Novel role of cardiovascular MRI to contextualise tuberculous pericardial inflammation and oedema as predictors of constrictive pericarditis.

Tuberculosis (TB) and human immunodeficiency virus/acquired immunodeficiency syndrome have reached epidemic proportions, particularly affecting vulnerable populations in low- and middle-income countries of sub-Saharan Africa. TB pericarditis is the commonest cardiac manifestation of TB and is the leading cause of constrictive pericarditis, a reversible (by surgical pericardiectomy) cause of diastolic heart failure in endemic areas. Unpacking the complex mechanisms underpinning constrictive haemodynamics in TB pericarditis has proven challenging, leaving various basic and clinical research questions unanswered. Subsequently, risk stratification strategies for constrictive outcomes have remained unsatisfactory. Unique pericardial tissue characteristics, as identified on cardiovascular magnetic resonance imaging, enable us to stage and quantify pericardial inflammation and may assist in identifying patients at higher risk of tissue remodelling and pericardial constriction, as well as predict the degree of disease reversibility, tailor medical therapy, and determine the ideal timing for surgical pericardiectomy.

Cardiovascular Disease in Total Knee Arthroplasty: An Analysis of Hospital Outcomes, Complications, and Mortality.

Background: Cardiovascular comorbidities have been identified as a significant risk factor for adverse outcomes following surgery. The purpose of this study was to investigate its prevalence and impact on postoperative outcomes, hospital metrics, and mortality in patients undergoing total knee arthroplasty (TKA). Our hypothesis was that patients with cardiovascular comorbidities would have worse outcomes, greater postoperative complication rates, and increased mortality compared to patients without cardiovascular disease. Methods: In this retrospective study, data from the National Inpatient Sample database from 2011 to 2020 were queried for patients who underwent TKA with preexisting cardiac comorbidities, including congestive heart failure (CHF), coronary artery disease (CAD), valvular dysfunction, and arrhythmia. Multivariate logistic regression analyses compared hospital metrics (length of stay, costs, and adverse discharge disposition), postoperative complications, and mortality rates while adjusting for demographic and clinical variables. All statistical analyses were performed using R studio 4.2.2 and Stata MP 17 and 18 with Python package. Results: A total of 385,585 patients were identified. Those with preexisting CHF, CAD, valvular dysfunction, or arrhythmias were found to be older and at higher risk of adverse outcomes, including prolonged length of stay, increased hospital charges, and increased mortality (p < 0.001). Additionally, all preexisting cardiac diagnoses led to an increased risk of postoperative myocardial infarction, acute kidney injury (AKI), and need for transfusion (p < 0.001). The presence of valvular dysfunction, arrhythmia, or CHF was associated with an increased risk of thromboembolic events (p < 0.001). The presence of CAD and valvular dysfunction was associated with an increased risk of urologic infection (p < 0.001). Conclusions: This study demonstrated that CHF, CAD, valvular dysfunction, and arrhythmia are prevalent among TKA patients and associated with worse hospital metrics, higher risk of perioperative complications, and increased mortality. As our use of TKA rises, a lower threshold for preoperative cardiology referral in older individuals and early preoperative counseling/intervention in those with known cardiac disease may be necessary to reduce adverse outcomes.

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data.

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used "robust" variance estimate of Barlow (Biometrics 50:1064-1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

Risk of progression of cervical intraepithelial neoplasia grade 2 in human papillomavirus-vaccinated and unvaccinated women: a population-based cohort study.

BACKGROUND: Many countries have implemented active surveillance (ie, leaving the lesion untreated) as an option among younger women with cervical intraepithelial neoplasia grade 2 because regression rates are high and excisional treatment increases the risk for preterm birth in subsequent pregnancies. However, early identification of women at increased risk for progression to cervical intraepithelial neoplasia grade 3 or worse is important to ensure timely treatment. Because women who have received a human papillomavirus vaccine have a lower risk for cervical cancer, they may have a lower risk for progression of untreated cervical intraepithelial neoplasia grade 2 to cervical intraepithelial neoplasia grade 3 or worse. OBJECTIVE: This study aimed to investigate if women who received a human papillomavirus vaccine and who are undergoing active surveillance for cervical intraepithelial neoplasia grade 2 are less likely to progress to cervical intraepithelial neoplasia grade 3 or worse when compared with women who did not receive the vaccine. STUDY DESIGN: We conducted a population-based cohort study in Denmark using data from national health registers. We identified all women aged 18 to 40 years who were undergoing active surveillance for cervical intraepithelial neoplasia grade 2 from January 1, 2007, to December 31, 2020. Women with a previous record of cervical intraepithelial neoplasia grade 2 or worse, hysterectomy, or a loop electrosurgical excision procedure were excluded. Exposure was defined as having received ≥1 dose of a human papillomavirus vaccine at least 1 year before the cervical intraepithelial neoplasia grade 2 diagnosis. We used cumulative incidence functions to estimate the risk for progression to cervical intraepithelial neoplasia grade 3 or worse within 28 months using hysterectomy, emigration, and death as competing events. We used modified Poisson regression to calculate crude and adjusted relative risks of progression during the 28-month surveillance period. Results were stratified by age at vaccination and adjusted for index cytology, disposable income, and educational level. RESULTS: The study population consisted of 7904 women of whom 3867 (48.9%) were vaccinated at least 1 year before a diagnosis of cervical intraepithelial neoplasia grade 2. At the time of cervical intraepithelial neoplasia grade 2 diagnosis, women who were vaccinated were younger (median age, 25 years; interquartile range, 23-27 years) than those who were not (median age, 29 years; interquartile range, 25-33 years). The 28-month cumulative risk for cervical intraepithelial neoplasia grade 3 or worse was significantly lower among women who were vaccinated before the age of 15 years (22.9%; 95% confidence interval, 19.8-26.1) and between the ages of 15 and 20 years (31.5%; 95% confidence interval, 28.8-34.3) when compared with women who were not vaccinated (37.6%; 95% confidence interval, 36.1-39.1). Thus, when compared with women who were not vaccinated, those who were vaccinated before the age of 15 years had a 35% lower risk for progression to cervical intraepithelial neoplasia grade 3 or worse (adjusted relative risk, 0.65; 95% confidence interval, 0.57-0.75), whereas women who were vaccinated between the ages of 15 and 20 years had a 14% lower risk (adjusted relative risk, 0.86; 95% confidence interval, 0.79-0.95). For women who were vaccinated after the age of 20 years, the risk was comparable with that among women who were not vaccinated (adjusted relative risk, 1.02; 95% confidence interval, 0.96-1.09). CONCLUSION: Women who were vaccinated and who were undergoing active surveillance for cervical intraepithelial neoplasia grade 2 had a lower risk for progression to cervical intraepithelial neoplasia grade 3 or worse during 28 months of follow-up when compared with women who were not vaccinated but only if the vaccine was administered by the age of 20 years. These findings may suggest that the human papillomavirus vaccination status can be used for risk stratification in clinical management of cervical intraepithelial neoplasia grade 2.

What can patients tell us in Sjögren's syndrome?

In Sjögren's Syndrome (SS), clinical heterogeneity and discordance between disease activity measures and patient experience are key obstacles to effective therapeutic development. Patient reported outcome measures (PROMs) are useful tools for understanding the unmet needs from the patients' perspective and therefore they are key for the development of patient centric healthcare systems. Initial concern about the subjectivity of PROMs has given way to methodological rigour and clear guidance for the development of PROMs. To date, several studies of patient stratification using PROMs have identified similar symptom-based subgroups. There is evidence to suggest that these subgroups may represent different disease endotypes with differing responses to therapeutic interventions. Stratified medicine approaches, alongside sensitive outcome measures, have the potential to improve our understanding of SS pathobiology and therapeutic development. The inclusion of PROMs is important for the success of such approaches. In this review we discuss the opportunities of using PROMs in understanding the pathogenesis of and therapeutic development for SS.

Comparison of ultrasound risk stratification systems for pediatric thyroid nodules.

Background: There is currently insufficient data to validate adult-based US risk stratification systems (RSSs) for the identification of malignant thyroid nodules in a pediatric population. Methods: From October 2016 and May 2023, 173 thyroid nodules of pediatric patients (age ≤ 18 years) with definitive pathology results and ultrasound (US) examination within 1 month before surgery or fine-needle aspiration (FNA) biopsy in our institution were enrolled in this study. The clinical and US characteristics of these nodules were retrospectively reviewed and categorized according to the ACR-TIRADS, C-TIRADS, and ATA guidelines. The diagnostic performance of US-based FNA criteria (original and simulating) of the three guidelines in thyroid cancer detection was estimated. Results: The three RSSs had similar AUC according to the categories(0.849-0.852, all P > 0.05). When combined with the original FNA criteria of the three RSSs to manage the nodules, the FNA rate of ACR-TIRADS and C-TIRADS were significantly less than ATA guidelines (53.18% vs. 64.63%, P < 0.05, and 52.60% vs. 64.63%, P < 0.05). The missed malignancy rate (MMR) and unnecessary FNA rate (UFR) of ATA guidelines (50.00%, 35.85%) was highest among the three RSSs, followed by the C-TIRADS (37.80%, 19.57%) and the ACR-TIRADS (37.04%, 19.57%). When nodules < 1 cm with the highest category in each RSS biopsied, that is when using the simulating FNA thresholds, the MMR was reduced overall (all P < 0.001), without a change in the UFR (all P > 0.05). All the three RSSs showed a substantial improvement in accuracy and malignant detection rate (all P < 0.05). Conclusion: The ACR-TIRADS, C-TIRADS, and ATA guidelines showed high missed malignancy rates when using their original recommended FNA criteria. When nodules < 1 cm with the highest category in each RSS biopsied, the missed malignancy rate of each RSS was decreased. Decreasing the FNA thresholds for highly suspicious malignant nodules may therefore be an effective means of managing malignant thyroid nodules in pediatric patients.

Evaluating Suicide Risk Stratification in Outpatient Settings: A Vignette-Based Approach.

Suicide risk assessment and stratification are a key suicide prevention strategy in mental health care systems that treat military service members and veterans. The aim of the current mixed-method project was to address a gap in our knowledge as to how therapists make these important clinical decisions. This manuscript reports the results of a project during which six vignettes were developed reflecting varying levels of risk according to the Rocky Mountain MIRECC Risk Stratification Table. Mental health therapists were asked to evaluate the risk level of each vignette, determine a treatment disposition, and provide justification for their ratings. The results of the study indicate that therapists can reliably evaluate risk, but that treatment planning tended to be based more on vignette-specific factors than essential features of the risk model. The qualitative findings revealed variations in the definition and perception of foundational concepts, suggesting a need for further research and training in these domains. Overall, the results support the use of vignettes as a method to assess clinical decision-making and provide several areas for further training and research.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification.

BACKGROUND: Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia. METHODS: Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P). RESULTS: Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance. CONCLUSION: Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.

Ensemble learning methods of inference for spatially stratified infectious disease systems.

Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

Comprehensive Sex-Stratified Genetic Analysis of 28 Blood Biomarkers and Depression Reveals a Significant Association between Depression and Low Levels of Total Protein in Females.

Introduction: Major depression (MD) is more common amongst women than men, and MD episodes have been associated with fluctuations in reproductive hormones amongst women. To investigate biological underpinnings of heterogeneity in MD, the associations between depression, stratified by sex and including perinatal depression (PND), and blood biomarkers, using UK Biobank (UKB) data, were evaluated, and extended to include the association of depression with biomarker polygenic scores (PGS), generated as proxy for each biomarker. Method: Using female (N = 39,761) and male (N = 38,821) UKB participants, lifetime MD and PND were tested for association with 28 blood biomarkers. A GWAS was conducted for each biomarker and genetic correlations with depression subgroups were estimated. Using independent data from the Australian Genetics of Depression Study, PGS were constructed for each biomarker, and tested for association with depression status (n [female cases/controls] = 9,006/6,442; n [male cases/controls] = 3,106/6,222). Regions of significant local genetic correlation between depression subgroups and biomarkers highlighted by the PGS analysis were identified. Results: Depression in females was significantly associated with levels of twelve biomarkers, including total protein (OR = 0.90, CI = [0.86, 0.94], p = 3.9 × 10-6) and vitamin D (OR = 0.94, CI = [0.90, 0.97], p = 2.6 × 10-4), and PND with five biomarker levels, also including total protein (OR = 0.88, CI = [0.81, 0.96], p = 4.7 × 10-3). Depression in males was significantly associated with levels of eleven biomarkers. In the independent Australian Genetics of Depression Study, PGS analysis found significant associations for female depression and PND with total protein (female depression: OR = 0.93, CI = [0.88, 0.98], p = 3.6 × 10-3; PND: OR = 0.91, CI = [0.86, 0.96], p = 1.1 × 10-3), as well as with vitamin D (female depression: OR = 0.93, CI = [0.89, 0.97], p = 2.0 × 10-3; PND: OR = 0.92, CI = [0.87, 0.97], p = 1.4 × 10-3). The male depression sample did not report any significant results, and the point estimate of total protein (OR = 0.98, CI = [0.92-1.04], p = 4.7 × 10-1) did not indicate any association. Local genetic correlation analysis highlighted significant genetic correlation between PND and total protein, located in 5q13.3 (rG = 0.68, CI = [0.33, 1.0], p = 3.6 × 10-4). Discussion and Conclusion: Multiple lines of evidence from genetic analysis highlight an association between total serum protein levels and depression in females. Further research involving prospective measurement of total protein and depressive symptoms is warranted.

Distinct differences of vertical phytoplankton community structure in mainstream and a tributary bay of the Three Gorges Reservoir, China.

The vertical distribution of phytoplankton plays a crucial role in shaping the dynamics and structure of aquatic communities. In highly dynamic reservoir systems, water level fluctuations significantly affect the physiochemical conditions and the phytoplankton community. However, the specific effects on the vertical characteristics of phytoplankton between the mainstream and the tributary bay of the reservoir remain unstudied. This study investigated the vertical aspects of phytoplankton density, biomass, α and ß diversity through monthly sampling over two years in the mainstream (Chang Jiang, CJ) and a tributary bay (Xiang Xi, XX) of the Three Gorges Reservoir in China. Phytoplankton density and biomass were significantly higher in XX, indicating an increased risk of algal blooms in the tributary. The phytoplankton community in CJ showed more stable species-environment relationships, a lower Shannon index and a higher evenness index, suggesting a relatively simple structure and a more uniform distribution of phytoplankton among different water layers. Conversely, XX showed greater differences between water layers (higher ß diversity), with significant negative correlations with water level and positive correlations with DO difference, dissolved silica (DSi) difference, and stratification. Peak phytoplankton density and biomass, as well as high ß diversity in XX, occurred during periods of decreased water levels with strong stratification in spring and summer. A structural equation model complemented by path analysis revealed that a decrease in water level could increase ß diversity either directly through internal processes with extended residence time or indirectly by modifying stratification and the vertical distribution of DSi in XX. Therefore, a proposed water quality management strategy for XX was to increase the water level or reduce ß diversity by implementing artificial mixing during stratification periods. Overall, this study lies in its comprehensive investigation of the vertical characteristics of the phytoplankton community in both the mainstream and the tributary bay of the Three Gorges Reservoir, elucidating the significant impact of water level fluctuations and providing insights for targeted water quality management strategies in the tributary bay to mitigate potential ecological impacts.

Emergency Colorectal Surgery in Those with Cirrhosis: A Population-based Study Assessing Practice Patterns, Outcomes and Predictors of Mortality.

Background: Those with cirrhosis who require emergency colorectal surgery are at risk for poor outcomes. Although risk predictions models exists, these tools are not specific to colorectal surgery, nor were they developed in a contemporary setting. Thus, the objective of this study was to assess the outcomes in this population and determine whether cirrhosis etiology and/or the Model for End Stage Liver Disease (MELD-Na) is associated with mortality. Methods: This population-based study included those with cirrhosis undergoing emergent colorectal surgery between 2009 and 2017. All eligible individuals in Ontario were identified using administrative databases. The primary outcome was 90-day mortality. Results: Nine hundred and twenty-seven individuals (57%) (male) were included. The most common cirrhosis etiology was non-alcoholic fatty liver disease (NAFLD) (50%) and alcohol related (32%). Overall 90-day mortality was 32%. Multivariable survival analysis demonstrated those with alcohol-related disease were at increased risk of 90-day mortality (hazards ratio [HR] 1.53, 95% confidence interval [CI] 1.2-2.0 vs. NAFLD [ref]). Surgery for colorectal cancer was associated with better survival (HR 0.27, 95%CI 0.16-0.47). In the subgroup analysis of those with an available MELD-Na score (n = 348/927, 38%), there was a strong association between increasing MELD-Na and mortality (score 20+ HR 6.6, 95%CI 3.9-10.9; score 10-19 HR 1.8, 95%CI 1.1-3.0; score <10 [ref]). Conclusion: Individuals with cirrhosis who require emergent colorectal surgery have a high risk of postoperative complications, including mortality. Increasing MELD-Na score is associated with mortality and can be used to risk stratify individuals.

CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study.

OBJECTIVES: The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS: A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS: 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION: The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.

Risk stratification of synchronous gastric cancers including alcohol-related genetic polymorphisms.

BACKGROUND AND AIM: We previously identified that ever-smoking and severe gastric atrophy in pepsinogen are risk factors for synchronous gastric cancers (SGCs). This study aimed to determine the association of alcohol drinking status or alcohol-related genetic polymorphism with SGCs and also stratify their risk. METHODS: This multi-center prospective cohort study included patients who underwent endoscopic submucosal dissection for the initial early gastric cancers at 22 institutions in Japan. We evaluated the association of alcohol drinking status or alcohol dehydrogenase 1B (ADH1B) and acetaldehyde dehydrogenase 2 (ALDH2) genotypes with SGCs. We then stratified the risk of SGCs by combining prespecified two factors and risk factors identified in this study. RESULTS: Among 802 patients, 130 had SGCs. Both the ADH1B Arg and ALDH2 Lys alleles demonstrated a significant association with SGCs on multivariate analysis (odds ratio, 1.77), although alcohol drinking status showed no association. The rates of SGCs in 0-3 risk factors in the combined evaluation of three risk factors (ever-smoking, severe gastric atrophy in pepsinogen, and both the ADH1B Arg and ALDH2 Lys alleles) were 7.6%, 15.0%, 22.0%, and 32.1%, respectively. The risk significantly increased from 0 to 3 risk factors on multivariate analysis (P for trend <0.001). CONCLUSIONS: Both the ADH1B Arg and ALDH2 Lys alleles were at high risk for SGCs. The risk stratification by these three factors may be a less invasive and promising tool for predicting their risk.

Cardiac Damage in Degenerative Mitral Regurgitation Treated With Transcatheter Mitral Edge-to-Edge Repair.

BACKGROUND: The extent of cardiac damage and its association with clinical outcomes in patients undergoing transcatheter edge-to-edge repair (TEER) for degenerative mitral regurgitation remains unclear. This study was aimed to investigate cardiac damage in patients with degenerative mitral regurgitation treated with TEER and its association with outcomes. METHODS: We analyzed patients with degenerative mitral regurgitation treated with TEER in the Optimized Catheter Valvular Intervention-Mitral registry, which is a prospective, multicenter observational data collection in Japan. The study subjects were classified according to the extent of cardiac damage at baseline: no extravalvular cardiac damage (stage 0), mild left ventricular or left atrial damage (stage 1), moderate left ventricular or left atrial damage (stage 2), or right heart damage (stage 3). Two-year mortality after TEER was compared using Kaplan-Meier analysis. RESULTS: Out of 579 study participants, 8 (1.4%) were classified as stage 0, 76 (13.1%) as stage 1, 319 (55.1%) as stage 2, and 176 (30.4%) as stage 3. Two-year survival was 100% in stage 0, 89.5% in stage 1, 78.9% in stage 2, and 75.3% in stage 3 (P=0.013). Compared with stage 0 to 1, stage 2 (hazard ratio, 3.34 [95% CI, 1.03-10.81]; P=0.044) and stage 3 (hazard ratio, 4.51 [95% CI, 1.37-14.85]; P=0.013) were associated with increased risk of 2-year mortality after TEER. Significant reductions in heart failure rehospitalization rate and New York Heart Association functional scale were observed following TEER (both, P<0.001), irrespective of the stage of cardiac damage. CONCLUSIONS: Advanced cardiac damage is associated with an increased risk of mortality in patients undergoing TEER for degenerative mitral regurgitation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: UMIN000023653.

Advances in uterine inflammatory myofibroblastic tumours: Diagnostic challenges and risk stratification.

The current understanding of inflammatory myofibroblastic tumours (IMTs) of the gynaecological tract has recently been enhanced by their increased recognition. This increase is largely due to greater accessibility to RNA-based molecular assays used to identify their defining ALK rearrangements. This review summarises the clinical characteristics, morphological spectrum, immunohistochemical profile and molecular underpinnings of uterine IMT. Additionally, this review discusses practical diagnostic considerations including overlap between uterine IMT and smooth muscle tumours as well as pregnancy-associated uterine IMT. Finally, we highlight recent literature demonstrating the potential for aggressive behaviour in uterine IMT, including a novel risk stratification model for identifying high-risk IMT.

Methods to predict heart failure in diabetes patients.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is one of the leading causes of cardiovascular disease and powerful predictor for new-onset heart failure (HF). AREAS COVERED: We focus on the relevant literature covering evidence of risk stratification based on imaging predictors and circulating biomarkers to optimize approaches to preventing HF in DM patients. EXPERT OPINION: Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Association between the insufficient improvement of the quantitative flow ratio and worsening outcomes in ST-segment elevated myocardial infarction: a multicentre prospective cohort study.

Background: Improved coronary physiological function after percutaneous coronary intervention (PCI) has been shown to improve prognosis in stable ischaemic heart disease, but has not yet been explored in ST-segment elevated myocardial infarction (STEMI). The study sought to determine whether an improvement in the quantitative flow ratio (QFR) could improve the prognosis of STEMI patients undergoing primary PCI. Methods: Patients diagnosed with STEMI who were receiving primary PCI were recruited for the study. Those with thrombolysis in myocardial infarction (TIMI) flow <2 after wiring were excluded. The ΔQFR was calculated using the following formula: ΔQFR = post-PCI QFR - pre-stent QFR. The primary endpoint was the composite event, including recurrent myocardial infarction (MI) and acute heart failure (AHF). Results: In total, 515 STEMI patients with a median follow-up of 364 days were enrolled in the study. Based on the cut-off value from the receiver operator characteristic (ROC) curve, the patients were divided into the following two groups: the lower ΔQFR group (≤0.25, N=332); and the normal ΔQFR group (>0.25, N=183). Patients with a lower ΔQFR had a relatively higher rate of MI/AHF (10.5% vs. 4.4%, P=0.019) and AHF (7.2% vs. 2.7%, P=0.044). A lower ΔQFR was significantly associated with a higher incidence of MI/AHF [hazard ratio (HR) =2.962, 95% confidence interval (CI): 1.358-6.459, P=0.006, respectively] after adjusting for potential confounders. Pre-stent angiographic microvascular resistance [odds ratio (OR) =1.027, 95% CI: 1.022-1.033, P<0.001] and the stent-to-vessel diameter ratio <1.13 (OR =1.766, 95% CI: 1.027-3.071, P=0.04) were independent predictors of a lower ΔQFR. Conclusions: An insufficient improvement in the QFR contributes to worsening outcomes and might be a useful tool for risk stratification in STEMI.