Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT.

BACKGROUND: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. OBJECTIVE: We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. METHODS: Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. RESULTS: Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. CONCLUSIONS: Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

Evaluating health information systems-related errors using the human, organization, process, technology-fit (HOPT-fit) framework.

Complex socio-technical health information systems (HIS) issues can create new error risks. Therefore, we evaluated the management of HIS-related errors using the proposed human, organization, process, and technology-fit framework to identify the lessons learned. Qualitative case study methodology through observation, interview, and document analysis was conducted at a 1000-bed Japanese specialist teaching hospital. Effective management of HIS-related errors was attributable to many socio-technical factors including continuous improvement, safety culture, strong management and leadership, effective communication, preventive and corrective mechanisms, an incident reporting system, and closed feedback loops. Enablers of medication errors include system sophistication and process factors like workarounds, variance, clinical workload, slips and mistakes, and miscommunication. The case management effectiveness in handling the HIS-related errors can guide other clinical settings. The potential of HIS to minimize errors can be achieved through continual, systematic, and structured evaluation. The case study validated the applicability of the proposed evaluation framework that can be applied flexibly according to study contexts to inform HIS stakeholders in decision-making. The comprehensive and specific measures of the proposed framework and approach can be a useful guide for evaluating complex HIS-related errors. Leaner and fitter socio-technical components of HIS can yield safer system use.

Lower doses of carvedilol in Japanese heart failure patients with reduced ejection fraction could show the potential to be non-inferior to higher doses in US patients: An international collaborative observational study.

The Japanese national guidelines recommend significantly lower doses of carvedilol for heart failure with reduced ejection fraction (HFrEF) management than the US guidelines. Using real-world data, we determined whether initial and target doses of carvedilol in Japanese patients (JPNs) differ from those in US patients (USPs), especially in Asian Americans (ASA) and Caucasians (CA), and investigated differences in outcomes. We collected data from the electronic medical records, including demographics, carvedilol dosing, tolerability, cardiac functional indicators like EF, cardiovascular events including all-cause deaths, and laboratory values from the University of California, San Diego Health and Osaka University. JPNs had significantly lower doses (mg/day) of carvedilol initiation (66 USPs composed of 38 CAs and 28 ASAs, 17.1±16.2; 93 JPNs, 4.3±4.2, p<0.001) and one year after initiation (33.0±21.8; 11.2±6.5, p<0.001), and a significantly lower relative rate (RR) of dose discontinuation and reduction than USPs (RR: 0.406, 95% confidence interval (CI): 0.181-0.911, p<0.05). CAs showed the highest reduction rate (0.184), and ASAs had the highest discontinuation rate (0.107). A slight mean difference with narrow 95% CI ranges straddling zero was observed between the two regions in the change from the baseline of each cardiac functional indicator (LVEF, -0.68 [-5.49-4.12]; LVDd, -0.55 [-3.24-2.15]; LVDd index, -0.25 [-1.92-1.43]; LVDs, -0.03 [-3.84-3.90]; LVDs index, -0.04 [-2.38-2.30]; heart rate, 1.62 [-3.07-6.32]). The event-free survival showed no difference (p = 0.172) among the races. Conclusively, despite JPNs exhibiting markedly lower carvedilol doses, their dose effectiveness has the potential to be non-inferior to that in USPs. Dose de-escalation, not discontinuation, could be an option in some Asian and ASA HFrEF patients intolerable to high doses of carvedilol.

Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study.

BACKGROUND: One life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups. OBJECTIVE: This study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns. METHODS: A web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared. RESULTS: A total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child's temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties. CONCLUSIONS: The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.

Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies.

Some multicenter clinical studies require the acquisition of clinical specimens from patients, and the centralized management and analysis of clinical specimens at a research institution. In such cases, it is necessary to manage clinical specimens with anonymized patient information. In addition, clinical specimens need to be managed in connection with clinical information in clinical studies. In this study, we have developed a clinical specimen information management system that works with electronic data capture system for efficient specimen information management and the system workflow has verified at Osaka University Hospital. In addition, by combining this system with medical image collection system that we have developed previously, the integrated management of clinical information, medical image, and clinical specimen information will become possible. This specimen information management system may be expected to provide the platform for integrated analysis utilizing clinical information, medical image, and data from clinical specimens in multicenter clinical studies.

Classification of Diagnostic Certainty in Radiology Reports with Deep Learning.

A radiology report is prepared for communicating clinical information about observed abnormal structures and clinically important findings with referring clinicians. However, such observations and findings are often accompanied by ambiguous expressions, which can prevent clinicians from accurately interpreting the content of reports. To systematically assess the degree of diagnostic certainty for each observation and finding in a report, we defined an ordinal scale comprising five classes: definite, likely, may represent, unlikely, and denial. Furthermore, we applied a deep learning classification model to determine its applicability to in-house radiology reports. We trained and evaluated the model using 540 in-house chest computed tomography reports. The deep learning model achieved a micro F1-score of 97.61%, which indicated that our ordinal scale was suitable for measuring the diagnostic certainty of observations and findings in a report.

Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital.

We implemented a multilingual medical questionnaire system, which allows patients to answer questionnaires both in and out of the hospital. The response data are sent to and stored as structured data on the server in hospital information system, and could be converted to Japanese and quoted as part of progress notes in the electronic medical record.

Antimicrobial resistance patterns of WHO priority pathogens isolated in hospitalized patients in Japan: A tertiary center observational study.

BACKGROUND: After issuing the "Global action plan on antimicrobial resistance" in 2015, the World Health Organization (WHO) established a priority pathogens list for supporting research and development of novel antimicrobials. We conducted a comprehensive analysis of the WHO priority organisms in a Japanese tertiary hospital to apprehend the local AMR epidemiology. METHODS: Data were obtained from electrical medical records in Osaka University Hospital between January 2010 and March 2021. The critical, high, and medium "priority pathogens list" categories of the WHO were used to compare results between the early (2010-2015) and late (2016-2021) phases. RESULTS: Out of 52,130 culture-positive specimens, a total of 9,872 (18.9%) contained WHO priority isolates. In comparison to early phases, late phases were likely to have higher rates of carbapenem resistance in Pseudomonas aeruginosa (15.7% vs 25.0%, P<0.001), 3rd generation cephalosporin resistance in Escherichia coli (11.5% vs 17.8%, P<0.001) as well as Klebsiella pneumoniae (1.6% vs 4.4%, P<0.001), and ampicillin resistance in Haemophilus influenzae (2.4% vs 3.9%, P<0.001). After 2015, however, the proportion of methicillin-resistant and vancomycin-intermediate Staphylococcus aureus was low. In this study, in-hospital mortality was comparable among patients with resistance to the three WHO priority pathogen types: critical (5.9%), high (3.9%), and medium (3.8%), and no significant change was observed between two phases in each category. However, significant interactions for in-hospital mortality were observed in subgroup analyses between "critical priority" AMR and the presence of comorbid conditions, such as chronic kidney disease or diabetes mellitus. CONCLUSIONS: To implement better antimicrobial stewardship policies and practices, local priority pathogens and "high-risk" patients for in-hospital death need to be acknowledged and evaluated periodically.

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation.

Background: Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. Objective: For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. Methods: Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model. Results: The microaveraged F1-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F1-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). Conclusions: Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.

Enhancing evidence-informed policymaking in medicine and healthcare: stakeholder involvement in the Commons Project for rare diseases in Japan.

BACKGROUND: Although stakeholder involvement in policymaking is attracting attention in the fields of medicine and healthcare, a practical methodology has not yet been established. Rare-disease policy, specifically research priority setting for the allocation of limited research resources, is an area where evidence generation through stakeholder involvement is expected to be effective. We generated evidence for rare-disease policymaking through stakeholder involvement and explored effective collaboration among stakeholders. METHODS: We constructed a space called 'Evidence-generating Commons', where patients, family members, researchers, and former policymakers can share their knowledge and experiences and engage in continual deliberations on evidence generation. Ten rare diseases were consequently represented. In the 'Commons', 25 consecutive workshops were held predominantly online, from 2019 to 2021. These workshops focused on (1) clarification of difficulties faced by rare-disease patients, (2) development and selection of criteria for priority setting, and (3) priority setting through the application of the criteria. For the first step, an on-site workshop using sticky notes was held. The data were analysed based on KJ method. For the second and third steps, workshops on specific themes were held to build consensus. The workshop agendas and methods were modified based on participants' feedback. RESULTS: The 'Commons' was established with 43 participants, resulting in positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants. The difficulties faced by patients with rare diseases were classified into 10 categories. Seven research topics were identified as priority issues to be addressed including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. This was performed by synthesising the results of the application of the two criteria that were particularly important to strengthen future research on rare diseases. We also clarified high-priority research topics by using criteria valued more by patients and family members than by researchers and former policymakers, and criteria with specific perspectives. CONCLUSION: We generated evidence for policymaking in the field of rare diseases. This study's insights into stakeholder involvement can enhance evidence-informed policymaking. We engaged in comprehensive discussions with policymakers regarding policy implementation and planned analysis of the participants' experiences in this project.

Stakeholder involvement is significant for effective policymaking in the field of rare diseases. However, practical methods for this involvement have not yet been established. Therefore, we developed the 'Commons project' to generate valuable policymaking information and explore effective ways for stakeholders' collaboration. This article explains the process and results of 25 continuous workshops, held from 2019 to 2021 with 43 participants, including patients, family members, researchers, and former policymakers. The main achievements of the discussion that took place in the 'Commons' included a presentation of the overview of the difficulties faced by patients with rare diseases and formulation of high priority research topics.First, the difficulties faced by patients with rare diseases were grouped into 10 categories. Second, seven research topics were identified as priority issues including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. During the project process, positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants, were identified. Beyond the context of the field of rare diseases and science of policy, these findings are useful for the future of society, including co-creation among stakeholders and patient and public involvement. Based on this study's results, we have initiated communications with policy stakeholders in the field of rare diseases, with the aim of policy implementation.

Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study.

BACKGROUND: Documentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation. OBJECTIVE: This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses. METHODS: An electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022. RESULTS: A total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group. CONCLUSIONS: The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

Safety and utility of Endoscopic Ultrasound with Bronchoscope-guided Fine Needle Aspiration (EUS-B-FNA) in suspected lung cancer patients with poor respiratory or general conditions: a prospective three-center observational study.

BACKGROUND: Although transbronchial diagnostic procedures are sometimes difficult to perform because of the patient's respiratory or general conditions, endoscopic ultrasound with bronchoscope-guided fine-needle aspiration (EUS-B-FNA), a known transesophageal diagnostic procedure, might be useful for such cases. We conducted this prospective three-center observational study to evaluate the safety and efficacy of EUS-B-FNA in suspected lung cancer patients with poor respiratory or general conditions. METHODS: Patients with suspected lung cancer with respiratory failure, Eastern Cooperative Oncology Group performance status of 2 or higher, or severe respiratory symptoms, were enrolled. The primary endpoints were the diagnostic yield of lung cancer and its safety, and the secondary endpoints were the success rate of molecular and programmed death ligand 1 (PD-L1) analyses, and the 6-month survival rate in patients with lung cancer. RESULTS: We enrolled 30 patients, of which 29 were included in the analysis. Among them, 26 were eventually diagnosed with lung cancer. The diagnostic yield for lung cancer was 100% (26/26). There were no adverse events associated with EUS-B-FNA requiring procedure discontinuation. The success rates of molecular analysis for EGFR, ALK, ROS-1, and BRAF were 100% (14/14), 100% (11/11), 100% (9/9), and 75% (6/8), respectively. The success rate of the PD-L1 analysis was 100% (15/15). The 6-month survival rate in patients with lung cancer was 53.8% (95% confidence interval [CI]: 33.4-76.4), and the median overall survival (OS) was 196 days (95% CI: 142-446). CONCLUSIONS: EUS-B-FNA is a safe and effective diagnostic method, even in patients with suspected lung cancer with poor respiratory or general conditions. TRIAL REGISTRATION: This clinical trial was registered at https://www.umin.ac.jp/ctr/index.htm (UMIN000041235, approved on 28/07/2020).

Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer's disease using multimodal data.

Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data, structural magnetic resonance imaging (MRI) data, APOE genotype data, and age at examination. The HML model showed an average F1 score of 0.721, which was comparable to the random forest method and had significantly more predictive accuracy than the CART method. The HML-generated decision tree was also used to classify-five subtypes of MCI. Each MCI subtype was characterized in terms of the degree of abnormality in CSF biomarkers, brain atrophy, and cognitive decline. The five subtypes of MCI were further categorized into three groups: one subtype with low conversion rates (similar to cognitively normal subjects); three subtypes with moderate conversion rates; and one subtype with high conversion rates (similar to AD dementia patients). The subtypes with moderate conversion rates were subsequently separated into a group with CSF biomarker abnormalities and a group with brain atrophy. The subtypes identified in this study exhibited varying MCI-to-AD conversion rates and differing biological profiles.

Relationships of Atrial Fibrillation at Diagnosis and Type of Atrial Fibrillation During Follow-up With Long-Term Outcomes for Heart Failure With Preserved Ejection Fraction.

Background: Few data are available regarding the impact of atrial fibrillation (AF) at diagnosis and type of AF during the follow-up period on long-term outcomes in patients with heart failure with preserved ejection fraction (HFpEF). Methods and Results: In all, 1,697 patients diagnosed as HFpEF between March 2010 and December 2017 were included in this study. At enrollment, 698 (41.1%) patients had AF. Over a median follow-up of 1,017 days, there were no significant differences between patients with and without AF in the adjusted hazard ratio (HR) for all-cause death or admission for heart failure. However, those with AF had a higher risk of stroke (HR 1.831; P=0.003). Of 998 patients with sinus rhythm at enrollment, 139 (13.9%) developed new-onset AF. Predictors of new-onset AF were pulse, hemoglobin, left ventricular end-diastolic dimension, and B-type natriuretic peptide. Compared with sinus rhythm, paroxysmal AF had a similar risk for all-cause death, admission for HF, and stroke; persistent AF had a lower risk of all-cause death (HR 0.701; P=0.015), but a higher risk for admission for HF (HR 1.608; P=0.002); and new-onset AF had a lower risk for all-cause death (HR 0.654; P=0.040), but a higher risk of admission for HF (HR 2.475; P<0.001). Conclusions: In patients with HFpEF, long-term outcome may differ by type of AF. Physicians need to consider individual risk with regard to AF type.

Immune Checkpoint Inhibitor-Induced Limbic Encephalitis during Treatment with Atezolizumab in a Patient with Small-Cell Lung Cancer: A Case Report and Review of the Literature.

Paraneoplastic neurological syndrome (PNS) is associated with malignancies, including small-cell lung cancer. Recently, PNS cases among patients with small-cell lung cancer (SCLC) induced by immune checkpoint inhibitors have increased. We herein report a 66-year-old man with SCLC who developed disorientation, dysphagia, and gait disturbance after three courses of treatment with atezolizumab. Brain magnetic resonance imaging revealed a high-intensity area in the bilateral temporal lobes. Blood test results were positive for anti-Hu and anti-Zic4 antibodies, which led to the diagnosis of limbic encephalitis as PNS. Some symptoms improved with intravenous administration of steroids and immunoglobulins.

Successful treatment with lorlatinib in a patient with meningeal carcinomatosis of ALK-positive non-small cell lung cancer resistant to alectinib and brigatinib: A case report.

RATIONALE: Although anaplastic lymphoma kinase (ALK) inhibitors are effective treatment options for ALK-positive non-small cell lung cancer (NSCLC) with central nervous system (CNS) metastasis, achieving long-term survival in patients with NSCLC with meningeal carcinomatosis resistant to ALK inhibitors is difficult. Lorlatinib, a third-generation ALK inhibitor, was designed for selective CNS penetration, and exerts potent antitumor activity against tumors resistant to first- and/or second-generation ALK inhibitors. However, there is limited information about the activity of lorlatinib in ALK inhibitor-resistant meningeal carcinomatosis. Here, we report a case of ALK-positive lung adenocarcinoma with meningeal carcinomatosis in which lorlatinib was used after resistance to alectinib and brigatinib. PATIENTS CONCERNS: A 55-year-old woman with no history of smoking presented to our hospital with a swelling on the left neck. Clinical imaging and histopathological examination revealed a tumor of adenocarcinoma histology in the left upper lung with no CNS metastasis. DIAGNOSES: The patient was diagnosed with ALK-positive lung adenocarcinoma (cT3N3M1b: stage IVA). INTERVENTIONS: She received the second-generation ALK inhibitors, alectinib and brigatinib, in the first and second-line settings, respectively. However, she developed meningeal carcinomatosis. Hence, treatment with lorlatinib was initiated in the third-line setting. OUTCOMES: The symptoms associated with meningeal carcinomatosis, such as disturbance of consciousness and diplopia, improved dramatically. At 8 months from the initiation of lorlatinib, the patient remained well without disease progression. LESSONS: Lorlatinib is an effective treatment option for patient with ALK-positive NSCLC who develop meningeal carcinomatosis resistant to second-generation ALK inhibitors. Therefore, lorlatinib should be considered in such cases, even when patients exhibit serious symptoms associated with meningeal carcinomatosis.

Practical use of a multicenter clinical research support system connected to electronic medical records.

BACKGROUND: Electronic medical records (EMRs) are widely used, but in many cases, they are used within a network physically separated from the Internet. Multicenter clinical studies use Internet-connected electronic data capture (EDC) systems to collect data, where data entered into the EMR are manually transcribed into the EDC system. In addition, medical images for clinical research are also collected manually. Variations in EMRs and differing data structures among vendors hamper the use of data for clinical research. METHODS: We solved this problem by developing a network infrastructure for clinical research between Osaka University Hospital and affiliated hospitals in the Osaka area and introducing a clinical data collection system (CDCS). In each hospital's EMR network, we implemented a CRF reporter that accumulated data for clinical research using a template and then sent the data to a management server in the Osaka University Hospital Data Center. To organize the patient profile data and clinical laboratory data stored in each EMR for use in clinical research, the data are retrieved from the template by an interface module developed by each vendor, according to our common data output interface specification. The data entered into the CRF reporter template for clinical research are also recorded in the EMR progress notes and sent to the data management server. This network infrastructure can also be used as a medical image collection system that automatically collects images for research from PACS at each hospital. These systems are managed under common subject numbers issued by the CDCS. RESULTS: A network infrastructure was established among 19 hospitals, and a CRF reporter was incorporated into the EMR. A medical image transfer system was introduced in 13 hospitals. Since 2013, 28 clinical studies have been conducted using this system, and data for 9,987 cases have been collected as of December 31, 2020. CONCLUSION: Incorporating a CRF reporter with medical image transfer system into the EMR has proven useful for collecting research data.

Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports.

BACKGROUND AND OBJECTIVE: In this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease lesion. Manually annotating nodules is costly in terms of time. Therefore, we used the report information to automatically produce training data for the object detection task. METHODS: First, we use semantic segmentation model PSP-Net to recognize lung fields described in the CXR reports. Next, a classification model ResNeSt-50 is used to discriminate the nodule in segmented right and left field. It also can provide attention map by Grad-Cam. If the attention region corresponds to the location of the nodule in the CXR reports, an attention bounding box is generated. Finally, object detection model Faster-RCNN was performed using generated attention bounding box. The bounding boxes predicted by Faster-RCNN were filtered to satisfy the location extracted from CXR reports. RESULTS: For lung field segmentation, a mean intersection of union of 0.889 was achieved in our best model. 15,156 chest radiographs are used for classification. The area under the receiver operating characteristics curve was 0.843 and 0.852 for the left and right lung, respectively. The detection precision of the generated attention bounding box was 0.341 to 0.531 depending on the binary setting for attention map. Through object detection process, the detection precisions of the bounding boxes were improved to 0.567 to 0.800. CONCLUSION: We successfully generated bounding boxes with nodule on CXR images based on the positional information of the diseases extracted from the CXR reports. Our method has the potential to provide bounding boxes for various lung lesions which can reduce the annotation burden for specialists. SHORT ABSTRACT: Machine learning for computer aided image diagnosis requires annotation of images, but manual annotation is time-consuming for medical doctor. In this study, we tried to create a machine-learning method that creates bounding boxes with disease lesions on chest X-ray (CXR) images using the positional information extracted from CXR reports. We set the nodule as the target lesion. First, we use PSP-Net to segment the lung field according to the CXR reports. Next, a classification model ResNeSt-50 was used to discriminate the nodule in segmented lung field. We also created an attention map using the Grad-Cam algorithm. If the area of attention matched the area annotated by the CXR report, the coordinate of the bounding box was considered as a possible nodule area. Finally, we used the attention information obtained from the nodule classification model and let the object detection model trained by all of the generated bounding boxes. Through object detection model, the precision of the bounding boxes to detect nodule is improved.

The efficacy and safety of left atrial low-voltage area guided ablation for recurrence prevention compared to pulmonary vein isolation alone in patients with persistent atrial fibrillation trial: Design and rationale.

Recurrence rates of atrial fibrillation (AF) after pulmonary vein isolation (PVI) are higher in patients with a left atrial low-voltage area (LVA) than those without. However, the efficacy of LVA guided ablation is still unknown. The purpose of this study-the Efficacy and Safety of Left Atrial Low-voltage Area Guided Ablation for Recurrence Prevention Compared to Pulmonary Vein Isolation Alone in Patients with Persistent Atrial Fibrillation trial (SUPPRESS-AF trial)-is to elucidate whether LVA guided ablation in addition to PVI is superior to PVI alone in patients with persistent AF. The Osaka Cardiovascular Conference will conduct a multicenter, randomized, open-label trial aiming to examine whether LVA guided ablation in addition to PVI is superior to PVI alone in patients with persistent AF and LVAs. The primary outcome is the recurrence of AF documented by scheduled or symptom-driven electrocardiography (ECG) during the 1 year follow-up period after the index ablation. The key secondary endpoints include all-cause death, symptomatic stroke, bleeding events, and other complications related to the procedure. A total of 340 patients with an LVA will be enrolled and followed up to 1 year. The SUPPRESS-AF trial is a randomized controlled trial designed to assess whether LVA guided ablation in addition to PVI is superior to PVI alone for patients with persistent AF and LVAs detected while undergoing their first catheter ablation.