Impact of a Cancer History on Cardiovascular Events Among Patients With Myocardial Infarction Who Received Revascularization.

BACKGROUND: It remains controversial whether a cancer history increases the risk of cardiovascular (CV) events among patients with myocardial infarction (MI) who undergo revascularization.Methods and Results: Patients who were confirmed as type 1 acute MI (AMI) by coronary angiography were retrospectively analyzed. Patients who died in hospital or those not undergoing revascularization were excluded. Patients with a cancer history were compared with those without it. A cancer history was examined in the in-hospital cancer registry. The primary outcome was a composite of cardiac death, recurrent type 1 MI, post-discharge coronary revascularization, heart failure hospitalization, and stroke. Among 551 AMI patients, 55 had a cancer history (cancer group) and 496 did not (non-cancer group). Cox proportional hazards model revealed that the risk of composite endpoint was significantly higher in the cancer group than in the non-cancer group (adjusted hazard ratio [HR]: 1.78; 95% confidence interval [CI]: 1.13-2.82). Among the cancer group, patients who were diagnosed as AMI within 6 months after the cancer diagnosis had a higher risk of the composite endpoint than those who were diagnosed as AMI 6 months or later after the cancer diagnosis (adjusted HR: 5.43; 95% CI: 1.55-19.07). CONCLUSIONS: A cancer history increased the risk of CV events after discharge among AMI patients after revascularization.

Extracting clinical terms from radiology reports with deep learning.

Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model comprising three types of clinical entities: observations, clinical findings, and modifiers. Furthermore, to determine its applicability for in-house radiology reports, we extracted clinical terms with state-of-the-art deep learning models and compared the results. We trained and evaluated models using 540 in-house chest computed tomography (CT) reports annotated by multiple medical experts. Two deep learning models were compared, and the effect of pre-training was explored. To investigate the generalizability of the model, we evaluated the use of other institutional chest CT reports. The micro F1-score of our best performance model using in-house and external datasets were 95.36% and 94.62%, respectively. Our results indicated that entities defined in our information model were suitable for extracting clinical terms from radiology reports, and the model was sufficiently generalizable to be used with dataset from other institutions.

The role of autophagy in cardiomyocytes in the basal state and in response to hemodynamic stress.

Autophagy, an evolutionarily conserved process for the bulk degradation of cytoplasmic components, serves as a cell survival mechanism in starving cells. Although altered autophagy has been observed in various heart diseases, including cardiac hypertrophy and heart failure, it remains unclear whether autophagy plays a beneficial or detrimental role in the heart. Here, we report that the cardiac-specific loss of autophagy causes cardiomyopathy in mice. In adult mice, temporally controlled cardiac-specific deficiency of Atg5 (autophagy-related 5), a protein required for autophagy, led to cardiac hypertrophy, left ventricular dilatation and contractile dysfunction, accompanied by increased levels of ubiquitination. Furthermore, Atg5-deficient hearts showed disorganized sarcomere structure and mitochondrial misalignment and aggregation. On the other hand, cardiac-specific deficiency of Atg5 early in cardiogenesis showed no such cardiac phenotypes under baseline conditions, but developed cardiac dysfunction and left ventricular dilatation one week after treatment with pressure overload. These results indicate that constitutive autophagy in the heart under baseline conditions is a homeostatic mechanism for maintaining cardiomyocyte size and global cardiac structure and function, and that upregulation of autophagy in failing hearts is an adaptive response for protecting cells from hemodynamic stress.

Hybrid endovascular treatment for complicated aortic dissection concomitant with true lumen obliteration: a case report.

Background: Thoracic endovascular aortic repair (TEVAR) has been widely introduced. However, unestablished transfemoral approach due to true lumen obliteration disables endovascular option. Case summary: A 74-year-old male with a history of 15-year-ago type B aortic dissection presented with chronic bilateral lower extremity claudication. CT angiography revealed that a large entry tear was located at distal to the left subclavian artery. The thoracic aneurysmal degeneration progressed and eventually required repair. True lumen of infrarenal aorta to bilateral common iliac arteries was totally collapsed by false lumen, and the re-entry tear was open at external iliac artery. Initially, we performed recanalization to the collapsed true lumen. Bidirectional approach was taken from right brachial and bifemoral arteries. The covered endovascular reconstruction of aortic bifurcation (CERAB) technique and double D-shape moulding technique (DDMT) was performed to create covered stent configuration. As secondary treatment, 1-debranching TEVAR with axillary artery bypass was successfully performed by utilizing femoral approach. Discussion: This case demonstrated feasibility of two-stage endovascular therapy for thoracic aneurysmal degeneration concomitant with true lumen obliteration. This combined technique of CERAB and DDMT was absolutely effective to minimize type Ⅲ endoleak in infrarenal segment. Hybrid endovascular treatment offered minimally invasive therapy to the patient.

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.

Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing.

Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in free text, and findings documented by radiologists may not be adequately addressed. In this study, focusing on pulmonary nodules, we evaluated whether cases in which radiologists described follow-up as recommended were receiving appropriate treatment. Reports recommending follow-up for pulmonary nodules were automatically extracted using natural language processing. In our evaluation, out of 10,507 reports, 1,501 cases (14.3%) were classified as "reports recommending follow-up for pulmonary nodules." Among these, 958 cases underwent additional imaging tests within 400 days. From the remaining 543 cases, we randomly sampled 42 cases and conducted chart reviews by clinicians to confirm patient care status. Our assessment found that follow-up was not documented in 17 of the 42 cases (40.5%), indicating a high likelihood that appropriate care was not provided.

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.

The observation of below-the-knee artery by optical frequency domain image and angioscopy: a case series.

Background: Endovascular treatment (EVT) is a well-established treatment for patients with chronic limb-threatening ischaemia, and below-the-knee (BTK) artery is its main target, although the re-intervention rate is still high. Understanding of the characteristics of BTK artery atherosclerosis would be required to overcome this issue. In this case series, we elucidated the characteristics of non-stenotic BTK artery atherosclerosis in the patients who received EVT of the superficial femoral artery (SFA) using optical frequency domain imaging (OFDI) and angioscopy. Case summary: We presented five patients who underwent EVT of SFA and subsequent observation of ipsilateral BTK artery using OFDI and angioscopy. Patients one and two had advanced atherosclerosis; however, patients three, four, and five had only mild atherosclerosis. Discussion: All patients had multiple risk factors for atherosclerosis and stenosis/occlusion of the SFA and ipsilateral BTK arteries. Furthermore, some patients had several other atherosclerotic vascular diseases suggesting the presence of advanced systemic atherosclerosis. On the other hand, some patients with multiple BTK artery stenosis/occlusion did not have advanced atherosclerosis in the examined BTK artery. The absence of significant atherosclerosis in a BTK artery in patients with multiple stenoses or occlusion in other ipsilateral BTK arteries may suggest some mechanism of vessel occlusion other than atherosclerosis. Further investigations are needed to clarify the mechanism.

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.

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.

Novel Algorithm for the Estimation of Cancer Incidence Using Claims Data in Japan: A Feasibility Study.

PURPOSE: We developed algorithms to identify patients with newly diagnosed cancer from a Japanese claims database to identify the patients with newly diagnosed cancer of the sample population, which were compared with the nationwide cancer incidence in Japan to assess the validity of the novel algorithms. METHODS: We developed two algorithms to identify patients with stomach, lung, colorectal, breast, and cervical cancers: diagnosis only (algorithm 1), and combining diagnosis, treatments, and medicines (algorithm 2). Patients with newly diagnosed cancer were identified from an anonymized commercial claims database (JMDC Claims Database) in 2017 with two inclusions/exclusion criteria: selecting all patients with cancer (extract 1) and excluding patients who had received cancer treatments in 2015 or 2016 (extract 2). We estimated the cancer incidence of the five cancer sites and compared it with the Japan National Cancer Registry incidence (calculated standardized incidence ratio with 95% CIs). RESULTS: The number of patients with newly diagnosed cancer ranged from 219 to 17,840 by the sites, algorithms, and exclusion criteria. Standardized incidence ratios were significantly higher in the JMDC Claims Database than in the national registry data for extract 1 and algorithm 1, extract 1 and algorithm 2, and extract 2 and algorithm 1. In extract 2 and algorithm 2, colorectal cancer in male and stomach, lung, and cervical cancers in females showed similar cancer incidence in the JMDC and national registry data. CONCLUSION: The novel algorithms are effective for extracting information about patients with cancer from claims data by using the combined information on diagnosis, procedures, and medicines (algorithm 2), with 2-year cancer-treatment history as an exclusion criterion (extract 2).

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.

Proposal of diagnostic process model for computer based diagnosis.

We aim at making a diagnosis support system that can be put to practical use. We proposed a diagnostic process model based on simple knowledge which can be gleaned from textbooks. We defined clinical finding (CF) as a general concept for patient's symptom or findings etc., whose value is expressed by Boolean. We call the combination of several CFs a "CF pattern", and a set of CF patterns with concomitant diseases "case base". We consider diagnosis as a process of searching an instance from the case base whose CF pattern is concomitant with that of a patient. The diseases which have the same CF pattern are candidates for diagnosis. Then we select a CF which is present in part of the candidates and check whether it is present or absent in the patient in order to narrow down the candidates. Because the case base does not exist in reality, the probability of CF pattern is calculated by the product of CF occurrence rate assuming that occurrence of CF is independent. Therefore the knowledge required for diagnosis is frequency of disease under sex and age group and CF-disease relation (CF and its occurrence rate in the disease). By processing these two types of knowledge, diagnosis can be made.

Prospects of Hospital Information Systems and Patient Safety in Japan.

OBJECTIVES: Approximately 20 years have passed since hospital information systems (HISs) featuring full-scale electronic medical records were first implemented in Japan. Patient safety is one of the most important of the several "safety" roles that HISs are expected to fulfill. However, insufficient research has analyzed the contribution of HISs to patient safety. This paper reviews the history of HISs in connection with patient safety in Japan and discusses the future of the patient safety function of HISs in a favorable environment for digitization. METHODS: A review on the history of HISs with functions that contribute to patient safety was conducted, analyzing evidence from reports published by the Japanese government and papers on patient safety and HISs published in various countries. RESULTS: Patient safety has become a concern, and initiatives to promote patient safety have progressed simultaneously with the spread of HISs. To address the problem of patient safety, most large hospitals prioritize patients' welfare when building HISs. However, no HIS-associated reduction in adverse events due to medical treatment could be confirmed. CONCLUSIONS: HISs are expected to help prevent medical accidents, such as patient- and drug-related errors. It is hoped that the patient safety functions of HISs will become generalized and contribute to patient safety in the future. To achieve this, the government and academic societies should provide regulations and guidelines on HISs and patient safety to the medical community and medical-device vendors. Furthermore, departments responsible for HISs and patient safety should collaborate to gather evidence for the effectiveness of HISs.

Temporary Rise in Blood Thrombogenicity in Patients with Acute Myocardial Infarction.

Objective Although blood thrombogenicity seems to be one of the determinant factors for the development of acute myocardial infarction (MI), it has not been dealt with in-depth. This study aimed to investigate blood thrombogenicity and its change in acute MI patients. Methods and Results We designed a prospective, observational study that included 51 acute MI patients and 83 stable coronary artery disease (CAD) patients who underwent cardiac catheterization, comparing thrombogenicity of the whole blood between: (1) acute MI patients and stable CAD patients; and (2) acute and chronic phase in MI patients. Blood thrombogenicity was evaluated by the Total Thrombus-Formation Analysis System (T-TAS) using the area under the flow pressure curve (AUC 30 ) for the AR-chip. Acute MI patients had significantly higher AUC 30 than stable CAD patients (median [interquartile range], 1,771 [1,585-1,884] vs. 1,677 [1,527-1,756], p = 0.010). Multivariate regression analysis identified acute MI with initial TIMI flow grade 0/1 as an independent determinant of high AUC 30 ( ß = 0.211, p = 0.013). In acute MI patients, AUC 30 decreased significantly from acute to chronic phase (1,859 [1,550-2,008] to 1,521 [1,328-1,745], p = 0.001). Conclusion Blood thrombogenicity was significantly higher in acute MI patients than in stable CAD patients. Acute MI with initial TIMI flow grade 0/1 was significantly associated with high blood thrombogenicity by multivariate analysis. In acute MI patients, blood thrombogenicity was temporarily higher in acute phase than in chronic phase.

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.

The I{kappa}B kinase {beta}/nuclear factor {kappa}B signaling pathway protects the heart from hemodynamic stress mediated by the regulation of manganese superoxide dismutase expression.

Cardiomyocyte death plays an important role in the pathogenesis of heart failure. The nuclear factor (NF)-kappaB signaling pathway regulates cell death, however, the effect of NF-kappaB pathway on cell death can vary in different cells or stimuli. The purpose of the present study was to clarify the in vivo role of the NF-kappaB pathway in response to pressure overload. First, we subjected C57Bl6/J mice to pressure overload by means of transverse aortic constriction (TAC) and examined the activity of the NF-kappaB pathway in response to pressure overload. IkappaB kinase (IKK) and NF-kappaB were activated after TAC. Then, we investigated the role of the activation using cardiac-specific IKKbeta-deficient mice (CKO). CKO displayed normal global cardiac structure and function compared with control littermates. We subjected CKO and control mice to pressure overload. One week after TAC, CKO showed cardiac dilation, dysfunction, and lung congestion, which are characteristics of heart failure. The number of apoptotic cells in the hearts of CKO mice increased significantly after TAC. The levels of manganese superoxide dismutase mRNA and protein expression in CKO after TAC were significantly attenuated compared with control mice. The levels of oxidative stress and c-Jun N-terminal kinase (JNK) activation in CKO after TAC were significantly greater than those in control mice. Isoproterenol-induced cell death of isolated adult CKO cardiomyocytes was inhibited by treatment with either a manganese superoxide dismutase mimetic or a JNK inhibitor. Thus, the IKKbeta/NF-kappaB signaling pathway plays a protective role in cardiomyocytes because of the attenuation of oxidative stress and JNK activation in a setting of acute pressure overload.