Aim: We aimed to determine the prognostic values of the National Early Warning Score 2 (NEWS2) and laboratory parameters during the first week of COVID-19. Materials & methods: All adult patients w... Lay abstract The COVID-19 pandemic is a grueling problem worldwide. There is a lack of knowledge about the predictive value of National Early Warning Score 2 (NEWS2) for severe COVID-19 illness. We...

Objective: The study aimed to investigate the reliability of ChatGPT’s answers to medical questions, including those sourced from patients and guide recommendations. The focus was on evaluating ChatGPT’s accuracy in responding to various types of infectious disease questions. Materials and Methods: The study was conducted using 200 questions sourced from social media, experts, and guidelines related to various infectious diseases like urinary tract infection, pneumonia, HIV, various types of hepatitis, COVID-19, skin infections, and tuberculosis. The questions were arranged for clarity and consistency by excluding repetitive or unclear ones. The answers were based on guidelines from reputable sources like the Infectious Diseases Society of America (IDSA), Centers for Disease Control and Prevention (CDC), European Association for the Study of Liver Disease (EASL) and Joint United Nations Programme on HIV/AIDS (UNAIDS) AIDSinfo. According to the scoring system, completely correct answers were given 1-point, and completely incorrect ones were given 4-points. To assess reproducibility, each question was posed twice on separate computers. Repeatability was determined by the consistency of the answers’ scores. Results: In the study, ChatGPT was posed with 200 questions: 107 from social media platforms and 93 from guidelines. The questions covered a range of topics: urinary tract infections (n=18 questions), pneumonia (n=22), HIV (n=39), hepatitis B and C (n=53), COVID-19 (n=11), skin and soft tissue infections (n=38), and tuberculosis (n=19). The lowest accuracy was 72% for urinary tract infections. ChatGPT answered 92% of social media platform questions correctly (scored 1-point) versus 69% of guideline questions (p=0.001; OR=5.48, 95% CI=2.29-13.11). Conclusion: Artificial intelligence is widely used in the medical field by both healthcare professionals and patients. Although ChatGPT answers questions from social media platforms quite properly, we recommend that healthcare professionals be conscientious when using it. Keywords: infectious disease, Chat-GTP, medical questions

Objective: Predictors of mortality that indicate disease severity plays an important role in COVID-19 management and treatment decisions. This study aimed to investigate the association between fibrosis-4 (FIB-4) score, aspartate aminotransferase-to-platelet ratio index (APRI), and novel biomarker-based score (SAD-60) with mortality in COVID-19 patients treated in a tertiary hospital. Materials and Methods: In this single-center retrospective study, patients ≥18 years of age who were admitted to our hospital for COVID-19 between December 1 and 31, 2021, were included. Patients were divided into two groups as deceased and survived. A comparative analysis was applied. Predictive abilities of the FIB-4, APRI, and SAD-60 scores for in-hospital mortality were evaluated. Results: Of the 453 patients enrolled in the study, 248 (54.6%) were male, and the mean age was 52.2±14.7 years. Mortality was recorded in 39 (8.5%) of the patients. The median values of APRI (0.43 and 0.58; p=0.001), FIB-4 score (1.66 and 2.91; p<0.001), and SAD-60 (2 and 8.25; p<0.001) were higher in deceased patients than in survivors. The optimal cut-off value for predicting mortality in the receiver operating characteristic (ROC) curve analysis was 0.58 for APRI (sensitivity=56.4%, specificity=63.6%); 2.14 for FIB-4 score (sensitivity=79.5%, specificity=68.2%); 4.25 for SAD-60 (sensitivity=90%, specificity=73.8%). In Cox regression analysis with a model that included gender, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD), FIB-4 (hazard ratio [HR]=4.013, 95% confidence interval [CI]=1.643-9.803; p=0.002), and SAD-60 (HR=8.850, 95% CI=1.035-75.696; p=0.046) were independent risk factors for mortality. Conclusion: SAD-60 and FIB-4 scores are easily applicable and may be used to predict mortality in COVID-19 patients. Keywords: APRI, FIB-4, SAD-160, COVID-19, mortality, predictors

Background: This study was designed to investigate the effectiveness of CoronaVac in preventing COVID-19 in healthcare workers (HCWs) during the Alpha variant-dominant period. Materials & methods: ... Plain language summary This study was designed to investigate the effectiveness of CoronaVac in preventing COVID-19 in healthcare workers (HCWs) during the alpha variant-dominant period. A total of...

We aimed to identify a rapid, accurate and accessible biomarker in the early stages of COVID-19 that can determine the prognosis of the disease in cancer patients. A total number of 241 patients with solid cancers who had COVID-19 diagnosis between March 2020 and February 2022 were included in the study. Factors and markers of inflammation were analyzed by year of diagnosis of COVID-19 and grouped by severity of infection. Hospitalization, referral to the intensive care unit (ICU), mechanical ventilation and death were more frequent in 2020 than in 2021 and 2022. (mortality rates respectively 18.8%, 3.8% and 2.5%) Bilateral lung involvement and chronic lung disease were independent risk factors for severe disease in 2020. In 2021-2022, only bilateral lung involvement was found as an independent risk factor for severe disease. The neutrophil to lymphocyte, platelet ratio (NLPR) with the highest area under the curve (AUC) value in 2020 had a sensitivity of 71.4% and specificity of 73.3% in detecting severe disease (cut-off > 0.0241, Area Under the Curve (AUC) =0.842, p < 0.001). In 2021-2022, the sensitivity of the C-reactive protein to lymphocyte ratio (CRP/L) with the highest AUC value was 70.0%, and the specificity was 73.3% (cut-off > 36.7, AUC = 0.829, p = 0.001). This is the first study to investigate the distribution and characteristics of cancer patients diagnosed with COVID-19. Based on the data from our study, bilateral lung involvement is an independent factor for severe disease, and the CRP/L inflammation index appears to be the most reliable prognostic marker.

Background: The aim was to explore a novel risk score to predict mortality in hospitalized patients with COVID-19 pneumonia. Methods: This was a retrospective, multicenter study. Results: A total o...