intensive Care Unit(ICU) during the COVID-19 pandemic. The non-survivors (cases) were matched with age and sex-matched survivors (control) in a ratio of 1: 3. The data regarding socio-demographics, clinical characteristics, complications, treatment, laboratory data, and outcomes were analysed.

Results: The most common signs and symptoms observed were fever (cases vs controls) (68.92 vs. 68.8%), followed by shortness of breath (62.2% Vs. 52.2%), and cough (47.3% Vs. 60.2%). Our analysis found no association between the presence of any of the comorbidities and mortality. At admission, laboratory markers such as LDH(Lactate Dehydrogenase), WBC(White Blood Count), creatinine, CRP(C-Reactive Protein), D-dimer, ferritin, and IL-6(Interleukin-6) were found to be significantly higher among the cases than among the controls. Complications such as development of seizure, bacteremia, acute renal injury, respiratory failure, and septic shock were seen to have a significant association with non-survivors.

Conclusions: Hypoxia, tachycardia, and tachypnoea at presentation were associated with higher mortality. The older adults in this study mostly presented with the typical clinical features of COVID-19 pneumonia. The presence of comorbid illnesses among them did not affect mortality. Higher death was seen among those with higher levels of CRP, LDH, D-dimer, and ferritin; and with lower lymphocyte counts.

A hospital-based case-control study was undertaken. Data was collected from the Intensive Care Unit(ICU) from December 2020 to September 2022. The sample size was calculated with a two-sided confidence level(1-α) of 95, 80% power, and with a ratio of controls to cases at 3:1. A sample size of 260 was calculated consisting of 195 controls and 65 cases. A Case was defined as a COVID-19-positive individual older than 60 years who, after being admitted or transferred to the ICU, did not survive, i.e., non-survivor. A Control was defined as a COVID-19-positive individual with age greater than 60 years who was admitted or transferred to the ICU, following which the patient recovered(survived) and was discharged alive from the hospital, i.e., survivor. Those patients who were admitted for post-COVID-19 complications or for COVID-19 unrelated medical conditions following discharge after initial treatment for COVID-19 pneumonia were excluded. The cases (non-survivors) were recruited according to the inclusion and exclusion criteria mentioned above and were then matched with an age and sex-matched control (survivor) in a ratio of 1: 3, respectively. The data regarding socio-demographics, clinical characteristics, complications, treatment, laboratory data, and outcomes were collected using a modified ISARIC form. The patient's identity was anonymized by assigning a code. The comorbidities and risk factors recorded in the study were chronic cardiac disease(including hypertension), chronic pulmonary disease(including asthma), chronic kidney disease, obesity, liver disease, asplenia, chronic neurological disorder, malignant neoplasm, chronic hematological disease, AIDS/HIV, diabetes mellitus, rheumatological disorder, dementia, tuberculosis, malnutrition, and smoking. Before the study's launch and data collection, approval was acquired from the Institutional Ethics Committee and the medical directors of the participating institutions. Data collection was done using Microsoft Excel. Data were analysed using the IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.

The data were expressed as mean and SD for continuous variables. Based on the type of distribution of data, a t-test or Mann-Whitney U test was applied. The categorical variables were analysed using Pearson's chi-square or Fisher's exact test based on the data distribution. 

Microsoft Excel, Word