Background and Aims: Although nintedanib has become widely used for idiopathic pulmonary fibrosis (IPF), biomarkers that predict the effects of nintedanib have not yet been established.Therefore, we monitored various fibrotic markers before and after nintedanib treatment.Methods: In this study, we prospectively enrolled 30 pirfenidone or nintedanib treatment-naive IPF cases.All included patients were diagnosed as having IPF based on the recent official guidelines.Patient sera were stored before treatment and 1, 3, 6, 12 months after treatment, and lung function and imaging were performed regularly. Results and Conclusions:The patient background showed an increase in serum fibrotic markers and an annual forced vital capacity (FVC) reduction of approximately 150 mL after nintedanib treatment.We monitored KL-6, SP-D, and other fibrotic markers.After treatment with nintedanib, the fibrotic markers increased in some cases and decreased in some cases, and the fluctuation was various.The rate of change in fibrotic markers, such as KL-6, SP-D, in 1 month after nintedanib treatment did not correlate with the rate of annual declining in FVC.This study suggests that previous fibrotic markers cannot be a predictor of disease progression in nintedanib-treated IPF cases.
Background Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19). Methods We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. The sample size calculation was not performed given the retrospective all-comer nature of the study. Results A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%– 98.1%), sensitivity of 92.3% (79.7%– 97.3%), specificity of 100% (80.6%– 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27–0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%– 91.3%), 77.5% (62.5%– 87.7%), and 100% (80.6%– 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%– 69.1%), 37.2% (32.0%– 42.7%), and 97.8% (95.2%– 99.0%), respectively. Interpretation AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.
We report a case of severe central sleep apnea incidentally diagnosed during polysomnography for suspected obstructive sleep apnea. Characteristic clinical features included episodic hyperventilation followed by apnea from hypocapnia, which did not follow a Cheyne-Stokes pattern. Combined with the identification of cerebellar and brainstem malformations known as the "molar tooth sign" on a brain magnetic resonance imaging, developmental delay, and motor coordination problems, Joubert syndrome (a congenital disease) was first diagnosed at the age of 50 years. Central apneas were also observed during wakefulness, although not continuously. During sleep, continuous positive airway pressure and adaptive servo-ventilation were ineffective at the referring clinic and at our hospital. Supplemental oxygen decreased the frequency of central apneas and significantly shortened the duration of each central sleep apnea compared with room air. In contrast, the opposite response was observed with acetazolamide administration.
Background: Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19).Methods: We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive.Results: A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The patient-level accuracy of 12-zone AI-POCUS for detecting CT-validated pneumonia was 94.5%, sensitivity was 92.3%, and specificity was 100%. When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9%, 77.5%, and 100%, respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3%, 37.2%, and 97.8%, respectively.Interpretation: AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.Funding Information: This work was partially supported by JSPS KAKENHI, with a Grant Number 21K18086. Declaration of Interests: Kagiyama and Daida are affiliated with a department funded by Philips Japan, Asahi KASEI Corporation, Inter Reha Co., Ltd, and Toho Holdings Co., Ltd., based on collaborative research agreements. Other authors have no conflict of interest to declare.Ethics Approval Statement: The study protocol complied with the Declaration of Helsinki and was approved by the institutional review board at Juntendo University Hospital (#E21-0197). Written informed consent was waived due to its purely observational nature.
Background and Aims: Although nintedanib has become widely used for idiopathic pulmonary fibrosis (IPF), biomarkers that predict the effects of nintedanib have not yet been established.Therefore, we monitored various fibrotic markers before and after nintedanib treatment.Methods: In this study, we prospectively enrolled 30 pirfenidone or nintedanib treatment-naive IPF cases.All included patients were diagnosed as having IPF based on the recent official guidelines.Patient sera were stored before treatment and 1, 3, 6, 12 months after treatment, and lung function and imaging were performed regularly. Results and Conclusions:The patient background showed an increase in serum fibrotic markers and an annual forced vital capacity (FVC) reduction of approximately 150 mL after nintedanib treatment.We monitored KL-6, SP-D, and other fibrotic markers.After treatment with nintedanib, the fibrotic markers increased in some cases and decreased in some cases, and the fluctuation was various.The rate of change in fibrotic markers, such as KL-6, SP-D, in 1 month after nintedanib treatment did not correlate with the rate of annual declining in FVC.This study suggests that previous fibrotic markers cannot be a predictor of disease progression in nintedanib-treated IPF cases.
Abstract Background: Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19). Methods: We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. Results: A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The patient-level accuracy of 12-zone AI-POCUS for detecting CT-validated pneumonia was 94.5%, sensitivity was 92.3%, and specificity was 100%. When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9%, 77.5%, and 100%, respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3 %, 37.2%, and 97.8 %, respectively. Interpretation: AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.
Objective Based on the increasing incidence of smell and taste dysfunction among coronavirus disease 2019 (COVID-19) patients, such issues have been considered an early symptom of infection. However, few studies have investigated the type of taste components that are most frequently affected in COVID-19 patients. This study investigated the difference in frequencies of the types of taste component disorders among hospitalized COVID-19 patients. Methods In this retrospective, single-center, observational study, patients' background characteristics, clinical course, laboratory and radiological findings, and details on taste and/or smell disorders were collected and analyzed from medical records. Patients A total of 227 COVID-19 patients were enrolled, among whom 92 (40.5%) complained of taste disorders. Results Multiple types of taste disorders (hypogeusia/ageusia and hypersensitivity, or hypersensitivity and changing tastes) were reported in 10 patients. In particular, 23 patients reported hypersensitivity to at least 1 type of taste, and 2 patients complained of a bitter taste on consuming sweet foods. Impairment of all taste components was found in 48 patients (52.2%). The most frequent taste disorder was salty taste disorder (81 patients, 89.0%). Hypersensitivity to salty taste was most frequently observed (19 patients, 20.9%). Conclusion Patients with COVID-19 develop multiple types of taste disorders, among which salty taste disorder was the most frequent, with many patients developing hypersensitivity to salty taste. As smell and taste are subjective senses, further studies with the combined use of objective examinations will be required to confirm the findings.
