Sociodemographic characteristics of non-institutional births and the association with neonatal and infant mortality in Japan.

OBJECTIVES: In this study, the association of non-institutional births with neonatal and infant mortality in Japan was investigated as well as sociodemographic characteristics of non-institutional births. METHODS: The Vital Statistics data in Japan (the birth data from 2012 to 2021 and mortality data from 2012 to 2022) were used. Births were classified into three types based on the place of birth and birth attendant: institutional births, non-institutional births with a physician or a midwife, and non-institutional births without a physician or a midwife. Modified Poisson regression was used in order to investigate the association between the type of birth and neonatal and infant mortality and the association between sociodemographic characteristics and the type of birth. RESULTS: A total of 9,422,942 births were used in the analysis. The results of regression analysis investigating an association between the type of birth and neonatal and infant mortality showed that non-institutional births were positively associated with neonatal and infant mortality regardless of the attendance of a physician or a midwife. Furthermore, the results of regression analysis investigating predictors of non-institutional births showed that factors such as non-urban regions, older maternal age groups, and unmarried status of mothers were positively associated with the two types of non-institutional births, and non-Japanese mother was positively associated with non-institutional births without a physician or a midwife. CONCLUSIONS: Non-institutional births were a predictor of neonatal and infant mortality regardless of the attendance of a physician or midwife, and some sociodemographic characteristics have been proven to be predictors of non-institutional births.

Performance Evaluation of Commonly Used Portable Hemoglobin Sensors in Comparison to Clinical Test Results.

Telehealth systems in underserved countries leverage various low-cost portable medical sensors to transmit patients' vital information to remote doctors, facilitating timely diagnoses and interventions. However, the potential risks associated with inaccurate data pose considerable threats to the health of individuals. This study focuses on identifying high-quality portable hemoglobin sensors, employing the Japanese clinical pathology laboratory as a gold standard. Out of the eight sensors evaluated in this study, four were found to be highly erroneous.

Inhibition of dynamin-related protein 1-filamin interaction improves systemic glucose metabolism.

BACKGROUND AND PURPOSE: Maintaining mitochondrial quality is attracting attention as a new strategy to treat diabetes and diabetic complications. We previously reported that mitochondrial hyperfission by forming a protein complex between dynamin-related protein (Drp) 1 and filamin, mediates chronic heart failure and cilnidipine, initially developed as an L/N-type Ca2+ channel blocker, improves heart failure by inhibiting Drp1-filamin protein complex. We investigated whether cilnidipine improves hyperglycaemia of various diabetic mice models. EXPERIMENTAL APPROACH: Retrospective analysis focusing on haemoglobin A1c (HbA1c) was performed in hypertensive and hyperglycaemic patients taking cilnidipine and amlodipine. After developing diabetic mice by streptozotocin (STZ) treatment, an osmotic pump including drug was implanted intraperitoneally, followed by weekly measurements of blood glucose levels. Mitochondrial morphology was analysed by electron microscopy. A Ca2+ channel-insensitive cilnidipine derivative (1,4-dihydropyridine [DHP]) was synthesized and its pharmacological effect was evaluated using obese (ob/ob) mice fed with high-fat diet (HFD). KEY RESULTS: In patients, cilnidipine was superior to amlodipine in HbA1c lowering effect. Cilnidipine treatment improved systemic hyperglycaemia and mitochondrial morphological abnormalities in STZ-exposed mice, without lowering blood pressure. Cilnidipine failed to improve hyperglycaemia of ob/ob mice, with suppressing insulin secretion. 1,4-DHP improved hyperglycaemia and mitochondria abnormality in ob/ob mice fed HFD. 1,4-DHP and cilnidipine improved basal oxygen consumption rate of HepG2 cells cultured under 25 mM glucose. CONCLUSION AND IMPLICATIONS: Inhibition of Drp1-filamin protein complex formation becomes a new strategy for type 2 diabetes treatment.

A Novel Kidney Failure Prediction Model in Individuals with CKD: Impact of Serum Bilirubin Levels.

CONTEXT: Predicting the progression of chronic kidney disease (CKD) to end-stage kidney disease (ESKD) is crucial for improving patient outcomes. OBJECTIVE: To reveal the highly predictive activity of serum bilirubin levels for the progression of CKD to ESKD, and to develop and validate a novel ESKD prediction model incorporating serum bilirubin levels. METHODS: We assessed the relative importance of 20 candidate predictors for ESKD, including serum bilirubin levels, in a CKD cohort (15< eGFR <60 mL/min/1.73 m2), and subsequently developed a prediction model using the selected variables. The development cohort comprised 4,103 individuals with CKD who underwent follow-up at Kyushu University Hospital, Japan, from 2008 to 2018. The primary outcome was incident ESKD, defined as an eGFR <15 mL/min/1.73 m2, chronic dialysis, or renal transplantation. RESULTS: The mean follow-up time was 7.0 ± 4.2 years, during which 489 individuals (11.9%) progressed to ESKD. The Cox proportional hazard model selected eGFR, serum bilirubin, proteinuria, age, diabetes, gender, hypertension, serum albumin, and hemoglobin in order of their importance. The predictive performance of the model was optimized by incorporating these 9 variables in discrimination evaluated by time-dependent area under the curve (AUC). This model also demonstrated excellent calibration. Additionally, this model exhibited excellent predictive performance in both discrimination (2-year AUC: 0.943, 5-year AUC: 0.935) and calibration in a validation cohort (n=2,799). CONCLUSION: Serum bilirubin levels were strong predictors for the progression of CKD to ESKD. Our novel model that incorporates serum bilirubin levels could accurately predict ESKD in individuals with CKD.

Predictive Performance of Machine Learning-Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study.

BACKGROUND: Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined. OBJECTIVE: This study aims to examine how much data-driven models with machine learning improve predictive performance for poststroke outcomes compared with conventional stroke prognostic scores and to elucidate how explanatory variables in machine learning-based models differ from the items of the stroke prognostic scores. METHODS: We used data from 10,513 patients who were registered in a multicenter prospective stroke registry in Japan between 2007 and 2017. The outcomes were poor functional outcome (modified Rankin Scale score >2) and death at 3 months after stroke. Machine learning-based models were developed using all variables with regularization methods, random forests, or boosted trees. We selected 3 stroke prognostic scores, namely, ASTRAL (Acute Stroke Registry and Analysis of Lausanne), PLAN (preadmission comorbidities, level of consciousness, age, neurologic deficit), and iScore (Ischemic Stroke Predictive Risk Score) for comparison. Item-based regression models were developed using the items of these 3 scores. The model performance was assessed in terms of discrimination and calibration. To compare the predictive performance of the data-driven model with that of the item-based model, we performed internal validation after random splits of identical populations into 80% of patients as a training set and 20% of patients as a test set; the models were developed in the training set and were validated in the test set. We evaluated the contribution of each variable to the models and compared the predictors used in the machine learning-based models with the items of the stroke prognostic scores. RESULTS: The mean age of the study patients was 73.0 (SD 12.5) years, and 59.1% (6209/10,513) of them were men. The area under the receiver operating characteristic curves and the area under the precision-recall curves for predicting poststroke outcomes were higher for machine learning-based models than for item-based models in identical populations after random splits. Machine learning-based models also performed better than item-based models in terms of the Brier score. Machine learning-based models used different explanatory variables, such as laboratory data, from the items of the conventional stroke prognostic scores. Including these data in the machine learning-based models as explanatory variables improved performance in predicting outcomes after stroke, especially poststroke death. CONCLUSIONS: Machine learning-based models performed better in predicting poststroke outcomes than regression models using the items of conventional stroke prognostic scores, although they required additional variables, such as laboratory data, to attain improved performance. Further studies are warranted to validate the usefulness of machine learning in clinical settings.

Evaluating the associations between compliance with CKD guideline component metrics and renal outcomes.

Understanding the association between compliance to the Chronic Kidney Disease (CKD) guidelines in real-world clinical settings and renal outcomes remains a critical gap in knowledge. A comprehensive analysis was conducted using data from a national, multicenter CKD registry. This study included 4,455 patients with an estimated glomerular filtration rate (eGFR) measurement on the index date and eight additional metrics recorded within six months. These metrics comprised serum electrolyte levels, low-density lipoprotein cholesterol, hemoglobin, and the use of renin-angiotensin system inhibitors. The primary outcome was a composite of renal events, defined by a decline in eGFR to < 15 mL/min/1.73 m2 or a reduction of ≥ 30% in eGFR, confirmed by follow-up tests. Over a median follow-up of 513 days, 838 renal events were observed. High serum potassium levels (> 5.4 mmol/L) were associated with increased event rates compared to lower levels. Similarly, low serum sodium-chloride levels (< 33) correlated with higher event rates. Usage of renin-angiotensin system inhibitors, low serum calcium (< 8.4 mg/dL), and high uric acid levels (> 7.0 mg/dL) were also linked to increased events. Conversely, higher hemoglobin levels (≥ 13 g/dL) were associated with lower event rates. Compliance to guidelines, categorized into quartiles based on the number of met metrics, revealed a significantly reduced risk of events in the highest compliance group (meeting 8 metrics) compared to the lowest (0-5 metrics). Compliance to CKD guidelines in clinical practice is significantly associated with improved renal outcomes, emphasizing the need for guideline-concordant care in the management of CKD.

Exploring the association between non-regular employment and adverse birth outcomes: an analysis of national data in Japan.

Background: As few studies have explored the association between non-regular or precarious employment in parents and adverse birth outcomes, this study aimed to investigate this association using national data in Japan. Methods: This study utilized the census data from 2020 and birth data from the vital statistics in 2021 and 2022 in the analysis. Adverse birth outcomes, including preterm birth, term low birth weight (TLBW), and small-for-gestational-age, were examined. Data linkage was conducted between birth data and census data to link parental employment statuses and educational attainments with birth data. Rates of adverse birth outcomes were calculated for each parental employment status. Additionally, regression analysis was used to determine adjusted risk ratios (RRs) of parental employment statuses for each birth outcome. Results: After data linkage, 334,110 birth records were included in the statistical analysis. Rates for non-regular workers were consistently higher than those for regular workers across all adverse birth outcomes for maternal employment status. Results of regression analyses indicated that the risks of preterm birth for non-regular workers were statistically significantly higher than those for regular workers, both in mothers and fathers with a RR (95% confidence intervals [CIs]) of 1.053 (1.004-1.104) and 1.142 (1.032-1.264), respectively. Furthermore, the risk of TLBW birth for non-regular workers was statistically significantly higher than that for regular workers in fathers (RR [95% CI]: 1.092 [1.043-1.143]). Conclusions: Our findings demonstrate that non-regular workers have a higher risk of some adverse birth outcomes compared to regular workers.

No clear influence of treatment escalation on flare prevention in serologically active clinically quiescent patients with systemic lupus erythematosus: a retrospective cohort study.

This study aimed to clarify the efficacy and safety of treatment escalation by initiating therapeutic agents in serologically active clinically quiescent (SACQ) patients with systemic lupus erythematosus (SLE). We retrospectively evaluated SACQ patients with SLE for ≥ 180 days, with the introduction of a therapeutic agent for SLE defined as exposure. The efficacy endpoints included the time to flare and time to remission, whereas the safety endpoint was the incidence of adverse events. The efficacy endpoints were assessed via Cox proportional hazards model with time-dependent covariates, which included exposure, serological activity, and prednisolone dose. Among 109 SACQ patients, 24 were initiated on the following therapeutic agents for SLE: hydroxychloroquine (10 patients), belimumab (6 patients), and immunosuppressive agents (8 patients). A total of 37 patients experienced a flare (8 and 29 patients during exposure and nonexposure periods, respectively). The time to flare was comparable between the exposure and control groups. Among 68 patients who were not in remission at the start of observation, 27 patients achieved remission (5 and 22 patients during exposure and nonexposure periods, respectively). Although both groups had a similar time to remission, the exposure group treated with belimumab had a significantly higher rate of remission than the control group. The adverse events were more frequent during the exposure period than during the nonexposure period. Thus, this study did not reveal a clear influence of treatment escalation on flare prevention and remission achievement.

Effects of ambient air pollution on the risk of small- and large-for-gestational-age births: an analysis using national birth data in Japan.

OBJECTIVES: Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births are major adverse birth outcomes related to newborn health. In contrast, the association between ambient air pollution levels and SGA or LGA births has not been investigated in Japan; hence, the purpose of our study is to investigate this association. METHODS: We used birth data from Vital Statistics in Japan from 2017 to 2021 and municipality-level data on air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), photochemical oxidants, and particulate matter 2.5 (PM2.5). Ambient air pollution levels throughout the first, second, and third trimesters, as well as the whole pregnancy, were calculated for each birth. The association between SGA/LGA and ambient levels of the air pollutants was investigated using crude and adjusted log-binomial regression models. In addition, a regression model with spline functions was also used to detect the non-linear association. RESULTS: We analyzed data from 2,434,217 births. Adjusted regression analyses revealed statistically significant and positive associations between SGA birth and SO2 level, regardless of the exposure period. Specifically, the risk ratio for average SO2 values throughout the whole pregnancy was 1.014 (95% confidence interval [CI] 1.009, 1.019) per 1 ppb increase. In addition, regression analysis with spline functions indicated that an increase in risk ratio for SGA birth depending on SO2 level was linear. Furthermore, statistically significant and negative associations were observed between LGA birth and SO2 except for the third trimester. CONCLUSIONS: It was suggested that ambient level of SO2 during the pregnancy term is a risk factor for SGA birth in Japan.

Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET®) in Japan.

BACKGROUND: The Medical Information Database Network (MID-NET®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET®. METHODS: This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated. RESULTS: A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%). CONCLUSION: This study developed valuable GIP identification algorithms for MID-NET®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms.

Performance Evaluation of the Commonly-Used Portable Cholesterol Sensors for Telehealth Services in the Unreached Communities.

Portable medical sensors play an important role in healthcare services, especially in rural communities. Many telehealth systems use these devices for providing patients' vital information from a distance to remote doctors. Erroneous data will not only mislead the remote doctor for correct diagnosis but it will cause health threats to these unreached community people. Therefore, it is very important to identify good sensors with an acceptable level of accuracy but within the affordable price of the available sensors in the market. This study aims to identify quality portable cholesterol sensors with high accuracy with the reference of the Japanese clinical pathology laboratory as a gold standard. We have considered cholesterol sensors that measure total cholesterol for this study that are commonly used in the developing countries of Asia. We found that out of four, three of them were very much erroneous and cannot be recommended even for primary healthcare.

Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data.

Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predictors of delirium were different from those of self-extubation after delirium. Data-driven decisions, rather than empirical decisions, on whether or not to use physical restraints or other actions that cause patient suffering will result in improved value in medical care.

Association Between Reduced Posterior Occlusal Contact and Alzheimer's Disease Onset in Older Japanese Adults: Results from the LIFE Study.

BACKGROUND: An association between poor oral health and cognitive decline has been reported. Most of these studies have considered the number of teeth as a criterion, only a few studies have analyzed the relationship between occlusal status and Alzheimer's disease (AD). OBJECTIVE: To elucidate whether posterior occlusal contact is associated with AD, focusing on the Eichner classification, among an older population aged 65 years or older in Japan. METHODS: This study used monthly claims data of National Health Insurance in Japan from April 2017 to March 2020. The outcome was newly diagnosed AD defined according to ICD-10 code G30. The number of teeth was estimated by dental code data, and occlusal contact was divided into three categories, namely A, B, and C, according to the Eichner classification. Multivariate Cox proportional hazards models were used to analyze the association between a new diagnosis of AD and the Eichner classification. RESULTS: A total of 22,687 participants were included, 560 of whom had newly diagnosed AD during a mean follow-up period of 12.2 months. The AD participants had a lower proportion of Eichner A and a higher proportion of Eichner C. After adjusting for covariates, hazard ratios (95% confidence intervals) with Eichner B and C were 1.34 (1.01-1.77) and 1.54 (1.03-2.30), respectively. CONCLUSION: In older people aged≥65 years old, reduced posterior occlusal contact as well as tooth loss have an impact on AD. This study emphasizes the importance of paying attention to occlusal contacts to reduce the risk of AD.

Regional difference in multi-psychotropic drug prescription in Japan and its associated factors: an ecological study using national health insurance claims data.

BACKGROUND: In Japan, regulations preventing the prescriptions of various types of psychotropic drugs have become stricter in recent years. However, the areas where multi-psychotropic drug prescriptions are common and the geographic factors that contribute to the regional difference, have not been studied. In this study, we used data from all claims in Japan to examine regional differences in the prescription for multi-psychotropic drugs using an ecological study. METHODS: The National Database of Health Insurance Claims and Specific Health Checkups of Japan Open data in 2019 were used. The outcome was the number of prescriptions for four or more different types of anxiolytics and hypnotics as well as for three or more of the same kind of psychotropic drugs (any one of anxiolytics, hypnotics, antipsychotics, antidepressants) for outpatients in each area. Among the 335 secondary medical areas in Japan, the data on 331 areas were used in the analysis. The standardized claim ratio (SCR), an indicator of the number of this multi-psychotropic drug prescription, correcting for regional differences in distribution of population by age and sex, was calculated for each of the secondary medical areas. The spatial cluster detection technique was used to locate a cluster of high-SCR areas. Furthermore, factors associated with regional differences in the SCRs were examined by a spatial statistics model. RESULTS: North Japanese regions tend to have high SCRs, and 13 areas in Hokkaido were identified as the most likely cluster (cluster with the highest likelihood ratio) for multi-psychotropic drug prescription. Furthermore, a spatial regression analysis revealed that the proportion of people with lower educational levels, the total number of prescriptions per capita, and the number of physicians working in psychiatric departments per capita were statistically positively associated with the SCR for the prescription of multiple psychotropic drugs. CONCLUSIONS: It was suggested that socioeconomic and medical characteristics of areas are related to the regional variation in the multi-psychotropic drug prescriptions, however, further research using individual-level data is required to confirm these results.