Diagnostic index for acute eosinophilic pneumonia without bronchoscopy in military smokers.

BACKGROUND/AIMS: Acute eosinophilic pneumonia (AEP) is common among military smokers; however, bronchoscopy is required for the diagnosis. We aimed to derive and validate a scoring system to diagnose AEP without bronchoscopy. METHODS: We conducted a retrospective study including patients diagnosed with AEP or any other pneumonia among military smokers hospitalized in the Armed Forces Capital Hospital from 15 November 2016 through 25 December 2019. The patients were divided into derivation and validation groups according to their admission day. Patient symptoms, laboratory findings, and computed tomography findings were candidate variables. Least absolute shrinkage and selection operator (LASSO) regression was used to calculate the scores for each variable. RESULTS: Among 414 patients, AEP was confirmed in 54 of 279 patients (19.4%) in the derivation group and in 18 of 135 patients (13.3%) in the validation group. Ten variables were selected using LASSO regression: new-onset or a recently increased smoking (≤ 4 weeks) (8 points), interlobular septal thickening (5 points), absence of sputum (3 points), ground glass opacity (3 points), acute onset (≤ 3 days) (2 points), dyspnea (2 points), chest pain (2 points), leukocytosis (2 points), bronchovascular bundle thickening (2 points), and bilateral involvement (2 points). The area under the receiver-operating characteristic curve of the score to diagnose AEP was 0.997 (95% confidence interval, 0.992 to 1.000) in the derivation group and 0.985 (95% confidence interval, 0.965 to 1.000) in the validation group. CONCLUSION: We introduce a scoring system that can distinguish AEP from other types of pneumonia in military smokers without the need for bronchoscopy.

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. OBJECTIVE: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics-baseline demographics, comorbidities, and symptoms. METHODS: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. CONCLUSIONS: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

BACKGROUND: Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. OBJECTIVE: This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak. METHODS: The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group. RESULTS: The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application. CONCLUSIONS: We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

A case of early diagnosis of pulmonary capillary hemangiomatosis in a worker with exposure to silica.

BACKGROUND: Pulmonary capillary hemangiomatosis (PCH) is a progressive and refractory vascular disease in the lung. Pulmonary hypertension is frequently combined with PCH when capillary proliferation invades to nearby pulmonary vascular systems. It is difficult to differentiate PCH from other diseases such as pulmonary venoocclusive disease and pulmonary arterial hypertension that cause pulmonary hypertension as they frequently overlap. CASE PRESENTATION: A 29-year-old female who had worked at a bathtub factory presented with progressive exertional dyspnea for the past 2 years. Computed tomography revealed centrilobular, diffusely spreading ground-glass opacities sparing subpleural parenchyma with some cystic lesions and air-trapping in both lungs, suggesting a peculiar pattern of interstitial lung disease with airway involvement. There was not any evidence of right heart failure or pulmonary hypertension on echocardiogram, as well as radiography. Microscopic examination of the lung by thoracoscopic resection showed atypical proliferation of capillary channels within alveolar walls and interlobar septa, without invasion of large vessels. CONCLUSION: We experienced a pathologically diagnosed PCH in a young female complaining progressive dyspnea with prior exposure to occupational silica or organic solvent without elevated right ventricular systolic pressure (RVSP) who showed atypical pattern of radiologic findings.

Incidence of Adverse Effects and Discontinuation Rate between Patients Receiving 250 Micrograms and 500 Micrograms of Roflumilast: A Comparative Study.

BACKGROUND: Roflumilast is the only approved oral phosphodiesterase-4 inhibitor for the treatment of severe chronic obstructive pulmonary disease (COPD) in patients with chronic bronchitis and a history of frequent exacerbations. The purpose of this study was to examine the incidence of adverse effects associated with roflumilast treatment in a real-world setting. Further, we compared the incidence of adverse effects and the discontinuation rate among patients receiving different doses. METHODS: We identified all outpatients diagnosed with COPD at Seoul St. Mary's Hospital between May 2011 and September 2016 and retrospectively reviewed their medical records. Roflumilast was prescribed to patients in doses of 500 µg and 250 µg. RESULTS: A total of 269 COPD patients were prescribed roflumilast in our hospital during the study period. Among them, 178 patients were treated with 500 µg and 91 patients were treated with 250 µg. The incidence of adverse effects was 38.2% in the 500 µg group and 25.3% in the 250 µg group (p=0.034). The discontinuation rate of roflumilast was 41.6% (n=74) in the 500 µg group and 23.1% (n=21) in the 250 µg group (p=0.003). When adjusted by age, sex, smoking status, and lung function, 500 µg dose was significantly associated with the discontinuation of roflumilast (odds ratio, 2.87; p<0.001). CONCLUSION: There was a lower incidence of adverse effects and discontinuation among patients treated with 250 µg compared with 500 µg dose. Further studies regarding the optimal dose of roflumilast are required.

Heterogeneity of asthma-COPD overlap syndrome.

Many patients suffering from asthma or COPD have overlapping features of both diseases. However, a phenotypical approach for evaluating asthma-COPD overlap syndrome (ACOS) has not been established. In this report, we examined the phenotypes in patients with ACOS. Patients diagnosed with ACOS between 2011 and 2015 were identified and classified into four phenotype groups. Group A was composed of patients who smoked <10 pack years and had blood eosinophil counts ≥300. Group B was composed of patients who smoked <10 pack years and had blood eosinophil counts <300. Group C was composed of patients who smoked ≥10 pack years and had blood eosinophil counts ≥300. Group D was composed of patients who smoked <10 pack years and had blood eosinophil counts <300. Clinical characteristics were analyzed and compared among groups. Comparisons were made among 103 ACOS patients. Patients in group D were oldest, while patients in group A were youngest. There were relatively more female patients in groups A and B; the majority of patients in groups C and D were male. The degree of airflow obstruction was most severe in group C. The rate of being free of severe exacerbation was significantly lower in group C than in the other groups. In this study, each ACOS phenotype showed different characteristics. The proportion of patients free of severe exacerbation differed significantly among groups. At this time, further studies on the phenotypes of ACOS are required.