Risk Stratification Of Patients Research Articles

Assessing the Efficacy of Early Warning Scores in Predicting Sepsis Outcomes in Emergency Departments

Background: Early recognition of sepsis is a critical challenge in emergency medicine, particularly in resource-constrained settings, where timely intervention is essential to improve patient outcomes. Objective: This study aimed to compare the effectiveness of different Early Warning Scores (EWS), including the National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick Sequential Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS), in predicting outcomes such as mortality, ICU admission, and septic shock in the emergency departments of Pakistan. Methods: A retrospective study was conducted at the Emergency Department of Khyber Teaching Hospital, Peshawar, from January to December 2024. Patients diagnosed with sepsis based on Sepsis-3 criteria were included. The predictive performance of each EWS was assessed using AUROC, logistic regression, and calibration plots. Results: NEWS demonstrated superior predictive accuracy for in-hospital mortality (AUROC: 0.873) and ICU admission (AUROC: 0.890) compared to MEWS, qSOFA, and SIRS. It showed robust sensitivity (88%) and specificity (75%) and was identified as an independent predictor of mortality and ICU admission through multivariate analysis. Conclusion: In resource-limited settings, NEWS proved to be the most effective tool for early risk stratification in sepsis patients. Its adoption could significantly enhance sepsis management and improve outcomes in emergency departments with constrained resources.

Severe Left Main Coronary Artery Stenosis as the First Finding in Newly Diagnosed Chronic Coronary Syndrome: Incidence and Clinical Predictors.

Severe left main coronary artery (LMCA) lesions (≥50% stenosis) portend a poor prognosis and require urgent revascularization. In this study, we identified the incidence and clinical predictors of severe LMCA stenosis in patients with chronic coronary syndrome (CCS) who had undergone coronary angiography for the first time. Using a nationwide database registry, all the patients with CCS who had undergone coronary angiography were included. Patients were classified based on having severe LMCA stenosis and they were compared based on the recommended therapeutic strategy after angiography. A multivariable binary logistic regression model was developed to identify the potential predictors of a severe LMCA lesion. Among 40,161 patients with CCS, a severe LMCA lesion was detected in a total of 1556 participants (3.87% [3.69; 6.07]). The multivariable logistic regression identified age (odds ratio [OR]: 1.04 [1.03;1.04]), male gender (OR:2.56 [2.28; 2.89]), dyslipidemia (OR:1.19 [1.06; 1.34]), and peripheral arterial disease (PAD) (OR:3.68 [1.06;12.83]) as predictors of a severe LMCA stenosis. Approximately 4% of patients with newly diagnosed CCS may suffer from severe LMCA disease. Age, male gender, dyslipidemia, and PAD are among the predicting factors of a severe LMCA stenosis and can be utilized in risk stratification of patients with CCS at greater risk of severe LMCA stenosis.

Navigating the Waves of Critical Care Echocardiography: Unveiling its Role, Advantages, and Pitfalls in the Cardiac Intensive Care Unit.

Critical Care Echocardiography (CCE) is now established as an important tool in the intensive care unit (ICU). This paper aims to examine the expanding role of cardiovascular ultrasound in the ICU, focusing on its applications, benefits, and challenges, while highlighting recent advancements shaping the future of critical care echocardiography. Non-invasive echocardiographic measurement of hemodynamic parameters including stroke volume, cardiac output, left ventricular filling pressures, and pulmonary pressures have been well-validated against invasive measurements. Myocardial perfusion can also be evaluated using ultrasound enhancing agent techniques to further risk-stratify patients with chest pain. Echocardiography enables clinicians to visualize cardiac anatomy and physiology directly at the bedside, providing immediate feedback in rapidly changing clinical situations. Assessment of stroke volume, cardiac output, and left ventricular filling pressures can be readily measured at the bedside and correspond with clinical outcomes including mortality. Measurement of central venous pressure and pulmonary pressures may guide clinical decisions in fluid management and mechanical ventilation strategies. Lastly, myocardial perfusion imaging can supplement the 2D echocardiographic evaluation to further risk-stratify patients presenting with chest pain.

Cardiac Magnetic Resonance Imaging in Diagnostics and Cardiovascular Risk Assessment

Cardiac magnetic resonance (CMR) allows for analysis of cardiac function and myocardial tissue characterization. Increased left ventricular mass (LVM) is an independent predictor of cardiovascular events; however, the diagnosis of left ventricular hypertrophy and its prognostic value strongly depend on the LVM indexation method. Evaluation of the quantity and distribution of late gadolinium enhancement assists in clinical decisions on diagnosis, cardiovascular assessment, and interventions, including the placement of cardiac implantable electronic devices and the choice of an optimal procedural approach. Novel CMR techniques, such as T1 and T2 mapping, may be used for the longitudinal follow-up of myocardial fibrosis and myocardial edema or inflammation in different groups of patients, including patients with systemic sclerosis, myocarditis, cardiac sarcoidosis, amyloidosis, and both ischemic and non-ischemic cardiomyopathy, among others. Moreover, CMR tagging and feature tracking techniques might improve cardiovascular risk stratification in patients with different etiologies of left ventricular dysfunction. This review summarizes the knowledge about the current role of CMR in diagnostics and cardiovascular risk assessment to enable more personalized approach in clinical decision making.

Precision Medicine for High-Risk Gene Fusions in Pediatric AML: a focus on KMT2A, NUP98, and GLIS2 Rearrangements.

Robust genetic characterization of paediatric AML has demonstrated that fusion oncogenes are highly prevalent drivers of AML leukemogenesis in young children. Identification of fusion oncogenes associated with adverse outcomes has facilitated risk stratification of patients, although successful development of precision medicine approaches for most fusion-driven AML subtypes have been historically challenging. This knowledge gap has been in part due to difficulties in targeting structural alterations involving transcription factors and in identification of a therapeutic window for selective inhibition of the oncofusion without deleterious effects upon essential wild-type proteins. Herein, we discuss the current molecular landscape and functional characterisation of three of the most lethal childhood AML fusion-oncogene driven subtypes harbouring KMT2A, NUP98, or CBFA2T3::GLIS2 rearrangements. We further review early-phase clinical trial data of novel targeted inhibitors and immunotherapies that have demonstrated initial success specifically for children with these poor-prognosis genetic subtypes of AML and provide appreciable optimism to improve clinical outcomes in the future.

Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study.

Rebleeding after recovery from esophagogastric variceal bleeding (EGVB) is a severe complication that is associated with high rates of both incidence and mortality. Despite its clinical importance, recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking. To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding. This study included 477 EGVB patients across 2 cohorts: The derivation cohort (n = 322) and the validation cohort (n = 155). The primary outcome was rebleeding events within 1 year. The least absolute shrinkage and selection operator was applied for predictor selection, and multivariate Cox regression analysis was used to construct the prognostic model. Internal validation was performed with bootstrap resampling. We assessed the discrimination, calibration and accuracy of the model, and performed patient risk stratification. Six predictors, including albumin and aspartate aminotransferase concentrations, white blood cell count, and the presence of ascites, portal vein thrombosis, and bleeding signs, were selected for the rebleeding event prediction following endoscopic treatment (REPET) model. In predicting rebleeding within 1 year, the REPET model exhibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort, alongside 0.862 and 0.127 in the validation cohort. Furthermore, the REPET model revealed a significant difference in rebleeding rates (P < 0.01) between low-risk patients and intermediate- to high-risk patients in both cohorts. We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive performance, which will improve the clinical management of rebleeding in EGVB patients.

How I Treat Higher-Risk MDS.

Myelodysplastic syndromes/neoplasms (MDS) are a widely heterogenous group of myeloid malignancies characterized by morphologic dysplasia, a defective hematopoiesis, and recurrent genetic abnormalities. The original and revised International Prognostic Scoring Systems (IPSS) have been used to risk-stratify patients with MDS to guide treatment strategies. In higher-risk MDS, the therapeutic approach is geared toward delaying leukemic transformation and prolonging survival. For over a decade, the hypomethylating agents azacitidine and decitabine have been the standard of care and, when feasible, an allogeneic hematopoietic stem cell transplantation (AHSCT) should be considered. However, the IPSS scoring systems solely rely on clinical, morphological, and cytogenetic features and do not account for somatic mutations present in over 80% of cases. These genetic abnormalities have been shown to play a crucial role in prognostication, prompting the development of molecular IPSS, and the integration of genomic features into MDS classification systems in recent years. In this review, we delineate our approach to higher-risk MDS in the context of updated classifications and the latest prognostication tools. We employ illustrative clinical cases to support our discussion and share insights from recent clinical trials, highlighting lessons learned.

Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.

The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain. This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain. All-cause death was ascertained from multiple sources, including the CDC National Death Index registry. Six machine learning models were trained for survival analysis using 73 morphological electrocardiogram features (80% training with 10-fold cross-validation and 20% testing), followed by a variational Bayesian Gaussian mixture model to define distinct risk groups. The resulting classification performance was compared against the HEART score. The derivation cohort included 4015 patients (age 59 ± 16 years, 47% women). The mortality rate was 20.3% after a median follow-up period of 3.05 years (interquartile range 1.75-5.32). Extra Survival Trees outperformed other forecasting models, and the derived risk groups successfully classified patients into low-, moderate-, and high-risk groups (log-rank test statistic = 121.14, P < .001). This model outperformed the HEART score, reducing the rate of missed events by >90% with a negative predictive value and sensitivity of 93.4% and 85.9%, compared to 89.0% and 75.0%, respectively. In an independent external testing cohort (N = 3095, age 59 ± 15 years, 44% women, 30-day mortality 3.5%), patients in the moderate [odds ratio 3.62 (1.35-9.74)] and high [odds ratio 6.12 (2.38-15.75)] risk groups had significantly higher odds of mortality compared to those in the low-risk group. The externally validated machine learning-based model, exclusively utilizing features from the 12-lead electrocardiogram, outperformed the HEART score in stratifying the mortality risk of patients with acute chest pain. This may have the potential to impact the precision of care delivery and the allocation of resources to those at highest risk of adverse events.

Illuminating the impact of CD38-induced adenosine formation in B-cell lymphoma

The expression of CD38 by cancer cells may mediate an immune-suppressive effect by producing Extracellular Adenosine (ADO) acting through G-protein-coupled cell surface receptors on cellular components and tumor cells. This can increase PD-1 expression and interaction with PD-L1, suppressing CD8 + cytotoxic T cells. This study examines the impact of heightened CD38 expression and extracellular ADO on various hematological and clinical parameters in patients with mature B-cell lymphoma, alongside their correlation with the soluble counterparts of the PD-1/PD-L1 axis. Our study was conducted on 90 patients, CD38-positive and CD38-negative (measured by flow cytometry), with mature B-cell lymphoma divided into CLL and B-NHL subtypes. Their serum ADO, soluble PD-1, and PD-L1 levels were measured using a sandwich ELISA. Our study revealed a positive correlation between CD38 expression, sADO, sPD-1, and sPD-L1 in mature B-cell lymphoma patients. CD38-positive patients had higher sADO, sPD-1, and sPD-L1 levels. Higher CD38 expression and extracellular ADO negatively affected HB level and PLT count and positively correlated with the higher risk stratification in mature B-cell lymphoma patients. This study explored the potential impact of CD38 expression and elevated extracellular ADO on B-cell lymphoma alongside their link with the PD-1/PD-L1 axis. Our findings underscore the influence of extracellular ADO on the neoplastic process of mature B-cell lymphoma. We also propose targeting the CD38-induced-ADO formation pathway, which could serve as a promising therapeutic immune target with multifaceted effects within mature B-cell neoplasms.

Efficacy of serum lactate/albumin ratio as a prognostic biomarker in patients with ventilator-associated pneumonia

Aims: This study aims to evaluate the efficacy of the serum lactate/albumin ratio (LAR) as a prognostic marker in patients with ventilator-associated pneumonia (VAP) who are hospitalized in the intensive care unit (ICU). Methods: This single-center retrospective observational clinical study was conducted between January 1, 2022, and October 1, 2024. The study group comprised 58 patients admitted to the ICUs of Malatya Training and Research Hospital, Turkiye, with intubation but without a diagnosis of pneumonia at the time of admission. These patients were diagnosed with VAP 48 hours after intubation. The serum LAR was calculated within the first 24 hours after admission to the ICU and correlated with mortality and morbidity. Results: The mean age of the patients was 68 years, with the majority being over 65 years of age. Of the 58 patients included in the study, 43 (74.1%) ultimately succumbed to their illness. The LAR of those who died in the study was significantly higher than that of those who survived. The LAR was identified as a reliable predictor, exhibiting a sensitivity of 83.7% and a specificity of 60% when a cutoff value of 1.13 was applied. The survival time of patients with a LAR of ≤1.13 was significantly longer than that of patients with a ratio of &gt;1.13. Conclusion: In our study, the mortality prediction performance of the LAR in patients with VAP was superior to that of the serum lactate level or serum albumin level alone. Therefore, the LAR may be a useful and readily available prognostic factor for early risk stratification of VAP patients.

Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer

Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Testing multiple integration strategies, most of them yield multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrate improved patient risk stratification compared to models built with routine clinical features only. Our study thus provides evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC datasets to develop and validate robust and powerful immunotherapy biomarkers.

Machine Learning for Predicting Primary Graft Dysfunction After Lung Transplantation: An Interpretable Model Study.

Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx. This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio. Independent risk factors for PGD3 were determined by integrating the univariate logistic regression and least absolute shrinkage and selection operator regression analyses. Subsequently, 9 ML models were used to construct prediction models for PGD3 based on selected variables. Their prediction performances were further evaluated. Besides, model stratification performance was assessed with 3 posttransplant metrics. Finally, the SHapley Additive exPlanations algorithm was used to understand the predictive importance of selected variables. We identified 9 independent clinical risk factors as selected variables. Among 9 ML models, the random forest (RF) model displayed optimal performance (area under the curve [AUC] = 0.9415, sensitivity [Se] = 0.8972, specificity [Sp] = 0.8795 in the training cohort; AUC = 0.7975, Se = 0.7520, Sp = 0.7313 in the internal validation cohort; and AUC = 0.8214, Se = 0.8235, Sp = 0.6667 in the external validation cohort). Further assessments on calibration and clinical usefulness indicated the promising applicability of the RF model in PGD3 prediction. Meanwhile, the RF model also performed best in terms of risk stratification for postoperative support (extracorporeal membrane oxygenation time: P < 0.001, mechanical ventilation time: P = 0.006, intensive care unit time: P < 0.001). The RF model had the optimal performance in PGD3 prediction and postoperative risk stratification for patients after Lung Tx.

Functional genotype classification groups distinguish disease severity in recessive dystrophic epidermolysis bullosa.

Recessive dystrophic epidermolysis bullosa (RDEB) is a genetic disorder due to pathogenic variants in the COL7A1 gene. In this study we determined the association between different categories of COL7A1 variants and clinical disease severity in 236 RDEB patients in North America. Published reports or in-silico predictions were used to assess the impact of pathogenic variants in COL7A1 on type VII collagen (C7) protein function. Three impact categories were postulated: genotypes that would likely cause a low impact on C7 function (splice B/missense, missense/missense), medium impact [premature termination codon (PTC)/splice B, splice A/splice B, PTC/missense, splice A/missense, splice B/splice B] and high impact (PTC/PTC, PTC/splice A, splice A/splice A). Splice A variants are predicted to cause downstream PTCs while splice B variants cause in-frame exon skipping and are therefore less deleterious. Severity of functional impact was significantly associated with history of gastrostomy tube placement, esophageal dilation, hand surgery, anemia, renal disease, chronic wounds, diffuse skin involvement and history of squamous cell carcinoma. Odds of death were 3.25 (1.24-8.50, p=0.02) higher in the high vs medium impact group. High impact group patients had worse clinical outcomes. Functional genotype categories are a feasible approach to risk stratify patients based on predicted C7 function.

Carotid artery atherosclerosis: mechanisms of instability and clinical implications.

Cardiovascular disease remains a prominent cause of disability and premature death worldwide. Within this spectrum, carotid artery atherosclerosis is a complex and multifaceted condition, and a prominent precursor of acute ischaemic stroke and other cardiovascular events. The intricate interplay among inflammation, oxidative stress, endothelial dysfunction, lipid metabolism, and immune responses participates in the development of lesions, leading to luminal stenosis and potential plaque instability. Even non-stenotic plaques can precipitate a sudden cerebrovascular event, regardless of the degree of luminal encroachment. In this context, carotid imaging modalities have proved their efficacy in providing in vivo characterization of plaque features, contributing substantially to patient risk stratification and clinical management. This review emphasizes the importance of identifying high-risk individuals by use of current imaging modalities, biomarkers, and risk stratification tools. Such approaches inform early intervention and the implementation of personalized therapeutic strategies, ultimately enhancing patient outcomes in the realm of cardiovascular disease management.

Relationship between VEGF to PEDF ratio and in-hospital mortality in acute respiratory distress syndrome patients

Acute respiratory distress syndrome (ARDS) has a high mortality rate worldwide; thus, identifying death risk factors related to ARDS is critical for risk stratification in patients with ARDS. In the present study, we conducted a single-center retrospective cohort analysis. Out of 278 patients with ARDS admitted from January 2016 to June 2022, 226 were included in this study. The patients were classified based on whether they were alive or dead during hospitalization. Their demographic and laboratory data and results were analyzed by performing a standard statistical analysis. Patients in the death group were older, with worse respiratory functions and blood biochemistry than those in the non-death group. Moreover, statistically significant differences were observed in the levels of vascular endothelial growth factor (VEGF) and pigment epithelium-derived factor (PEDF) between the two groups. Multivariate stepwise logistic regression analysis showed that the VEGF/PEDF ratio was strongly associated with the risk of death. The area under the curve of the VEGF/PEDF ratio was 0.829 (95% confidence interval: 0.772–0.885; P < 0.001), sensitivity was 86.3%, and specificity was 68.0%. Therefore, a VEGF/PEDF ratio is positively correlated with the risk of death in patients with ARDS.

Clinical decision instruments for predicting mortality in patients with cirrhosis seeking emergency department care.

Clinical decision instruments (CDIs) could be useful to aid risk stratification and disposition of emergency department (ED) patients with cirrhosis. Our primary objective was to derive and internally validate a novel Cirrhosis Risk Instrument for Stratifying Post-Emergency department mortality (CRISPE) for the outcomes of 14- and 30-day post-ED mortality. Secondarily, we externally validated the existing Model for End-Stage Liver Disease (MELD) scores for explicit use in ED patients and prediction of the same outcomes. A cohort of 2093 adults with cirrhosis, at 16 sites in a statewide health system, was analyzed for 119 candidate variables available at ED disposition. LASSO with 10-fold cross-validation was used in variable selection for 14-day (CRISPE-14) and 30-day (CRISPE-30) logistic regression models. Area under the receiver operating characteristic curve (AUROC) was calculated for each variant of the CRISPE and MELD scores and compared via Delong's test. Predictions were compared to actual ED disposition for predictive value and reclassification statistics. Median (interquartile range [IQR]) characteristics of the cohort were age 62 (53-70) years and MELD 3.0 13.0 (8.0-20.0). Mortality was 4.3% and 8.5% at 14 and 30 days, respectively. CRISPE-14 and CRISPE-30 outperformed each MELD variant, achieving AUROC of 0.824 (95% CI: 0.781-0.866) and 0.829 (0.796-0.861), respectively. MELD 3.0 AUROCs were 0.724 (0.667-0.781) and 0.715 (0.672-0.781), respectively. Compared to ED disposition, CRISPE-14, CRISPE-30, and MELD 3.0 significantly improved positive and negative predictive value and net reclassification index at multiple cutoffs. Applying CRISPE-30 (cutoff 4.5) favorably reclassified one net ED disposition for mortality for every 12 patients, while MELD 3.0 net reclassified one disposition per 84 patients. CDIs may be useful in risk-stratifying ED patients with cirrhosis and aiding disposition decision making. The novel CRISPE CDI showed powerful performance and requires external validation, while the existing MELD 3.0 score has moderate performance and is now externally-validated in an ED population for short-term mortality.

Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states

Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing. The results demonstrate a new sub-clustering of the prostate cancer cell-line into two groups—protein-rich and lipid-rich sub-cellular components—which we believe to be mechanistically linked. This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. Applications may lead to more targeted diagnoses, prognoses and clinical treatment decisions via molecularly-informed stratification that would benefit patients. A method that could discover distinct disease-state features that are mechanistically linked could also assist in the development of more effective broad-spectrum treatments that simultaneously target linked disease-state processes.

Hemodynamic evaluation of symptomatic and asymptomatic intracranial atherosclerotic stenosis using cerebral angiographic images: an exploratory study

BackgroundPrevious studies suggest that it may be inappropriate to determine treatment strategies solely based on the degree of stenosis for patients with intracranial atherosclerotic stenosis (ICAS). In order to better...

Mitral valve prolapse in sudden cardiac arrest survivors - coincidence or causal relationship?

Sudden cardiac arrest (SCA) risk stratification in patients with mitral valve prolapse (MVP) may be complicated by other potential causes of arrhythmia. We aimed to characterize SCA survivors with isolated (iMVP) and non-isolated MVP (non-iMVP) and to assess their long-term follow-up. This ambispective study included 75 patients with MVP who experienced SCA and were treated in our center between 2009-2024. They were divided into three groups depending on probability of relation between SCA and comorbidities. The control group comprised 112 subjects with MVP but without a history of SCA. We analyzed all available electrocardiograms, Holter ECG monitoring and echocardiograms, including longitudinal strain. A novel parameter, the systolic atrial-directed notch (SADN) was tested. SCA survivors with iMVP (n=28) had higher prevalence of mitral annular disjunction (MAD) (80% vs. 35.7%, P=0.006), Pickelhaube sign (60% vs. 8.3%, P=0.008), SADN > 2 mm (69.6% vs. 14.3%, P=0.001) as well as higher absolute longitudinal strain values in basal and mid segments of inferior and inferolateral wall than patients with SCA and non-iMVP without other defined structural heart diseases (SHD(-)) (n=14). The differences were also observed in comparison to control group. The cumulative incidence of appropriate implantable cardioverter-defibrillator shocks within 6 years was 62% in patients with iMVP and 23% in the group with non-iMVP SHD(-). Echocardiographic findings such as MAD, SADN, Pickelhaube sign and increased segmental strain may be useful in the assessment of relation between SCA and MVP. Malignant arrhythmias often recur in SCA survivors with iMVP.

Mac-2 binding protein glycosylation isomer as a novel predictor of early recurrence after resection for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) frequently recurs after radical resection, resulting in a poor prognosis. This study assessed the prognostic value of Mac-2 binding protein glycosylation isomer (M2BPGi) for early recurrence (ER) in patients with HCC. Patients who underwent radical resection for HCC between 2015 and 2021. HCC recurrence within one year after curative resection was defined as ER. The 150 patients were divided into two groups: non-ER (116, 77.3%) and ER (34, 22.7%). The ER group had a lower overall survival rate (p < 0.0001) and significantly higher levels of M2BPGi (1.06 vs. 2.74 COI, p < 0.0001) than the non-ER group. High M2BPGi levels (odds ratio [OR] 1.78, 95% confidence interval [CI] 1.31-2.41, p < 0.0001) and a large tumor size (OR 1.31, 95% CI, 1.05-1.63; p = 0.0184) were identified as independent predictors of ER. M2BPGi was the best predictor of ER according to a receiver operating characteristic (ROC) analysis (area under the ROC curve 0.82, p < 0.0001). M2BPGi can predict ER after surgery and is useful for risk stratification in patients with HCC.