Respiratory Medicine and Research最新文献

Objectives: We aimed to visualize CDC data to identify trends in respiratory illnesses-COVID-19, influenza, and RSV-across sex, age, and racial demographics, while advancing concepts of data-driven public health interventions.

Methods: We conducted a demographic analysis of CDC emergency department surveillance data (July 2023-June 2024) using Python-based visualizations developed collaboratively with generative AI. Our approach stratified COVID-19, influenza, and RSV visit percentages by sex, age group, and race/ethnicity to identify temporal patterns and high-risk populations across respiratory conditions.

Results: Analysis indicated no significant sex differences in visit rates, although females showed slightly higher rates for COVID-19, while males had higher rates for RSV. Age trends highlighted the highest visit rates for COVID-19 among young children (0-4) and the elderly (65+), influenza predominantly affecting children (0-4 and 5-17), and RSV primarily occurring in very young children (0-4). Racial disparities were observed, with Asian individuals exhibiting higher COVID-19 visit rates, while Hispanic individuals reported elevated rates for influenza and RSV.

Conclusions: Findings underscore the need for targeted public health interventions tailored to demographic-specific vulnerabilities, particularly for high-risk age groups and racial/ethnic communities disproportionately impacted by respiratory illnesses. Recommendations include culturally appropriate vaccination campaigns for Hispanic communities and specialized respiratory care for young children. This illustrates the potential of generative AI in enhancing analytical capabilities within public health, enabling a more nuanced understanding of respiratory illness trends and informing equitable health strategies that address systemic disparities across diverse populations. By integrating advanced data analytics, public health efforts can be more effectively aligned with demographic needs, ultimately working toward a healthier and more equitable society.

Background: Practical application of the classification of pulmonary hypertension (PH) occasionally presents challenges, particularly for idiopathic pulmonary arterial hypertension (iPAH) with a lung phenotype, which can overlap with group 3 PH. This study aimed to evaluate cardiac MRI as a discriminatory tool between classical iPAH, iPAH with a lung phenotype and group 3 PH.

Methods: This retrospective study included incident patients diagnosed with iPAH or group 3 PH referred to a PH reference center. All included patients underwent cardiac MRI, right heart catheterization, and chest computed tomography within 48 hours of diagnosis.

Results: The analysis included 34 patients, 11 with classical iPAH, 11 with iPAH and a lung phenotype (per predefined criteria) and 12 with group 3 PH (7 with chronic obstructive pulmonary disease and 5 with interstitial lung disease). No significant differences were observed regarding ventricular functions and flow rates. T1 mapping values in the interventricular septum and inferior ventricular insertion point were significantly higher in patients with classical iPAH compared to patients with iPAH and a lung phenotype (1305 [1234 - 1365] ms versus 1210 [1194 - 1251] ms, p=0.028 and 1398 [1355 - 1604] ms versus 1229 [1201 - 1289] ms, p=0.007, respectively. T1 mapping measurements were similar in the iPAH and a lung phenotype compared to group 3.

Interpretation: cardiac MRI with T1 mapping, specifically targeting fibrotic areas, effectively discriminated classical iPAH and iPAH and a lung phenotype. These MRI measurements revealed divergent patterns that could explain the different treatment responses and prognoses observed across these PH subtypes.

Clinical trial: This trial was registered before inclusion and analyses in www.

Clinicaltrial: gov (NCT05624242).

Introduction: COMISA, defined as the co-occurrence of obstructive sleep apnoea (OSA) and chronic insomnia, is a common clinical phenotype associated with increased morbidity. While OSA and insomnia have been described as potential cardiovascular (CV) risk factors, the combined impact of COMISA is poorly defined.

Objective: This review aims to synthesize the available data on the association between COMISA and CV risk in adults, including closely related phenotypes (disturbed sleep in OSA patients).

Methods: A systematic search of PubMed and Web of Science databases was performed in October 2024. Included studies were published between 2015 and 2024 and examined the associations between OSA, insomnia, disturbed sleep and CV pathologies.

Results: Seventeen studies remained after rigorous selection. Limited studies suggest an association between COMISA and resistant hypertension or prevalent CV comorbidities. However, follow-up of large longitudinal cohorts shows no significant excess risk of incident CV events after adjustment for confounders. OSA patients with disturbed sleep do not appear to have an independent increased CV risk, in contrast to those with marked sleepiness.

Conclusion: Although initial data suggest a potential synergistic effect of OSA and insomnia on CV risk, results remain mixed and limited by considerable methodological heterogeneity. Integrated management focusing on the multiple vulnerabilities of COMISA patients seems to be the most appropriate approach, regardless of a direct association with CV risk.