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Abstract BackgroundThe increasing demand for personal health record (PHR) systems is driven by individuals’ desire to actively manage their health care. However, the limited functionality of current PHR systems has affected users’ willingness to adopt them, leading to lower-than-expected usage rates. The HL7 (Health Level Seven) PHR System Functional Model (PHR-S FM) was proposed to address this issue, outlining all possible functionalities in PHR systems. Although the PHR-S FM provides a comprehensive theoretical framework, its practical effectiveness and applicability have not been fully explored. ObjectiveThis study aimed to design and develop a tethered PHR prototype in accordance with the guidelines of the PHR-S FM. It sought to explore the feasibility of applying the PHR-S FM in PHR systems by comparing the prototype with the results of previous research. MethodsThe PHR-S FM profile was defined to meet broad clinical data management requirements based on previous research. We designed and developed a PHR prototype as a web application using the Fast Healthcare Interoperability Resources R4 (FHIR) and Logical Observation Identifiers Names and Codes (LOINC) coding system for interoperability and data consistency. We validated the prototype using the Synthea dataset, which provided realistic synthetic medical records. In addition, we compared the results produced by the prototype with those of previous studies to evaluate the feasibility and implementation of the PHR-S FM framework. ResultsThe PHR prototype was developed based on the PHR-S FM profile. We verified its functionality by demonstrating its ability to synchronize data with the FHIR server, effectively managing and displaying various health data types. Validation using the Synthea dataset confirmed the prototype’s accuracy, achieving 100% coverage across 1157 data items. A comparison with the findings of previous studies indicated the feasibility of implementing the PHR-S FM and highlighted areas for future research and improvements. ConclusionsThe results of this study offer valuable insights into the potential for practical application and broad adoption of the PHR-S FM in real-world health care settings.

Background: Polypharmacy is associated with an increased risk of adverse events due to the higher number of drugs used. This is particularly notable in patients with chronic coronary syndrome (CCS), who are known to use a large number of drugs. Therefore, we investigated polypharmacy in patients with CCS, using CLIDAS, a multicenter database of patients who underwent percutaneous coronary intervention. Method and results: Between 2017 and 2020, 1411 CCS patients (71.5 ± 10.5 years old; 77.3 % male) were enrolled. The relationship between cardiovascular events occurring during the median follow-up of 514 days and the number of drugs at the time of PCI was investigated. The median number of drugs prescribed was nine. Major adverse cardiovascular events (MACE), defined as cardiovascular death, myocardial infarction, stroke, heart failure, transient ischemic attack, or unstable angina, occurred in 123 patients, and all-cause mortality occurred in 68 patients. For each additional drug, the adjusted hazard ratios for MACE and all-cause mortality increased by 2.069 (p = 0.003) and 1.102 (p = 0.010). The adjusted hazard ratios for MACE and all-cause mortality were significantly higher in the group using nine or more drugs compared to the group using eight or fewer drugs (1.646 and 2.253, both p

Abstract BackgroundThe National Disaster Management Agency (Badan Nasional Penanggulangan Bencana ObjectiveThis study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR. MethodsWe extracted variables from 2 EMT MDS medical records—the WHO and Association of Southeast Asian Nations (ASEAN) versions—and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server. ResultsThe total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap. ConclusionsWe successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters.

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.

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.

IntroductionPerinatal 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).MethodsNine 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 DiscussionIn 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.

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.

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.

Background and Objective: The collection of patient-generated health data (PGHD) is important for understanding a patient's daily status for efficient treatment. Mobile applications are effective for continuously collecting patient data, and it is desirable to promptly integrate such data into electronic medical records. However, most hospital information systems have limited connections with external mobile applications. Therefore, in this study, we developed a simple system that can collect data from patients with inflammatory bowel disease (IBD) and transfer the data to electronic medical records without a direct connection to a hospital information system. Methods: We developed patient-facing mobile applications and physician-facing user-defined form templates for the hospital information system. The PGHD were transferred via QR codes using a two-way linkage. The persistence rates were measured and analyzed to clarify the factors affecting the continuous usage of the application. Results: A mobile application connected to a hospital information system was implemented and used in on-site operations. Among patients with IBD using this application, 84.6%–91.7% continued to use it over six months and 72.2%–84.5% continued for over one year. Particularly, patients who used the application during the first two visits tended to be significantly frequent users. Conclusions: We developed a mobile application connected to a hospital information system using a QR code, which is a simple way to continuously collect data from patients and enables physicians to use the data efficiently for patient-centered medical care.

BackgroundThe 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).MethodsThis 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.ResultsIn 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.ConclusionCLIDAS 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.

BackgroundAfter 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. ObjectiveThis study aimed to facilitate the sharing of clinical data of patients undergoing hemodialysis among different hemodialysis facilities. MethodsWe 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. ResultsIn 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%. ConclusionsOur 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.

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.

A coating comprising Ni, Sn, and Fe was deposited on a Ni mesh by cathodic polarization from a bath containing Ni ^2+ , Sn ^2+ , and Fe ^3+ salts in solution, and the oxygen evolution reaction properties of the thus obtained electrode were investigated. The lattice volume of the Ni–Sn–Fe-based coating was estimated to be 77.1 Å ^3 , a much larger value than that (44.0 Å ^3 ) of the Ni–Fe-based coating obtained in the absence of Sn ^2+ . The Ni–Sn–Fe-coated electrode manufactured by cathodic polarization at a current density of −120 mA cm ^−2 afforded a current density of 10 mA cm ^−2 at an overpotential of 276 mV in 1 M KOH. The said electrode’s Tafel slope was estimated to be 37 mV dec ^−1 . When electrolyzed at constant current density (+50 mA cm ^−2 to +800 mA cm ^−2 ), it exhibited a stable potential for at least 162 h. The Ni–Sn–Fe-coated electrode was also used as the anode in a two-electrode cell (80 °C; 30 wt% KOH) and electrolyzed for 3 d. Evidence indicated a low cell voltage of 1.81 V at a current density of +600 mA cm ^−2 , which is at an industrial level.

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Copyright of New Food Industry is the property of NFI LLC. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)