Safe and effective antitussive therapy remains a significant area of unmet need for cough management. Antitussive drugs are commonly used cough suppressants and include centrally acting (opioids and non-opioids) cough suppressants and peripherally acting antitussives. Authors searched PubMed, Google Scholar and additional studies from reference lists via cross-referencing to identify studies assessing levodropropizine for the treatment of cough. Of the 748 studies identified, 13 were included. Recent clinical evidence, guideline recommendations and findings from this review suggest that levodropropizine is a peripheral antitussive which reduces cough intensity, frequency, and nocturnal awakenings in children and adults and provides better efficacy outcomes with a more favourable risk/benefit ratio compared to centrally acting antitussive agents which pose greater safety concerns and present an unacceptable risk–benefit profile. This review is aimed at Indian primary care physicians for making effective cough management decisions where the clinical evidence needs to be translated to clinical practice. Key Messages The usage of currently available centrally acting antitussive agent is greatly limited by their central depressing action and frequent side effect. The findings of this review indicate that levodropropizine is an effective antitussive agent and well tolerated in the management of cough in patients of all ages. Show less

Introduction People living with HIV (PLHIV) are susceptible to developing non- communicable chronic respiratory diseases. Our objective was to study the spirometric profile of this population. Material and methods This was a descriptive and analytical cross-sectional retro-prospective study conducted from March 15 to June 15, 2022 and relating to the analysis of the medical files of asymptomatic and eligible for spirometry PLHIV, aged 18 years and above. They were received in the voluntary counselling and testing (VCT) centres of one of the two pulmonology departments in Abidjan. Results The study involved 54 subjects including 22 men (40.7%) and 32 women (59.3%) with an average age of 48.9 years. The majority of patients were non-smokers (81.4%) and the main history was pulmonary tuberculosis (35.2%). Only 29.6% had chronic respiratory symptoms and 42.6% had a normal BMI. The frequency of spirometric abnormalities was 57.4%. These spirometric abnormalities included 40.7% peripheral obstructive pattern; 9.3% restrictive pattern; 3.7% asthma and 3.7% COPD. A more than 10 years duration of HIV infection (p=0.001 OR= 0.2 (0.1 – 0.7)) and a duration of ART of at least 10 years (p=0.001 OR= 0, 2 (0.1 – 0.7)) were significantly associated with the existence of ventilatory abnormalities. Conclusion The high frequency of ventilatory anomalies in PLHIV independently of the existence of chronic respiratory signs leads us to propose spirometry in the follow-up assessment of PLHIV while paying particular attention to those on ARVs for more than 10 years. Show less

Introduction Mortality is one of the most important demographic phenomena in public health, and its rate is the primary indicator of a population's state of health. The aim of this study was to describe the profile of patients in the pneumo-phthisiology department of the Hospital National Ignace Deen CHU in Conakry. Methodology This was a retrospective descriptive study lasting one year, from 01 January to 31 December 2023, on 176 records of hospitalized patients who died in the pneumophthisiology department of the Ignace Deen National Hospital. Results A total of 1043 patients were hospitalized during our study period, 176 of whom died, giving a mortality rate of 16.87%. The mean age of the deceased patients was 49.57±18.8 years, with a male predominance (sex ratio=1.93). Clinical signs on admission were dominated by dyspnoea (70.45%) and chest pain (61.36%). Tuberculosis was the most common diagnosis with a frequency of 42.61%, followed by TB/HIV co-infection with a frequency of 22.16%. The average length of hospitalization was 8.79 days. Probable cause of death was dominated by respiratory distress (54.44%) and decompensated anaemia (32.95%). Conclusion Mortality in the pneumo-phthisiology department of the Ignace Deen National Hospital remains high. Anemia and respiratory distress were the most common diagnoses of severity, hence the need to improve management of these probable causes of death. Show less

Respiratory diseases remain a major contributor to hospital morbidity and mortality worldwide, particularly among elderly patients and individuals with severe pulmonary compromise. Accurate prediction of respiratory mortality is clinically important for triage, resource allocation, ICU utilization, and early intervention. Traditional statistical models frequently demonstrate limited predictive sensitivity because respiratory mortality is influenced by complex interactions among demographic, diagnostic, physiologic, and severity-related variables. In this study, a machine learning framework was developed to predict in-hospital mortality among patients with respiratory disease using administrative and clinically derived variables, including age, sex, length of stay (LOS), diagnostic descriptions, risk of mortality and severity scores. A Random Forest classifier with balanced class weighting was developed and implemented to address nonlinear relationships and class imbalance within the dataset. Initial modeling demonstrated good overall discrimination performance, with receiver operating characteristic area under the curve (ROC-AUC) values approaching 0.84; however, mortality recall remained limited because deceased patients represented a minority class within the original dataset. To improve mortality detection, a physiologically informed synthetic augmentation strategy was developed. Synthetic clinical variables included oxygen saturation, ICU status, ventilator support, sepsis status, systolic blood pressure, creatinine, and lactate levels. Conditional physiologic consistency rules were incorporated during augmentation to preserve clinically plausible relationships among respiratory failure, hemodynamic instability, and organ dysfunction. The augmented dataset substantially improved model sensitivity and balanced mortality classification performance. Final model evaluation demonstrated strong predictive capability, achieving approximately 97% classification accuracy with balanced precision and recall across mortality classes. Confusion matrix analysis revealed marked reduction in false-negative mortality predictions compared with baseline modeling approaches. Feature importance analysis identified physiologic instability markers, respiratory severity classifications, LOS, and diagnostic respiratory categories as dominant predictors of mortality. These findings suggest that hybrid simulation-augmented machine learning frameworks may provide a valuable strategy for respiratory mortality analytics, particularly in datasets with limited real-world mortality prevalence and incomplete physiologic representation. Show less