Automatic adjustment of oxygen concentration during high-flow nasal cannula treatment using a targeted SpO2 feedback system.

AIM: To test whether targeted SpO2 feedback (TSF), an automatic control system for fraction of inspired oxygen (FiO2), achieves more time in the optimal SpO2 range and/or reduces the frequency of manual adjustments to administered FiO2 compared with conventional manual titration in patients with hypoxia on high-flow nasal cannula (HFNC) therapy. STUDY DESIGN: Twenty-two patients were recruited from two hospitals. For each, two sessions of manual mode and two sessions of TSF were applied in a random order, each session lasting 2 h. The target SpO2 on TSF was 95%. Oxygen monitoring levels were classified into four SpO2 ranges: hypoxia (≤ 89%), borderline (90%-93%), optimal (94%-96%) and hyperoxia (≥ 97%). The two modes were compared based on the proportion of time spent in each SpO2 range and the number of manual FiO2 adjustments. RESULTS: The proportion of time in the optimal SpO2 range was 20.5% under manual titration mode and 65.4% under TSF (p < .01). The proportions of time in the hypoxia range were 1.1% and 0.4%, respectively (p = .31), in the borderline range 4.7% and 3.5%, respectively (p = .54), and in the hyperoxia range 73.7% and 30.7%, respectively (p < .01). There were statistical differences only in the optimal and hyperoxia SpO2 ranges. During the 8 h, the frequency of manual FiO2 adjustment was 0.7 times for the manual mode and 0.2 times for TSF, showing no statistically significant difference (p = 0.076). CONCLUSION: Compared with manual titration, TSF achieved greater time of the optimal SpO2 and less time of hyperoxia during HFNC. The frequency of manual adjustments on TSF tended to be less than on manual titration mode. RELEVANCE TO CLINICAL PRACTICE: Automatic closed-loop algorithm FiO2 monitoring systems can achieve better oxygen treatments than conventional monitoring and may reduce nurse workloads. In the era of pandemic respiratory diseases, this system can also facilitate contactless SpO2 monitoring during HFNC therapy.

Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database.

BACKGROUND: The Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis mycobacterium (NTM) infection, or latent TB infection (LTBI). We also determined the prevalence and specific traits of polymorphisms in N-acetyltransferase-2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) phenotypes using this prospective multinational cohort. METHODS: Until August 2021, 964, 167, and 95 patients with TB, NTM infection, and LTBI, respectively, were included. Clinical, laboratory, and radiographic data were collected. NAT2 and SLCO1B1 phenotypes were classified by genomic DNA analysis. RESULTS: Patients with TB were older, had lower body mass index (BMI), higher diabetes rate, and higher male proportion than patients with LTBI. Patients with NTM infection were older, had lower BMI, lower diabetes rate, higher previous TB history, and higher female proportion than patients with TB. Patients with TB had the lowest albumin levels, and the prevalence of the rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 39.2%, 48.1%, and 12.7%, respectively. The prevalence of rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 42.0%, 44.6%, and 13.3% for NTM infection, and 42.5%, 48.3%, and 9.1% for LTBI, respectively, which did not differ significantly from TB. The prevalence of the normal, intermediate, and lower transporter SLCO1B1 phenotypes in TB, NTM, and LTBI did not differ significantly; 74.9%, 22.7%, and 2.4% in TB; 72.0%, 26.1%, and 1.9% in NTM; and 80.7%, 19.3%, and 0% in LTBI, respectively. CONCLUSIONS: Understanding disease characteristics and identifying pharmacokinetic traits are fundamental steps in optimizing treatment. Further longitudinal data are required for personalized precision medicine. TRIAL REGISTRATION: This study registered ClinicalTrials.gov NO. NCT05280886.

Pleuroparenchymal fibroelastosis in rheumatoid arthritis-associated interstitial lung disease.

BACKGROUND: Pleuroparenchymal fibroelastosis (PPFE) is a rare interstitial lung disease (ILD) featuring dense fibrosis of the visceral pleura and subpleural parenchyma, mostly in the upper lobes. PPFE can present in other ILDs, including rheumatoid arthritis-associated ILD (RA-ILD). The aim of this retrospective study was to investigate the prevalence and clinical implications of coexistent PPFE in RA-ILD. METHODS: Overall, 477 patients with RA-ILD were recruited from two cohorts; their clinical data and HRCT images were analysed. The criteria for diagnosing PPFE were (1) pleural thickening with bilateral subpleural dense fibrosis in the upper lobes, (2) evidence of disease progression, and (3) absence of other identifiable aetiologies. RESULTS: The median follow-up duration was 3.3 years. The mean age of the patients was 63.4 years, and 60.0% were women. PPFE was identified in 31 patients (6.5%). The PPFE group showed significantly lower body mass index and forced vital capacity (FVC) and more frequent usual interstitial pneumonia (UIP)-like pattern on HRCT than no-PPFE group. The risk factors for all-cause mortality were older age, lower FVC, and the presence of UIP-like pattern on HRCT; PPFE was not significantly associated with mortality in both all patients and a subgroup with a UIP-like pattern. The presence of PPFE was associated with a significantly increased risk of pneumothorax and greater decline in diffusing capacity. CONCLUSIONS: PPFE was not rare in patients with RA-ILD and was significantly associated with an increased risk of pneumothorax and greater lung function decline, though we found no significant association with mortality.

Estimating the prevalence and clinical significance of chronic obstructive pulmonary disease-obstructive sleep apnea overlap in South Korea.

INTRODUCTION: Concurrent obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) are referred to as COPD-OSA overlap. We investigated the prevalence and clinical significance of COPD-OSA overlap in the general population of South Korea. METHODS: Data were obtained from the 2019 Korea National Health and Nutrition Examination Survey. Participants ≥40 years of age with complete STOP-Bang questionnaire and spirometry data were included. OSA was presumed in individuals with a STOP-Bang score of ≥3. COPD was determined using forced expiratory volume at 1 s/forced vital capacity <0.7. Participants were classified on the basis of the STOP-Bang score and spirometry findings. Clinical characteristics, comorbidities, and quality of life (using the EuroQoL 5-dimension instrument) were compared between the overlap group and COPD-alone or OSA-alone groups. RESULTS: Among the 3157 participants, 6.9 % demonstrated COPD-OSA overlap. Individuals with OSA alone and COPD alone were 31.8 % and 5.2 %, respectively. The overlap group included more males, ever smokers, and frequent alcohol drinkers than the COPD- or OSA-alone groups. The overlap group had more diagnoses of hypertension, diabetes, and stroke than the COPD-alone group. The risk of anxiety/depression was approximately 2.5 times higher in the overlap group than in the COPD-alone group. COPD-OSA overlap was a significant risk factor for anxiety/depression after adjusting for age, sex, household income, and education levels. CONCLUSION: COPD-OSA overlap is not rare within the general population of South Korea. Patients with overlap showed more comorbidities and higher levels of anxiety/depression than those in the COPD-alone group.

Sex-related disparities in cough-associated symptoms across different age groups.

BACKGROUND: Sex-related disparities in the prevalence of chronic cough have been consistently reported globally, with varying male-to-female ratios. OBJECTIVES: This study aimed to evaluate sex-related differences by comparing correlations between cough-related symptoms in males and females of different age groups. DESIGN: Adult patients with chronic cough who completed the Leicester Cough Questionnaire (LCQ) were recruited from 16 respiratory centers. METHODS: Correlation networks were constructed based on Spearman's correlation coefficients in males and females of various age groups. The distinct relationships of cough-related symptoms between subgroups were validated by an independent cohort. RESULTS: A total of 255 patients were enrolled in this study (male-to-female ratio, 1:1.71). The following LCQ items were highly correlated: embarrassment and interference with daily work, anxiety, and interference with overall life enjoyment/feeling of being fed up, interference with daily work and overall life enjoyment, interference with overall life enjoyment and feeling of being fed up, and feeling of being fed up and annoyance to partner/family/friends. The patterns of these correlations between LCQ items varied in males and females of different ages. The strongest interrelationship was observed in male patients aged >50 years old, which was similar to those in the validation cohort. CONCLUSION: The correlation patterns between cough-related symptoms vary significantly according to age and sex. Understanding the mechanisms underlying the development of cough-related symptoms may facilitate sex- and age-specific strategies for chronic cough.

Unilateral diffuse alveolar haemorrhage with microscopic polyangiitis: A case report.

Diffuse alveolar haemorrhage (DAH) is a life-threatening condition caused by widespread damage to the small pulmonary vessels. Common chest imaging findings in patients with DAH show bilateral diffuse airspace opacities. DAH complicating antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis, including microscopic polyangiitis, should be considered as a differential diagnosis in patients with progressive dyspnea, reduced haemoglobin levels, and alveolar opacities on chest imaging. We report the case of a 78-year-old woman who presented with unilateral DAH, severe dyspnea, and anaemia. DAH was confirmed using bronchoalveolar lavage. Laboratory test results, including ANCA, positive anti-myeloperoxidase antibody, and negative anti-proteinase 3, led to a diagnosis of microscopic polyangiitis. Rituximab and methylprednisolone were administered. The patient's symptoms, laboratory test results, and chest radiography findings improved after the initiation of treatment. This case highlights the importance of prompt recognition of clinical symptoms and signs, including dyspnea and anaemia, for the diagnosis of DAH.

Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.

Background: We aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19). Methods: This was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation. Results: A total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively. Conclusions: Our prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.

Clinical phenotypes of chronic cough categorised by cluster analysis.

BACKGROUND: Chronic cough is a heterogeneous disease with various aetiologies that are difficult to determine. Our study aimed to categorise the phenotypes of chronic cough. METHODS: Adult patients with chronic cough were assessed based on the characteristics and severity of their cough using the COugh Assessment Test (COAT) and the Korean version of the Leicester Cough Questionnaire. A cluster analysis was performed using the K-prototype, and the variables to be included were determined using a correlation network. RESULTS: In total, 255 participants were included in the analysis. Based on the correlation network, age, score for each item, and total COAT score were selected for the cluster analysis. Four clusters were identified and characterised as follows: 1) elderly with mild cough, 2) middle-aged with less severe cough, 3) relatively male-predominant youth with severe cough, and 4) female-predominant elderly with severe cough. All clusters had distinct demographic and symptomatic characteristics and underlying causes. CONCLUSIONS: Cluster analysis of age, score for each item, and total COAT score identified 4 distinct phenotypes of chronic cough with significant differences in the aetiologies. Subgrouping patients with chronic cough into homogenous phenotypes could provide a stratified medical approach for individualising diagnostic and therapeutic strategies.

Impact of prior vaccination on clinical outcomes of patients with COVID-19.

Fully vaccinated people remain at risk of Coronavirus Disease 2019 (COVID-19). We examined association between prior vaccination and clinical outcomes in patients with COVID-19. Overall, 387 patients with mild-to-severe COVID-19 were enrolled. Patients were considered fully vaccinated at least 14, 7, and 14 days after receiving the second dose of ChAdOx1 nCoV-19 or mRNA-1273, second dose of BNT162b2, or single dose of Ad26.COV2.S, respectively. The primary outcomes (risk of pneumonia, requirement of supplemental oxygen, and progression to respiratory failure) were compared between vaccinated and unvaccinated patients. Logistic regression analysis was performed to identify factors associated with the outcomes. There were 204 and 183 patients in the vaccinated and unvaccinated groups, respectively. The vaccinated group was significantly older and had more comorbidities than the unvaccinated group. Patients in the unvaccinated group were significantly more likely to develop pneumonia (65.6% vs. 36.8%) or require supplemental oxygen (29.0 vs. 15.7%) than the vaccinated group. The vaccinated group had a significantly shorter time from symptom onset to hospital discharge than the unvaccinated group (10 vs. 11 days; p<0.001). The proportion of patients who progressed to respiratory failure did not differ significantly between groups. In multivariable analyses, vaccination was associated with an approximately 70% and 82% lower likelihood of pneumonia and supplemental oxygen requirement, respectively. Being vaccinated was associated with a significantly lower risk of pneumonia and severe disease when breakthrough infection developed. Our findings support continuous efforts to increase vaccine coverage in populations.

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

Background: Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters. Methods: Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between -600 and -250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2). Results: A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters. Conclusions: The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.