Clinical characteristics and survival analysis of hospitalised COVID-19 patients in Ogun State, Nigeria
DOI:
https://doi.org/10.38029/babcockuniv.med.j..v9i1.638Keywords:
COVID-19, Clinical characteristics, Outcome, Survival analysis, PredictorsAbstract
Objective: Despite the end of the pandemic, COVID-19 remains a disease of global concern. This study aimed to describe the clinical characteristics, outcomes, and survival analysis of hospitalised COVID-19 patients in Ogun State, Nigeria, with a view to providing evidence on the survival of hospitalised COVID-19 patients.
Methods: The study examined the medical records of COVID-19 patients at the Olabisi Onabanjo University Teaching Hospital COVID-19 Isolation Centre in Sagamu, Ogun State, between March and December 2020. Data was analysed using SPSS version 22, with chi-square tests for association and logistic regression for mortality predictors. Kaplan-Meier curves and Log-rank tests were used for survival analysis.
Results: The study involved 273 patients, with a mean age of 45.33±16.9 years. The majority were males, had symptoms (51.6%), and had SPO2 ≥ 94% (82.4%) at presentation. Most were discharged (94.1%), while 5.1% died. Over half presented with fever (55.3%) and cough (51.8%), and one-third had comorbidities. Most of those with comorbidities had hypertension (73.3%). The presence of two or more comorbid conditions (AOR 9.5, 95% CI 1.8 – 50.6; p = 0.008) and oxygen saturation less than 94% at admission (AOR 19.5, 95%CI 3.0 – 128.0; p = 0.002) were predictors of mortality. A significant difference was observed in the Kaplan-Meier curve regarding age group, symptom presence, comorbid conditions, and oxygen saturation at admission.
Conclusion: The study found higher mortality rates due to co-morbidities and low oxygen saturation at admission, emphasising the need for early diagnosis, prompt referral, and management of patients with co-morbidities.
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Copyright (c) 2025 Bamidele JO, Adeyemi O, Egbetola BO, Ayeni VA, Adefuye BO, Fatungase OM, Jaiyesimi EO, Coker MO, Soyinka FO, Alabi AD, Olaitan AO, Asare TD, Gbadebo AA, Ojo AA, Daniel OJ

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
