Advancing the management of acute intermediate-high-risk pulmonary embolism: The enduring legacy of Professor Guy Meyer.

Only few years after the first report on diagnosing acute pulmonary embolism (PE) with pulmonary angiography, studies began to investigate the effectiveness and safety of thrombolytic therapy for achieving early reperfusion. In 1992, Guy Meyer demonstrated the fast improvement of pulmonary haemodynamics after alteplase administration; this drug has remained the mainstay of thrombolysis for PE over almost 35 years. In the meantime, algorithms for PE risk stratification continued to evolve. The landmark Pulmonary Embolism International Thrombolysis (PEITHO) trial, led by Guy Meyer, demonstrated the clinical efficacy of thrombolysis for intermediate-risk PE, albeit at a relatively high risk of major, particularly intracranial bleeding. Today, systemic thrombolysis plays an only minor role in the real-world treatment of acute PE in the United States and Europe, but major trials are underway to test safer reperfusion regimens. Of those, the PEITHO-3 study, conceived by Guy Meyer and other European and North American experts, is an ongoing randomised, placebo-controlled, double-blind, multinational academic trial. The primary objective is to assess the efficacy of reduced-dose intravenous thrombolytic therapy against the background of heparin anticoagulation in patients with intermediate-high-risk PE. In parallel, trials with similar design are testing the efficacy and safety of catheter-directed local thrombolysis or mechanical thrombectomy. Increasingly, focus is being placed on long-term functional and patient-reported outcomes, including quality of life indicators, as well as on the utilization of health care resources. The pioneering work of Guy Meyer will thus continue to have a major impact on the management of PE for years to come.

Natural History, Phenotype Spectrum and Clinical Outcomes of Desmin (DES)-Associated Cardiomyopathy.

Background: Pathogenic/likely pathogenic (P/LP) desmin (DES) variants cause heterogeneous cardiomyopathy and/or skeletal myopathy phenotypes. Limited data suggest a high incidence of major adverse cardiac events (MACE), including cardiac conduction disease (CCD), sustained ventricular arrhythmias (VA), and heart failure (HF) events (HF hospitalization, LVAD/cardiac transplant, HF-related death), in patients with P/LP DES variants. However, pleiotropic presentation and small cohort sizes have limited clinical phenotype and outcome characterization. Objectives: We aimed to describe the natural history, phenotype spectrum, familial penetrance and outcomes in patients with P/LP DES variants through a systematic review and individual patient data meta-analysis using published reports. Methods: We searched Medline (PubMed) and Embase for studies that evaluated cardiac phenotypes in patients with P/LP DES variants. Cardiomyopathy diagnosis or occurrence of MACE were considered evidence of cardiac involvement/penetrance. Lifetime event-free survival from CCD, sustained VA, HF events, and composite MACE was assessed. Results: Out of 4,212 screened publications, 71 met the inclusion criteria. A total of 230 patients were included (52.6% male, 52.2% probands, median age: 31 years [22.0; 42.8] at first evaluation, median follow-up: 3 years [0; 11.0]). Overall, 124 (53.9%) patients were diagnosed with cardiomyopathy, predominantly dilated cardiomyopathy (14.8%), followed by restrictive cardiomyopathy (13.5%), whereas other forms were less common: arrhythmogenic cardiomyopathy (7.0%), hypertrophic cardiomyopathy (6.1%), arrhythmogenic right ventricular cardiomyopathy (5.2%), and other forms (7.4%). Overall, 132 (57.4%) patients developed MACE, with 96 [41.7%] having CCD, 36 [15.7%] sustained VA, and 43 [18.7%] HF events. Familial penetrance of cardiac disease was 63.6% among relatives with P/LP DES variants. Male sex was associated with increased risk of sustained VA (HR 2.28, p=0.02) and HF events (HR 2.45, p=0.008). Conclusions: DES cardiomyopathy exhibits heterogeneous phenotypes and distinct natural history, characterized by high familial penetrance and substantial MACE burden. Male patients face higher risk of sustained VA events.

Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.

PURPOSE: To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients. METHODS: 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test. RESULTS: The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001). CONCLUSION: The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.

A novel risk stratification approach and molecular subgroup characterization based on coagulation related genes in colon adenocarcinoma.

Colon adenocarcinoma (COAD) represents a significant health concern within the population. Advancing our understanding of COAD is imperative for early detection, enabling personalized treatment interventions, and facilitating the development of effective preventive measures. The coagulation system plays a role in tumor-related pathological processes; however, its specific involvement in COAD and potential contributors remain unclear. This study aimed to establish a novel risk stratification approach by analyzing coagulation related genes (CRGs) associated with COAD. Through a comprehensive bioinformatics analysis of data from public databases, we screened COAD associated CRGs and characterized the associated molecular subtypes. After a comprehensive analysis of the characteristics of each subtype, we applied differentially expressed genes in CRG subtypes to establish a new risk stratification method. Clinical subgroup analysis, immunoinfiltration analysis, therapeutic reactivity prediction and other analytical methods suggest the potential clinical value of the established risk stratification method. As one of the selected targets, the effect of MS4A4A on the proliferation and invasion of COAD was confirmed by in vitro experiments, which partially verified the reliability of bioinformatics results. Our findings delineate CRGs potentially implicated in COAD pathogenesis and offer fresh insights into the influence of the coagulation process on tumorigenesis and progression.

Enhanced motor noise in an autism subtype with poor motor skills.

BACKGROUND: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise. METHODS: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task. RESULTS: Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions. LIMITATIONS: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed. CONCLUSIONS: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.

Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset.

Aims: We aimed to externally validate the SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset-a large multi-centre cohort of patients undergoing CRT implantation. Methods and results: The SEMMELWEIS-CRT score is a machine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation. This tool demonstrated impressive performance during internal validation but has not yet been validated externally. To this end, we applied it to the data of 1367 patients from the European CRT Survey I dataset. The SEMMELWEIS-CRT predicted 1-year mortality with an area under the receiver operating characteristic curve (AUC) of 0.729 (0.682-0.776), which concurred with the performance measured during internal validation [AUC: 0.768 (0.674-0.861), P = 0.466]. Moreover, the SEMMELWEIS-CRT score outperformed multiple conventional statistics-based risk scores, and we demonstrated that a higher predicted probability is not only associated with a higher risk of death [odds ratio (OR): 1.081 (1.061-1.101), P < 0.001] but also with an increased risk of hospitalizations for any cause [OR: 1.013 (1.002-1.025), P = 0.020] or for heart failure [OR: 1.033 (1.015-1.052), P < 0.001], a less than 5% improvement in left ventricular ejection fraction [OR: 1.033 (1.021-1.047), P < 0.001], and lack of improvement in New York Heart Association functional class compared with baseline [OR: 1.018 (1.006-1.029), P = 0.003]. Conclusion: In the European CRT Survey I dataset, the SEMMELWEIS-CRT score predicted 1-year all-cause mortality with good discriminatory power, which confirms the generalizability and demonstrates the potential clinical utility of this machine learning-based risk stratification tool.

Echocardiography for Management of Cardiovascular Disease in Pregnancy.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) continues to be a leading contributor to maternal mortality and morbidity. Echocardiography is an essential tool for patients with suspected and known CVD to establish symptom etiology, treatment strategy, and prognosis. We summarize the current status of conventional and novel techniques for assessment of CVD during pregnancy. RECENT FINDINGS: Conventional techniques are still useful for evaluation of known or suspected CVD. Advanced technology using speckle tracking continues to evolve and is increasingly applied for diagnosis of subclinical disease including hypertensive disorders of pregnancy and left ventricular (LV) dysfunction. Precise recommendations on how frequently echocardiography should be performed and for whom remain in flux. However, a recently published consensus statement and new screening tool for pregnancy assessment of patients with valvular heart disease provide additional advice on using this modality. Echocardiography remains the diagnostic modality of choice for evaluation and risk stratification in pregnancy.

Assays of Variant Effect and Automated Patch Clamping Improve KCNH2-LQTS Variant Classification and Cardiac Event Risk Stratification.

BACKGROUND: Long QT syndrome is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2. Variant classification is difficult, often because of lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes. METHODS: We quantified cell-surface trafficking of 18 796 variants in KCNH2 using a multiplexed assay of variant effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping. We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian long QT syndrome penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events. RESULTS: Variant MAVE trafficking scores and automated patch clamping peak tail currents were highly correlated (Spearman rank-order ρ=0.69; n=433). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian long QT syndrome penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became nonsignificant when peak tail current and penetrance estimates were also available. The area under the receiver operator characteristic curve for 20-year event outcomes based on patient-specific sex and QTc (area under the curve, 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (area under the curve, 0.86 [0.83-0.89]) or attainable automated patch clamping peak tail current data (area under the curve, 0.84 [0.81-0.88]). CONCLUSIONS: High-throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale, whereas long QT syndrome penetrance estimates and automated patch clamping peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Leukocyte Extracellular Vesicles Predict Progression of Systolic Dysfunction in Heart Failure with Mildly Reduced Ejection Fraction (LYCHEE) - A Prospective, Multicentre Cohort Study.

Risk stratification in heart failure with mildly-reduced ejection fraction (HFmrEF) remains challenging. We evaluated the predictive value of advanced glycation end products (AGEs) and plasma concentrations of extracellular vesicles (EVs) for the systolic and diastolic dysfunction progression in HFmrEF patients. Skin AGE accumulation was measured using AGE Reader. Plasma EV concentrations were measured using flow cytometry. Among 74 patients enrolled, 13 (18%) had systolic dysfunction progression and 5 (7%) had diastolic dysfunction progression during 6.5 months follow-up. Leukocyte EVs concentrations were higher in patients with systolic dysfunction progression (p = 0.002) and predicted the progression with 75.0% sensitivity and 58.3% specificity, independent of other clinical variables (OR 4.72, 95% CI 0.99-22.31). Skin AGE levels and concentrations of other EV subtypes were not associated with systolic or diastolic dysfunction progression. Increased leukocyte EVs concentrations are associated with 4.7-fold higher odds of systolic dysfunction progression in HFmrEF patients.

MRI as an assessment tool for prognostic risk stratification of endometrial carcinoma patients based on molecular classification.

BACKGROUND: Non-invasive risk stratification for patients with endometrial carcinoma (EC) is important for developing personalised treatment plans. Our study aimed to explore the ability of quantitative MRI parameters to predict the risk stratification of EC patients based on molecular classification. METHODS: Fifty-three patients with histologically proven EC who underwent pelvic MRI and surgical treatment at our hospital between January 2020 and August 2022 were assessed. The tumour volume (TV) and uterine volume (UV) were estimated with the ellipsoid formula and used to calculate the tumour volume ratio (TVR). The mean apparent diffusion coefficient (ADC) of the tumour was measured on a workstation. Quantitative MRI parameters were compared among different risk groups via unpaired Student's t-tests or Mann-Whitney's U-tests. RESULTS: The TV and TVR were significantly different between the low- and high-risk groups (p < 0.001), and cut-off values of 5342 mm3 and 0.055 allowed the differentiation of the high-risk group from the low-risk group, with 77% and 85% sensitivity and 78% and 78% specificity, respectively. There was a significant difference in the ADC between the two groups (p = 0.026), and a cut-off value of 0.65 × 10-3 mm2/s allowed differentiation of the risk groups, with 93% sensitivity and 39% specificity. CONCLUSIONS: Quantitative MRI parameters such as the TV, TVR and ADC may be helpful in preoperatively assessing the risk stratification of patients with EC based on molecular classification.

For patients with endometrial carcinoma (EC), it is important to assess the risk stratification based on molecular classification for developing treatment plans, but risk stratification is obtained most accurately from postoperative samples. We used non-invasive and easily accessible quantitative parameters of magnetic resonance imaging for preoperatively evaluating the risk stratification in these patients. We found that the quantitative parameters may be helpful in preoperatively assessing the risk stratification of patients with EC on the basis of molecular classification.

Validation of Novel Risk Prediction Models in Patients with Brugada Syndrome: A Multicenter Study in Japan.

BACKGROUND: Risk stratification in patients with Brugada syndrome (BrS) is challenging, especially in those at intermediate risk. The Predicting Arrhythmic evenT (PAT) score has recently been demonstrated to be excellent for predicting future arrhythmic events in patients without prior ventricular fibrillation (VF). However, validation studies are lacking. OBJECTIVE: This study aimed to assess the performance of a novel risk stratification model in predicting future VF events in patients with BrS in a Japanese multicenter cohort. METHODS: The PAT score was calculated for 413 patients with BrS (mean age, 50.9±13.6 years; 395 men) from 59 hospitals in Japan, including 314 patients without prior VF. The incidence of developing VF during the follow-up period was investigated. RESULTS: During the 106.8-month follow-up period, 54 patients (13.1%) experienced VF events. Of the 314 patients without prior VF at enrollment, 14 (4.5%) experienced VF events. The incidence of VF events during the follow-up period was significantly higher in patients with PAT scores ≥10 than in those with scores <10 (41/173 [23.7%] vs. 13/240 [5.4%], p<0.0001) in the total cohort. No difference was observed in the incidence of VF events between patients with PAT scores ≥10 and <10 among the 314 patients without prior VF (6/86 [7.0%] vs. 8/228 [3.5%], p=0.22). PAT scores ≥10 predicted future VF events with a sensitivity and specificity of 42.9% and 73.3%, respectively. CONCLUSION: This Japanese multicenter registry demonstrated that the novel risk stratification model could not accurately predict future VF events in patients with BrS, but without prior VF.

Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis.

OBJECTIVE: We explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB). METHODS: This retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity. RESULTS: There were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348-41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS. CONCLUSION: ASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.

Estimating effects of time-varying exposures on mortality risk.

Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization process via linked administrative data. A generalized Cox regression model is proposed using a time-dependent stratification variable to summarize lifetime service utilization. Recognizing the service utilization over time as an internal covariate in the survival analysis, conventional likelihood methods are inapplicable. We present an estimating function based procedure for estimating model parameters, and provide a testing procedure for updating the stratification levels. The proposed approach is examined both asymptotically and numerically via simulation. We motivate and illustrate the proposed approach using an on-going program pertaining to opioid agonist treatment (OAT) management for individuals identified with opioid use disorders. Our analysis of the OAT data indicates that the OAT effect on mortality risk decreases in successive OAT attempts, in which two risk classes based on an individual's treatment episode number are established: one with 1-3 OAT episodes, and the other with 4+ OAT episodes.

DNAm scores for serum GDF15 and NT-proBNP levels associate with a range of traits affecting the body and brain.

BACKGROUND: Plasma growth differentiation factor 15 (GDF15) and N-terminal proB-type natriuretic peptide (NT-proBNP) are cardiovascular biomarkers that associate with a range of diseases. Epigenetic scores (EpiScores) for GDF15 and NT-proBNP may provide new routes for risk stratification. RESULTS: In the Generation Scotland cohort (N ≥ 16,963), GDF15 levels were associated with incident dementia, ischaemic stroke and type 2 diabetes, whereas NT-proBNP levels were associated with incident ischaemic heart disease, ischaemic stroke and type 2 diabetes (all PFDR < 0.05). Bayesian epigenome-wide association studies (EWAS) identified 12 and 4 DNA methylation (DNAm) CpG sites associated (Posterior Inclusion Probability [PIP] > 95%) with levels of GDF15 and NT-proBNP, respectively. EpiScores for GDF15 and NT-proBNP were trained in a subset of the population. The GDF15 EpiScore replicated protein associations with incident dementia, type 2 diabetes and ischaemic stroke in the Generation Scotland test set (hazard ratios (HR) range 1.36-1.41, PFDR < 0.05). The EpiScore for NT-proBNP replicated the protein association with type 2 diabetes, but failed to replicate an association with ischaemic stroke. EpiScores explained comparable variance in protein levels across both the Generation Scotland test set and the external LBC1936 test cohort (R2 range of 5.7-12.2%). In LBC1936, both EpiScores were associated with indicators of poorer brain health. Neither EpiScore was associated with incident dementia in the LBC1936 population. CONCLUSIONS: EpiScores for serum levels of GDF15 and Nt-proBNP associate with body and brain health traits. These EpiScores are provided as potential tools for disease risk stratification.

Comparative Clinical Assessment and Risk Stratification of COVID-19 and Influenza Infections in Adults and Children: A Comprehensive Systematic Review and Meta-Analysis.

Objective: This study aimed to compare the clinical risks and outcomes of COVID-19 and influenza. Methods: The search for relevant articles was conducted using both a database search method and a manual search, which involved searching through the reference lists of articles related to the topic for additional studies. The Quality assessment was carried out using the Newcastle Ottawa tool, and the data analysis was conducted using the Review Manager Software (RevMan 5.4.1). Results: The meta-analysis results indicated that COVID-19 patients had similar lengths of hospital stays (SMD: -0.25; 95% CI: -0.60-0.11; p=0.17). However, COVID-19 patients had significantly higher mortality rates (RR: 0.28; 95% CI: 0.21-0.37; p<0.0001), in-hospital complications (RR: 0.57; 95% CI: 0.50-0.65; p<0.00001), intensive care unit (ICU) admissions (OR: 0.48; 95% CI: 0.37-0.61; p<0.00001), length of ICU stay (SMD: -0.45; 95% CI: -0.83-0.06; p=0.02), and mechanical ventilation use (OR: 0.36; 95% CI: 0.28-0.46; p<0.00001). Conclusion: The findings suggested that COVID-19 was more severe than influenza. Therefore, "flu-like" symptoms should not be dismissed without a clear diagnosis, especially during the winter when influenza is more prevalent.

Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network.

Objective: The study aims to identify distinct population-specific comorbidity progression patterns, timely detect potential comorbidities, and gain better understanding of the progression of comorbid conditions among patients. Methods: This work presents a comorbidity progression analysis framework that utilizes temporal comorbidity networks (TCN) for patient stratification and comorbidity prediction. We propose a TCN construction approach that utilizes longitudinal, temporal diagnosis data of patients to construct their TCN. Subsequently, we employ the TCN for patient stratification by conducting preliminary analysis, and typical prescription analysis to uncover potential comorbidity progression patterns in different patient groups. Finally, we propose an innovative comorbidity prediction method by utilizing the distance-matched temporal comorbidity network (TCN-DM). This method identifies similar patients with disease prevalence and disease transition patterns and combines their diagnosis information with that of the current patient to predict potential comorbidity at the patient's next visit. Results: This study validated the capability of the framework using a real-world dataset MIMIC-III, with heart failure (HF) as interested disease to investigate comorbidity progression in HF patients. With TCN, this study can identify four significant distinctive HF subgroups, revealing the progression of comorbidities in patients. Furthermore, compared to other methods, TCN-DM demonstrated better predictive performance with F1-Score values ranging from 0.454 to 0.612, showcasing its superiority. Conclusions: This study can identify comorbidity patterns for individuals and population, and offer promising prediction for future comorbidity developments in patients.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer.

BACKGROUND: Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM: To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS: The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS: Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION: The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Prognostic value of ventricular arrhythmia in early post-infarction left ventricular dysfunction: the French nationwide WICD-MI study.

BACKGROUND AND AIMS: Prophylactic implantable cardioverter-defibrillators (ICDs) are not recommended until left ventricular ejection fraction (LVEF) has been reassessed 40 to 90 days after an acute myocardial infarction. In the current therapeutic era, the prognosis of sustained ventricular arrhythmias (VAs) occurring during this early post-infarction phase (i.e. within 3 months of hospital discharge) has not yet been specifically evaluated in post-myocardial infarction patients with impaired LVEF. Such was the aim of this retrospective study. METHODS: Data analysis was based on a nationwide registry of 1032 consecutive patients with LVEF ≤ 35% after acute myocardial infarction who were implanted with an ICD after being prescribed a wearable cardioverter-defibrillator (WCD) for a period of 3 months upon discharge from hospital after the index infarction. RESULTS: ICDs were implanted either because a sustained VA occurred while on WCD (VA+/WCD, n = 72) or because LVEF remained ≤35% at the end of the early post-infarction phase (VA-/WCD, n = 960). The median follow-up was 30.9 months. Sustained VAs occurred within 1 year after ICD implantation in 22.2% and 3.5% of VA+/WCD and VA-/WCD patients, respectively (P < .0001). The adjusted multivariable analysis showed that sustained VAs while on WCD independently predicted recurrence of sustained VAs at 1 year (adjusted hazard ratio [HR] 6.91; 95% confidence interval [CI] 3.73-12.81; P < .0001) and at the end of follow-up (adjusted HR 3.86; 95% CI 2.37-6.30; P < .0001) as well as 1-year mortality (adjusted HR 2.86; 95% CI 1.28-6.39; P = .012). CONCLUSIONS: In patients with LVEF ≤ 35%, sustained VA during the early post-infarction phase is predictive of recurrent sustained VAs and 1-year mortality.