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.

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.

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.

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.

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.

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.

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.

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.

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.

Kidney outcomes associated with haematuria and proteinuria trajectories among patients with IgA nephropathy in real-world clinical practice: The Japan Chronic Kidney Disease Database.

AIM: Among patients with Immunoglobulin A (IgA) nephropathy, we aimed to identify trajectory patterns stratified by the magnitude of haematuria and proteinuria using repeated urine dipstick tests, and assess whether the trajectories were associated with kidney events. METHODS: Using a nationwide multicentre chronic kidney disease (CKD) registry, we analysed data from 889 patients with IgA nephropathy (mean age 49.3 years). The primary outcome was a sustained reduction in eGFR of 50% or more from the index date and thereafter. During follow-up (median 49.0 months), we identified four trajectories (low-stable, moderate-decreasing, moderate-stable, and high-stable) in both urine dipstick haematuria and proteinuria measurements, respectively. RESULTS: In haematuria trajectory analyses, compared to the low-stable group, the adjusted hazard ratios (HRs) (95% confidence interval [CI]) for kidney events were 2.59 (95% CI, 1.48-4.51) for the high-stable, 2.31 (95% CI, 1.19-4.50) for the moderate-stable, and 1.43 (95% CI, (0.72-2.82) for the moderate-decreasing groups, respectively. When each proteinuria trajectory group was subcategorized according to haematuria trajectories, the proteinuria group with high-stable and with modest-stable haematuria trajectories had approximately 2-times higher risk for eGFR reduction ≥50% compared to that with low-stable haematuria trajectory. CONCLUSION: Assessments of both haematuria and proteinuria trajectories using urine dipstick could identify high-risk IgA nephropathy patients.

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.

Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study.

BACKGROUND: Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE: The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS: The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS: A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS: The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer.

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

Impact of the pretreatment prognostic nutritional index on the survival after first-line immunotherapy in non-small-cell lung cancer patients.

BACKGROUND: Immunotherapy has become a standard-of-care for patients with non-small-cell lung cancer (NSCLC). Although several biomarkers, such as programmed cell death-1, have been shown to be useful in selecting patients likely to benefit from immune checkpoint inhibitors (ICIs), more useful and reliable ones should be investigated. The prognostic nutritional index (PNI) is a marker of the immune and nutritional status of the host, and is derived from serum albumin level and peripheral lymphocyte count. Although several groups reported its prognostic role in patients with NSCLC receiving a single ICI, there exist no reports which have demonstrated its role in the first-line ICI combined with or without chemotherapy. MATERIALS AND METHODS: Two-hundred and eighteen patients with NSCLC were included in the current study and received pembrolizumab alone or chemoimmunotherapy as the first-line therapy. Cutoff value of the pretreatment PNI was set as 42.17. RESULTS: Among 218 patients, 123 (56.4%) had a high PNI (≥42.17), while 95 (43.6%) had a low PNI (<42.17). A significant association was observed between the PNI and both the progression-free survival (PFS; hazard ratio [HR] = 0.67, 95% confidence interval [CI]: 0.51-0.88, p = 0.0021) and overall survival (OS; HR = 0.46, 95% CI: 0.32-0.67, p < 0.0001) in the entire population, respectively. The multivariate analysis identified the pretreatment PNI as an independent prognosticator for the PFS (p = 0.0011) and OS (p < 0.0001), and in patients receiving either pembrolizumab alone or chemoimmunotherapy, the pretreatment PNI remained an independent prognostic factor for the OS (p = 0.0270 and 0.0006, respectively). CONCLUSION: The PNI might help clinicians appropriately identifying patients with better treatment outcomes when receiving first-line ICI therapy.

Non-linear association between body weight and functional outcome after acute ischemic stroke.

This study aimed to determine whether body weight is associated with functional outcome after acute ischemic stroke. We measured the body mass index (BMI) and assessed clinical outcomes in patients with acute ischemic stroke. The BMI was categorized into underweight (< 18.5 kg/m2), normal weight (18.5-22.9 kg/m2), overweight (23.0-24.9 kg/m2), and obesity (≥ 25.0 kg/m2). The association between BMI and a poor functional outcome (modified Rankin Scale [mRS] score: 3-6) was evaluated. We included 11,749 patients with acute ischemic stroke (70.3 ± 12.2 years, 36.1% women). The risk of a 3-month poor functional outcome was higher for underweight, lower for overweight, and did not change for obesity in reference to a normal weight even after adjusting for covariates by logistic regression analysis. Restricted cubic splines and SHapley Additive exPlanation values in eXtreme Gradient Boosting model also showed non-linear relationships. Associations between BMI and a poor functional outcome were maintained even after excluding death (mRS score: 3-5) or including mild disability (mRS score: 2-6) as the outcome. The associations were strong in older patients, non-diabetic patients, and patients with mild stroke. Body weight has a non-linear relationship with the risk of a poor functional outcome after acute ischemic stroke.

Long-term outcome of chronic thromboembolic pulmonary hypertension using direct oral anticoagulants and warfarin: a Japanese prospective cohort study.

BACKGROUND: Chronic thromboembolic pulmonary hypertension (CTEPH) requires lifelong anticoagulation. Long-term outcomes of CTEPH under current anticoagulants are unclear. OBJECTIVES: The CTEPH AC registry is a prospective, nationwide cohort study comparing the safety and effectiveness of direct oral anticoagulants (DOACs) and warfarin for CTEPH. PATIENTS/METHODS: Patients with CTEPH, both tre atment-naïve and on treatment, were eligible for the registry. Inclusion criteria were patients aged ≥20 years and those who were diagnosed with CTEPH according to standard guidelines. Exclusion criteria were not specified. The primary efficacy outcome was a composite morbidity, and mortality outcome comprised all-cause death, rescue reperfusion therapy, initiation of parenteral pulmonary vasodilators, and worsened 6-minute walk distance and WHO functional class. The safety outcome was clinically relevant bleeding, including major bleeding. RESULTS: Nine hundred twenty-seven patients on oral anticoagulants at baseline were analyzed: 481 (52%) used DOACs and 446 (48%) used warfarin. The 1-, 2-, and 3-year rates of composite morbidity and mortality outcome were comparable between the DOAC and warfarin groups (2.6%, 3.1%, and 4.2% vs 3.0%, 4.8%, and 5.9%, respectively; P = .52). The 1-, 2-, and 3-year rates of clinically relevant bleeding were significantly lower in DOACs than in the warfarin group (0.8%, 2.4%, and 2.4% vs 2.5%, 4.8%, and 6.4%, respectively; P = 0.036). Multivariable Cox proportional-hazards regression models revealed lower risk of clinically relevant bleeding in the DOAC group than the warfarin group (hazard ratio: 0.35; 95% CI: 0.13-0.91; P = .032). CONCLUSION: This registry demonstrated that under current standard of care, morbidity and mortality events were effectively prevented regardless of anticoagulants, while the clinically relevant bleeding rate was lower when using DOACs compared with warfarin.

Association of maternal nationality with preterm birth and low birth weight rates: analysis of nationwide data in Japan from 2016 to 2020.

BACKGROUND: The rate of low birth weight or preterm birth is known to vary according to the birth place of mothers. However, in Japan, studies that investigated the association between maternal nationalities and adverse birth outcomes are few. In this study, we investigated the association between maternal nationalities and adverse birth outcomes. METHODS: We obtained live birth data from the Vital Statistics 2016-2020 of the Ministry of Health, Labour, and Welfare. We used data on maternal age, sex, parity, gestational age, birth weight, number of fetuses, household occupation, paternal nationality, and maternal nationality for each infant. We compared the rates of preterm birth and low birth weight at term among mothers whose nationalities were Japan, Korea, China, Philippines, Brazil, and other countries. Log binomial regression model was used to investigate the association between maternal nationality and the two birth outcomes using the other infants' characteristics as covariates. RESULTS: In the analysis, data on 4,290,917 singleton births were used. Mothers from Japan, Korea, China, the Philippines, Brazil, and other nations had preterm birth rates of 4.61%, 4.16%, 3.97%, 7.43%, 7.69%, and 5.61%, respectively. The low birth weight rate among Japanese mothers was 5.36% and was the highest among the maternal nationalities. Regression analysis showed that the relative risk for preterm birth among Filipino, Brazilian, and mothers from other countries (1.520, 1.329, and 1.222, respectively) was statistically significantly higher compared with Japanese mothers. In contrast, the relative risk for Korean and Chinese mothers (0.870 and 0.899, respectively) was statistically significantly lower compared with Japanese mothers. Mothers from Korea, China, the Philippines, Brazil, and other nations had a relative risk for low birth weight that was statistically significantly lower than that of Japanese mothers (0.664, 0.447, 0.867, 0.692, and 0.887, respectively). CONCLUSIONS: Support for mothers from the Philippines, Brazil, and other countries are necessary to prevent preterm birth. A future study is necessary to investigate the differences in characteristics among mothers of different nationalities in order to uncover the reason for the high risk for low birth weight among Japanese mothers.