At altitude, factors such as decreased barometric pressure, low temperatures, and acclimatization might affect lung function. The effects of exposure and acclimatization to high-altitude on lung function were assessed in 39 subjects by repetitive spirometry up to 6022 m during a high-altitude expedition. Subjects were classified depending on the occurrence of acute mountain sickness (AMS) and summit success to evaluate whether lung function relates to successful climb and risk of developing AMS. Peak expiratory flow (PEF), forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) increased with progressive altitude (max. +20.2 %pred, +9.3 %pred, and +6.7 %pred, all p<0.05). Only PEF improved with acclimatization (BC1 vs. BC2, +7.2 %pred, p=0.044). At altitude FEV1 (p=0.008) and PEF (p<0.001) were lower in the AMS group. The risk of developing AMS was associated with lower baseline PEF (p<0.001) and longitudinal changes in PEF (p=0.008) and FEV1 (p<0.001). Lung function was not related to summit success (7126 m). Improvement in PEF after acclimatization might indicate respiratory muscle adaptation.

OBJECTIVE: The Japanese Society for Respiratory Care and Rehabilitation, Japanese Association on Sarcopenia and Frailty, Japanese Society of Respiratory Physical Therapy, and Japanese Association of Rehabilitation Nutrition proposed the definition and diagnosis of respiratory sarcopenia using low respiratory muscle strength and appendicular skeletal muscle mass (ASM; ASM/height2) instead of respiratory muscle mass; however, these parameters have not been validated. This study aimed to confirm the validity of the respiratory sarcopenia definition proposed by these four professional organizations.
METHODS: Participants of our cohort study in 2015 of 468 community-dwelling older people who were evaluated for sarcopenia and underwent spirometry were included in this analysis. We determined two respiratory sarcopenia models based on low skeletal muscle mass and respiratory muscle strength. Low skeletal muscle mass was defined by low ASM/height2, and low respiratory muscle strength was defined by peak expiratory flow rate (PEFR) or percentage of predicted PEFR (%PEFR). Survival status was assessed 5 years after baseline assessment (in 2020). To evaluate the validity of the cut-off values for PEFR and %PEFR, we determined different respiratory sarcopenia models by sequentially varying the cut-off values for each parameter, including ASM/height2, from high to low. We subsequently calculated the hazard ratio (HR) for mortality for each respiratory sarcopenia model using the Cox proportional hazards model. Additionally, we plotted the HR for each combination of cut-off values for ASM/height2 and PEFR or %PEFR on a three-dimensional chart to observe the relationship between the different cut-off values and HR.
RESULTS: A total of 31 people died during the 5-year observation period. With ASM/height2 cut-off values of approximately 7.0 kg/m2 for men and 5.7 kg/m2 for women and %PEFR cut-off values of 66-75%, respiratory sarcopenia was associated with mortality risk (HR, 2.36-3.27, point estimation range).
CONCLUSIONS: The definition of respiratory sarcopenia by the four professional organizations is related to future health outcomes, and this definition is valid. Geriatr Gerontol Int 2024; ••: ••-••.

UNASSIGNED: The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden.
UNASSIGNED: To establish baseline disease characteristics of the study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored.
UNASSIGNED: This was a multicenter, observational, 24-week, proof-of-concept study conducted in the United States.
UNASSIGNED: Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels.
UNASSIGNED: Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h.
UNASSIGNED: This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.

BACKGROUND: The English PUMA questionnaire emerges as an effective COPD case-finding tool. We aimed to use the PUMA questionnaire in combination with peak expiratory flow rate (PEFR) to improve case-finding efficacy in Chinese population.
METHODS: This cross-sectional, observational study included two stages: translating English to Chinese PUMA (C-PUMA) questionnaire with linguistic validation and psychometric evaluation, followed by clinical validation. Eligible participants (with age ≥40 years, respiratory symptoms, smoking history ≥10 pack-years) were enrolled and subjected to three questionnaires (C-PUMA, COPD assessment test [CAT], and generic health survey [SF-12V2]), PEFR measurement, and confirmatory spirometry. The C-PUMA score and PEFR were incorporated into a PUMA-PEFR prediction model applying binary logistic regression coefficients to estimate the probability of COPD (PCOPD).
RESULTS: C-PUMA was finalized through standard forward-backward translation processes and confirmation of good readability, comprehensibility, and reliability. In clinical validation, 240 participants completed the study. 78/240 (32.5%) were diagnosed with COPD. C-PUMA exhibited significant validity (correlated with CAT or physical component scores of SF-12V2, both P<0.05, respectively). PUMA-PEFR model had higher diagnostic accuracy than C-PUMA alone (area under ROC curve, 0.893 vs. 0.749, P<0.05). The best cutoff values of C-PUMA and PUMA-PEFR model (PCOPD) were ≥6 and ≥0.39, accounting for a sensitivity/specificity/numbers needed to screen of 77%/64%/3 and 79%/88%/2, respectively. C-PUMA ≥5 detected more underdiagnosed patients, up to 11.5% (vs. C-PUMA ≥6).
CONCLUSIONS: C-PUMA is well-validated. The PUMA-PEFR model provides more accurate and cost-effective case-finding efficacy than C-PUMA alone in at-risk, undiagnosed COPD patients. These tools can be useful to detect COPD early.

Background and objective COVID-19 is a respiratory disease that is highly contagious and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptoms vary from mild to severe, where most of the patients suffer from high fever, severe headaches, dry cough, and exhaustion, while the less common symptoms are diarrhea, loss of taste, sore throat, and loss of smell. Following recovery from COVID-19, some patients displayed a restricted pattern in the function of their lungs. As a result, documenting the effects of COVID-19 after infection is essential since it provides a better understanding of the long-term consequences of COVID-19. Hence, the objective of the present study was to assess pulmonary functions in post-convalescent COVID-19 patients. Methodology A cross-sectional comparative study was conducted among students and staff members of Gulf Medical University for a duration of one year from 2021 to 2022. Through a convenient sampling method, a total of 100 participants were recruited for the present study, in which pulmonary function tests (PFTs) were performed using a spirometer, and O2 levels were measured using a pulse oximeter. Additionally, respiratory rate and pulse rate were monitored. Results The present study highlighted the comparison of PFTs in post-convalescent COVID-19 patients and concluded that smoker and convalescent COVID-19 groups showed non-significant decrease (p>0.05) in forced vital capacity (FVC) prediction, forced expiratory volume in the first second (FEV1) prediction, FEV1/FVC%, forced mid-expiratory flow rate (FEF25-75%) prediction, peak expiratory flow rate (PEFR) prediction, respiratory rate, and pulse rate in comparison to the control group. In comparison to the convalescent COVID-19 group, convalescent COVID-19 smoking patients showed a significant increase in FEV1/FVC% (p=0.04). Additionally, in comparison to the convalescent COVID-19 group, a significant increase in PEFR prediction values was observed with a p-value of 0.045 and in comparison to the smoker group with a p-value of 0.006. Moreover, oxygen saturation (SpO2) levels demonstrated non-significant changes between the groups. Conclusion The study concluded that for FEV1/FVC% and PEFR prediction values among the convalescent COVID-19 smoking patient group, a significant increase was observed in comparison to the convalescent COVID-19 group. This aids healthcare professionals in amending strategies to prevent consequences resulting from post-COVID-19 infection.

We investigated the association of peak expiratory flow (PEF) with dementia; cognitive impairment, no dementia (CIND); and transition from CIND to dementia, and possible underlying neuropathological mechanisms.
A population-based cohort of adults aged 60+ was followed over 15 years to detect dementia (Diagnostic and Statistical Manual of Mental Disorders, 4th edition criteria), CIND (assessed through a cognitive battery), and progression from CIND to dementia, in relation to baseline PEF observations. A subsample (n = 462) had 6-year follow-up data on brain magnetic resonance imaging markers of neurodegeneration and small vessel disease.
In fully adjusted models, poor PEF performance (< 10th vs. ≥ 80th percentile) was associated with increased hazards for dementia (hazard ratio [HR] = 1.89; 95% confidence interval [CI] = 1.23-2.92) and CIND (HR = 1.55; 95% CI = 1.01-2.38) and CIND progression to dementia, although not statistically significantly (HR = 2.44; 95% CI = 0.78-6.88). People with poor PEF also experienced the fastest ventricular enlargement (β coefficient = 0.67 mL/year; 95% CI = 0.13-1.21) and had the highest likelihood of developing lacunes (odds ratio = 5.05; 95% CI = 1.01-25.23).
Poor lung function contributes to cognitive deterioration possibly through accelerated brain atrophy and microvascular damage.
Poor lung function increased the risk of dementia and mild cognitive impairment (MCI). Poor lung function accelerated the progression from MCI to dementia. Poor lung function was linked to brain microvascular damage and global brain atrophy.

Prolonged ventilation remains the most common cause of tracheal stenosis. Tracheal reconstruction is the gold standard treatment in these cases. The success of surgery is based on results of the pulmonary function test (PFT) which relies on patients\' performance and their cooperation. The objective of the study was to investigate the impact of tracheal reconstruction in cases of tracheal stenosis on blood gas indices as well as airflow indices and whether arterial blood gas (ABG) can be a better surrogate of adequacy of tracheal reconstruction. This was a retrospective observational study of 61 patients with tracheal stenosis between the ages of 21 and 65 years who underwent tracheal reconstruction. The preoperative and postoperative values of various blood gas indices like partial pressure of oxygen (pO2), partial pressure of carbon dioxide (pCO2), and potential of hydrogen (pH) and airflow indices like forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) were evaluated and found that all the parameters showed significant improvement after tracheal reconstruction with P < 0.0001. Tracheal reconstruction resulted in improvement of oxygenation and airflow parameters which led to relief of symptoms. Hence, ABG can be used as a surrogate marker for denoting the success of the surgery.

UNASSIGNED: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD).
UNASSIGNED: This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD.
UNASSIGNED: A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value.
UNASSIGNED: Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

Assessing cough effectiveness, using Cough Peak Flow, is crucial for patients with Neuromuscular Diseases, such as Amyotrophic Lateral Sclerosis. Impaired cough function can contribute to respiratory decline and failure. The goal of the study is to determine the correlation between diaphragmatic excursion and cough expiratory phase, potentially utilizing ultrasonographic indices to estimate Cough Peak Flow in these patients. Twenty-two patients were enrolled in this study. The upward displacement of the diaphragm was measured with ultrasonography during voluntary cough expiration and Cough Peak Flow was simultaneously measured. A multivariable linear regression model was built to quantify the association between Cough Peak Flow and diaphragm expiratory excursion. There is significative relationship between Cough Peak Flow and diaphragm excursion with a Pearson\'s r coefficient of 0.86 observed in the patients group. Multiple linear regression analysis for Cough Peak Flow (Adjusted R2 = 0.86) revealed significant associations between Cough Peak Flow and expiratory excursion (adjusted β-coefficient: 64.78, 95 %, CI: 51.50-78.07, p<0.001) and sex (adjusted β-coefficient: -69.06; 95 % CI: -109.98 to -28.15, p=0.001). Our results predict the cough effectiveness by using M-mode diaphragmatic sonography with a potentially significant impact on therapeutic choices.

Decreased cough strength in myasthenia gravis (MG) leads to aspiration and increases the risk of MG crisis. The aim of this study was to clarify the reliability and validity of cough peak flow (CPF) measurements in MG. A total of 26 patients with MG who underwent CPF measurements using the peak flow meter by themselves were included. MG symptoms were evaluated by pulmonary function tests and clinical MG assessment scales before and after immune-treatments. The relationship between CPF and pulmonary function tests and MG comprehensive were assessed. The cut-off value of CPF for aspiration risk was determined and the area under the curve (AUC) was calculated. The intraclass correlation coefficient was more than 0.95 for pre-and post-treatment. Positive correlations were found between CPF and almost all spirometric values as well as between the differences of pre-and post-treatment in CPF and quantitative myasthenia gravis score. The CPF for identifying the aspiration risk was used to calculate the CPF cut-off value of 205 L/min with a sensitivity of 0.77, specificity of 0.90, and AUC of 0.85. The CPF, a convenient measure by patients themselves, has a high reliability in patients with MG, and is a useful biomarker reflecting MG symptoms.