Faculty Bibliography

STUDY OBJECTIVES/OBJECTIVE:We have previously estimated that the prevalence of obstructive sleep apnea (OSA) among World Trade Center (WTC) rescue and recovery workers is 75% and identified that having symptoms of chronic rhinosinusitis (CRS) is an independent risk factor for OSA in this population. Nasal inflammation and/or elevated awake nasal resistance that carried over into sleep could explain this association. To understand the mechanism(s) for the elevated risk of OSA observed in WTC responders with chronic rhinosinusitis (CRS) symptoms we examined if elevated awake supine nasal resistance was associated with OSA, CRS and/or nasal inflammatory biomarkers. METHODS:) enrolled in the WTC Health Program and without significant pre-9/11 snoring, underwent two nights of home sleep apnea testing, measurements of anterior rhinomanometry in the supine position, and nasal lavage. RESULTS:Awake supine nasal resistance was not associated with OSA; 74.8% and 74.4% of the participants with low and high nasal resistance respectively, had OSA (P=NS). Patients with CRS had elevated nasal inflammatory markers (IL6, IL8, ECP and Neut) but did not have high nasal resistance. Nasal inflammatory markers were not correlated with nasal resistance. CONCLUSIONS:As awake nasal resistance did not explain the relationship of CRS to OSA in this large and well characterized dataset, our findings suggest that either "sleep" nasal resistance or other factors such as increased supraglottic inflammation, perhaps through impairing upper airway reflex mechanisms, or systemic inflammation are involved in the pathophysiology of OSA in the WTC population.

BACKGROUND:The novel coronavirus-associated ARDS (COVID-19 ARDS) often requires invasive mechanical ventilation. A spectrum of atypical ARDS with different phenotypes (high vs low static compliance) has been hypothesized in COVID-19. METHODS:test, chi-square test, ANOVA test, and Pearson correlation was used to identify relationship between subject variables and respiratory mechanics. The primary outcome was duration of mechanical ventilation. Secondary outcomes were correlation between fluid status, C- reactive protein, PEEP, and D-dimer with respiratory and ventilatory parameters. RESULTS:= .02). CONCLUSIONS:In our cohort of mechanically ventilated COVID-19 ARDS subjects, high PEEP and D-dimer were associated with increase in physiologic dead space without significant effect on oxygenation, raising the question of potential microvascular dysfunction.

RATIONALE/BACKGROUND:) may reduce discomfort in those with high nasal resistance and improve adherence in this subgroup. OBJECTIVES/OBJECTIVE:improves adherence over CPAP in subjects with high nasal resistance. METHODS:versus CPAP in World Trade Center dust-exposed subjects with OSA stratified by nasal resistance measured by 4-Phase Rhinomanometry. RESULTS:(mean Δ hours (95% CI)) in subjects with low resistance (0.33h (-0.10, 0.76)) or high nasal resistance (0.26h (-0.14, 0.66)). No significant differences were observed in any of the secondary outcomes between PAP modes. CONCLUSIONS:than to CPAP in subjects with high or low nasal resistance, and, show clinically insignificant better adherence overall with CPAP. Clinical Trial registered with Clinicaltrials.gov (NCT01753999).

To explore demographics, comorbidities, transfers, and mortality in critically ill patients with confirmed severe acute respiratory syndrome coronavirus 2.

BACKGROUND:Many respiratory conditions have been attributed to toxic dust and fume exposure in World Trade Center (WTC) rescue and recovery workers, who frequently report symptoms of OSA. We examined the prevalence of new-onset OSA and tested if the prevalence and severity of OSA are related to the presence of chronic rhinosinusitis (CRS). METHODS:) enrolled in the WTC Health Program, excluding those with significant pre-September 11, 2001, snoring or prior CRS, underwent two nights of home sleep testing. OSA was defined as Apnea Hypopnea Index 4% ≥ 5 events/h or respiratory disturbance index of ≥ 15 events/h. CRS was assessed using nasal symptom questionnaires. RESULTS:The prevalence of OSA was 75% (25% no OSA, 46% mild OSA, 19% moderate OSA, and 10% severe OSA), and the prevalence of CRS was 43.5%. Compared with no CRS, new and worsening CRS was a significant risk factor for OSA with an OR of 1.80 (95% CI, 1.18-2.73; P = .006) unadjusted and 1.76 (95% CI, 1.08-2.88; P = .02) after adjustment for age, BMI, sex, gastroesophageal reflux disorder, and alcohol use. CONCLUSIONS:The high prevalence of OSA in WTC responders was not explained fully by obesity and sex. Possible mechanisms for the elevated risk of OSA in subjects with CRS include increased upper airway inflammation and/or elevated nasal/upper airway resistance, but these need confirmation.

RATIONALE/BACKGROUND:Obstructive Sleep Apnea (OSA) is associated with recurrent obstruction, sub-epithelial edema, and airway inflammation. The resultant inflammation may influence or be influenced by the nasal microbiome. OBJECTIVES/OBJECTIVE:To evaluate whether the composition of the nasal microbiota is associated with obstructive sleep apnea and inflammatory biomarkers. METHODS:Two large cohorts were utilized: 1) a discovery cohort of 472 subjects from the WTCSNORE cohort; and 2) a validation cohort of 93 subjects from the Zaragoza Sleep cohort. Sleep apnea was diagnosed using home sleep tests. Nasal lavages were obtained from cohort subjects to measure: 1) microbiome composition (based on 16S rRNA gene sequencing); 2) biomarkers for inflammation (inflammatory cells, IL-8, and IL-6). Longitudinal 3 months samples were obtained in the validation cohort including post-CPAP treatment when indicated. RESULTS:In both cohorts, we identified that: 1) severity of OSA correlated with differences in microbiome diversity and composition; 2) the nasal microbiome of subjects with severe OSA were enriched with Streptococcus, Prevotella, and Veillonella; 3) the nasal microbiome differences were associated with inflammatory biomarkers. Network analysis identified clusters of co-occurring microbes that defined communities. Several common oral commensals (e.g., Streptococcus, Rothia, Veillonella, and Fusobacterium) correlated with apnea-hypopnea index. Three months of treatment with CPAP did not change the composition of the nasal microbiota. CONCLUSIONS:We demonstrate that the presence of an altered microbiome in severe OSA is associated with inflammatory markers. Further experimental approaches to explore causal links are needed.