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Background: Case fatality rate of COVID-19 patients in Surabaya is higher than global cases. Thus, it is important to identify risk factors to reduce the mortality rate. This study aimed to assess the factors associated with hospital mortality of COVID-19 patients, and develop a prediction score based on these findings. Methods: We analyzed 111 patients, who were diagnosed with COVID-19 based on reverse-transcriptase polymerase chain reaction. The following patient characteristics were obtained from records: age, gender, type of symptoms, onset of symptoms, neutrophil lymphocyte ratio (NLR), absolute lymphocyte count, chest x-ray abnormalities, lung involvement, type of lesion, radiographic assessment of the quantity of lung edema (RALE) score, and mortality. Data were analyzed using SPSS 25.0. Results Multivariate analysis showed that age >50 years (p=0.043), NLR score >5.8 (p=0.016) and RALE score >2 (p=0.002) can predict the mortality of COVID-19 patients in the hospital. ROC curve analysis of the score ability to predict mortality showed an area under the curve of 0.794. The cut-off point is 4.5, with a sensitivity of 96.7% and specificity of 49.4% to predict the mortality of COVID-19 patient in the hospital. Conclusions Age, NLR score and RALE score were associated with mortality of COVID-19 patients in the hospital and could be used as a predictor for discharge probability of COVID-19 patients in low health care resource setting. The prediction score may be useful for frontline physicians to effectively manage patients with a higher score to prevent mortality.

Susac syndrome is a rare autoimmune disorder characterised by the clinical triad of encephalopathy, retinopathy (branch retinal artery occlusions) and hearing loss. The diagnosis of Susac syndrome may be difficult initially, and it is not uncommon for patients with Susac syndrome to be misdiagnosed with multiple sclerosis. In this case report, we describe a patient who was diagnosed as having multiple sclerosis for three years, with further deterioration after starting treatment with interferon beta-1a. The patient had the triad of encephalopathy, branch retinal artery occlusions and sensorineural hearing loss. She had the classic magnetic resonance imaging appearance, with normal magnetic resonance imaging of the spinal cord and absence of oligoclonal bands in the cerebrospinal fluid. Our patient responded well to treatment with a combination therapy and discontinuation of interferon beta-1a. Our observations raise awareness about the importance of the early and correct diagnosis of Susac syndrome, which usually affects young patients, with an excellent prognosis if treated aggressively at an early stage of the disease. Susac syndrome is underdiagnosed and is not uncommonly misdiagnosed as multiple sclerosis. Susac syndrome is a great mimicker of multiple sclerosis, and establishing diagnostic criteria for this syndrome is very useful. In any patient presenting with a progressive disabling neurological disorder associated with callosal lesions and/or hearing loss, and/or visual loss especially in women, Susac syndrome should be suspected.

The exhaled breath analysis is a procedure of measuring several types of gases that aim to identify various diseases in the human body. The purpose of this study is to analyze the gases contained in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize the subjects of asthma with different severity. The result of this study showed that the system provided a low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects. The use of five gas sensors in the electronic nose system has the best accuracy in the classification results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower concentration of the marker gases.