Factors Influencing the Deaths of Coronavirus Disease 2019 (COVID-19) Patients, Phang-nga Province
Abstract
This research aimed to describe the epidemiological characteristics of patients with Coronavirus Disease 2019 (COVID-19) and 2) to analyze risk factors for mortality in patients with COVID-19. A total of 369 study subjects were purposively selected from COVID-19 patients who were treated at hospitals in Phang-Nga province during the outbreak period from April 1, 2021 - May 30, 2022. Research findings revealed that majority of the samples were in the age group > 60 years old (72.9%), Thai nationality (98.4%), marital status (79.9%), Buddhism (77.8%), unemployed/housewife (51.2%), no underlying disease (58.3%), high risk contact patients (68.3%), the source of infection was at home (44.7%), X-Ray results of lung lesions were normal (78.0%), oxygen saturation 96 – 100 % (79.7 %) and receiving antiretroviral drugs (73.7%). Risk factors influencing mortality in patients with COVID-19 with statistical significance (P-value < 0.05) by multiple logistic regression analysis were: comorbidities (aOR 11.49, 95% CI 1.34 - 98.60, P-value = 0.02), radiographic results of lung lesions (aOR 66.06, 95% CI 16.15 - 270.14, P-value < 0.01), hypertension (aOR 4.89, 95% CI 1.11 - 21.42, P-value = 0.03), shortness of breath (aOR 8.56, 95% CI 2.59 - 28.33, P-value < 0.01), having sputum (aOR 29.44, 95% CI 4.48 - 193.46, P-value < 0.01), Neutrophil < 70 % (aOR 16.43, 95% CI 4.45 - 60.59, P-value < 0.01), and N Gene (Cycle Threshold) (aOR 5.33, 95% CI 1.58 - 17.98, P-value < 0.01). The research findings concluded that comorbidities, hypertension, abnormal pulmonary lesion radiographs, shortness of breath, having sputum, neutrophils < 70%, and N Gene (Cycle Threshold) < 20 were pivotal predictors for COVID-19 mortality. Thus, patients with underlying diseases must be promptly diagnosed and appropriately treated for both COVID-19 and its comorbidities. The neutrophil content and number of cycles of SARS-CoV-2): 2019-nCoV N gene could be used to predict patient severity and mortality.
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