CHEST pulmonary最新文献

Background

Care management programs for chronic lung disease attempt to reduce hospitalizations, yet have not reliably achieved this goal. A key limitation of many programs is that they target patients with characteristics associated with hospitalization risk, but do not specifically modify the mechanisms that lead to hospitalization.

Research Question

What are the common mechanisms underlying known patient-level risk characteristics leading to hospitalizations for acute exacerbations of chronic lung disease?

Study Design and Methods

We conducted a qualitative study of patients admitted to the University of Pennsylvania Health System with acute exacerbations of chronic lung disease between January and September 2019. We interviewed patients, their family caregivers, and their inpatient and outpatient clinicians about experiences leading up to the hospitalization. We analyzed the interview transcripts using triangulation and abductive analytic methods.

Results

We conducted 69 interviews focused on the admission of 22 patients with a median age of 66 years (interquartile range, 60-70 years), of whom 16 patients (73%) were female and 14 patients (64%) were Black. We interviewed 22 patients, 14 caregivers, 19 inpatient clinicians, and 14 outpatient clinicians. We triangulated the available interview data for each patient admission and identified the underlying mechanisms of how several known patient characteristics associated with risk actually led to hospitalization. These mechanisms included limited capacity for home management of acute symptom changes, barriers to accessing care, chronic functional limitations, and comorbid behavioral health disorders. Importantly, many of the clinical, social, and behavioral mechanisms underlying hospitalizations were present for months or years before the symptoms that prompted inpatient care.

Interpretation

Care management programs should be built to target specific clinical, social, and behavioral mechanisms that directly lead to hospitalization. Upstream interventions that reduce hospitalization risk are possible given that many contributory mechanisms are present for months or years before the onset of acute exacerbations.

Background

Exponential growth of knowledge in asthma has advanced the diagnosis and treatment of personalized, phenotype- and endotype-driven care in asthma leading to improved clinical outcomes. However, many knowledge and practice gaps persist among health care professionals. Microlearning has demonstrated efficacy in educational outcomes but is often limited by fragmentation and incomplete information. To integrate the benefits of microlearning into a comprehensive asthma curriculum that addresses knowledge gaps in this rapidly growing field, we built an extensive curriculum in bite-sized, “one breath at a time” portions for physicians managing asthma.

Research question

Is creating an asthma curriculum online educational program with modules of microlearning feasible and useful in improving knowledge of clinicians?

Study Design and Methods

The CHEST Curriculum Pathway for Asthma was published online on the American College of Chest Physicians (CHEST) learning management site in September 2022. It was derived from existing asthma education at the CHEST organization and was vetted by asthma experts. Pretests and posttests, satisfaction surveys, and user engagement data were collected.

Results

In the first 6 months, 523 participants engaged with the CHEST Curriculum Pathway for Asthma. Participant satisfaction was high, and participants answered 68% of the knowledge pretest correctly and 82% of the posttest correctly. Participants completed 82.8% of the individual videos that they opened. Completion rates for each asthma chapter ranged from 22.6% to 57.9%.

Interpretation

This educational program is, to our knowledge, the first comprehensive microlearning online curriculum issued by a medical association on asthma. Our results suggest that the CHEST Curriculum Pathway for Asthma was effective in improving knowledge and had high user satisfaction. Comprehensive online educational programs broken into microlearning components may be useful in other topics in pulmonary medicine.

Background

Common diagnostic tests for pulmonary disorders include chest radiography and pulmonary function tests (PFTs). Although essential, these tests only offer a static assessment. Chest dynamic digital radiography (DDR) integrates lung and diaphragm motion in one study with limited radiation exposure. DDR is relatively easy to obtain, but barriers to its clinical adoption include time-consuming manual analysis and unclear correlation with PFTs.

Research Question

Can a machine learning pipeline automate DDR analysis? What is the strength of the relationship between PFT measures and automated DDR-based lung area measurements?

Study Design and Methods

PFT and DDR studies were obtained in 55 participants. We developed an analysis pipeline using convolutional neural networks capable of quantifying lung areas in DDR images to generate DDR-based PFTs (dPFTs). PFT and dPFT measures were correlated in patients with normal, obstructive, and restrictive lung physiology.

Results

We observed statistically significant (P < 1 × 10^-6), strong correlations between dPFT areas and PFT volumes, including total lung capacity (r = 0.764), FEV₁ (r = 0.591), vital capacity (r = 0.763), and functional residual capacity (r = 0.756). Automated DDR and lung shape tracking revealed differences between normal, restrictive, and obstructive physiology using diaphragm curvature indices and strain analysis measurements. Linear regressions allowed for derivation of PFT values from dPFT measurements.

Interpretation

Statistically significant correlations found between dPFTs and PFTs suggest that dPFTs can act as a surrogate to PFTs when these are not available or unable to be performed. This study contributes to the potential integration of DDR as a reliable alternative to PFTs.

Background

Research Question

Study Design and Methods

Results

Interpretation