ABSTRACT
BACKGROUND: Biomarkers that are cost-effective and accurate for predicting severe coronavirus disease 2019 (COVID-19) are urgently needed. We would like to assess the role of various inflammatory biomarkers on admission as disease severity predictors and determine the optimal cut-off of the neutrophile-to-lymphocyte ratio (NLR) for predicting severe COVID-19. METHODS: We conducted a cross-sectional study in six hospitals in Bali and recruited real-time PCR-confirmed COVID-19 patients aged >18 y from June to August 2020. Data collection included each patient's demographic, clinical, disease severity and hematological data. Multivariate and receiver operating characteristic curve analyses were performed. RESULTS: A total of 95 Indonesian COVID-19 patients were included. The highest NLR among severe patients was 11.5±6.2, followed by the non-severe group at 3.3±2.8. The lowest NLR was found in the asymptomatic group (1.9±1.1). The CD4+ and CD8+ values were lowest in the critical and severe disease groups. The area under the curve of NLR was 0.959. Therefore, the optimal NLR cut-off value for predicting severe COVID-19 was ≥3.55, with sensitivity at 90.9% and a specificity of 16.7%. CONCLUSIONS: Lower CD4+ and CD8+ and higher NLR values on admission are reliable predictors of severe COVID-19 among Indonesian people. NLR cut-off ≥3.55 is the optimal value for predicting severe COVID-19.
ABSTRACT
INTRODUCTION: The spectrum of illness and outcomes of coronavirus disease 2019 (COVID-19) patients may vary. This study reports the characteristics of COVID-19 patients in Bali, Indonesia, and evaluates the diagnostic value of their clinical symptoms. METHOD: This observational study was conducted in eight hospitals. The patients were classified as non-severe COVID-19, severe COVID-19, and non-COVID-19. Demographics, clinical, laboratory, and radiologic characteristics, and outcomes of COVID-19 patients were collected. Factors associated with the severity and outcomes were assessed using the chi-squared test or ANOVA when appropriate. We also compared the clinical features of non-severe COVID-19 and non-COVID-19 patients to evaluate the diagnostic accuracy. RESULTS: This study included 92 patients: 41 non-COVID-19 and 51 COVID-19 patients, comprising 45 non-severe and six severe cases. The most common symptoms of COVID-19 were cough (47.1%), fever (31.0%), and dyspnea (25.3%). Cough, fatigue, and anosmia have high accuracy, and combining these complaints in clinical diagnostics offered a higher accuracy in predicting COVID-19 patients (60.1%). We found lower lymphocyte counts and interleukin-1R levels and higher levels of C-reactive protein, interleukin-6, and interleukin-8 in severe compared than in non-severe COVID-19 patients. Lactate dehydrogenase was associated with intensive care unit admission and ventilator use, while other markers such as neutrophil-lymphocyte ratio, C-reactive protein, and interleukin-6 were not. CONCLUSION: A battery of symptoms, including cough, fatigue, and anosmia, is likely associated with COVID-19 in Bali. Clinicians should be aware of these symptoms to ensure a prompt diagnostic test for COVID-19, beyond other causes of acute febrile illnesses.