Comparative analysis of neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio with respect to outcomes of in-hospital coronavirus disease 2019 patients: A retrospective study.

Introduction and objectives: In patients with coronavirus disease 2019 (COVID-19), several abnormal hematological biomarkers have been reported. The current study aimed to find out the association of neutrophil to lymphocyte ratio (NLR) and derived NLR (dNLR) with COVID-19. The objective was to compare the accuracy of both of these markers in predicting the severity of the disease. Materials and methods: The study was conducted in a single-center having patients with COVID-19 with a considerable hospital stay. NLR is easily calculated by dividing the absolute neutrophil count (ANC) with the absolute lymphocyte count (ALC) {ANC/ALC}, while dNLR is calculated by ANC divided by total leukocyte count minus ANC {ANC/(WBC-ANC)}. Medians and interquartile ranges (IQR) were represented by box plots. Multivariable logistic regression was performed obtaining an odds ratio (OR), 95% CI, and further adjusted to discover the independent predictors and risk factors associated with elevated NLR and dNLR. Results: A total of 1,000 patients with COVID-19 were included. The baseline NLR and dNLR were 5.00 (2.91-10.46) and 4.00 (2.33-6.14), respectively. A cut-off value of 4.23 for NLR and 2.63 for dNLR were set by receiver operating characteristic (ROC) analysis. Significant associations of NLR were obtained by binary logistic regression for dependent outcome variables as ICU stay (p < 0.001), death (p < 0.001), and invasive ventilation (p < 0.001) while that of dNLR with ICU stay (p = 0.002), death (p < 0.001), and invasive ventilation (p = 0.002) on multivariate analysis when adjusted for age, gender, and a wave of pandemics. Moreover, the indices were found correlating with other inflammatory markers such as C-reactive protein (CRP), D-dimer, and procalcitonin (PCT). Conclusion: Both markers are equally reliable and sensitive for predicting in-hospital outcomes of patients with COVID-19. Early detection and predictive analysis of these markers can allow physicians to risk assessment and prompt management of these patients.

Comparative analysis of neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio with respect to outcomes of in-hospital coronavirus disease 2019 patients: A retrospective study

Introduction and objectives In patients with coronavirus disease 2019 (COVID-19), several abnormal hematological biomarkers have been reported. The current study aimed to find out the association of neutrophil to lymphocyte ratio (NLR) and derived NLR (dNLR) with COVID-19. The objective was to compare the accuracy of both of these markers in predicting the severity of the disease. Materials and methods The study was conducted in a single-center having patients with COVID-19 with a considerable hospital stay. NLR is easily calculated by dividing the absolute neutrophil count (ANC) with the absolute lymphocyte count (ALC) {ANC/ALC}, while dNLR is calculated by ANC divided by total leukocyte count minus ANC {ANC/(WBC-ANC)}. Medians and interquartile ranges (IQR) were represented by box plots. Multivariable logistic regression was performed obtaining an odds ratio (OR), 95% CI, and further adjusted to discover the independent predictors and risk factors associated with elevated NLR and dNLR. Results A total of 1,000 patients with COVID-19 were included. The baseline NLR and dNLR were 5.00 (2.91–10.46) and 4.00 (2.33–6.14), respectively. A cut-off value of 4.23 for NLR and 2.63 for dNLR were set by receiver operating characteristic (ROC) analysis. Significant associations of NLR were obtained by binary logistic regression for dependent outcome variables as ICU stay (p < 0.001), death (p < 0.001), and invasive ventilation (p < 0.001) while that of dNLR with ICU stay (p = 0.002), death (p < 0.001), and invasive ventilation (p = 0.002) on multivariate analysis when adjusted for age, gender, and a wave of pandemics. Moreover, the indices were found correlating with other inflammatory markers such as C-reactive protein (CRP), D-dimer, and procalcitonin (PCT). Conclusion Both markers are equally reliable and sensitive for predicting in-hospital outcomes of patients with COVID-19. Early detection and predictive analysis of these markers can allow physicians to risk assessment and prompt management of these patients.

Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey.

Background: The use of Artificial intelligence (AI) has gained popularity during the last few decades and its use in medicine is increasing globally. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare. There is a need to incorporate AI in the health system which may help not only in expediting diagnosis and management but also injudicious resource allocation. Objective: To determine the knowledge, attitude, and practice of AI among doctors and medical students in Pakistan. Materials and methods: We conducted a cross-sectional study using an online questionnaire-based survey regarding demographic details, knowledge, perception, and practice of AI. A sample of 470 individuals including doctors and medical students were selected using the convenient sampling technique. The chi-square test was applied for the comparison of variables. Results: Out of 470 individuals, 223(47.45%) were doctors and 247(52.55%) were medical students. Among these, 165(74%) doctors and 170(68.8%) medical students had a basic knowledge of AI but only 61(27.3%) doctors and 48(19.4%) students were aware of its medical applications. Regarding attitude, 237(76.7%) individuals supported AI's inclusion in curriculum, 368(78.3%) and 305(64.9%), 281(59.8%) and 269(57.2%) acknowledged its necessity in radiology, pathology, and COVID-19 pandemic respectively. Conclusion: The majority of doctors and medical students lack knowledge about AI and its applications, but had a positive view of AI in the field of medicine and were willing to adopt it.

People living with mental disorder in Nigeria amidst COVID-19: Challenges, implications, and recommendations.

COVID-19 has become a global public health obstacle. This disease has caused negligence on mental health institutions, decreased trust in the healthcare system and traditional and religious beliefs, and has created a widespread stigma on people living with mental health illness, specifically in Nigeria. The increase of COVID-19 cases that have exhausted the healthcare system in Nigeria have brought further negligence to people living with mental disorder, thus increasing the burden of the disease on these patients. Overall, this article considerably highlighted the need for equal accessibility to healthcare resources, as well as the requirement of proper attention and care for mental health patients in Nigeria. This article discusses the challenges that surfaced because of the COVID-19 pandemic on people living with mental illness and their implications, as well as suggesting necessary actions and recommendations.