Predicting COVID 19-Associated Pulmonary Aspergillosis Risk in Low- and Middle-Income Countries: A Matched Case-Control Study.

Background: Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is a life-threatening fungal infection. Studies focusing on CAPA in low- and middle-income countries are limited. Methods: This retrospective matched case-control study was conducted at a tertiary care center in South India. Cases of CAPA were classified using the 2020 European Confederation of Medical Mycology/International Society for Human and Animal Mycology consensus criteria. A total of 95 cases were matched 1:1 with COVID-19 patients without CAPA. Matching was done based on age and period of admission. Inverse probability weighting was used to account for imbalances in COVID-19 severity and intensive care unit (ICU) admission. Data on demographics, clinical details, microbiologic and radiologic data, and treatment outcomes were collected. A predictive score for CAPA was developed from baseline risk factors. Results: The predictive score identified lymphopenia, European Organisation for Research and Treatment of Cancer risk factors, and broad-spectrum antibiotic use as the main risk factors for CAPA. Positivity for bacterial pathogens in blood or bronchoalveolar lavage samples reduced the risk of CAPA. The predictive model performed well in cross-validation, with an area under the curve value of 82%. CAPA diagnosis significantly increased mortality and shift to ICU. Conclusions: The predictive model derived from the current study offers a valuable tool for clinicians, especially in high-endemic low- and middle-income countries, for the early identification and treatment of CAPA. With further validation, this risk score could improve patient outcomes.

Characterization and predictive risk scoring of long COVID in a south indian cohort after breakthrough COVID infection; a prospective single centre study.

BACKGROUND: With the World Health Organization (WHO) declaring an end to the COVID-19 pandemic, the focus has shifted to understanding and managing long-term post-infectious complications. "Long COVID," characterized by persistent or new onset symptoms extending beyond the initial phase of infection, is one such complication. This study aims to describe the incidence, clinical features and risk profile of long COVID among individuals in a South Indian cohort who experienced post-ChAdOx1 n-Cov-2 vaccine breakthrough infections. METHODS: A single-centre hospital-based prospective observational study was conducted from October to December 2021. The study population comprised adult patients (> 18 years) with a confirmed COVID-19 diagnosis who had received at least a single dose of vaccination. Data was collected using a specially tailored questionnaire at week 2, week 6, and week 12 post-negative COVID-19 test. A propensity score based predictive scoring system was developed to assess the risk of long COVID. RESULTS: Among the 414 patients followed up in the study, 164 (39.6%) reported long COVID symptoms persisting beyond 6 week's post-infection. The presence of long COVID was significantly higher among patients above 65 years of age, and those with comorbidities such as Type II Diabetes Mellitus, hypertension, dyslipidemia, coronary artery disease, asthma, and cancer. Using backwards selection, a reduced model was developed, identifying age (OR 1.053, 95% CI 0.097-1.07, p < 0.001), hypertension (OR 2.59, 95% CI 1.46-4.59, p = 0.001), and bronchial asthma (OR 3.7176, 95% CI 1.24-11.12, p = 0.018) as significant predictors of long COVID incidence. A significant positive correlation was observed between the symptomatic burden and the number of individual comorbidities. CONCLUSIONS: The significant presence of long COVID at 12 weeks among non-hospitalised patients underscores the importance of post-recovery follow-up to assess for the presence of long COVID. The predictive risk score proposed in this study may help identify individuals at risk of developing long COVID. Further research is needed to understand the impact of long COVID on patients' quality of life and the potential role of tailored rehabilitation programs in improving patient outcomes.

Incidence and Characterization of Post-COVID-19 Symptoms in Hospitalized COVID-19 Survivors to Recognize Syndemic Connotations in India: Single-Center Prospective Observational Cohort Study.

BACKGROUND: Long COVID, or post-COVID-19 syndrome, is the persistence of signs and symptoms that develop during or after COVID-19 infection for more than 12 weeks and are not explained by an alternative diagnosis. In spite of health care recouping to prepandemic states, the post-COVID-19 state tends to be less recognized from low- and middle-income country settings and holistic therapeutic protocols do not exist. Owing to the syndemic nature of COVID-19, it is important to characterize post-COVID-19 syndrome. OBJECTIVE: We aimed to determine the incidence of post-COVID-19 symptoms in a cohort of inpatients who recovered from COVID-19 from February to July 2021 at a tertiary-care center in South India. In addition, we aimed at comparing the prevalence of post-COVID-19 manifestations in intensive care unit (ICU) and non-ICU patients, assessing the persistence, severity, and characteristics of post-COVID-19 manifestations, and elucidating the risk factors associated with the presence of post-COVID-19 manifestations. METHODS: A total of 120 adult patients admitted with COVID-19 in the specified time frame were recruited into the study after providing informed written consent. The cohort included 50 patients requiring intensive care and 70 patients without intensive care. The follow-up was conducted on the second and sixth weeks after discharge with a structured questionnaire. The questionnaire was filled in by the patient/family member of the patient during their visit to the hospital for follow-up at 2 weeks and through telephone follow-up at 6 weeks. RESULTS: The mean age of the cohort was 55 years and 55% were men. Only 5% of the cohort had taken the first dose of COVID-19 vaccination. Among the 120 patients, 58.3% had mild COVID-19 and 41.7% had moderate to severe COVID-19 infection. In addition, 60.8% (n=73) of patients had at least one persistent symptom at the sixth week of discharge and 50 (41.7%) patients required intensive care during their inpatient stay. The presence of persistent symptoms at 6 weeks was not associated with severity of illness, age, or requirement for intensive care. Fatigue was the most common reported persistent symptom with a prevalence of 55.8%, followed by dyspnea (20%) and weight loss (16.7%). Female sex (odds ratio [OR] 2.4, 95% CI 1.03-5.58; P=.04) and steroid administration during hospital stay (OR 4.43, 95% CI 1.9-10.28; P=.001) were found to be significant risk factors for the presence of post-COVID-19 symptoms at 6 weeks as revealed by logistic regression analysis. CONCLUSIONS: Overall, 60.8% of inpatients treated for COVID-19 had post-COVID-19 symptoms at 6 weeks postdischarge from the hospital. The incidence of post-COVID-19 syndrome in the cohort did not significantly differ across the mild, moderate, and severe COVID-19 severity categories. Female sex and steroid administration during the hospital stay were identified as predictors of the persistence of post-COVID-19 symptoms at 6 weeks.

Leeds sleep evaluation questionnaire in Jordanian university students. A psychometric investigation using comparative confirmatory factor analysis.

OBJECTIVES: To analyze the fit of different competing factor models (a one-factor model, 3 2-factor models, and 2 4-factor models) of the Leeds sleep evaluation questionnaire (LSEQ) in the data from a Jordanian student population. METHODS: A cross-sectional study was conducted on university students, with 2 sleep-related tools - the LSEQ and the sleep hygiene index (SHI). The students (n=166) at Jordan University of Science and Technology, Irbid, Jordan participated in this study from January-April, 2019. A total of 12 LSEQ models (6 models with all 10-items, and 6 models with one item deleted) were evaluated by using confirmatory factor analysis. The summary statistics of correlation coefficients, descriptive measures of item analysis, the model fit, and Cronbach's alpha were determined. RESULTS: The findings show that a 4-factor correlated solution was a plausible model for the LSEQ with 9-items, compared to a one-factor, 2-factor, and other 4-factor variant models. The deletion of one item from the original LSEQ improved the data fit significantly in the studied population. Moreover, correlation analysis between the LSEQ and SHI confirmed the divergent validity of the LSEQ. CONCLUSION: The results support the validity of a 4-factor structure of the LSEQ with 9-items with adequate internal consistency and divergent validity.