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Background: This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity. Methods: We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals. Results: Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January–June 2021), 90.4% and 87.5% for moderate or higher status (July–December 2021), and 92.3% and 85.7% for severe status (July–December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%. Conclusion: The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

ABSTRACT Aims The utilization of long‐term effect of internet of things (IoT) on glycemic control is controversial. This trial aimed to examine the effect of an IoT‐based approach for type 2 diabetes. Materials and Methods This randomized controlled trial enrolled 1,159 adults aged 20–74 years with type 2 diabetes with a HbA1c of 6.0–8.9% (42–74 mmol/mol), who were using a smartphone on a daily basis were randomly assigned to either the IoT‐based approach group (ITG) or the control group (CTG). The ITG were supervised to utilize an IoT automated system that demonstrates a summary of lifelogging data (weight, blood pressure, and physical activities) and provides feedback messages that promote behavioral changes in both diet and exercise. The primary end point was a HbA1c change over 52 weeks. Results Among the patients, 581 were assigned to the ITG and 578 were in the CTG. The changes in HbA1c from baseline to the final measurement at 52 weeks [mean (standard deviation)] were −0.000 (0.6225)% in ITG and − 0.006 (0.6449)% in CTG, respectively (P = 0.8766). In the per protocol set, including ITG using the IoT system almost daily and CTG, excluding those using the application almost daily, the difference in HbA1c from baseline to 52 weeks were −0.098 (0.579)% and 0.027 (0.571)%, respectively (P = 0.0201). We observed no significant difference in the adverse event profile between the groups. Conclusions The IoT‐based approach did not reduce HbA1c in patients with type 2 diabetes. IoT‐based intervention using data on the daily glycemic control and HbA1c level may be required to improve glycemic control.

ABSTRACT Aims/Introduction To investigate whether the COVID‐19 pandemic affected behavioral changes and glycemic control in patients with diabetes and to conduct a survey of telemedicine during the pandemic. Materials and Methods In this retrospective study, a total of 2,348 patients were included from 15 medical facilities. Patients were surveyed about their lifestyle changes and attitudes toward telemedicine. Hemoglobin A1c (HbA1c) levels were compared among before (from June 1 to August 31, 2019) and in the first (from June 1 to August 31, 2020) and in the second (from June 1 to August 31, 2021) year of the pandemic. A survey of physician attitudes toward telemedicine was also conducted. Results The HbA1c levels were comparable between 2019 (7.27 ± 0.97%), 2020 (7.28 ± 0.92%), and 2021 (7.25 ± 0.94%) without statistical difference between each of those 3 years. Prescriptions for diabetes medications increased during the period. The frequency of eating out was drastically reduced (51.7% in 2019; 30.1% in 2020), and physical activity decreased during the pandemic (48.1% in 2019; 41.4% in 2020; 43.3% in 2021). Both patients and physicians cited increased convenience and reduced risk of infection as their expectations for telemedicine, while the lack of physician–patient interaction and the impossibility of consultation and examination were cited as sources of concern. Conclusions Our data suggest that glycemic control did not deteriorate during the COVID‐19 pandemic with appropriate intensification of diabetes treatment in patients with diabetes who continued to attend specialized diabetes care facilities, and that patients and physicians shared the same expectations and concerns about telemedicine.

Purpose: Achromatopsia (ACHM) is an early-onset cone dysfunction caused by 5 genes with cone-specific functions (CNGA3, CNGB3, GNAT2, PDE6C, and PDE6H) and by ATF6, a transcription factor with ubiquitous expression. To improve the relatively low variant detection ratio in these genes in a cohort of exome-sequenced Japanese patients with inherited retinal diseases (IRD), we performed genome sequencing to detect structural variants and intronic variants in patients with ACHM. Methods: Genome sequencing of 10 ACHM pedigrees was performed after exome sequencing. Structural, non-coding, and coding variants were filtered based on segregation between the affected and unaffected in each pedigree. Variant frequency and predicted damage scores were considered in identifying pathogenic variants. Results: A homozygous deletion involving exon 18 of RPGRIP1 was detected in 5 of 10 ACHM probands, and variant inheritance from each parent was confirmed. This deletion was relatively frequent (minor allele frequency = 0.0023) in the Japanese population but was only homozygous in patients with ACHM among the 199 Japanese IRD probands analyzed by the same genome sequencing pipeline. Conclusion: The deletion involving exon 18 of RPGRIP1 is a prevalent cause of ACHM in Japanese patients and contributes to the wide spectrum of RPGRIP1-associated IRD phenotypes, from Leber congenital amaurosis to ACHM.

Abstract Aim While accumulating evidence suggests a protective role of healthy diet against depression, evidence on this issue is limited among healthcare workers combating COVID‐19 pandemic. The aim of this study was to determine the cross‐sectional association between frequency of balanced meal consumption and depressive symptoms among Japanese hospital workers during the COVID‐19 pandemic. Methods Participants were 2,457 workers of the National Center for Global Health and Medicine who responded to a questionnaire survey in October or December 2020. Depressive symptoms were assessed using the patient health questionnaire‐9 (PHQ‐9). The number of days per week of eating two or more balanced meals was categorized into four from ≤1 day/week to daily. The association between frequency of balanced meal consumption and depressive symptoms was assessed using logistic regression analysis, with adjustment for lifestyle and COVID‐19‐related factors. Results The prevalence of depressive symptoms was 14.8%. The odds of depressive symptoms increased with decreasing frequency of balanced meal consumption. The multivariable‐adjusted odds ratios (95% confidence intervals) of depressive symptoms were 1.00 (reference), 1.09 (0.75‐1.58), 1.62 (1.17‐2.24), and 2.21 (1.54‐3.17) for balanced meal consumption categories of daily, 4‐5 days/week, 2‐3 days/week, and ≤1 day/week, respectively (P for trend

Patients with underlying medical conditions are at high risk of developing serious symptoms of the coronavirus disease 2019 than healthy individuals; therefore, it is necessary to evaluate the immune response to vaccination among them to formulate precision and personalized vaccination strategies. However, inconsistent evidence exists regarding whether patients with underlying medical conditions have lower anti-SARS-CoV-2 spike IgG antibody titers. We performed a cross-sectional study enrolling 2762 healthcare workers who received second doses of BNT162b2 vaccination from three medical and research institutes between June and July, 2021. Medical conditions were surveyed by a questionnaire, and spike IgG antibody titers were measured with chemiluminescent enzyme immunoassay using serum collected on the median of 62 days after the second vaccination. Multilevel linear regression model was used to estimate geometric mean and ratio of mean (95% confidence interval, CI) for the presence and absence of medical conditions and treatments. Among all participants (median age, 40 years [interquartile range, 30–50]; male proportion, 29.4%), the prevalence of hypertension, diabetes, chronic lung disease, cardiovascular disease, and cancer was 7.5%, 2.3%, 3.8%, 1.8%, and 1.3%, respectively. Patients with treated hypertension had lower antibody titers than those without hypertension; the multivariable-adjusted ratio of mean (95% CI) was 0.86 (0.76–0.98). Patients with untreated and treated diabetes had lower antibody titers than those without diabetes; the multivariable-adjusted ratio of mean (95% CI) was 0.63 (0.42–0.95) and 0.77 (0.63–0.95), respectively. No substantial difference was observed between the presence or absence of chronic lung disease, cardiovascular disease, or cancer. Patients with untreated hypertension and patients with untreated and treated diabetes had lower spike IgG antibody titers than participants without those medical conditions, suggesting that continuous monitoring of antibody titers and further booster shots could be necessary to maintain adaptive immunity in patients with hypertension or diabetes.

ObjectiveThis study aimed to investigate the cross-sectional association between the presence of chronic physical conditions and depressive symptoms among hospital workers at a national medical institution designated for COVID-19 treatment in Tokyo, Japan. We also accounted for the combined association of chronic physical conditions and SARS-CoV-2 infection risk at work in relation to depressive symptoms, given that occupational infection risk might put additional psychological burden among those with chronic physical conditions with risk of severe COVID-19 outcome.MethodsThe study sample consisted of 2,440 staff members who participated in a health survey conducted at the national medical institution during period between October 2020 and December 2020. Participants who reported at least one chronic physical condition that were deemed risk factors of severe COVID-19 outcome were regarded as having chronic physical conditions. Depressive symptoms were assessed using the patient health questionnaire-9 (PHQ-9). We performed logistic regression analysis to assess the association between chronic physical conditions and depressive symptoms.ResultsOur results showed that the presence of chronic physical conditions was significantly associated with depressive symptoms (odds ratio (OR) = 1.49, 95% confidence interval (CI) = 1.10-2.02). In addition, the prevalence of depressive symptoms was significantly higher among healthcare workers with chronic physical conditions who were at a higher occupational infection risk (OR = 1.81, 95% CI = 1.04-3.16).ConclusionOur findings suggest the importance of providing more assistance to those with chronic physical conditions regarding the prevention and control of mental health issues, particularly among frontline healthcare workers engaging in COVID-19-related work.

Objectives To examine whether engagement in COVID-19-related work was associated with an increased prevalence of depressive symptoms among the staff members working in a designated medical institution for COVID-19 in Tokyo, Japan.Design A cross-sectional study.Setting Data were obtained from a health survey conducted in July 2020 among the staff members of a designated medical institution for COVID-19 in Tokyo, Japan.Participants A total of 1228 hospital workers.Exposure of interest Engagement in COVID-19-related work (qualitatively (ie, the risk of SARS-CoV-2 infection at work or affiliation to related departments) as well as quantitatively (ie, working hours)) and job categories.Outcome measures Depressive symptoms.Results There was no significant association between depressive symptoms and engagement in work with potential exposure to SARS-CoV-2 or affiliation to COVID-19-related departments. However, working for longer hours in March/April, when Japan witnessed a large number of infected cases, was significantly associated with depressive symptoms (≥11 hours/day: prevalence ratio (PR)=1.45, 95% CI=1.06 to 1.99, compared with ≤8 hours/day). Nurses were more likely to exhibit depressive symptoms than did doctors (PR=1.70, 95% CI=1.14 to 2.54).Conclusions This study suggests that the risk of SARS-CoV-2 infection at work or having an affiliation to related departments might not be linked with a higher prevalence of depressive symptoms among Japanese hospital workers; contrarily, long working hours appeared to increase the prevalence of depressive symptoms.

In multicenter clinical research, case-reported clinical data are managed for each research project. Participating institutions manage the mapping between standardized codes and in-house codes. To use the data extracted from electronic medical records in case report forms, it is necessary to pay attention to the gap in the semantic hierarchy. Managing mapping information between in-house and standardized codes is centralized in Resource Description Framework (RDF) stores. The relationship between standardized and in-house codes is described in RDF and stored in RDF stores. RESTful APIs for accessing RDF stores in SPARQL was developed and verified. The relationship between standardized codes and in-house codes of pharmaceuticals was expressed in RDF triples. As a +result of the operational verification of the implemented APIs, it was confirmed that data management with knowledge bases expressed in RDF graphs is possible. The ability to dynamically modify mapping definitions enables flexible data management and ease of operational restrictions. [ABSTRACT FROM AUTHOR]

This paper reports a case study in which an improvement in nursing work was achieved through the introduction of a traceability system compatible with radio-frequency identification (RFID) and bar codes for the management of medical materials. In verifying the RFID-based system in an operating room, the work time was reduced to approximately one-tenth of that of barcode reading. In the catheterization laboratory, a system consisting of a cartel management system utilizing bar codes and an RFID-compatible inventory management cabinet was introduced. The system eliminates the need to cut and paste packages onto vouchers after the cases are completed and digitizes the data sent to the medical affairs department for secure billing. We are implementing this system at many facilities, and, in addition to improving the work of nurses, we are taking new steps to improve hospital management through data linkage. [ABSTRACT FROM AUTHOR]

By applying the Bayesian network method to the clinical registry database J-DREAMS (Japan Diabetes compREhensive database project based on an Advanced electronic Medical record System), we have developed a reference model that summarizes the exploration of the patient group's state and facilitates a bird's-eye view. This visualization method would help registry researchers to screen the registry database. [ABSTRACT FROM AUTHOR]