Impact of rapid investigation clinic on timeliness of lung cancer diagnosis and treatment.

BACKGROUND: Guidelines recommend timely evaluation of patients with suspected lung cancer. We evaluated the impact of a Rapid Investigation Clinic (RIC) on timeliness of lung cancer diagnosis and treatment between February 2010 and December 2011. METHODS: Investigation within the RIC was conducted by a pulmonologist and a nurse clinician. Controls were patients with lung cancer, investigated outside the RIC at the same institution during the same time period. The primary outcome was time between first contact with a local physician for suspected lung cancer (T0) and first treatment. Factors associated with the delay from T0 to first treatment were examined using multivariate analysis. Completeness of lung cancer staging according to guidelines was assessed. RESULTS: A total of 195 patients were investigated within the RIC vs. 132 patients outside the RIC. The median delay between T0 and first treatment was 65 days (interquartile range [IQR] 46-92 days) in the RIC and 78 days (IQR 49-119 days) in the non-RIC patients (p ≤ 0.01). Time from T0 to pathological diagnosis was shorter in the RIC (median 26 days; IQR 14-42 days) vs. non-RIC patients (median 40 days; IQR 16-68 days). In multivariate analysis, investigation in the RIC was associated with a reduction in time to first treatment of 24 days (95% confidence interval [CI] 12-35 days) when adjusted for relevant confounders. Guideline-concordant investigation occurred more frequently in RIC patients, based on the quality indicators examined. CONCLUSIONS: A Rapid Investigation Clinic reduces delays to lung cancer diagnosis and treatment, and impacts quality of care.

Blood Eosinophil Count Stability and Clinical Outcomes in Patients With Chronic Obstructive Pulmonary Disease in a High Endemic Area of Parasitic Infection: A Prospective Study.

Background: The blood eosinophil count (BEC) is an effective biomarker for predicting inhaled corticosteroid responsiveness in patients with chronic obstructive pulmonary disease (COPD). Methods: A 12-month prospective observational study was conducted in patients with COPD. BEC was measured at enrolment, and after 6 and 12 months. Patients were classified into three groups according to their baseline BEC: <100, 100 - 299, and ≥300 cells/µL. We aimed to describe the patterns of blood eosinophil stability in patients with stable COPD and compare the exacerbation rates and other clinical outcomes at 6 and 12 months. Results: A total of 252 patients with COPD were included. The <100, 100 - 299, and ≥ 300 cells/µL groups consisted of 14.7, 38.9, and 46.4% of patients, respectively. BEC stability was highest (85%) in the ≥300 cells/µL group for both durations. The lowest stability was observed in the <100 cells/µL group at 57 and 46% after 6 and 12 months, respectively. The persistent ≥ 300 cells/µL group had a higher incidence of moderate-to-severe exacerbation (IRR 2.44, 95% confidence interval (CI): 1.13-5.27, p value 0.023, as well as severe exacerbation (IRR 2.19, 95%CI: 1.39-3.45, p value 0.001). Other patient-reported outcomes did not differ significantly between groups. Conclusion: Blood eosinophil levels had good stability in patients with COPD with BEC ≥300 cells/µL and was associated with a high risk of exacerbation in the persistent ≥300 cells/µL group. The variability of BEC was higher in patients with COPD with BEC <300 cells/µL.

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation.

BACKGROUND: The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention. OBJECTIVE: We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. METHODS: A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients. RESULTS: The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available. CONCLUSIONS: The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.

MSTAC: A Multi-Stage Automated Classification of COVID-19 Chest X-ray Images Using Stacked CNN Models.

This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable for disease classification. The study collected CXR images from public datasets and aimed to differentiate between COVID-19, non-COVID-19, and healthy cases. MSTAC employs two classification stages: the first distinguishes healthy from unhealthy cases, and the second further classifies COVID-19 and non-COVID-19 cases. Compared to a single CNN-Multiclass model, MSTAC demonstrated superior classification performance, achieving 97.30% accuracy and sensitivity. In contrast, the CNN-Multiclass model showed 94.76% accuracy and sensitivity. MSTAC's effectiveness is highlighted in its promising results over the CNN-Multiclass model, suggesting its potential to assist healthcare professionals in efficiently diagnosing COVID-19 cases. The system outperformed similar techniques, emphasizing its accuracy and efficiency in COVID-19 diagnosis. This research underscores MSTAC as a valuable tool in medical image analysis for enhanced disease classification.

Characteristics, Outcomes, and Factors Affecting Mortality in Hospitalized Patients with CAP Due to Different Variants of SARS-CoV-2 and Non-COVID-19 CAP.

The study was conducted from October 2020 to March 2022 in a province in southern Thailand. The inpatients with community-acquired pneumonia (CAP) and more than 18 years old were enrolled. Of the 1511 inpatients with CAP, COVID-19 was the leading cause, accounting for 27%. Among the patients with COVID-19 CAP, mortalities, mechanical ventilators, ICU admissions, ICU stay, and hospital costs were significantly higher than of those with non-COVID-19 CAP. Household and workplace contact with COVID-19, co-morbidities, lymphocytopenia and peripheral infiltration in chest imaging were associated with CAP due to COVID-19. The delta variant yielded the most unfavorable clinical and non-clinical outcomes. While COVID-19 CAP due to B.1.113, Alpha and Omicron variants had relatively similar outcomes. Among those with CAP, COVID-19 infection as well as obesity, a higher Charlson comorbidity index (CCI) and APACHE II score were associated with in-hospital mortality. Among those with COVID-19 CAP, obesity, infection due to the Delta variant, a higher CCI and higher APACHE II score were associated with in-hospital mortality. COVID-19 had a great impact on the epidemiology and outcomes of CAP.

Clinical characteristics of anti-MDA5 antibody-positive interstitial lung disease.

Clinically amyopathic dermatomyositis (CADM) with anti-melanoma differentiation-associated gene 5 antibody is associated with rapidly progressive interstitial lung disease (RP-ILD) which results in up to 50% mortality, especially within six months of diagnosis. However, limited data are available on this disease. This is the first case series of six patients in Thailand diagnosed with CADM with ILD. All patients presented with respiratory symptoms, such as progressive dyspnoea, dyspnoea on exertion, or cough. High-resolution computed tomography of the chest showed predominantly subpleural and peripheral consolidation in both lower lungs. Four patients had RP-ILD and three of the RP-ILD patients died within seven weeks of diagnosis. These cases illustrate the clinical characteristics, chest imaging, treatments, and clinical outcomes of the patients diagnosed with CADM and ILD.

Efficacy of whole lung lavage in pulmonary alveolar proteinosis: a 20-year experience at a reference center in Thailand.

BACKGROUND: Pulmonary alveolar proteinosis (PAP) is a rare lung syndrome. The current standard treatment is whole lung lavage (WLL). We reviewed PAP cases treated with WLL during a 243-month period. The primary objective was to describe the efficacy of WLL. We compared chest imaging resolution and pulmonary function tests (PaO2 and DLCO) before the first and after the last WLL. The secondary objectives were to compare mMRC dyspnea scores, other lung function parameters, and complications of WLL. METHODS: We retrospectively reviewed PAP patients from 1 January 2000 to 31 March 2020. Demographic data, pulmonary function tests, and the efficacy of WLL were collected from the electronic medical database and analyzed by descriptive analysis. Differences in data used the student t-test to compare parameters pre- and post-WLL. RESULTS: A total of 19 PAP patients and 50 WLL procedures were included. Eleven patients (57.9%) were females and the mean age was 51.5±11.7 years. Dyspnea (100%) and cough (94.7%) were the two leading symptoms. The most common indication for WLL was progressive dyspnea. There were significant improvements in SpO2 from 86% to 94% (P<0.001), PaO2 from 49.3 to 66.1 (P<0.001), DLCO from 31.8% to 52.5% predicted (P=0.013), and the mMRC dyspnea score from 3 to 2 (P<0.001) without major complications. CONCLUSIONS: WLL is an effective standard treatment for PAP cases. It is safe and can be used as a primary treatment in case of inhaled anti GM-CSF is not available.

Efficacy and Safety of Enteral Erythromycin Estolate in Combination With Intravenous Metoclopramide vs Intravenous Metoclopramide Monotherapy in Mechanically Ventilated Patients With Enteral Feeding Intolerance: A Randomized, Double-Blind, Controlled Pilot Study.

BACKGROUND: In this pilot study, we aimed to determine the efficacy and safety of enteral erythromycin estolate in combination with intravenous metoclopramide compared to intravenous metoclopramide monotherapy in mechanically ventilated patients with enteral feeding intolerance. METHODS: This randomized, double-blind, controlled pilot study included 35 mechanically ventilated patients with feeding intolerance who were randomly assigned to receive 10-mg metoclopramide intravenously every 6-8 hours in combination with 250-mg enteral erythromycin estolate (study group) or placebo every 6 hours for 7 days. The primary outcome was an administered-to-target energy ratio of ≥80% at 48 hours, indicating a successful feeding. Secondary, prespecified outcomes were daily average gastric residual volume (GRV), total energy intake, administered-to-target energy ratio, hospital length of stay, in-hospital mortality, and 28-day mortality. RESULTS: The rate of successful feeding was not significantly different between the study and placebo groups (47.1% and 61.1%, respectively; P = .51). The average daily GRV was significantly lower in the study group than in the placebo group (ß = 91.58 [95% Wald CI, -164.35 to -18.8]), determined by generalized estimating equation. Other secondary outcomes were comparable, and the incidence of adverse events was not significantly different between the 2 groups. One common complication was cardiac arrhythmia, which was mostly self-terminated. CONCLUSION: Although the combination therapy of enteral erythromycin estolate and intravenous metoclopramide reduced GRV, the successful feeding rate and other patient-specific outcomes did not improve in mechanically ventilated patients with feeding intolerance.

Short-Term Lung Function Changes and Predictors of Progressive Systemic Sclerosis-Related Interstitial Lung Disease.

BACKGROUND: Systemic sclerosis (SSc) involves multiple organ systems and has the highest mortality among connective tissue diseases. Interstitial lung disease is the most common cause of death among SSc patients and requires closer studies and follow-ups. This study aimed to identify lung function changes and predictors of progressive disease in systemic sclerosis-related interstitial lung disease (SSc-ILD). METHODS: A retrospective study extracted SSc patients from an electronic database January 2002-July 2019. Eligible cases were SSc patients >age 15 diagnosed with SSc-ILD. Factors associated with progressive disease were analyzed by univariate and multivariate logistic regression analyses. RESULTS: Seventy-eight SSc-ILD cases were enrolled. Sixty-five patients (83.3%) were female, with mean age of 44.7±14.4, and 50 (64.1%) were diffuse type SSc-ILD. Most SSc-ILD patients had crackles (75.6%) and dyspnea on exertion (71.8%), and 19.2% of the SSc-ILD patients had no abnormal respiratory symptoms but had abnormal chest radiographic findings. The most common diagnosis of SSc-ILD patients was non-specific interstitial pneumonia (43.6%). The lung function values of diffusing capacity of the lung for carbon monoxide (DLCO) and DLCO per unit alveolar volume declined in progressive SSc-ILD during a 12-month follow-up. Male and no previous aspirin treatment were the two significant predictive factors of progressive SSc-ILD with adjusted odds ratios of 5.72 and 4.99, respectively. CONCLUSION: This present study showed that short-term lung function had declined during the 12-month follow-up in progressive SSc-ILD. The predictive factors in progressive SSc-ILD were male sex and no previous aspirin treatment. Close follow-up of the pulmonary function tests is necessary for early detection of progressive disease.

Yield of EBUS-TBNA for the diagnosis of sarcoidosis: impact of operator and cytopathologist experience.

BACKGROUND: Studies have reported a high diagnostic yield of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for the diagnosis of sarcoidosis. We sought to review the yield of EBUS-TBNA for the diagnosis of sarcoidosis at our institution over time, and examine factors that may influence this yield. METHODS: Patients who underwent EBUS-TBNA for suspected sarcoidosis between December 2008 and November 2011 were identified. EBUS was performed without rapid on-site evaluation (ROSE) of samples. The final diagnosis was based on the results of all invasive diagnostic procedures and/or clinical follow-up. Logistic regression analysis was used to examine the effect of various factors on yield. RESULTS: 43 patients underwent 45 EBUS-TBNA procedures for suspected sarcoidosis. A total of 115 lymph nodes were sampled. The 21 G needle was used in 51% of procedures. The mean number of lymph node stations sampled was 2.6 (SD 0.7) and the mean number of needle passes per procedure was 7.8 (SD 2.0). Non-necrotising granulomatous inflammation was detected in EBUS-TBNA samples from 34/45 (76%) procedures. The overall diagnostic yield increased to 36/45 (80%) following a cytopathology review for this study. Needle gauge, number of lymph node stations sampled and number of needle passes were not associated with diagnostic yield. The yield of EBUS-TBNA increased significantly after the first 15 procedures performed for suspected sarcoidosis; the 2 additional cases diagnosed after the cytopathology review were part of this early experience. CONCLUSIONS: EBUS-TBNA is a valuable technique for the diagnosis of sarcoidosis when performed without ROSE. The yield of the procedure improved significantly over time, based on operator and cytopathologist experience.

Optimizing tissue sampling for the diagnosis, subtyping, and molecular analysis of lung cancer.

Lung cancer has entered the era of personalized therapy with histologic subclassification and the presence of molecular biomarkers becoming increasingly important in therapeutic algorithms. At the same time, biopsy specimens are becoming increasingly smaller as diagnostic algorithms seek to establish diagnosis and stage with the least invasive techniques. Here, we review techniques used in the diagnosis of lung cancer including bronchoscopy, ultrasound-guided bronchoscopy, transthoracic needle biopsy, and thoracoscopy. In addition to discussing indications and complications, we focus our discussion on diagnostic yields and the feasibility of testing for molecular biomarkers such as epidermal growth factor receptor and anaplastic lymphoma kinase, emphasizing the importance of a sufficient tumor biopsy.