Protein Cryoprotectant Ability of the Aqueous Zwitterionic Solution.

Protein cryopreservation is important for the long-term storage of unstable proteins. Recently, we found that N-acetylglucosaminyltransferase-V (GnT-V) can be cryopreserved in a deep freezer without temperature control using a dilute binary aqueous solution of 3-(1-(2-(2-methoxyethoxy)ethyl)imidazol-3-io)butane-1-carboxylate (OE2imC3C) [10 wt %, mole fraction of solute (x) = 7.75 × 10-3], an artificial zwitterion. However, it is unclear which solvent properties are required in these media to preserve unstable proteins, such as GnT-V. In this study, we investigated the melting phenomena and solution structure of dilute binary aqueous OE2imC3C solutions [x = 0-2.96 × 10-2 (0-30 wt %)] using differential scanning calorimetry (DSC) and Raman and Fourier transform infrared (FTIR) spectroscopies combined with molecular dynamics (MD) simulation to compare the cryoprotectant ability of OE2imC3C with two general cryoprotectants (CPAs), glycerol and dimethyl sulfoxide. DSC results indicated that aqueous OE2imC3C solutions can be melted at lower temperatures with less energy than the control CPA solution, with increasing x, primarily due to OE2imC3C having a higher content of unfrozen water molecules. Moreover, Raman and FTIR results showed that the high content of unfrozen water molecules in aqueous OE2imC3C solutions was due to the hydration around the ionic parts (the COO- group and imidazolium ring) and the OCH2CH2O segment. In addition, the MD simulation results showed that there were fewer structured water molecules around the OCH2CH2O segment than the hydration water molecules around the ionic parts. These solvent properties suggest that dilute aqueous OE2imC3C solutions are effective in preventing freezing, even in a deep freezer. Therefore, this medium has the potential to act as a novel cryoprotectant for proteins in biotechnology and biomedical fields.

BNP level predicts bleeding event in patients with heart failure after percutaneous coronary intervention.

OBJECTIVE: This study aimed to investigate the association between heart failure (HF) severity measured based on brain natriuretic peptide (BNP) levels and future bleeding events after percutaneous coronary intervention (PCI). BACKGROUND: The Academic Research Consortium for High Bleeding Risk presents a bleeding risk assessment for antithrombotic therapy in patients after PCI. HF is a risk factor for bleeding in Japanese patients. METHODS: Using an electronic medical record-based database with seven tertiary hospitals in Japan, this retrospective study included 7160 patients who underwent PCI between April 2014 and March 2020 and who completed a 3-year follow-up and were divided into three groups: no HF, HF with high BNP level and HF with low BNP level. The primary outcome was bleeding events according to the Global Use of Streptokinase and t-PA for Occluded Coronary Arteries classification of moderate and severe bleeding. The secondary outcome was major adverse cardiovascular events (MACE). Furthermore, thrombogenicity was measured using the Total Thrombus-Formation Analysis System (T-TAS) in 536 consecutive patients undergoing PCI between August 2013 and March 2017 at Kumamoto University Hospital. RESULTS: Multivariate Cox regression showed that HF with high BNP level was significantly associated with bleeding events, MACE and all-cause death. In the T-TAS measurement, the thrombogenicity was lower in patients with HF with high BNP levels than in those without HF and with HF with low BNP levels. CONCLUSIONS: HF with high BNP level is associated with future bleeding events, suggesting that bleeding risk might differ depending on HF severity.

Prediction of respiratory failure risk in patients with pneumonia in the ICU using ensemble learning models.

The aim of this study was to develop early prediction models for respiratory failure risk in patients with severe pneumonia using four ensemble learning algorithms: LightGBM, XGBoost, CatBoost, and random forest, and to compare the predictive performance of each model. In this study, we used the eICU Collaborative Research Database (eICU-CRD) for sample extraction, built a respiratory failure risk prediction model for patients with severe pneumonia based on four ensemble learning algorithms, and developed compact models corresponding to the four complete models to improve clinical practicality. The average area under receiver operating curve (AUROC) of the models on the test sets after ten random divisions of the dataset and the average accuracy at the best threshold were used as the evaluation metrics of the model performance. Finally, feature importance and Shapley additive explanation values were introduced to improve the interpretability of the model. A total of 1676 patients with pneumonia were analyzed in this study, of whom 297 developed respiratory failure one hour after admission to the intensive care unit (ICU). Both complete and compact CatBoost models had the highest average AUROC (0.858 and 0.857, respectively). The average accuracies at the best threshold were 75.19% and 77.33%, respectively. According to the feature importance bars and summary plot of the predictor variables, activetx (indicates whether the patient received active treatment), standard deviation of prothrombin time-international normalized ratio, Glasgow Coma Scale verbal score, age, and minimum oxygen saturation and respiratory rate were important. Compared with other ensemble learning models, the complete and compact CatBoost models have significantly higher average area under the curve values on the 10 randomly divided test sets. Additionally, the standard deviation (SD) of the compact CatBoost model is relatively small (SD:0.050), indicating that the performance of the compact CatBoost model is stable among these four ensemble learning models. The machine learning predictive models built in this study will help in early prediction and intervention of respiratory failure risk in patients with pneumonia in the ICU.

Implementation of a Patient Summary Web Application According to the International Patient Summary and Validation in Common Use Cases in Japan.

BACKGROUND: The application of standardized patient summaries would reduce the risk of information overload and related problems for physicians and nurses. Although the International Patient Summary (IPS) standard has been developed, disseminating its applications has challenges, including data conversion of existing systems and development of application matching with common use cases in Japan. This study aimed to develop a patient summary application that summarizes and visualizes patient information accumulated by existing systems. METHODS: We converted clinical data from the Standardized Structured Medical Information eXchange version 2 (SS-MIX2) storage at Tohoku University Hospital into the Health Level 7 Fast Healthcare Interoperability Resource (FHIR) repository. Subsequently, we implemented a patient summary web application concerning the IPS and evaluated 12 common use cases of the discharge summary. RESULTS: The FHIR resources of seven of the necessary IPS sections were successfully converted from existing SS-MIX2 data. In the main view of the application we developed, all the minimum necessary patient information was summarized and visualized. All types of mandatory or required sections in the IPS and all structured information items of the discharge summary were displayed. Of the discharge summary, 75% of sections and 61.7% of information items were completely displayed, matching 12 common use cases in Japan. CONCLUSIONS: We implemented a patient summary application that summarizes and visualizes patient information accumulated by existing systems and is evaluated in common use cases in Japan. Efficient sharing of the minimum necessary patient information for physicians is expected to reduce information overload, workload, and burnout.

Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability.

Introduction: Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV). Methods: Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested. Results and Discussion: In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

Impact of heart failure severity and major bleeding events after percutaneous coronary intervention on subsequent major adverse cardiac events.

Background: Heart failure (HF) is associated with a high bleeding risk after percutaneous coronary intervention (PCI). Additionally, major bleeding events increase the risk of subsequent major adverse cardiac events (MACE). However, whether brain natriuretic peptide (BNP) levels and major bleeding events following PCI are associated with MACE and all-cause death remains unknown. This study aimed to investigate the impact of HF severity or bleeding on subsequent MACE and all-cause death. Methods: The Clinical Deep Data Accumulation System (CLIDAS), a multicenter database involving seven hospitals in Japan, was developed to collect data from electronic medical records. This retrospective analysis included 7160 patients who underwent PCI between April 2014 and March 2020 and completed a three-year follow-up. Patients were divided according to the presence of HF with high BNP (HFhBNP) (>100 pg/ml) and major bleeding events within 30 days post-PCI (30-day bleeding): HFhBNP with bleeding (n = 14), HFhBNP without bleeding (n = 370), non-HFhBNP with bleeding (n = 74), and non-HFhBNP without bleeding (n = 6702). Results: In patients without 30-day bleeding, HFhBNP was a risk factor for MACE (hazard ratio, 2.19; 95% confidence interval, 1.56-3.07) and all-cause death (hazard ratio, 1.60; 95% confidence interval, 1.60-2.23). Among HFhBNP patients, MACE incidence was higher in patients with 30-day bleeding than in those without bleeding, but the difference was not significant (p = 0.075). The incidence of all-cause death was higher in patients with bleeding (p = 0.001). Conclusions: HF with high BNP and bleeding events in the early stage after PCI might be associated with subsequent MACE and all-cause death.

Validation study on definition of cause of death in Japanese claims data.

Identifying the cause of death is important for the study of end-of-life patients using claims data in Japan. However, the validity of how cause of death is identified using claims data remains unknown. Therefore, this study aimed to verify the validity of the method used to identify the cause of death based on Japanese claims data. Our study population included patients who died at two institutions between January 1, 2018 and December 31, 2019. Claims data consisted of medical data and Diagnosis Procedure Combination (DPC) data, and five definitions developed from disease classification in each dataset were compared with death certificates. Nine causes of death, including cancer, were included in the study. The definition with the highest positive predictive values (PPVs) and sensitivities in this study was the combination of "main disease" in both medical and DPC data. For cancer, these definitions had PPVs and sensitivities of > 90%. For heart disease, these definitions had PPVs of > 50% and sensitivities of > 70%. For cerebrovascular disease, these definitions had PPVs of > 80% and sensitivities of> 70%. For other causes of death, PPVs and sensitivities were < 50% for most definitions. Based on these results, we recommend definitions with a combination of "main disease" in both medical and DPC data for cancer and cerebrovascular disease. However, a clear argument cannot be made for other causes of death because of the small sample size. Therefore, the results of this study can be used with confidence for cancer and cerebrovascular disease but should be used with caution for other causes of death.

Sex Differences in Long-Term Outcomes in Patients With Chronic Coronary Syndrome After Percutaneous Coronary Intervention　- Insights From a Japanese Real-World Database Using a Storage System.

BACKGROUND: Several studies have reported some sex differences in patients with coronary artery diseases. However, the results regarding long-term outcomes in patients with chronic coronary syndrome (CCS) are inconsistent. Therefore, the present study investigated sex differences in long-term outcomes in patients with CCS after percutaneous coronary intervention (PCI).Methods and Results: This was a retrospective, multicenter cohort study. We enrolled patients with CCS who underwent PCI between April 2013 and March 2019 using the Clinical Deep Data Accumulation System (CLIDAS) database. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, or hospitalization for heart failure. In all, 5,555 patients with CCS after PCI were included in the analysis (4,354 (78.4%) men, 1,201 (21.6%) women). The median follow-up duration was 917 days (interquartile range 312-1,508 days). The incidence of MACE was not significantly different between the 2 groups (hazard ratio [HR] 1.20; 95% confidential interval [CI] 0.97-1.47; log-rank P=0.087). After performing multivariable Cox regression analyses on 4 different models, there were still no differences in the incidence of MACE between women and men. CONCLUSIONS: There were no significant sex differences in MACE in patients with CCS who underwent PCI and underwent multidisciplinary treatments.

Relationships Among Heart Rate, ß-Blocker Dosage, and Prognosis in Patients With Coronary Artery Disease in a Real-World Database Using a Multimodal Data Acquisition System.

BACKGROUND: The optimal heart rate (HR) and optimal dose of ß-blockers (BBs) in patients with coronary artery disease (CAD) have been unclear. We sought to clarify the relationships among HR, BB dose, and prognosis in patients with CAD using a multimodal data acquisition system.Methods and Results: We evaluated the data for 8,744 CAD patients who underwent cardiac catheterization from 6 university hospitals and the National Cerebral and Cardiovascular Center and who were registered using the Clinical Deep Data Accumulation System. Patients were divided into quartile groups based on their HR at discharge: Q1 (HR <60 beats/min), Q2 (HR 60-66 beats/min), Q3 (HR 67-74 beats/min), and Q4 (HR ≥75 beats/min). Among patients with acute coronary syndrome (ACS) and patients with chronic coronary syndrome (CCS), those in Q4 (HR ≥75 beats/min) had a significantly greater incidence of major adverse cardiac and cerebral events (MACCE) compared with those in Q1 (ACS patients: hazard ratio 1.65, P=0.001; CCS patients: hazard ratio 1.45, P=0.019). Regarding the use of BBs (n=4,964), low-dose administration was significantly associated with MACCE in the ACS group (hazard ratio 1.41, P=0.012), but not in patients with CCS after adjustment for covariates. CONCLUSIONS: HR ≥75 beats/min was associated with worse outcomes in patients with CCS or ACS.

Hemodialysis Record Sharing: Solution for Work Burden Reduction and Disaster Preparedness.

BACKGROUND: After the Great East Japan Earthquake in 2011, backup systems for clinical information were launched in Japan. The system in Miyagi Prefecture called the Miyagi Medical and Welfare Information Network (MMWIN) is used as a health information exchange network to share clinical information among various medical facilities for patients who have opted in. Hospitals and clinics specializing in chronic renal failure require patients' data and records during hemodialysis to facilitate communication in daily clinical activity and preparedness for disasters. OBJECTIVE: This study aimed to facilitate the sharing of clinical data of patients undergoing hemodialysis among different hemodialysis facilities. METHODS: We introduced a document-sharing system to make hemodialysis reports available on the MMWIN. We also recruited hospitals and clinics to share the hemodialysis reports of their patients and promoted the development of a network between emergency and dialysis clinics. RESULTS: In addition to basic patient information as well as information on diagnosis, prescription, laboratory data, hospitalization, allergy, and image data from different facilities, specific information about hemodialysis is available, as well as a backup of indispensable information in preparation for disasters. As of June 1, 2021, 12 clinics and 10 hospitals of 68 dialysis facilities in Miyagi participated in the MMWIN. The number of patients who underwent hemodialysis in Miyagi increased by more than 40%. CONCLUSIONS: Our backup system successfully developed a network of hemodialysis facilities. We have accumulated data that are beneficial to prevent the fragmentation of patient information and would be helpful in transferring patients efficiently during unpredictable disasters.

Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records.

BACKGROUND: Few studies have developed automatic systems for identifying social distress, spiritual pain, and severe physical and phycological symptoms from text data in electronic medical records. AIM: To develop models to detect social distress, spiritual pain, and severe physical and psychological symptoms in terminally ill patients with cancer from unstructured text data contained in electronic medical records. DESIGN: A retrospective study of 1,554,736 narrative clinical records was analyzed 1 month before patients died. Supervised machine learning models were trained to detect comprehensive symptoms, and the performance of the models was tested using the area under the receiver operating characteristic curve (AUROC) and precision recall curve (AUPRC). SETTING/PARTICIPANTS: A total of 808 patients was included in the study using records obtained from a university hospital in Japan between January 1, 2018 and December 31, 2019. As training data, we used medical records labeled for detecting social distress (n = 10,000) and spiritual pain (n = 10,000), and records that could be combined with the Support Team Assessment Schedule (based on date) for detecting severe physical/psychological symptoms (n = 5409). RESULTS: Machine learning models for detecting social distress had AUROC and AUPRC values of 0.98 and 0.61, respectively; values for spiritual pain, were 0.90 and 0.58, respectively. The machine learning models accurately identified severe symptoms (pain, dyspnea, nausea, insomnia, and anxiety) with a high level of discrimination (AUROC > 0.8). CONCLUSION: The machine learning models could detect social distress, spiritual pain, and severe symptoms in terminally ill patients with cancer from text data contained in electronic medical records.

Coverage of Clinical Research Data Retrieved from Standardized Structured Medical Information eXchange Storage.

Clinical researchers hold high expectations for the utility of health data sourced from hospital information systems. In Japan, the standardized structured medical information eXchange version 2 (SS-MIX2) storage is a common resource for obtaining clinical data from different medical databases. However, little is known about the coverage of the data types derived from the SS-MIX2 storage. In this regard, we calculated the proportions of a dataset that could be extracted via SS-MIX2 for various clinical study categories listed in various articles published in the New England Journal of Medicine. In the 95 articles reviewed, the proportions varied from 13.3% ± 13.3% (mean ± SD) for dementia to 61.8% ± 13.7% for diabetes. For cardiology, the proportion of data accessed in a unique format (SEAMAT) increased significantly. We further noted that there was room for improvement in the coverage of SS-MIX2 data.

A Pilot Study for Return of Individual Pharmacogenomic Results to Population-Based Cohort Study Participants.

Introduction: Pharmacogenomic (PGx) testing results provide valuable information on drug selection and appropriate dosing, maximization of efficacy, and minimization of adverse effects. Although the number of large-scale, next-generation-sequencing-based PGx studies has recently increased, little is known about the risks and benefits of returning PGx results to ostensibly healthy individuals in research settings. Methods: Single-nucleotide variants of three actionable PGx genes, namely, MT-RNR1, CYP2C19, and NUDT15, were returned to 161 participants in a population-based Tohoku Medical Megabank project. Informed consent was obtained from the participants after a seminar on the outline of this study. The results were sent by mail alongside sealed information letter intended for clinicians. As an exception, genetic counseling was performed for the MT-RNR1 m.1555A > G variant carriers by a medical geneticist, and consultation with an otolaryngologist was encouraged. Questionnaire surveys (QSs) were conducted five times to evaluate the participants' understanding of the topic, psychological impact, and attitude toward the study. Results: Whereas the majority of participants were unfamiliar with the term PGx, and none had undergone PGx testing before the study, more than 80% of the participants felt that they could acquire basic PGx knowledge sufficient to understand their genomic results and were satisfied with their potential benefit and use in future prescriptions. On the other hand, some felt that the PGx concepts or terminology was difficult to fully understand and suggested that in-person return of the results was desirable. Conclusions: These results collectively suggest possible benefits of returning preemptive PGx information to ostensibly healthy cohort participants in a research setting.

Electronic Phenotyping to Identify Patients with Arrythmia Disease from a Hospital Information System.

Electronic phenotyping is an important method to identify a disease group by collecting clinical data from hospital information systems. This study aimed to extract accurate cases of supraventricular arrythmia, ventricular arrythmia, and bradycardia from clinical data of a hospital information system. The electronic phenotyping algorithm was improved using the machine learning method. Subsequently, it showed a higher area under the curve for prediction and higher specificity. However, the algorithm needs further improvement to classify each arrythmia disease accurately. In conclusion, phenotyping using clinical data from hospital information systems has some affinities and issues depending on the disease.

Layered Manganese Dioxide Thin Films Intercalated with Ag+ Ions Reduceable In Situ for Oxygen Reduction Reaction.

The purpose of this study is to propose a new strategy based on electrodeposition to create binder-free composites of metallic silver supported on MnO2. The process involves in situ reduction of the Ag+ ions incorporated in the interlayer spaces of layered MnO2 in an alkaline electrolyte without Ag+ ions. The reduction process of the incorporated Ag+ was monitored in situ based on the characteristic surface plasmon resonance in the visible region, and the resulting metallic Ag was identified by X-ray photoelectron spectroscopy. Because the formation of metallic Ag is only possible via electron injection into the Ag+ ions between MnO2 layers, the growth of Ag metals was inevitably limited, although the reduced Ag did not remain immobilized in the interlayers of MnO2. The thus-formed Ag in the MnO2 composite functioned as an electrocatalyst for the oxygen reduction reaction in a gas diffusion electrode system, showing a much better mass activity compared to Ag particles electrodeposited from an aqueous solution containing AgNO3.

Associations among cardiovascular and cerebrovascular diseases: Analysis of the nationwide claims-based JROAD-DPC dataset.

Cardiovascular and cerebrovascular diseases are frequently interconnected due to underlying pathology involving atherosclerosis and thromboembolism. The aim of this study was to investigate the impact of clinical interactions among cardiovascular and cerebrovascular diseases on patient outcomes using a large-scale nationwide claims-based dataset. Cardiovascular diseases were defined as myocardial infarction, heart failure, atrial fibrillation, and aortic dissection. Cerebrovascular diseases were defined as cerebral infarction, intracerebral hemorrhage, and subarachnoid hemorrhage. This retrospective study included 2,736,986 inpatient records (1,800,255 patients) at 911 hospitals from 2015 to 2016 from Japanese registry of all cardiac and vascular disease-diagnostic procedure combination dataset. Interactions among comorbidities and complications, rehospitalization, and clinical outcomes including in-hospital mortality were investigated. Among hospitalization records that involved cardiovascular disease, 5.9% (32,686 records) had cerebrovascular disease as a comorbidity and 2.1% (11,362 records) included an incident cerebrovascular complication after hospitalization. Cerebrovascular disease as a comorbidity or complication was associated with higher in-hospital mortality than no cerebrovascular disease (adjusted odds ratio (OR) [95% confidence interval]: 1.10 [1.06-1.14], 2.02 [1.91-2.13], respectively). Among 367,904 hospitalization records that involved cerebrovascular disease, 17.7% (63,647 records) had cardiovascular disease listed as comorbidity and 3.3% (11,834 records) as a complication. Only cardiovascular disease as a complication was associated with higher in-hospital mortality (adjusted OR [95% confidence interval]: 1.29 [1.22-1.37]). In addition, in-hospital mortality during rehospitalization due to the other disease was significantly higher than mortality during the hospitalization due to the first disease. In conclusion, substantial associations were observed between cardiovascular and cerebrovascular disease in a large-scale nationwide claims-based dataset; these associations had a significant impact on clinical outcomes. More intensive prevention and management of cardiovascular and cerebrovascular disease might be crucial.

Validity of identification algorithms combining diagnostic codes with other measures for acute ischemic stroke in MID-NET®.

PURPOSE: We aimed to develop a reliable identification algorithm combining diagnostic codes with several treatment factors for inpatients with acute ischemic stroke (AIS) to conduct pharmacoepidemiological studies using the administrative database MID-NET® in Japan. METHODS: We validated 11 identification algorithms based on 56 different diagnostic codes (International Classification of Diseases, Tenth Revision; ICD-10) using Diagnosis Procedure Combination (DPC) data combined with information on AIS therapeutic procedures added as "AND" condition or "OR" condition. The target population for this study was 366 randomly selected hospitalized patients with possible cases of AIS, defined as relevant ICD-10 codes and diagnostic imaging and prescription or surgical procedure, in three institutions between April 1, 2015 and March 31, 2017. We determined the positive predictive values (PPVs) of these identification algorithms based on comparisons with a gold standard consisting of chart reviews by experienced specialist physicians. Additionally, the sensitivities of them among 166 patients with the possible cases of AIS at a single institution were evaluated. RESULTS: The PPVs were 0.618 (95% confidence interval [CI]: 0.566-0.667) to 0.909 (95% CI: 0.708-0.989) and progressively increased with adding or limiting information on AIS therapeutic procedures as "AND" condition in the identification algorithms. The PPVs for identification algorithms based on diagnostic codes I63.x were >0.8. However, the sensitivities progressively decreased to a maximum of ~0.2 after adding information on AIS therapeutic procedures as "AND" condition. CONCLUSIONS: The identification algorithms based on the combination of appropriate ICD-10 diagnostic codes in DPC data and other AIS treatment factors may be useful to studies for AIS at a national level using MID-NET®.

Hyperuricemia predicts increased cardiovascular events in patients with chronic coronary syndrome after percutaneous coronary intervention: A nationwide cohort study from Japan.

Background: The causal relationship between hyperuricemia and cardiovascular diseases is still unknown. We hypothesized that hyperuricemic patients after percutaneous coronary intervention (PCI) had a higher risk of major adverse cardiovascular events (MACE). Methods: This was a large-scale multicenter cohort study. We enrolled patients with chronic coronary syndrome (CCS) after PCI between April 2013 and March 2019 using the database from the Clinical Deep Data Accumulation System (CLIDAS), and compared the incidence of MACE, defined as a composite of cardiovascular death, myocardial infarction, and hospitalization for heart failure, between hyperuricemia and non-hyperuricemia groups. Results: In total, 9,936 patients underwent PCI during the study period. Of these, 5,138 patients with CCS after PCI were divided into two group (1,724 and 3,414 in the hyperuricemia and non-hyperuricemia groups, respectively). The hyperuricemia group had a higher prevalence of hypertension, atrial fibrillation, history of previous hospitalization for heart failure, and baseline creatinine, and a lower prevalence of diabetes than the non-hyperuricemia group, but the proportion of men and age were similar between the two groups. The incidence of MACE in the hyperuricemia group was significantly higher than that in the non-hyperuricemia group (13.1 vs. 6.4%, log-rank P < 0.001). Multivariable Cox regression analyses revealed that hyperuricemia was significantly associated with increased MACE [hazard ratio (HR), 1.52; 95% confidential interval (CI), 1.23-1.86] after multiple adjustments for age, sex, body mass index, estimated glomerular filtration rate, left main disease or three-vessel disease, hypertension, diabetes mellitus, dyslipidemia, history of myocardial infarction, and history of hospitalization for heart failure. Moreover, hyperuricemia was independently associated with increased hospitalization for heart failure (HR, 2.19; 95% CI, 1.69-2.83), but not cardiovascular death or myocardial infarction after multiple adjustments. Sensitive analyses by sex and diuretic use, B-type natriuretic peptide level, and left ventricular ejection fraction showed similar results. Conclusion: CLIDAS revealed that hyperuricemia was associated with increased MACE in patients with CCS after PCI. Further clinical trials are needed whether treating hyperuricemia could reduce cardiovascular events or not.

Use of Medical Information and Digital Services for Self-Empowerment before, during, and after a Major Disaster.

Disaster response procedures have been developed and improved following the Great East Japan Earthquake. Innovative services have also been created through digital transformation, including an acceleration and deepening of artificial intelligence technology. Things that were once technically impossible are now possible. These innovative technologies will spread across various fields, and disaster response will not be an exception. The Ministry of Health, Labour and Welfare is promoting the use of personal health records in a way that effectively supports the management of treatments by using data from wearable devices and specific applications. During the COVID-19 pandemic, the trade-off between protecting personal information and enabling social benefits, such as in the use of digital tracking, and infodemics, including misinformation, have become new social challenges. Reviewing past disaster preparedness and the services and value provided by digital transformation indicates what new disaster preparedness should be. Digital transformation does not require literacy (ability to collect, analyze, and use information) but competence (beneficial behavioral traits derived from experience). Understanding behavior through data and enabling rational behavior are crucial. By increasing human productivity, we can save time and improve self- and mutual-help in times of disaster. Medical information and digital services must be properly used in normal times. A society that uses such services will be more disaster resilient.

Development of an application concerning fast healthcare interoperability resources based on standardized structured medical information exchange version 2 data.

BACKGROUND AND OBJECTIVE: A mobile application for personal health records (PHR) would allow patients to access their clinical data easily. When PHR connects with multiple electronic health records (EHRs), doctors and patients can exchange large quantities of patient data from the EHR (e.g., medication list, diagnoses, allergies, and laboratory data). Furthermore, personal daily records can also be retrieved from PHR (e.g., blood pressure, pulse, dietary habits, and exercise). However, no standard interoperability between EHRs and PHR has been established. This study aims to convert clinical data in EHRs into the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) data format while developing a PHR application to present the FHIR data. METHODS: In Japan, Standardized Structured Medical Information eXchange version 2 (SS-MIX2) is typically utilized as a health information exchange to preserve and elicit clinical data from EHRs. We converted clinical data in the SS-MIX2 storage at Tohoku University Hospital into the FHIR repository server using the R4 standard. Additionally, we used the Swift programming language to build a PHR application. RESULTS: We converted patients' basic information, disease names, diagnostic reports, prescriptions, and injection data from the SS-MIX2 to the FHIR server. Besides, we launched a PHR application that could retrieve data from the FHIR server to display patients' clinical information. CONCLUSIONS: Our work demonstrated the conversion of SS-MIX2 data into the FHIR and presented them with our PHR application. This mechanism may be useful to accelerate the sharing of clinical information among doctors and patients.