Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

Introduction: Systemic Lupus Erythematosus (SLE) is a very complicated autoimmune disease which is characterized by the presence of abnormal neutrophils known as Low Density Granulocytes (LDGs). These LDGs have increased capacity to produce Neutrophil Extracellular Traps (NETs). Vitamin D levels in SLE patients were significantly lower than that of healthy subjects. This study aims to investigate the effects of vitamin D [1,25 (OH) 2 D3] on NETosis and endothelial cell apoptosis in SLE patients with vitamin D deficiency.

Background: Residency program, including pediatric residency, has long been considered stressful and for some students, depressing. Many studies have shown that depression or emotional impairment in resident physicians is more common than that in the general population, about 29% and increased with each year of training. Nearly half of the depressed residents seemed unaware of their condition. Knowing the cause and risk of mental health problem during medical training is important for informing efforts to prevent, treat, and identify.Objective: This study aim to analyze factors affecting anxiety and depression among pediatric residents in Dr. Soetomo Hospital, to minimize its effect towards residents' mental health. Material and Methods: A cross-sectional study of the Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI) was performed on pediatric residents in Dr. Soetomo General Hospital. We collected resident's demographic data (age, sex, marital status, parental status, number of children, and duration of work experience before residency), as well as Beck Anxiety, Beck Depression, and Likert Scale Questionnaire on how residents feel about their academic burden, non-academic burden, and patients-related duties, were all taken using an online questionnaire. The comparison and correlation of data were analyzed using Mann-Whitney and Spearman tests. The difference will be considered significant if the p-value<0.05, and a strong correlation will be considered if r>0.5. Results: Higher BDI score was found in female residents (37.95; P=0.008), and unmarried residents (41.39; P=0.025). Age was negatively correlated with BAI (R = -0.281; P = 0.021;) and also BDI (R = -0.273; P = 0.025). Duration of work experience before residency period was also negatively correlated with BAI (R = -0.334; P = 0.005) and BDI (R = -0.308; P = 0.011). Meanwhile, Likert Scale on how residents feel about their academic burden was positively correlated with BAI (R = 0.26; P=0.033; and BDI (R = 0.257; P = 0.036). Conclusion:Female and unmarried residents have significantly higher BDI. Age and duration of work experience were negatively correlated with both BAI and BDI. Academic burden was positively correlated with BAI and BDI

Purpose. This study was conducted to investigate possible risk factors that could increase the occurrence of drug-resistant epilepsy (DRE), in hopes that the results could be used to educate the patient and their caregivers as well as increase early detection efforts. Methods. Case control study was conducted at neurology outpatient pediatric RSDS between May to December 2022. Risk factor of DRE such as sex, age of onset, type of seizure, initial seizure frequencies, history of cranial hemorrhage, cerebral infection, febrile seizure, status epilepticus, neonatal seizure, neonatal asphyxia, family history of epilepsy, present of neurological deficit, electroencephalogram (EE) finding, and result of neuroimaging examination were obtained through anamnesis and clinical examination. Risk factors were analyzed with bivariate analysis and multivariate analysis. A model was generated to predict probabilities of DRE in children with epilepsy. Results. DRE was observed in 84/137 (54%) patients. Bivariate analysis showed age of onset <1 years old (OR 2.31, p = 0.016), initial seizure frequencies >5 times/day (OR 3.0, p = 0.011), neonatal seizure (OR 3, p = 0.034), presence of neurologic deficit (OR 3.1, p=0.002), and abnormality of EEG (OR 2.82, p = 0.013) are significantly associated with DRE. Logistic regression revealed that initial seizure frequencies > 5 times/day (OR=2.5; 95%CI 1.051 to 6.028; P=0.038), present of neurological deficit (OR=2.58; 95%CI 1.205 to 5.531; P=0.031), and EE abnormality (OR=2.84; 95%CI 1.170 6.914; P=0.021) were significantly correlated with DRE. Our model sensitivity was 75.3% and 55.76% to predict DRE (AUC = 0.704, p=0.000). Conclusion. Seizure onsets of >5 times, neurological deficits, and EEG abnormality were found to be associated with drug resistant epilepsy.