Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

Selective elimination of undifferentiated human pluripotent stem cells using pluripotent state-specific immunogenic antigen Glypican-3.

Immunogenicity of immature pluripotent stem cells is a topic of intense debate. Immunogenic antigens, which are specific in pluripotent states, have not been described previously. In this study, we identified glypican-3 (GPC3), a known carcinoembryonic antigen, as a pluripotent state-specific immunogenic antigen. Additionally, we validated the applicability of human leukocyte antigen (HLA)-class I-restricted GPC3-reactive cytotoxic T lymphocytes (CTLs) in the removal of undifferentiated pluripotent stem cells (PSCs) from human induced pluripotent stem cell (hiPSC)-derivatives. HiPSCs uniquely express GPC3 in pluripotent states and were rejected by GPC3-reactive CTLs, which were sensitized with HLA-class I-restricted GPC3 peptides. Furthermore, GPC3-reactive CTLs selectively removed undifferentiated PSCs from hiPSC-derivatives in vitro and inhibited tumor formation in vivo. Our results demonstrate that GPC3 works as a pluripotent state-specific immunogenic antigen in hiPSCs and is applicable to regenerative medicine as a method of removing undifferentiated PSCs, which are the main cause of tumor formation.

Enhanced optineurin E50K-TBK1 interaction evokes protein insolubility and initiates familial primary open-angle glaucoma.

Glaucoma is the leading cause for blindness affecting 60 million people worldwide. The optineurin (OPTN) E50K mutation was first identified in familial primary open-angle glaucoma (POAG), the onset of which is not associated with intraocular pressure (IOP) elevation, and is classified as normal-tension glaucoma (NTG). Optineurin (OPTN) is a multifunctional protein and its mutations are associated with neurodegenerative diseases such as POAG and amyotrophic lateral sclerosis (ALS). We have previously described an E50K mutation-carrying transgenic (E50K-tg) mouse that exhibited glaucomatous phenotypes of decreased retinal ganglion cells (RGCs) and surrounding cell death at normal IOP. Further phenotypic analysis of these mice revealed persistent reactive gliosis and E50K mutant protein deposits in the outer plexiform layer (OPL). Over-expression of E50K in HEK293 cells indicated accumulation of insoluble OPTN in the endoplasmic reticulum (ER). This phenomenon was consistent with the results seen in neurons derived from induced pluripotent stem cells (iPSCs) from E50K mutation-carrying NTG patients. The E50K mutant strongly interacted with TANK-binding kinase 1 (TBK1), which prohibited the proper oligomerization and solubility of OPTN, both of which are important for OPTN intracellular transition. Treatment with a TBK1 inhibitor, BX795, abrogated the aberrant insolubility of the E50K mutant. Here, we delineated the intracellular dynamics of the endogenous E50K mutant protein for the first time and demonstrated how this mutation causes OPTN insolubility, in association with TBK1, to evoke POAG.

Analysis of cardiomyocyte movement in the developing murine heart.

The precise assemblage of several types of cardiac precursors controls heart organogenesis. The cardiac precursors show dynamic movement during early development and then form the complicated heart structure. However, cardiomyocyte movements inside the newly organized mammalian heart remain unclear. We previously established the method of ex vivo time-lapse imaging of the murine heart to study cardiomyocyte behavior by using the Fucci (fluorescent ubiquitination-based cell cycle indicator) system, which can effectively label individual G1, S/G2/M, and G1/S-transition phase nuclei in living cardiomyocytes as red, green, and yellow, respectively. Global analysis of gene expression in Fucci green positive ventricular cardiomyocytes confirmed that cell cycle regulatory genes expressed in G1/S, S, G2/M, and M phase transitions were upregulated. Interestingly, pathway analysis revealed that many genes related to the cell cycle were significantly upregulated in the Fucci green positive ventricular cardiomyocytes, while only a small number of genes related to cell motility were upregulated. Time-lapse imaging showed that murine proliferating cardiomyocytes did not exhibit dynamic movement inside the heart, but stayed on site after entering the cell cycle.

Time-lapse imaging of cell cycle dynamics during development in living cardiomyocyte.

Mammalian cardiomyocytes withdraw from the cell cycle shortly after birth, although it remains unclear how cardiomyocyte cell cycles behave during development. Compared to conventional immunohistochemistry in static observation, time-lapse imaging can reveal comprehensive data in hard-to-understand biological phenomenon. However, there are no reports of an established protocol of successful time-lapse imaging in mammalian heart. Thus, it is valuable to establish a time-lapse imaging system to enable the observation of cell cycle dynamics in living murine cardiomyocytes. This study sought to establish time-lapse imaging of murine heart to study cardiomyocyte cell cycle behavior. The Fucci (fluorescent ubiquitination-based cell cycle indicator) system can effectively label individual G1, S/G2/M, and G1/S-transition phase nuclei red, green and yellow, respectively, in living mammalian cells, and could therefore be useful to visualize the real-time cell cycle transitions in living murine heart. To establish a similar system for time-lapse imaging of murine heart, we first developed an ex vivo culture system, with the culture conditions determined in terms of sample state, serum concentration, and oxygen concentration. The optimal condition (slice culture, oxygen concentration 20%, serum concentration 10%) successfully mimicked physiological cardiomyocyte proliferation in vivo. Time-lapse imaging of cardiac slices from E11.5, E14.5, E18.5, and P1 Fucci-expressing transgenic mice revealed an elongated S/G2/M phase in cardiomyocytes during development. Our time-lapse imaging of murine heart revealed a gradual elongation of the S/G2/M phase during development in living cardiomyocytes.

Graph Representation Learning-Based Fixed-Length Clinical Feature Vector Generation from Heterogeneous Medical Records.

Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data manipulation-thus, manual feature design is labor-intensive. In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which utilizes unsupervised feature extraction based on graph embedding. Unsupervised learning is performed on a heterogeneous graph using Graph2Vec, and the inclusion of clinically useful data in the obtained embedding representation is evaluated by predicting readmission within 30 days of discharge based on it. The embedded representations are observed to improve predictive performance significantly as the information contained in the graph increases, indicating the suitability of the proposed method for feature design corresponding to clinical information.

Providing Practical Knowledge and Skills to Handle Real-World Data? Lessons Learned from Med RWD Program.

Both lectures and hands-on education are essential for the development of human resources that can use real-world data (RWD). The University of Tokyo has launched a new hybrid-style RWD educational program entitled "Medical Real World Data Utilization Human Resource Development Project" from FY2019 onwards. We present an overview of the overall picture of the project, including the development process of the educational program and the challenges associated with it.

Immediate postnatal prediction of death or bronchopulmonary dysplasia among very preterm and very low birth weight infants based on gradient boosting decision trees algorithm: A nationwide database study in Japan.

INTRODUCTION: Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data. METHODS: Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values. RESULTS: We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn. CONCLUSIONS: GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.

Etiology-Based Prognosis of Extracorporeal CPR Recipients After Out-of-Hospital Cardiac Arrest: A Retrospective Multicenter Cohort Study.

BACKGROUND: A better understanding of the relative contributions of various factors to patient outcomes is essential for optimal patient selection for extracorporeal CPR (ECPR) therapy for patients with out-of-hospital cardiac arrest (OHCA). However, evidence on the prognostic comparison based on the etiologies of cardiac arrest is limited. RESEARCH QUESTION: What is the etiology-based prognosis of patients undergoing ECPR for OHCA? STUDY DESIGN AND METHODS: This retrospective multicenter registry study involved 36 institutions in Japan and included all adult patients with OHCA who underwent ECPR between January 2013 and December 2018. The primary etiology for OHCA was determined retrospectively from all hospital-based data at each institution. We performed a multivariable logistic regression model to determine the association between etiology of cardiac arrest and two outcomes: favorable neurologic outcome and survival at hospital discharge. RESULTS: We identified 1,781 eligible patients, of whom 1,405 (78.9%) had cardiac arrest because of cardiac causes. Multivariable logistic regression analysis for favorable neurologic outcome showed that accidental hypothermia (adjusted OR, 5.12; 95% CI, 2.98-8.80; P < .001) was associated with a significantly higher rate of favorable neurologic outcome than cardiac causes. Multivariable logistic regression analysis for survival showed that accidental hypothermia (adjusted OR, 5.19; 95% CI, 3.15-8.56; P < .001) had significantly higher rates of survival than cardiac causes. Acute aortic dissection/aneurysm (adjusted OR, 0.07; 95% CI, 0.02-0.28; P < .001) and primary cerebral disorders (adjusted OR, 0.12; 95% CI, 0.03-0.50; P = .004) had significantly lower rates of survival than cardiac causes. INTERPRETATION: In this retrospective multicenter cohort study, although most patients with OHCA underwent ECPR for cardiac causes, accidental hypothermia was associated with favorable neurologic outcome and survival; in contrast, acute aortic dissection/aneurysm and primary cerebral disorders were associated with nonsurvival compared with cardiac causes.

Factors associated with favourable neurological outcomes following cardiopulmonary resuscitation for out-of-hospital cardiac arrest: A retrospective multi-centre cohort study.

Aim: To investigate the factors associated with favourable neurological outcomes in adult patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest (OHCA). Methods: This retrospective observational study used secondary analysis of the SAVE-J II multicentre registry data from 36 institutions in Japan. Between 2013 and 2018, 2157 patients with OHCA who underwent ECPR were enrolled in SAVE-J II. A total of 1823 patients met the study inclusion criteria. Adult patients (aged ≥ 18 years) with OHCA, who underwent ECPR before admission to the intensive care unit, were included in our secondary analysis. The primary outcome was a favourable neurological outcome at hospital discharge, defined as a Cerebral Performance Category score of 1 or 2. We used a multivariate logistic regression model to examine the association between factors measured at the incident scene or upon hospital arrival and favourable neurological outcomes. Results: Multivariable analysis revealed that shockable rhythm at the scene [odds ratio (OR); 2.11; 95% confidence interval (CI), 1.16-3.95] and upon hospital arrival (OR 2.59; 95% CI 1.60-4.30), bystander CPR (OR 1.63; 95% CI 1.03-1.88), body movement during resuscitation (OR 7.10; 95% CI 1.79-32.90), gasping (OR 4.33; 95% CI 2.57-7.28), pupillary reflex on arrival (OR 2.93; 95% CI 1.73-4.95), and male sex (OR 0.43; 95% CI 0.24-0.75) significantly correlated with neurological outcomes. Conclusions: Shockable rhythm, bystander CPR, body movement during resuscitation, gasping, pupillary reflex, and sex were associated with favourable neurological outcomes in patients with OHCA treated with ECPR.

Association between pupillary examinations and prognosis in patients with out-of-hospital cardiac arrest who underwent extracorporeal cardiopulmonary resuscitation: a retrospective multicentre cohort study.

BACKGROUND: In some cases of patients with out-of-hospital cardiac arrest (OHCA) who underwent extracorporeal cardiopulmonary resuscitation (ECPR), negative pupillary light reflex (PLR) and mydriasis upon hospital arrival serve as common early indicator of poor prognosis. However, in certain patients with poor prognoses inferred by pupil findings upon hospital arrival, pupillary findings improve before and after the establishment of ECPR. The association between these changes in pupillary findings and prognosis remains unclear. This study aimed to clarify the association of pupillary examinations before and after the establishment of ECPR in patients with OHCA showing poor pupillary findings upon hospital arrival with their outcomes. To this end, we analysed retrospective multicentre registry data involving 36 institutions in Japan, including all adult patients with OHCA who underwent ECPR between January 2013 and December 2018. We selected patients with poor prognosis inferred by pupillary examinations, negative pupillary light reflex (PLR) and pupil mydriasis, upon hospital arrival. The primary outcome was favourable neurological outcome, defined as Cerebral Performance Category 1 or 2 at hospital discharge. Multivariable logistic regression analysis was performed to evaluate the association between favourable neurological outcome and pupillary examination after establishing ECPR. RESULTS: Out of the 2,157 patients enrolled in the SAVE-J II study, 723 were analysed. Among the patients analysed, 74 (10.2%) demonstrated favourable neurological outcome at hospital discharge. Multivariable analysis revealed that a positive PLR at ICU admission (odds ration [OR] = 11.3, 95% confidence intervals [CI] = 5.17-24.7) was significantly associated with favourable neurological outcome. However, normal pupil diameter at ICU admission (OR = 1.10, 95%CI = 0.52-2.32) was not significantly associated with favourable neurological outcome. CONCLUSION: Among the patients with OHCA who underwent ECPR and showed poor pupillary examination findings upon hospital arrival, 10.2% had favourable neurological outcome at hospital discharge. A positive PLR after the establishment of ECPR was significantly associated with favourable neurological outcome.

Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records.

The diversity of patient information recorded on electronic medical records generally, presents a challenge for converting it into fixed-length vectors that align with clinical characteristics. To address this issue, this study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical records into a fixed-length vector. Infograph, one of the unsupervised graph representation learning algorithms was applied to the graphed inpatient information, resulting in embedded vectors of fixed length. The embedded vectors were then evaluated for whether the clinical information was preserved in it. The results indicated that the embedded representation contained information that could predict readmission within 30 days, demonstrating the feasibility of using unsupervised graph representation learning to transform patient information into fixed-length vectors that retain clinical characteristics.

Sex- and Age-Based Disparities in Public Access Defibrillation, Bystander Cardiopulmonary Resuscitation, and Neurological Outcome in Cardiac Arrest.

Importance: Younger females with out-of-hospital cardiac arrest (OHCA) in public locations have less likelihood to receive public access defibrillation and bystander cardiopulmonary resuscitation (CPR). However, the association between age- and sex-based disparities and neurological outcomes remains underexamined. Objective: To investigate the association between sex and age and the rate of receiving bystander CPR, automated external defibrillator defibrillation, and neurological outcomes in patients with OHCA. Design, Setting, and Participants: This cohort study used the All-Japan Utstein Registry, a prospective, population-based, nationwide database in Japan containing data on 1 930 273 patients with OHCA between January 1, 2005, and December 31, 2020. The cohort comprised patients with OHCA of cardiac origin that was witnessed by citizens and treated by emergency medical service personnel. The data were analyzed from September 3, 2022, to May 5, 2023. Exposure: Sex and age. Main Outcomes and Measures: The primary outcome was favorable neurological outcome at 30 days after an OHCA. Favorable neurological outcome was defined as a Cerebral Performance Category score of 1 (indicating good cerebral performance) or 2 (indicating moderate cerebral disability). The secondary outcomes were the rates of receiving public access defibrillation and bystander CPR. Results: The 354 409 included patients who experienced bystander-witnessed OHCA of cardiac origin had a median (IQR) age of 78 (67-86) years and 136 520 were females (38.5%). The rate of receiving public access defibrillation was higher in males than females (3.2% vs 1.5%; P < .001). Stratified by age, age- and sex-based disparities in prehospital lifesaving interventions by bystanders and in neurological outcomes were observed. Although younger females had a lower rate of receiving public access defibrillation and bystander CPR than males, younger females had a higher favorable neurological outcome compared with males of the same age (odds ratio [OR], 1.19; 95% CI, 1.08-1.31). In younger females with OHCA that was witnessed by nonfamily bystanders, receiving public access defibrillation (OR, 3.51; 95% CI, 2.34-5.27) or bystander CPR (OR, 1.62; 95% CI, 1.20-2.22) was associated with a favorable neurological outcome. Conclusions and Relevance: Results of this study suggest a pattern of significant sex- and age-based differences in bystander CPR, public access defibrillation, and neurological outcomes in Japan. Improvement in neurological outcomes in patients with OHCA, especially younger females, was associated with increased use of public access defibrillation and bystander CPR.

Myocardial cell sheet therapy and cardiac function.

Heart failure (HF) is the leading cause of death in developed countries. Regenerative medicine has the potential to drastically improve treatment for advanced HF. Stem cell-based medicine has received attention as a promising candidate therapy over the past decade; however, it has not yet realized this potential in terms of reliability. The cell sheet is an innovative technology for constructing aligned graft cells, and several cell sources have been investigated for making a feasible cell sheet. The most representative thus far is skeletal myoblast, although such cells raise the issue of arrhythmogenicity. Regenerative cardiomyocytes (CMs) derived from pluripotent stem cells (PSCs), such as embryonic stem cells or induced PSCs, are the most promising, because a myocardial cell sheet (MCS) constructed with regenerative CMs can potentially enable contraction recovery and electromechanical coupling with host CMs. The functional outcomes of experimental MCS are reduction of ventricular wall stress and paracrine effects rather than contraction recovery. Several technical obstacles still hamper the clinical application of MCSs, with graft survival the most pivotal issue. Ischemia, apoptosis, inflammation, and immune response can all cause graft cell death, and a stable blood supply to the MCS is critical for successful engraftment. Ventricular tachycardia must also be considered in any myocardial cell therapy, and multiple layering of MCS (>3 layers) is necessary to reconstruct human myocardium. Innervation is also a potential issue. The future application of myocardial cell therapy with MCS for advanced HF depends on resolving these difficulties.

Zac1 is an essential transcription factor for cardiac morphogenesis.

RATIONALE: The transcriptional networks guiding heart development remain poorly understood, despite the identification of several essential cardiac transcription factors. OBJECTIVE: To isolate novel cardiac transcription factors, we performed gene chip analysis and found that Zac1, a zinc finger-type transcription factor, was strongly expressed in the developing heart. This study was designed to investigate the molecular and functional role of Zac1 as a cardiac transcription factor. METHODS AND RESULTS: Zac1 was strongly expressed in the heart from cardiac crescent stages and in the looping heart showed a chamber-restricted pattern. Zac1 stimulated luciferase reporter constructs driven by ANF, BNP, or alphaMHC promoters. Strong functional synergy was seen between Zac1 and Nkx2-5 on the ANF promoter, which carries adjacent Zac1 and Nkx2-5 DNA-binding sites. Zac1 directly associated with the ANF promoter in vitro and in vivo, and Zac1 and Nkx2-5 physically associated through zinc fingers 5 and 6 in Zac1, and the homeodomain in Nkx2-5. Zac1 is a maternally imprinted gene and is the first such gene found to be involved in heart development. Homozygous and paternally derived heterozygous mice carrying an interruption in the Zac1 locus showed decreased levels of chamber and myofilament genes, increased apoptotic cells, partially penetrant lethality and morphological defects including atrial and ventricular septal defects, and thin ventricular walls. CONCLUSIONS: Zac1 plays an essential role in the cardiac gene regulatory network. Our data provide a potential mechanistic link between Zac1 in cardiogenesis and congenital heart disease manifestations associated with genetic or epigenetic defects in an imprinted gene network.

Pathway importance by graph convolutional network and Shapley additive explanations in gene expression phenotype of diffuse large B-cell lymphoma.

Deep learning techniques have recently been applied to analyze associations between gene expression data and disease phenotypes. However, there are concerns regarding the black box problem: it is difficult to interpret why the prediction results are obtained using deep learning models from model parameters. New methods have been proposed for interpreting deep learning model predictions but have not been applied to genetics. In this study, we demonstrated that applying SHapley Additive exPlanations (SHAP) to a deep learning model using graph convolutions of genetic pathways can provide pathway-level feature importance for classification prediction of diffuse large B-cell lymphoma (DLBCL) gene expression subtypes. Using Kyoto Encyclopedia of Genes and Genomes pathways, a graph convolutional network (GCN) model was implemented to construct graphs with nodes and edges. DLBCL datasets, including microarray gene expression data and clinical information on subtypes (germinal center B-cell-like type and activated B-cell-like type), were retrieved from the Gene Expression Omnibus to evaluate the model. The GCN model showed an accuracy of 0.914, precision of 0.948, recall of 0.868, and F1 score of 0.906 in analysis of the classification performance for the test datasets. The pathways with high feature importance by SHAP included highly enriched pathways in the gene set enrichment analysis. Moreover, a logistic regression model with explanatory variables of genes in pathways with high feature importance showed good performance in predicting DLBCL subtypes. In conclusion, our GCN model for classifying DLBCL subtypes is useful for interpreting important regulatory pathways that contribute to the prediction.

Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development.

BACKGROUND: Treatment discontinuation (TD) is one of the major prognostic issues in diabetes care, and several models have been proposed to predict a missed appointment that may lead to TD in patients with diabetes by using binary classification models for the early detection of TD and for providing intervention support for patients. However, as binary classification models output the probability of a missed appointment occurring within a predetermined period, they are limited in their ability to estimate the magnitude of TD risk in patients with inconsistent intervals between appointments, making it difficult to prioritize patients for whom intervention support should be provided. OBJECTIVE: This study aimed to develop a machine-learned prediction model that can output a TD risk score defined by the length of time until TD and prioritize patients for intervention according to their TD risk. METHODS: This model included patients with diagnostic codes indicative of diabetes at the University of Tokyo Hospital between September 3, 2012, and May 17, 2014. The model was internally validated with patients from the same hospital from May 18, 2014, to January 29, 2016. The data used in this study included 7551 patients who visited the hospital after January 1, 2004, and had diagnostic codes indicative of diabetes. In particular, data that were recorded in the electronic medical records between September 3, 2012, and January 29, 2016, were used. The main outcome was the TD of a patient, which was defined as missing a scheduled clinical appointment and having no hospital visits within 3 times the average number of days between the visits of the patient and within 60 days. The TD risk score was calculated by using the parameters derived from the machine-learned ranking model. The prediction capacity was evaluated by using test data with the C-index for the performance of ranking patients, area under the receiver operating characteristic curve, and area under the precision-recall curve for discrimination, in addition to a calibration plot. RESULTS: The means (95% confidence limits) of the C-index, area under the receiver operating characteristic curve, and area under the precision-recall curve for the TD risk score were 0.749 (0.655, 0.823), 0.758 (0.649, 0.857), and 0.713 (0.554, 0.841), respectively. The observed and predicted probabilities were correlated with the calibration plots. CONCLUSIONS: A TD risk score was developed for patients with diabetes by combining a machine-learned method with electronic medical records. The score calculation can be integrated into medical records to identify patients at high risk of TD, which would be useful in supporting diabetes care and preventing TD.

Deep learning model to detect significant aortic regurgitation using electrocardiography.

BACKGROUND: Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG). METHODS: Our dataset comprised 29,859 paired data of ECG and echocardiography, including 412 AR cases, from January 2015 to December 2019. This dataset was divided into training, validation, and test datasets. We developed a multi-input neural network model, which comprised a two-dimensional convolutional neural network (2D-CNN) using raw ECG data and a fully connected deep neural network (FC-DNN) using ECG features, and compared its performance with the performances of a 2D-CNN model and other machine learning models. In addition, we used gradient-weighted class activation mapping (Grad-CAM) to identify which parts of ECG waveforms had the most effect on algorithm decision making. RESULTS: The area under the receiver operating characteristic curve of the multi-input model (0.802; 95% CI, 0.762-0.837) was significantly greater than that of the 2D-CNN model alone (0.734; 95% CI, 0.679-0.783; p<0.001) and those of other machine learning models. Grad-CAM demonstrated that the multi-input model tended to focus on the QRS complex in leads I and aVL when detecting AR. CONCLUSIONS: The multi-input deep learning model using 12-lead ECG data could detect significant AR with modest predictive value.

The complement C3-complement factor D-C3a receptor signalling axis regulates cardiac remodelling in right ventricular failure.

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure.