Faculty Bibliography

TOPIC IMPORTANCE/UNASSIGNED:Patients with neuromuscular disease and other thoracic restrictive disorders are at increased risk of hypoxia during high-altitude travel due to an ineffective hypoxic ventilatory response. Efforts to identify clinical parameters that successfully predict the need for hypoxic challenge testing, a tool used to assess the risk of hypobaric hypoxia, and its outcomes in this population have been inconsistent and limited to mainly small, retrospective studies. Consequently, recommendations regarding the evaluation and management of these patients prior to high-altitude travel have been uncertain and differ across medical societies. We provide a clinical management algorithm for this patient population that draws on the available data. REVIEW FINDINGS/RESULTS:Patients should be assessed for contraindications to high-altitude travel prior to undergoing a safety screen that incorporates travel history, pulmonary co-morbidities, pulmonary function test parameters, and measures of oxygenation and ventilation. Hypoxic challenge testing is recommended for those who do not pass the safety screen. Resolving any contraindications to high-altitude travel or barriers to pre-flight testing should be prioritized so as to preserve the quality of life of these patients. SUMMARY/CONCLUSIONS:Prospective studies are needed to identify and validate pulmonary function testing parameters and exercise testing outcomes that may be predictive of hypoxic challenge test failure, as well as potentially new hypoxic challenge testing protocols that incorporate non-invasive ventilation titration. Such endeavors will add more clarity to an area of medicine that has many uncertainties and important implications for the well-being of a vulnerable patient population.

RATIONALE/UNASSIGNED:Dysfunctional breathing (DB) is a commonly identified abnormality in post-acute sequelae of SARS-CoV-2 (PASC) patients undergoing cardiopulmonary exercise testing (CPET), and is potentially a contributor to ongoing symptoms. Currently, this oscillating, irregular breathing pattern is identified by visual observation of CPET data by an experienced interpreter, which is subjective. We hypothesise that approximate entropy (ApEn), a regularity statistic that quantifies the unpredictability of time-series data can reliably distinguish DB from normal breathing states. METHODS/UNASSIGNED:) and breathing frequency (BF) over time data were normalised with 100% considered as the ventilation at anaerobic threshold (AT) and detrended before ApEn was calculated. Analysis was initiated at 25 W and ceased at AT. RESULTS/UNASSIGNED:and ApEn BF were similar among all PASC patients despite visually recognised DB, but significantly greater than controls. CONCLUSIONS/UNASSIGNED:is an objective metric that can reliably differentiate DB from normal breathing patterns on CPET. This can be a valuable addition to keen visual scrutiny of CPET data.

Studies suggest that environmental disasters have a big impact on population health conditions including metabolic risk factors, such as obesity and hypertension. The World Trade Center (WTC) destruction from the 9/11 terrorist attack resulted in environmental exposures to community members (Survivors) with potential for metabolic effects. We now examine the impact of WTC exposure on Body Mass Index (BMI) using the data from 7136 adult participants enrolled in the WTC Environmental Health Center (EHC) from August 1, 2005, to December 31, 2022. We characterized WTC-related exposures by multiple approaches including acute dust-cloud exposure, occupational or residential exposures, and latent exposure patterns identified by synthesizing multiplex exposure questions using latent class analysis. Employing multivariable linear and quantile regressions for continuous BMI and ordered logistic regression for BMI categories, we found significant associations of BMI with WTC exposure categories or latent exposure patterns. For example, using exposure categories, compared to the group of local residents, local workers exhibited an average BMI increase of 1.71 kg/m² with 95% confidence intervals (CI) of (1.33, 2.09), the rescue/recovery group had an increase of 3.13 kg/m² (95% CI: 2.18, 4.08), the clean-up worker group had an increase of 0.75 kg/m² (95% CI: 0.09, 1.40), and the other mixer group had an increase of 1.01 kg/m² (95% CI: 0.43, 1.58). Furthermore, quantile regression analysis demonstrated that WTC exposures adversely affected the entire distribution of BMI in the WTC EHC Survivors, not merely the average. Our analysis also extended to blood pressure and hypertension, demonstrating statistically significant associations with WTC exposures. These outcomes highlight the intricate connection between WTC exposures and metabolic risk factors including BMI and blood pressure in the WTC Survivor population.

BACKGROUND/UNASSIGNED:Isolated small airway abnormalities may be demonstrable at rest in patients with normal spirometry; however, the relationship of these abnormalities to exertional symptoms remains uncertain. This study uses an augmented cardiopulmonary exercise test (CPET) to include evaluation of small airway function during and following exercise to unmask abnormalities not evident with standard testing in individuals with dyspnoea and normal spirometry. METHODS/UNASSIGNED:volume curves during exercise to assess for dynamic hyperinflation and expiratory flow limitation; and 2) post-exercise spirometry and oscillometry to evaluate for airway hyperreactivity. RESULTS/UNASSIGNED:0.05). CONCLUSIONS/UNASSIGNED:We uncovered mechanisms for exertional dyspnoea in subject with normal spirometry that was attributable to either small airway dysfunction during exercise and/or small airway hyperreactivity following exercise. The similarity of findings in WTC environmentally exposed and clinically referred cohorts suggests broad relevance for these evaluations.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Recently, "Technical standards for respiratory oscillometry" was published, which reviewed the physiological basis of oscillometric measures and detailed the technical factors related to equipment and test performance, quality assurance and reporting of results. Here we present a review of the clinical significance and applications of oscillometry. We briefly review the physiological principles of oscillometry and the basics of oscillometry interpretation, and then describe what is currently known about oscillometry in its role as a sensitive measure of airway resistance, bronchodilator responsiveness and bronchial challenge testing, and response to medical therapy, particularly in asthma and COPD. The technique may have unique advantages in situations where spirometry and other lung function tests are not suitable, such as in infants, neuromuscular disease, sleep apnoea and critical care. Other potential applications include detection of bronchiolitis obliterans, vocal cord dysfunction and the effects of environmental exposures. However, despite great promise as a useful clinical tool, we identify a number of areas in which more evidence of clinical utility is needed before oscillometry becomes routinely used for diagnosing or monitoring respiratory disease.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Introduction: Exertional respiratory symptoms are prominent in patients with environmental lung injury following inhalation of World Trade Center dust. Baseline pulmonary function testing in these patients is frequently normal, leaving symptoms unexplained. Although small airway dysfunction has been identified at rest, its role in producing exertional symptoms is unclear. In this study exercise evaluation with assessment of airway function was employed to uncover mechanisms for exertional dyspnea.
Method(s): 27 subjects were studied: 20 with unexplained dyspnea (normal spirometry) and 6 asymptomatic controls. Baseline pulmonary function testing was conducted along with respiratory oscillometry to assess small airway function. An incremental exercise protocol was performed that included a focused evaluation of airway function: (1) examination of tidal flow vs. volume curves during exercise to assess for dynamic hyperinflation and expiratory flow limitation; and (2) airway reactivity post-exercise using spirometry and oscillometry. Baseline: By design spirometry values were within normal limits in all subjects. Symptomatic individuals tended to have greater mean R5, R20, R5-20, and AX at baseline compared with asymptomatic controls (R5: 4.80+/-1.79 vs. 3.66+/-1.06; R20: 3.52+/-1.12 vs. 2.98+/-0.68; R5-20: 1.28+/-1.02 vs. 0.70+/-0.53; AX: 13.44+/-10.74 vs. 5.48+/-5.21). Exercise: Dyspnea was reproduced with exercise in symptomatic subjects (mean Borg dyspnea score 1.38+/-1.48 at baseline, 4.20+/-2.28 at peak exercise). Asymptomatic controls did not report significant dyspnea (mean Borg dyspnea score 0 at baseline, 1.60+/-1.14 at peak exercise). Expiratory flow limitation during exercise was noted in 13/20 symptomatic subjects compared with 0 controls. Post Exercise: Bronchial hyper-reactivity was evident in post-exercise spirometry (>10% decline in FEV1) in 3/20 symptomatic subjects vs. 1/6 controls; the fall in FEV1 was predominantly attributable to a fall in FVC, consistent with small airway dysfunction. An additional six symptomatic subjects demonstrated isolated small airway hyper-reactivity that was only revealed on oscillometry.
Conclusion(s): In patients with unexplained dyspnea and normal spirometry, symptoms were reproduced during exercise. Focused airway assessment uncovered small airway dysfunction both during and following exercise that contributed to the development of dyspnea

This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance. Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case-control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease. Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading. This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight.