Background: Mechanical ventilators are essential life-support devices, yet evidence regarding device-related vulnerabilities remains limited compared with the extensive literature on human-factor-related adverse events. This study applied text-mining techniques to a large post-market surveillance database to examine ventilator malfunction reporting patterns and the effect of stratifying reports by device category.Methods: Adverse event reports related to mechanical ventilators submitted to the U.S. Food and Drug Administration's Manufacturer and User Facility Device Experience database between January 2010 and December 2025 were stratified into life-supporting and noninvasive devices and analyzed using KH Coder. A random sample of the life-supporting reports was drawn for the primary analysis. Frequency analysis, co-occurrence network analysis, hierarchical cluster analysis, and correspondence analysis were performed. Device brand and year were used as external variables in the correspondence analysis.Results: A total of 193,926 reports met the eligibility criteria, of which 177,641 were classified as malfunctions. The analyses were restricted to reports classified as malfunctions. Among life-supporting ventilators, the reported malfunctions spanned several broad themes, including power and battery failures, circuit-board and electronic faults, breathing-circuit and gas-delivery problems, and issues involving monitoring and control functions. Among noninvasive devices, reports were concentrated in terms associated with a single foam-related recall, as well as connector corrosion and patient-reported symptoms. Nearly 1 in 5 malfunction reports did not document a confirmed device failure during patient use.Conclusions: Text mining of ventilator-related adverse event reports revealed distinct reporting patterns across life-supporting and noninvasive devices. These findings are exploratory and do not establish the frequency or causes of device failure. Stratification of reports by device category helped prevent the reporting patterns of individual devices from being obscured by a single recall. This study demonstrates the potential value of combining post-market surveillance data with text-mining methods to support medical device safety surveillance.
Pulmonary rehabilitation (PR) is a cornerstone of therapy for people with chronic lung disease. Over the years, there has been a tremendous amount of research in the field. Researchers have studied improved delivery methods for PR that improve patient outcomes. This article will review several important articles in the field over the last year.
Background: Negative-pressure ventilation (NPV) avoids a positive-pressure facial interface, but contemporary multi-center pediatric data comparing NPV with noninvasive positive-pressure ventilation (NIV) are limited. We describe modern NPV epidemiology and compare outcomes with NIV in children younger than 3 years with acute respiratory failure. Methods: We conducted a multi-center retrospective cohort study using the Virtual Pediatric Systems (VPS) database. Children younger than 3 years who received NPV or NIV during a pediatric ICU (PICU) admission for acute or acute-on-chronic respiratory failure were included; outpatient use of either modality was excluded. Subjects were categorized according to initial respiratory support modality. The primary outcome was endotracheal intubation after initiation of noninvasive respiratory support. The primary analysis used multivariable mixed-effects logistic regression with center included as a random intercept. Propensity-score matching using Pediatric Risk of Mortality III score, age, sex, weight, diagnostic variables, and center was performed as a sensitivity analysis.Results: Crude intubation rates were higher in the NPV group than in the NIV group (27.3% vs 10.4%, P < .001). After adjustment for measured subject-level factors and center-level clustering, ventilation modality was not independently associated with intubation (NIV vs NPV adjusted OR 0.675, 95% CI 0.393-1.158, P = .154). In the propensity-score-matched cohort, intubation remained numerically higher in the NPV group but was not statistically significant (NPV vs NIV OR 1.64, 95% CI 0.92-2.93, P = .12). Conclusions: In this multi-center retrospective cohort, NPV was used in a small minority of children younger than 3 years with acute respiratory failure. After adjustment for measured confounders and center-level clustering, ventilation modality was not independently associated with intubation. Prospective studies are needed to define optimal patient selection and the role of NPV in pediatric acute respiratory failure.
Background: Obstructive sleep apnea (OSA) is a prevalent public health concern linked to cardiovascular, metabolic, and neurocognitive consequences. Although polysomnography (PSG) is the diagnostic standard, the availability can be limited with long wait time. Snoring is a common symptom of OSA, but all individuals with snoring do not have OSA. In this study, we hypothesized the characteristics of snoring may be used to detect OSA. We applied an unsupervised clustering method to classify snoring patterns and examine their correlations with OSA parameters.Methods: We enrolled 287 adults with snoring symptoms who underwent overnight PSG at 2 teaching hospitals in Taiwan. Snoring sounds were recorded, and the dominant frequency and loudness were analyzed. A K-means clustering model was first developed using a training cohort (n = 50) and applied to classify frequency patterns into 5 clusters. Each cluster was further divided into 2 subclusters based on a loudness threshold (70 dB). An independent validation cohort (n = 237) was used to develop an apnea-hypopnea index (AHI) prediction model and evaluate discriminative performance using receiver operating characteristic analysis.Results: Clusters 1, 2, and 5 were significantly associated with higher AHI, oxygen desaturation index, and apnea index. In contrast, clusters 3 and 4 showed little or no association. An AHI prediction model was developed using age, BMI, and significant acoustic clusters. For PSG-defined OSA thresholds of AHI >= 5, >= 15, and >= 30 events/h, the AUC values were 0.90, 0.83, and 0.82, respectively, with sensitivity ranging from 70.5% to 77.5% and specificity ranging from 73.7% to 100%.Conclusions: Our study demonstrates an AI-based clustering analysis of snoring patterns can be used to evaluate OSA severity. The snoring pattern analysis can also be an additional screening tool for OSA.
