Update review on SUDEP: Risk assessment, background & seizure detection devices.

This review focusses on sudden unexpected death in epilepsy patients (SUDEP) and incorporates risk stratification (through SUDEP risk factors and SUDEP risk scores), hypotheses on the mechanism of SUDEP and eligible seizure detection devices (SDDs) for further SUDEP prevention studies. The main risk factors for SUDEP are the presence and the frequency of generalized tonic-clonic seizures (GTC). In Swedish population-based case control study, the Odds ratio of the presence of GTC in the absence of bedroom sharing is 67. SUDEP risk scoring systems express a score that represents the cumulative presence of SUDEP risk factors, but not the exact effect of their combination. We describe 4 of the available scoring systems: SUDEP-7 inventory, SUDEP-3 inventory, SUDEP-ClinicAl Risk scorE (SUDEP-CARE score) and Kempenhaeghe SUDEP risk score. Although they all include GTC, their design is often different. Three of 4 scoring systems were validated (SUDEP-7 inventory, SUDEP-3 inventory and SUDEP-CARE score). None of the available scoring systems has been sufficiently validated for the use in a general epilepsy population. Plausible mechanisms of SUDEP are discussed. In the MORTEMUS-study (Mortality in Epilepsy Monitoring Unit Study), SUDEP was a postictal cardiorespiratory arrest after a GTC. The parallel respiratory and cardiac dysfunction in SUDEP suggests a central dysfunction of the brainstem centers that are involved in the control of respiration and heart rhythm. In the (consequent) adenosine serotonin hypotheses SUDEP occurs when a postictal adenosine-mediated respiratory depression is not compensated by the effect of serotonin. Other (adjuvant) mechanisms and factors are discussed. Seizure detection devices (SDDs) may help to improve nocturnal supervision. Five SDDs have been validated in phase 3 studies for the detection of TC: Seizure Link®, Epi-Care®, NightWatch, Empatica, Nelli®. They have demonstrated a sensitivity of at least 90 % combined with an acceptable false positive alarm rate. It has not yet been proven that the use will actually lead to SUDEP prevention, but clinical experience supports their effectiveness.

Development and validation of a relatively accurate gastric cancer high-risk group screening scoring system in urban residents.

PURPOSE: Our study aimed to develop a relatively accurate gastric cancer (GC) screening score system for urban residents and to validate the screening efficacy. METHODS: The present study included a derivation cohort (n = 3406) and a validation cohort (n = 868) of urban residents. Applying the full-stack engineering intelligent system platform of Hualian Health Big Data of Shandong University, the clinical physical examination data of subjects were collected. Univariate and multivariate analyses were used to identify risk factors for GC, and subsequently, an optimal prediction rule was established to create three distinct scoring systems. RESULTS: In the GC-risk scoring system I, age, plateletocrit (PCT), carcinoembryonic antigen (CEA), glucose, albumin, creatinine were independent risk factors of GC, with scores ranging from 0 to 28 and optimal cut-off was 15.5. The second scoring system consisted of age, PCT, RDW-CV, CEA, glucose, albumin, and creatinine, with scores ranging from 0 to 31. The optimal cut-off point was determined to be 15.5. The scoring system III comprise of age, sex, PCT, RDW CV, CEA, glucose, with scores ranging from 0 to 21 and optimal cut-off was 10.5. All three scoring systems demonstrated excellent discrimination for GC, achieving an AUC of 0.884, 0.89, and 0.876, respectively. In external validation, the AUC values were 0.654, 0.658, and 0.714. Notably, the GC-risk scoring system III exhibited the highest screening efficiency. CONCLUSIONS: Urban residents benefited from the effective and verified GC-risk scoring systems, which demonstrated excellent performance in identifying individuals with an elevated risk of GC.

Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients.

BACKGROUND: Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. METHODS: We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. RESULTS: Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. CONCLUSIONS: This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.

Prognostic aging gene-based score for colorectal cancer: unveiling links to drug resistance, mutation burden, and personalized treatment strategies.

OBJECTIVE: Colorectal cancer (CRC) is characterized by high incidence and mortality rates worldwide. In this study, we present a novel aging-related gene-based risk scoring system (Aging score) as a predictive tool for CRC prognosis. METHOD: We identified prognostic aging-related genes using univariate Cox regression analysis, revealing key biological processes in CRC progression. We then constructed a robust prognostic model using LASSO and multivariate Cox regression analyses, including four critical genes: CAV1, FOXM1, MAD2L1, and WT1. RESULT: The Aging score demonstrated high prognostic performance across the training, testing, and entire TCGA-CRC datasets, proving its reliability. High-risk patients identified by the Aging score had significantly shorter overall survival times than low-risk patients, indicating its potential for patient stratification and personalized treatment. The Aging score remained an independent prognostic factor compared to age, gender, and tumor stage. Additionally, the score was linked to tumor mutation burden and microsatellite instability, indicators of immune checkpoint inhibitor response. High-risk patients also showed higher estimated IC50 values for common chemotherapeutic drugs, suggesting possible treatment resistance. CONCLUSION: Our findings highlight the Aging score's potential to enhance clinical decision-making and pave the way for personalized CRC management.

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis.

Background: Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). Methods: We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. Results: A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. Conclusions: This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

"Predicting diabetic kidney disease in youth with type 1 diabetes: Insights from genetic risk assessment".

OBJECTIVE: Diabetic kidney disease (DKD) is influenced by multiple factors, yet its precise progression mechanisms remain largely unclear. This study aimed to create a clinical risk-scoring system based on genetic polymorphisms in the AFF3, CARS, CERS2, ERBB4, GLRA3, RAET1L, TMPO, and ZMIZ1 genes. METHODS: The study included a DKD group diagnosed with diabetic kidney disease before age 18 and a WDC group matched by age, gender, and age at diabetes diagnosis. Genetic data and clinical data from diabetes diagnosis to moderately increased albuminuria (MIA) detection were compared between the groups. RESULTS: Among 43 DKD cases, 22 were girls and 21 were boys. At MIA diagnosis, mean body weight SDS was -0.24 ± 0.94, height SDS was 0.34 ± 1.15, and BMI SDS was -0.26 ± 0.94. Systolic blood pressure was at the 72nd percentile (2-99), and diastolic blood pressure was at the 74th percentile (33-99). Significant differences in rs267734, rs267738, and rs942263 polymorphisms were found between DKD and non-complication diabetic groups (13[30.2 %] vs 5[11.6 %], p = 0.034; 14[32.6 %] vs 5[11.6 %], p = 0.019; 26[60.5 %] vs 40[93 %], p < 0.001). CONCLUSION: Several factors were identified as significant in DKD onset, including low follow-up weight SDS, elevated diastolic blood pressure, presence of rs267734, and absence of rs942263 polymorphisms. The model demonstrated a specificity of 81.4 % and a sensitivity of 74.4 %.

Integrative analysis regarding the correlation between collagen-related genes and prostate cancer.

PURPOSE: Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen's critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. METHODS: Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. RESULTS: Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. CONCLUSION: The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing.

A novel scoring system based on sIL-2R for predicting IVIG resistance in Chinese children with KD.

OBJECTIVE: This study aimed to develop a novel scoring system utilizing circulating interleukin (IL) levels to predict resistance to intravenous immunoglobulin (IVIG) in Chinese patients with Kawasaki disease (KD). We further compared this scoring system against six previously established scoring methods to evaluate its predictive performance. METHODS: A retrospective analysis was conducted on KD patients who were treated at the cardiovascular medical ward of our institution from January 2020 to December 2022. Six scoring systems (Egami, Formosa, Harada, Kobayashi, Lan and Yang) were analyzed, and a new scoring system was developed based on our data. RESULTS: In our study, 521 KD patients were recruited, 42 of whom (8.06%) were identified as resistant to IVIG. Our study indicated that IVIG-resistant KD patients were at an increased risk for the development of coronary arterial lesions (CALs) (P = 0.001). The evaluation of IVIG resistance using various scoring systems revealed differing levels of sensitivity and specificity, as follows: Egami (38.10% and 88.52%), Formosa (95.24% and 41.13%), Harada (78.57% and 43.22%), Kobayashi (66.67% and 74.95%), Lan (66.67% and 73.49%), and Yang (69.05% and 77.24%). Our novel scoring system utilizing sIL-2R demonstrated the highest sensitivity and specificity of 69.29% and 83.91%, respectively, and calibration curves indicated a favorable predictive accuracy of the model. CONCLUSION: Our newly developed scoring system utilizing sIL-2R demonstrated superior predictive performance in identifying IVIG resistance among Chinese patients with KD.

Rationale and Design of SCOT-HEART 2 Trial: CT Angiography for the Prevention of Myocardial Infarction.

Coronary artery disease continues to be the leading cause of death globally. Identifying patients who are at risk of coronary artery disease remains a public health priority. At present, the focus of cardiovascular disease prevention relies heavily on probabilistic risk scoring despite no randomized controlled trials demonstrating their efficacy. The concept of using imaging to guide preventative therapy is not new, but has previously focused on indirect measures such as carotid intima-media thickening or coronary artery calcification. In recent trials, patients found to have coronary artery disease on computed tomography (CT) coronary angiography were more likely to be started on preventative therapy and had lower rates of cardiac events. This led to the design of the SCOT-HEART 2 (Scottish Computed Tomography of the Heart 2) trial, which aims to determine whether screening with the use of CT coronary angiography is more clinically effective than cardiovascular risk scoring to guide the use of primary preventative therapies and reduce the risk of myocardial infarction.

A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring.

OBJECTIVE: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification. MATERIALS AND METHODS: The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients. RESULTS: Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands. DISCUSSION: According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition. CONCLUSION: Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.

Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.

PURPOSE: To develop a comorbidity risk score specifically for lung resection surgeries. METHODS: We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI). RESULTS: The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset. CONCLUSIONS: The new machine learning model could predict postoperative complications with acceptable accuracy. CLINICAL REGISTRATION NUMBER: 2020-0375.

Hibiscus score: Developing and validating a predictive tool for intravenous immunoglobulin treatment resistance in Malaysian children with Kawasaki disease.

BACKGROUND: Children with intravenous immunoglobulin (IVIG) resistant Kawasaki disease (KD) are at higher risk of developing coronary artery (CA) aneurysm. Early identification of high-risk patients using a predictive tool would allow for earlier interventions to prevent cardiac complications. METHODS: Children with KD who were admitted to five selected hospitals in Malaysia between 2008 and 2018 and received 2 g/kg of IVIG within 10 days from the onset of illness were included. Predictors of IVIG resistance in KD were determined using multiple logistic regression analysis. An optimal cut-off point was set using receiver operative characteristic curve and a final multiple logistic regression analysis was performed entering these cut-off points. A new scoring system was constructed. RESULTS: A total of 276 patients were included. IVIG resistance occurred in 9.1 % of them. Total bilirubin [OR 7.37; 95 % CI (2.18, 24.83)], male sex [OR 0.34; 95 % CI (0.10, 1.19)], C-reactive protein (CRP) [OR 0.17; 95 % CI (0.02, 1.38)] and neutrophils [OR 0.25; 95 % CI (0.05, 1.21)] were found to be significant predictors for IVIG resistance. The findings led to the development of a new predictive tool called the Hibiscus score, which scored 1 point each for neutrophils ≥60 %, CRP ≥80 mg/L, and male sex, while total bilirubin ≥9.4 µmol/L scored 2 points. A cut-off point of ≥4 with this prediction score yielded a sensitivity of 78.9 % and specificity of 80.5 %, with area under the curve of 0.835 [95 % CI (0.752, 0.919)]. CA aneurysms occurred in 6.7 % of IVIG responders and 32 % of IVIG-resistant children (p < 0.001). CONCLUSION: The findings suggest that the Hibiscus score has a higher predictive power than the existing scoring systems for IVIG resistance in children with KD in Malaysia. However, external validation is required to enable its use to guide treatment decisions.

ABCD2-I Score Predicts Unplanned Emergency Department Revisits within 72 Hours Due to Recurrent Acute Ischemic Stroke.

BACKGROUND: the ABCD2 score is valuable for predicting early stroke recurrence after a transient ischemic attack (TIA), and Doppler ultrasound can aid in expediting stroke triage. The study aimed to investigate whether combining the ABCD2 score with carotid duplex results can enhance the identification of early acute ischemic stroke after TIA. METHODS: we employed a retrospective cohort design for this study, enrolling patients diagnosed with TIA who were discharged from the emergency department (ED). The modified ABCD2-I (c50) score, which incorporates a Doppler ultrasound assessment of internal carotid artery stenosis > 50%, was used to evaluate the risk of acute ischemic stroke within 72 h. Patients were categorized into three risk groups: low risk (with ABCD2 and ABCD2-I scores = 0-4), moderate risk (ABCD2 score = 4-5 and ABCD2-I score = 5-7), and high risk (ABCD2 score = 6-7 and ABCD2-I score = 8-9). RESULTS: between 1 January 2014, and 31 December 2019, 1124 patients with new neurological deficits were screened, with 151 TIA patients discharged from the ED and included in the analysis. Cox proportional hazards analysis showed that patients in the high-risk group, as per the ABCD2-I (c50) score, were significantly associated with revisiting the ED within 72 h due to acute ischemic stroke (HR: 3.12, 95% CI: 1.31-7.41, p = 0.0102), while the ABCD2 alone did not show significant association (HR: 1.12, 95% CI: 0.57-2.22, p = 0.7427). CONCLUSION: ABCD2-I (c50) scores effectively predict early acute ischemic stroke presentations to the ED within 72 h after TIA.

Making Our Hospitals a Safe Workplace: Hazard Identification and Risk Assessment at a Tertiary-Level Public Hospital in Eastern India.

BACKGROUND: Hospitals are complex places with a large number of employees, patients, furniture, equipment, etc. Healthcare workers (HCWs), patients, or the general public are vulnerable to injuries and illness due to unseen hazards at the workplace. This study aims to identify the hazards and assess the risks at a hospital to ensure safety for HCWs, patients, and the public and generate awareness about the same. It helps in reducing the financial obligation of the institution due to the treatment of illnesses of staff, absenteeism, and service disruption and slows down manpower turnover. Hazard Identification and Risk Assessment (HIRA) helps reduce human errors and promote safe behavior. OBJECTIVE: This study aims to identify and study the hazards in a hospital, assess the risks associated with the hazards, and recommend methods to reduce or eliminate the hazards based on the outcomes of the study. METHODOLOGY: An observational study was conducted at a 1000-bed tertiary-level teaching public sector hospital in eastern India. A checklist was used for direct observation, conducting staff interviews, and document reviews. A risk scoring tool was used, and hazards were ranked as per the risk score. RESULTS: Thirty-eight hazards were identified in the study and classified under the categories of natural, physical, chemical, biological, ergonomic, psychological, and safety. The fire risk and occurrence of cyclones had the highest risk scores. CONCLUSIONS: The study identified hazards through direct observations, record reviews, and staff interviews. These findings can guide the prioritization of areas requiring necessary action in risk reduction, ensuring a safe workplace for healthcare workers (HCWs), patients, and the public. They can also help the institution shift from a reactive approach to a proactive method for HCW safety.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Prognostic value of CD8+T cells related genes and exhaustion regulation of Notch signaling pathway in hepatocellular carcinoma.

Immunotherapy has emerged as the primary treatment modality for patients with advanced Hepatocellular carcinoma (HCC). However, its clinical efficacy remains limited, benefiting only a subset of patients, while most exhibit immune tolerance and face a grim prognosis. The infiltration of immune cells plays a pivotal role in tumor initiation and progression. In this study, we conducted an analysis of immune cell infiltration patterns in HCC patients and observed a substantial proportion of CD8+T cells. Leveraging the weighted gene co-expression network analysis (WGCNA), we identified 235 genes associated with CD8+T cell and constructed a risk prediction model. In this model, HCC patients were stratified into a high-risk and low-risk group. Patients in the high-risk group exhibited a lower survival rate, predominantly presented with intermediate to advanced stages of cancer, displayed compromised immune function, showed limited responsiveness to immunotherapy, and demonstrated elevated expression levels of the Notch signaling pathway. Further examination of clinical samples demonstrated an upregulation of the Notch1+CD8+T cell exhaustion phenotype accompanied by impaired cytotoxicity and cytokine secretion functions that worsened with increasing Notch activation levels. Our study not only presents a prognostic model but also highlights the crucial involvement of the Notch pathway in CD8+T cell exhaustion-a potential target for future immunotherapeutic interventions.

The value of LCI-based modified Kyoto classification risk scoring system in predicting the risk of early gastric cancer.

OBJECTIVE: To study and compare the value of the Kyoto classification risk scoring system and the modified Kyoto classification risk scoring system based on linked color imaging (LCI) in predicting the risk of early gastric cancer. METHODS: One hundred and fifty patients with pathologically confirmed non-cardia early gastric cancer by endoscopic LCI and 150 non-gastric cancer patients matched for age and gender were included. Basic patient data and whole gastric endoscopic images under LCI were collected, and the images were scored according to the LCI-based Kyoto classification risk scoring system and the LCI-based modified Kyoto classification risk scoring system. RESULTS: Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based modified Kyoto classification risk scoring system had a higher AUC for predicting the risk of early gastric cancer (0.723 vs. 0.784, p = 0.023), with a score of ≥3 being the best cutoff value for predicting the risk of early gastric cancer (sensitivity 61.33%, specificity 86.00%), and scores of 3 to 5 were significantly associated with early gastric carcinogenesis significantly (OR = 9.032, 95% CI: 4.995-16.330, p < 0.001). CONCLUSIONS: Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based Kyoto modified classification risk scoring system has a better value for predicting the risk of early gastric cancer, and the score of 3 to 5 is a high-risk factor for the risk of early gastric cancer development, which is more strongly correlated with the risk of early gastric cancer.

Scores predicting atrial fibrillation after mitral valve surgery: Do we need a more specific score?

Background: Atrial fibrillation after cardiac surgery (POAF) is associated with increased morbidity and mortality. Several scores were used to predict POAF, with variable results. Thus, this study assessed the performance of several scoring systems to predict POAF after mitral valve surgery. Additionally, we identified the risk factors for POAF in those patients. Methods: This retrospective cohort included 1381 recruited from 2009 to 2021. The patients underwent mitral valve surgery, and POAF occurred in 233 (16.87%) patients. The performance of CHADS2, CHA2DS2-VASc, POAF, EuroSCORE II, and HATCH scores was evaluated. Results: The median age was higher in patients who developed POAF (60 vs. 54 years; p < .001). CHA2-DS2-VASc, POAF, EuroSCORE II, and HATCH scores significantly predicted POAF, with areas under the curve of the receiver operator curve (AUCROC) of 0.56, 0.61, 0.58, and 0.54, respectively. We identified age > 58 years, body mass index > 28 kg/m2, creatinine clearance < 90 mL/min, reoperative surgery, and preoperative inotropic and intra-aortic balloon pump use as predictors of POAF. We constructed a score from these variables (PSCC-AF). A score > 2 significantly predicted POAF (p < .001). The AUCROC of this score was 0.67, which was significantly higher than the AUCROC of the POAF score (p = .009). Conclusion: POAF after mitral valve surgery can be predicted based on preoperative patient characteristics. The new PSCC-AF score significantly predicted POAF after mitral valve surgery and can serve as a bedside diagnostic tool for POAF risk screening. Further studies are needed to validate the PSCC-AF-mitral score externally.

Machine learning-based survival analysis approaches for predicting the risk of pneumonia post-stroke discharge.

BACKGROUND: Post-stroke pneumonia (PSP) is common among stroke patients. PSP occurring after hospital discharge continues to increase the risk of poor functional outcomes and death among stroke survivors. Currently, there is no prediction model specifically designed to predict the occurrence of PSP beyond the acute stage of stroke. This study aimed to explore the use of machine learning (ML) methods in predicting the risk of PSP after hospital discharge. METHODS: This study analyzed data from 5,754 hospitalized stroke patients. The dataset was randomly divided into a training set and a holdout test set, with a ratio of 80:20. Several clinical and laboratory variables were utilized as predictors and different ML algorithms were employed to model time-to-event data. The ML model's predictive performance was compared to existing risk-scoring systems. A model-agnostic method based on Shapley additive explanations was utilized to interpret the ML model. RESULTS: The study found that 5.7% of the study patients experienced pneumonia within one year after discharge. Based on repeated 5-fold cross-validation on the training set, the random survival forest (RSF) model had the highest C-index among the various ML algorithms and traditional Cox regression analysis. The final RSF model achieved a C-index of 0.787 (95% confidence interval: 0.737-0.840) on the holdout test set, outperforming five existing risk-scoring systems. The top three important predictors were the Glasgow Coma Scale score, age, and length of hospital stay. CONCLUSIONS: The RSF model demonstrated superior discriminative ability compared to other ML algorithms and traditional Cox regression analysis, suggesting a non-linear relationship between predictors and outcomes. The developed ML model can be integrated into the hospital information system to provide personalized risk assessments.