mucormycosis is an uncommon, life-threatening opportunistic fungal infection which affects immunocompromised patients such as diabetes, recipients of stem cell or organ transplant, and has worse outcomes in those with hematologic malignancy or neutropenia.Methods: A 35 years old male patient with uncontrolled type II Diabemellitus presented to casualty with complaints of cough associated with thick dark brownish sputum for 1 month, fever, loss of appetite and weight since 15 days.His vitals were stable at the time of admission.Chest auscultation revealed diminished vesicular breath sounds heard in right infraclavicular and suprascapular area.Complete blood count showed leucocytosis with neutrophilic predominance.He also had hyponatremia and hypokalaemia.Chest x-ray showed large irregular thick walled cavity in right mid and lower zone.CT thorax revealed central bronchopleural fistula involving right main bronchus with collapse and consolidation involving right middle and lower lobe.Patient underwent diagnostic bronchoscope which showed distorted right secondary carina and there was a large communication in right main bronchus extending to pleural space with thick brownish collection.Right upper lobe bronchial mucosa was unhealthy, covered with thick brownish slough.Results: Bronchoalveolar lavage KOH smear and Transbronchial lung biopsy showed fungal organisms with broad aseptate hyphae suggestive of mucormycosis and he was started on intravenous amphotericin deoxycholate 50mg.Right pneumonectomy was performed in view of large BPF. Conclusion:We evidenced a positive clinical outcome in a poorly controlled diabetic state with early surgical resection and a combination of antifungals.It highlights the importance of the early diagnosis, treatment and timely surgical debridement for the therapy of mucormycosis.
dysmotility symptoms score.A multivariate linear regression revealed that the mean LCQ total score was independently associated with current smoker, fibrocavitary type, bilateral cavitary lesion, and FSSG total score (Table 2).Conclusions: Cough-and sputum-related QOL was impaired in NTM patients with current smoking, radiographical characteristics, and comorbid GERD being associated with the cough-specific QOL.
dysmotility symptoms score.A multivariate linear regression analysis revealed that the mean LCQ total score was independently associated with current smoker, fibrocavitary type, bilateral cavitary lesion, and FSSG total score (Table 2).Conclusions: Cough-and sputum-related QOL was impaired in NTM patients with current smoking, radiographical characteristics, and comorbid GERD being associated with the cough-specific QOL.
ABSTRACT Background Rapid and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD) is problematic in acute-care settings, particularly in the presence of infective comorbidities. Objective The aim of this study was to develop a rapid, smartphone-based algorithm for the detection of COPD, in the presence or absence of acute respiratory infection, and then evaluate diagnostic accuracy on an independent validation set. Methods Subjects aged 40-75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis or emphysema, were recruited into the study. The algorithm analysed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. Results The algorithm demonstrated high percent agreement (PA) with clinical diagnosis for COPD in the total cohort (n=252, Positive PA=93.8%, Negative PA=77.0%, AUC=0.95); in subjects with pneumonia or infective exacerbations of COPD (n=117, PPA=86.7%, NPA=80.5%, AUC=0.93) and in subjects without an infective comorbidity (n=135, PPA=100.0%, NPA=74.0%, AUC=0.97.) In those who had their COPD confirmed by spirometry (n=229), PPA = 100.0% and NPA = 77.0%, AUC=0.97. Conclusions The algorithm demonstrates high agreement with clinical diagnosis and rapidly detects COPD in subjects presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens and identify those at increased risk of mortality due to seasonal or other respiratory ailments.
Analysis of the National Health Insurance data has been actively carried out for the purpose of academic research and establishing scientific evidences for health care service policy asthma.However, there has been a limitation for the accuracy of the data extracted through conventional operational definition.Aim: To establish an operational definition that predicts asthma more accurately.Methods: From Jan 2017 to Jan 2018, we extracted patients with asthma using the conventional operational definition in St. Paul's Hospital at the Catholic University of Korea.Among these, 10% of patients were randomly sampled and analyzed.Using the decision tree model, we established the most accurate operational definition of asthma and validated it.Results: Total 85 patients were enrolled in this study.Twenty-eight patients were not eligible for asthma.Of these, 22 patients were chronic obstructive pulmonary disease.By decision tree model (Figure 1), operational definition of asthma was most precise when it included inhaled corticosteroid and excluded long-acting muscarinic antagonists.As a result of validation of this model, the overall accuracy was 80.0 % (95 % confidence interval: 0.593-0.9317),sensitivity was 75.0 % and specificity was 82.4 % (Figure 2). Conclusions:The conventional operational definition of asthma has limitation to extract true asthma patients in real world.Therefore, it is necessary to establish an accurate standardized operational definition of asthma.
Introduction: Identifying exacerbations in patients with COPD is necessary to allow the implementation of timely and appropriate treatment but is resource intensive and may be confused with other conditions. Aim: To determine the accuracy of exacerbation detection in subjects with COPD aged over 40 years using a smartphone-based algorithm that analyses cough sounds and patient-reported symptoms. Methods: A diagnostic model (index test) was developed using five cough sounds and three-patient reported features (age, fever and acute cough) obtained from a training set of subjects with COPD. The reference diagnosis was defined as the presence or absence of COPD exacerbation as determined by expert clinicians. The agreement of the model with the clinical diagnosis was then evaluated in an independent testing set in two age groups. Results: There were 86 with diagnosed COPD exacerbations and 78 without COPD exacerbations in those aged over 40 years. Of these 64/86 and 63/78 were aged over 65 years. The algorithm demonstrated greater than 82% positive per cent agreement (PPA) and 88% negative per cent agreement (NPA) with the clinical diagnosis for both test groups (Table 1). Conclusion: The model accurately predicted the exacerbations of COPD, which may improve initial management and aid therapy decisions in remote or resource-limited locations.
BACKGROUND Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. OBJECTIVE The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. METHODS Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. RESULTS The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. CONCLUSIONS The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. CLINICALTRIAL Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939
Background Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. Objective The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. Methods Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. Results The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. Conclusions The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939
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