Value Of Risk Score Research Articles

Predictive value of the hospital frailty risk score in perioperative complications of artificial hip and knee arthroplasty in elderly patients

Background Although artificial hip and knee arthroplasty has been widely used, the incidence of perioperative complications remains relatively high due to factors such as long operation time and large surgical incisions. However, the Hospital Frailty Risk Score (HFRS) clinical value for patients undergoing artificial hip and knee arthroplasty in China is not yet clear. This study aims to explore its clinical value in this population. Objective To explore predictive value of the HFRS in perioperative complications of artificial hip and knee arthroplasty in elderly patients. Methods Elderly patients who underwent artificial hip and knee arthroplasty in our hospital from March 2020 to March 2022 were selected as the study subjects. The patients were divided into the non-frail risk group (HFRS <5 points) and the frail risk group (HFRS ≥5 points) on the basis of the literature grading method. 5-factor modified frailty index (mFI-5) and Charlson Comorbidity Index (CCI) scores were assessed. General data including age, sex, ASA classification, preoperative course, and surgical time were collected through the electronic medical record system. The occurrence of postoperative deep venous thrombosis, periprosthetic infection, hematoma, anemia, and overall complications was recorded. Hospitalization days, surgical costs, and total treatment costs were also calculated for all patients. Results There were no significant differences in age, sex, ASA classification, preoperative course, or surgical time between the two groups ( p > 0.05). The frail risk group had significantly higher mFI-5 and CCI scores ( p < 0.05). This group also had a higher incidence of deep venous thrombosis and overall complications ( p < 0.05), but similar rates of periprosthetic infection, hematoma, and anemia ( p > 0.05). ROC analysis showed HFRS had the highest predictive value for postoperative complications (AUC = 0.851) compared to mFI-5 (0.786) and CCI (0.739). Surgical costs were similar ( p > 0.05), but the frail group had longer hospital stays and higher total treatment costs ( p < 0.05). Conclusion HFRS has better predictive value for perioperative complications in elderly patients undergoing artificial hip and knee arthroplasty compared to mFI-5 and CCI scores, and it can be used for predicting perioperative complications in elderly patients with this surgery.

Construction of AML prognostic model with CYP2E1 and GALNT12 biomarkers based on golgi- associated genes.

Acute myeloid leukaemia (AML) was originally an aggressive malignancy of the bone marrow and one of the deadliest forms of acute leukaemia. The 5-year mortality benefit for patients with AML was only 28.3%. Moreover, a large proportion of patients experienced frequent relapses even after remission, thus predicting a bleak prognosis. This research employed differential expression analysis of AML and normal samples sourced from the GSE30029 database, as well as weighted gene co-expression network analysis (WGCNA). We discovered differential golgi apparatus-related genes (DGARGs) specifically associated with AML. Via regressivity analysis and machine learning algorithm, the cancer genome atlas-acute myeloid leukemia (TCGA-AML) cohort developed a prognostic model using characteristic prognostic genes. The performance value of risk score was analysed using Kaplan-Meier (KM) curves and Cox regression. A predictive nomogram was developed to assess the outcome. The association between prognostic trait genes and the immune microenvironment was examined. Finally, immunoactivity and drug susceptibilities were evaluated in various risk groups identified by prognostic signature genes. A total of 77 DGARGs were obtained by differential expression analysis with WGCNA analysis. Following univariate Cox regression and LASSO regression, six prognostic signature genes (ARL5B, GALNT12, MANSC1, PDE4DIP, NCALD and CYP2E1) were utilized to develop a prognostic model. This model was calibrated via KM survival and receiver operating characteristic (ROC) curves, which concluded that it had a predictive impact on the prognosis of AML. Further analysis of the tumour microenvironment in AML patients demonstrated notable variances in immune cell APC_co_inhibition, CCR, Parainflammation, Type_I_IFN_Response, and Type_II_IFN_Response between the high-risk and low-risk groups. A prognostic model was devised in this study using six prognostic genes linked to the Golgi apparatus. The exactness of the model in guiding the prognosis of AML was established. As a result of expression validation, CYP2E1 and GALNT12 will be used as biomarkers to offer fresh insights into the prognosis and treatment of AML patients.

Immune-oncology targets and therapeutic response of cell pyroptosis-related genes with prognostic implications in neuroblastoma

ObjectiveConstruction of a neuroblastoma (NB) prognostic predictive model based on pyroptosis-related genes (PRGs) to improve individualized management of NB patients.MethodsThe NB cohort GSE49711 was obtained from the Gene Expression Omnibus (GEO) database, and a total of 498 patients were enrolled into the study, which were randomized into a training set and a test set at a ratio of 1:1, with 250 patients in the training set and 248 patients in the test set. A risk prediction model was constructed using the training set, and the GSE49711 cohort and test set were used as internal validation to verify the reliability of the model. Independent predictors associated with prognosis were screened using univariate and multivariate COX regression analyses, and risk score models were constructed. Single-cell gene set enrichment analysis (ssGSEA) was used to assess the relationship between PRGs and the tumor immune microenvironment. Nomograms were constructed to extend the clinical usability of the model and the reliability of the model was verified using ROC curves and calibration curves. Protein interaction networks of risk genes were mapped using the String database, and the expression of PRGs in NB cell lines was staged using the CCLE database.ResultsA prognostic model was first developed with the training set: the risk score formula was (− 0.30 × GSDMB) + (− 0.46 × IL-18) + (− 0.21 × NLRP3) + (0.56 × AIM2). Patients were categorized into high- and low-risk groups based on the median risk score value. Survival analysis showed that NB patients in the high-risk group had a significantly lower survival rate than those in the low-risk group (P < 0.001). In both the GSE49711 overall cohort and the test cohort, survival analyses showed that patients in the high-risk group had significantly lower survival than those in the low-risk group (P < 0.001). Single-cell gene set enrichment analysis was used to assess the relationship between PRGs and the tumor immune microenvironment. Time-dependent ROC curves assessed the predictive performance of the nomogram in 5-, 7.5-, and 10-year survival with areas under the curve (AUC) of 0.843, 0.802 and 0.797, respectively. The calibration curves show good clinical predictive performance for nomograms.ConclusionThe results suggest that PRGs may serve as a novel prognostic marker for NB patients to provide new immunotherapeutic targets for the clinical treatment of NB patients.

The added value of deep learning to submaximal exercise electrocardiogram for the 10-year prediction of major adverse cardiovascular and cerebrovascular events

Abstract Background The added value of exercise ECG data to traditional risk factors in predicting cardiovascular events remains unclear. Deep learning of ECG signals has demonstrated state-of-the-art performance in detecting subtle abnormalities indicative of cardiovascular disease. We investigated whether deep learning analysis of exercise ECGs could improve the prediction of major cardiovascular and cerebrovascular events (MACCE) with respect to established risk models in a large population-based cohort. Purpose To assess the added value of submaximal exercise ECG measurements to established cardiovascular risk prediction models. Methods We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We created two deep neural network ECG risk scores: one derived from conventional ECG parameters, measured at rest, peak exercise and late-stage recovery, and another based on the complete ECG (Q-ECG). We assessed the added value of conventional ECG parameters and Q-ECG risk scores to the UK's current prediction algorithm (QRISK3). We estimate the association between ECG parameters and MACCE, using Cox Proportional hazard models, following adjustment for traditional risk factors. All models were internally validated via 5-fold cross-validation and 1000 bootstrap iterations. Predictive performance was evaluated using Harrel's C-index, Net Reclassification Index (NRI) and net benefit. Findings: Incident MACCE was reported in 4,082 (9.9%) individuals in the study population and 3,463(9.7%) individuals with valid ECG parameters over a median follow-up period of 12.5 years. We found combined conventional ECG and Q-ECG scores were independently associated with MACCE, following adjustment for multiple testing: adjusted hazard ratio [HZ] = 1.76 (95% CI:1.63-1.91); HZ = 1.14 (95% CI:1.10-1.18), respectively, per standard deviation increase. Both conventional ECG parameters and Q-ECG were predictive of MACCE, independent of clinical risk factors, C-index = 0.64 (95%CI 0.63-0.65); net benefit = 0.09(95% CI 0.07 - 0.11) and C-index = 0.56 (95% CI 0.55-0.57); net benefit = 0.07(95% CI 0.05 - 0.09). ECG measurement's modestly improved model discrimination over the bassline QRISK3 risk score when combined with QRISK3 risk factors for conventional markers and Q-ECG score, respectively; ΔC-index 0.03 (95% CI: 0.02 – 0.04) and ΔC-index 0.03 (95% CI: 0.02 – 0.03); However, we observed no significant improvements in classification at the current recommended threshold of 10%. Conclusion In individuals without a history of prior cardiovascular disease, ECG measures independently predict the risk of MACCE. When combined with QRISK3, neural-network-derived ECG risk scores marginally improve cardiovascular risk prediction over QRISK3.

Cardiovascular events in CML patients treated with nilotinib: validation of the HFA-ICOS baseline risk score

Abstract Background The therapeutic landscape of chronic myeloid leukaemia (CML) has been transformed by tyrosine kinase inhibitors (TKI). Nilotinib, a potent 2nd generation TKI, showed higher rates of major molecular response than imatinib, however was associated with higher rates of cardiovascular (CV) toxicity. Objectives To describe the CV events associated with nilotinib in a real-world CML population and assess the predictive value of the HFA-ICOS baseline risk score in clinical practice. Methods The HFA-ICOS baseline risk was calculated (low, medium, high /very high) for patients with CML treated with nilotinib between 2006 and 2021. The incidence of all CV events, ischaemic events and Major CV Adverse Events (MACE) were reviewed and compared among the different risk groups. Results A total of two hundred and twenty-nine eligible patients were included in the analysis. The incidence of CV events, ischaemic events, and MACE, while on nilotinib, were 20.9% (95% CI: 15.7% to 26.2%), 12.7% (95% CI: 8.4% to 16.9%), and 12.2% (95% CI: 7.9% to 16.5%) respectively, following a median duration of treatment with nilotinib of 34.4 months for the entire cohort. Patients with a higher HFA-ICOS baseline risk score had higher rates of all CV events (low: 11.2%, medium: 28.2% [HR: 2.8, 95% CI: 1.2 to 6.2], high/very high: 32.4% [HR: 3.5, 95% CI: 1.8 to 6.8]), ischaemic events (low: 5.2%, medium: 17.9% [HR: 3.7, 95% CI: 1.2 to 10.9] , high/very high: 21.6% [HR: 3.7, 95% CI: 1.4 to 9.7]) and MACE (low: 7.8% , medium: 10.3% [HR:1.2, 95% CI: 0.38 to 4.1], high/very high: 20.2% [HR: 2.2, 95% CI: 0.9 to 5.3]). The predictive tool presented an 89% NPV, and an area under the ROC curve: 0.65 for all CV events, an NPV of 95% and AUC 0.68 for ischaemic events and NPV 92% and AUC 0.62 for MACE. Conclusions The HFA-ICOS risk stratification tool has been shown to be an efficient discriminator at low, medium and high/very high risk of developing cardiovascular events, with an overall positive trend towards increasing cardiotoxicity rates with rising risk categories. This study provides evidence to support the use of this predictive tool in nilotinib treated patients, as it also underscores the potential for further improvement in the overall discriminatory ability of the model.

Identification of m6A-related lncRNAs prognostic signature for predicting immunotherapy response in cervical cancer

BackgroundN6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) play a crucial role in the cancer progression and immunotherapeutic efficacy. The potential function of m6A-related lncRNAs signature in cervical cancer has not been systematically clarified. MethodsRNA-seq and the clinical data of cervical cancer were extracted from The Cancer Genome Atlas. All of the patients were randomly classified into training and testing cohorts. The m6A-related lncRNAs prognostic model was constructed by LASSO regression using data in the training cohort.The predictive value of the signature was validated in the whole cohort and testing cohort. Cervical cancer patients were divided into low- and high-risk subgroups by the median value of risk scores. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used for further evaluation. We also examined the immune response and potential drug sensitivity targeting this model. ResultsSeventy-nine prognostic m6A-related lncRNAs were screened. The risk model comprising four m6A-related lncRNAs (AL139035.1, AC015922.2, AC073529.1, AC008124.1) was identified and verified as an independent prognostic predictor of cervical cancer. A nomogram based on age, tumor grade, clinical stage, TNM stage, and four m6A-related lncRNAs risk signatures was generated. It displayed good accuracy and reliability in predicting the overall survival of patients with CC. Based on our risk model, cervical cancer patients with potential immunotherapy benefits from the candidate drugs could be effectively screened. ConclusionThe four m6A-related lncRNAs signature may provide new targets and allow the prediction of immunotherapy response, which can assist developing individualized treatment for cervical cancer.

Prediction of complications after laparoscopic partial nephrectomy: feasibility of E‑PASS score.

With significant advancements in laparoscopic procedures and tools over the last decade, the partial nephrectomy (PN) surgical approach has gradually transformed from open to laparoscopic partial nephrectomy (LPN). Although numerous studies evaluating the postoperative complications of laparoscopic partial nephrectomy can be found in the literature, a scoring system that addresses both preoperative physical condition and intraoperative risk factors has not yet been established. The Estimation of Physiological Ability and Surgical Stress (E-PASS) score is a scoring system that considers both the patient's preoperative condition and intraoperative variables to predict the risk of postoperative complications in surgically treated patients. This study aimed to assess the applicability of the E-PASS scoring system for predicting postoperative complications following LPN. We analyzed data of 196 patients who underwent LPN between 2017 and 2024 by a single surgeon in this single-center retrospective study. Demographic data of the patients, parameters reflecting patients' preoperative physical condition, and intraoperative risk factors were recorded. Postoperative complications were classified using the Clavien-Dindo system. The E-PASS score and its sub-scores were calculated for each patient. Various degrees of complications developed in 46 patients (23.5%). Patients who developed complications had significantly higher mean age, performance scores, tumor sizes, length of hospital stay, and E-PASS values. The cutoff value of the E-PASS Comprehensive Risk Score (CRS) for predicting the development of postoperative complications was determined to be -0.1692 (AUC = 0.676; 95% CI 0.581-0.771; p < 0.001). According to the results of the multivariate analysis, the presence of E-PASS CRS > -0.1692 (OR 4.872; 95% CI 2.384-9.957; p < 0.001) and tumor size (OR 1.021; 95% CI 1-1.041; p = 0.047) was identified as independent risk factors predicting postoperative complications. Patients with a CRS higher than the cutoff value had a 4.87 times higher rate of postoperative complications after LPN. The E-PASS scoring model successfully predicts postoperative complications in patients undergoing LPN by utilizing preoperative data on the patient's physical condition and surgical risk factors. The E-PASS score and its sub-scores can serve as objective criteria to determine the risk of postoperative complications both preoperatively and immediately postoperatively.

The Relationship of Trace Element Levels with Obesity and Cardiovascular Health

Objective: One of the most important factors affecting cardiovascular health is obesity. Trace elements, which play a role in every stage of metabolism, are also related to our cardiovascular health. The aim of this study was to determine serum trace element levels in individuals and to examine the relationship between trace elements and cardiovascular risk. Methods: This cross-sectional study was conducted with individuals who applied to the outpatient clinic between 01.03.2022-31.04.2022. Sociodemographic characteristics, BMI, and Framingham risk score values were recorded. Serum iron, zinc, copper and selenium levels were analyzed in the laboratory. Results: A total of 180 individuals were included in the study. Of the individuals, 33.3% were overweight and 32.8% were obese. The mean Framingham Risk Score (FRS) was 9.31±7.99 (1-37). FRS values of normal weight individuals (7.84±3.05) were lower than the mean FRS values of overweight (8.80±3.39) and obese (13.39 ± 6.24) groups (p

Effect of aberrant fructose metabolism following SARS-CoV-2 infection on colorectal cancer patients' poor prognosis.

Most COVID-19 patients have a positive prognosis, but patients with additional underlying diseases are more likely to have severe illness and increased fatality rates. Numerous studies indicate that cancer patients are more prone to contract SARS-CoV-2 and develop severe COVID-19 or even dying. In the recent transcriptome investigations, it is demonstrated that the fructose metabolism is altered in patients with SARS-CoV-2 infection. However, cancer cells can use fructose as an extra source of energy for growth and metastasis. Furthermore, enhanced living conditions have resulted in a notable rise in fructose consumption in individuals' daily dietary habits. We therefore hypothesize that the poor prognosis of cancer patients caused by SARS-CoV-2 may therefore be mediated through fructose metabolism. Using CRC cases from four distinct cohorts, we built and validated a predictive model based on SARS-CoV-2 producing fructose metabolic anomalies by coupling Cox univariate regression and lasso regression feature selection algorithms to identify hallmark genes in colorectal cancer. We also developed a composite prognostic nomogram to improve clinical practice by integrating the characteristics of aberrant fructose metabolism produced by this novel coronavirus with age and tumor stage. To obtain the genes with the greatest potential prognostic values, LASSO regression analysis was performed, In the TCGA training cohort, patients were randomly separated into training and validation sets in the ratio of 4: 1, and the best risk score value for each sample was acquired by lasso regression analysis for further analysis, and the fifteen genes CLEC4A, FDFT1, CTNNB1, GPI, PMM2, PTPRD, IL7, ALDH3B1, AASS, AOC3, SEPINE1, PFKFB1, FTCD, TIMP1 and GATM were finally selected. In order to validate the model's accuracy, ROC curve analysis was performed on an external dataset, and the results indicated that the model had a high predictive power for the prognosis prediction of patients. Our study provides a theoretical foundation for the future targeted regulation of fructose metabolism in colorectal cancer patients, while simultaneously optimizing dietary guidance and therapeutic care for colorectal cancer patients in the context of the COVID-19 pandemic.

Comprehensive Analysis of Characteristics of Cuproptosis-Related LncRNAs Associated with Prognosis of Lung Adenocarcinoma and Tumor Immune Microenvironment.

As a novel discovered mechanism of cell death, cuproptosis is copper-dependent and induces protein toxicity related to advanced tumors, disease prognosis, and human innate and adaptive immune response. However, it has not yet been fully established how the prognosis of lung adenocarcinoma (LUAD) is related to the immune microenvironment of cuproptosis-related lncRNAs using several bioinformatic techniques. In the study, 19 genes related to cuproptosis were collected. Subsequently, 783 lncRNAs related to the co-expression of cuproptosis were obtained. Moreover, the Cox model revealed and constructed four lncRNA (AC012020.1, AC114763.1, AL161431.1, AC010260.1) prognostic markers related to cuproptosis. Based on the median risk score (RS) values, patients were categorized into two groups: high risk and low risk. The Kaplan-Meier (KM) survival curve depicted a statistically significant overall survival (OS) rate among two groups. Principal component analysis (PCA) and receiver operator characteristic curve (ROC) proved that the model had promising ability in prognosis. The analysis of univariate and multivariate Cox regression revealed that RS served as an independent prognostic factor. Moreover, multivariate Cox regression was employed for the establishment of a nomogram of prognostic indicators. The tumor mutational burden (TMB) depicted a considerable difference between the two risk groups. The immunotherapy response of LUAD patients with high risk was improved compared to low risk patients. The study also revealed that drug sensitivity associated with LUAD was significantly linked to RS. The findings could be helpful to establish a good diagnosis, prognosis, and management regime for patients with LUAD.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Predictive value of polygenic risk score for prostate cancer incidence and prognosis in the Han Chinese

Although prostate cancer is a common occurrence among males, the relationship between existing risk prediction models remains unclear. The objective of this hospital-based retrospective study is to investigate the impact of polygenic risk scores (PRSs) on the incidence and prognosis of prostate cancer in the Han Chinese population. A total of 24,778 male participants including 903 patients with prostate cancer at Taichung Veterans General Hospital were enrolled in the study. PRS was calculated using 269 single nucleotide polymorphisms and their corresponding effect sizes from the polygenic score catalog. The association between PRS and the risk prostate cancer was evaluated using Cox proportional hazards regression model. Among the 24,778 participants, 903 were diagnosed with prostate cancer. The risk of prostate cancer was significantly higher in the highest quartile of PRS distribution compared to the lowest (hazard ratio = 4.770, 95% CI = 3.999–5.689, p < 0.0001), with statistical significance across all age groups. Patients in the highest quartile were diagnosed with prostate cancer at a younger age (66.8 ± 8.3 vs. 69.5 ± 8.8, p = 0.002). Subgroup analysis of patients with localized or stage 4 prostate cancer showed no significant differences in biochemical failure or overall survival. This hospital-based cohort study observed that a higher PRS was associated with increased susceptibility to prostate cancer and younger age of diagnosis. However, PRS was not found to be a significant predictor of disease stage and prognosis. These findings suggest that PRS could serve as a useful tool in prostate cancer risk assessment.

Relationship between dietary oxidative balance score, anthropometric measurements and socioeconomic factors in women at low risk of cardiovascular disease

IntroductionThe aim of this study was to investigate the relationship between the dietary oxidative balance score (OBS), an indicator of oxidative stress, anthropometric measures and socioeconomic factors in women at low risk of cardiovascular disease. MethodsThe participants’ 3-day dietary intake, demographic information, anthropometric measurements and blood pressure values were recorded, and the Framingham Risk Score (FRS) and OBS values were determined. Oxidative balance score consists of prooxidant and antioxidant scores. Prooxidant scores were calculated from red meat consumption, total iron and polyunsaturated fatty acid intake, alcohol and cigarette consumption parameters, while antioxidant scores were calculated by assessing cruciferous consumption, dietary total vitamin C, vitamin E, β-carotene, β-cryptoxanthin, β-carotene, β-cryptoxanthin, lycopene, lutein+zeaxanthin and selenium intake. ResultsA total of 145 women were included in the study. Education level was associated with anthropometric measurements, income status with antioxidant and prooxidant scores, and exercise status with OBS (p<0.05). Weight, waist, hip, BMI, waist/hip, and waist/height ratio were significantly lower in subjects with low prooxidant score (p<0.05); there was no significant relationship between age, systolic, diastolic, FRS (p>0.05). ConclusionThe study, conducted in healthy women, showed that dietary oxidative balance scoring is promising in preventing the development of CVD and reducing the burden of disease, and that prospective cohort studies should be conducted in this area.

Hub metastatic gene signature and risk score of breast cancer patients with small tumor sizes using WGCNA

BackgroundBreast cancer (BC) is the most common cancer in women and accounts for approximately 15% of all cancer deaths among women globally. The underlying mechanism of BC patients with small tumor size and developing distant metastasis (DM) remains elusive in clinical practices.MethodsWe integrated the gene expression of BCs from ten RNAseq datasets from Gene Expression Omnibus (GEO) database to create a genetic prediction model for distant metastasis-free survival (DMFS) in BC patients with small tumor sizes (≤ 2 cm) using weighted gene co-expression network (WGCNA) analysis and LASSO cox regression.ResultsABHD11, DDX39A, G3BP2, GOLM1, IL1R1, MMP11, PIK3R1, SNRPB2, and VAV3 were hub metastatic genes identified by WGCNA and used to create a risk score using multivariable Cox regression. At the cut-point value of the median risk score, the high-risk score (≥ median risk score) group had a higher risk of DM than the low-risk score group in the training cohort [hazard ratio (HR) 4.51, p < 0.0001] and in the validation cohort (HR 5.48, p = 0.003). The nomogram prediction model of 3-, 5-, and 7-year DMFS shows good prediction results with C-indices of 0.72–0.76. The enriched pathways were immune regulation and cell–cell signaling. EGFR serves as the hub gene for the protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3.ConclusionPrognostic gene signature was predictive of DMFS for BCs with small tumor sizes. The protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3 connected by EGFR merits further experiments for elucidating the underlying mechanisms.Graphical abstract

Diagnostic Role of Non-Invasive Risk Scores in Prediction of Simple Steatosis versus Non-Alcoholic Steatohepatitis

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is currently considered one of the most common liver diseases in the Western world. NAFLD includes a spectrum of diseases from simple fatty liver disease, to non-alcoholic steatohepatitis (NASH), liver cirrhosis and hepatocellular carcinoma (HCC). NASH, the critical stage in NAFLD progression, is the only bridge between NAFLD and liver cirrhosis, and its persistent existence has become one of the leading causes of liver cirrhosis and even HCC. Aim of the Work To assess the value of non-invasive risk scores in differentiation between simple steatosis and NASH Patients and Methods This was prospective cohort study, conducted Internal Medicine and Gastroenterology outpatient clinics and ward in Ain Shams University Hospitals, on 60 patients with NAFLD divided into 2 groups: (Group I): Patients with normal liver enzymes, (Group II): Patients with persistant elevation of liver enzymes, during a period of 6 months. Results There was statistically insignificant difference between the two studied groups and sex and age. There were 12 (40%) of the group I were males and 18 (60.0%) were females. There were 16(53.3%) of the group II were males and 14(46.7%) were females. The mean age of group I was 40.63 ± 10.53 SD with range (25.0 – 59.0). The mean age of group II was 40.83 ± 9.06SD with range (27.0 – 54.0). There was statistically significant difference between the two studied groups and weight, BMI and waist circumference. There was statistically insignificant difference between the two studied groups and height, DM and HTN. There was statistically significant difference between the two studied groups and ALT, AST, GGT, ALP, ALB and Platelets. There was statistically significant difference between the two studied groups and TC, HDL, LDL, TG and AST/ALT ratio. There was statistically insignificant difference between the two studied groups and FBG. There was statistically significant difference between the two studied groups and FLI, NFS, BARD, APRI, FIB-4 and Fibrosis score. There was statistically significant difference between the two studied groups and Ultrasound (Size, Findings and Fibrosis score). The mean size in group I was 10.03 ± 1.56 SD with range (8.0 – 12.0). The mean size in group II was 13.63 ± 1.22SD with range (12.0 – 16.0). Conclusion A combination of Fibroscan, Fib-4, NFS, BARD and APRI provides a valuable non- invasive method in diagnosis of NASH patients, and this can eliminate the need for liver biopsy in patients without clear indication, BMI, platelet count, HDL cholesterol level, ALT level, and the AST/ALT ratio were independent predictors of NAFLD.

The prognostic value of the MAGGIC risk score in patients with acute pulmonary embolism

ObjectivesAcute pulmonary embolism (PE) is a potentially life-threatening condition characterized by the sudden blockage of the pulmonary arteries. Although the MAGGIC risk score has emerged as a valuable tool in predicting outcomes in patients with chronic heart failure, it has also been demonstrated and identified as a prognostic model in various cardiac diseases other than heart failure. In this study, we aimed to investigate the relationship between MAGGIC score and adverse outcomes in patients with PE. Materials and MethodsA total of 302 consecutive patients diagnosed with acute PE were retrospectively included in the present study. For each patient, the MAGGIC score was calculated. The study population was divided into two groups according to the median value of MAGGIC score. ResultsPatients with high MAGGIC score had a significantly higher proportion of elderly and female individuals, lower BMI, higher presence of CAD, DM, AFib, HF, HT, CKD, COPD, and ACEI/ARB and NOAC usage. Logistic regression analyses was carried out using univariate and multivariate analysis to predict the in-hospital and 30-day mortality predictors in the included PE patients. For in-hospital mortality, diastolic blood pressure, heart rate, RV dilatation, and the MAGGIC score (HR: 1.166, 95 % CI 1.077–1.263, p < 0.001) and for short-term mortality, sPESI and the MAGGIC score (HR: 1.925, 95 % CI 1.243–2.983, p:0.003) were found to be independent predictors for adverse outcomes in patients with acute PE. ConclusionOur study demonstrates that the MAGGIC score can be applied as a valuable prognostic tool for acute pulmonary embolism.

Exploring the role of UMP as a molecular marker in the tumor microenvironment of lung adenocarcinoma.

e20064 Background: Currently, there is a relatively extensive research emphasis on tumor microenvironment based on single-cell transcriptomics. However, the investigation of the metabolic aspects of tumor microenvironment is relatively rare due to technological limitations. To address this gap, our study employes scFEA and MEBOCOST, which are metabolic level prediction tools based on single-cell transcriptomics, to explore the metabolic landscape of tumor microenvironment in lung adenocarcinoma(LUAD). Methods: This study utilized four single-cell sequencing (scRNA-seq) datasets, two targeted metabolomics datasets, and the TCGA-LUAD RNA sequencing dataset obtained from the Cancer Genomics Data Portal. To perform dimensionality reduction, the Seurat package was employed. Cell annotation was carried out using scType, followed by manual correction using the FindAllMarkers function. Tumor cell clusters were identified by utilizing tumor marker genes and the R package inferCNV. Single-cell metabolic levels were inferred using scFEA. The TCGA-LUAD datasets underwent analysis to estimate and analyze immune levels using estimate and cibersort algorithms. Additionally, scFEA was used for metabolic level analysis, and survival analysis was conducted. UMP differential expression analysis was performed on the targeted metabolomics datasets. Results: Across all four single-cell sequence datasets, metabolites such as (GlcNAc)4 (Man)3 (Asn)1[G00015], B-Alanine[C00099], and UMP[C00105] exhibited significantly higher levels in immune cells compared to tumor cells (P &lt; 0.05). These findings were further validated using the TCGA-LUAD datasets. Cox regression analysis was performed using immune scores and tumor purity scores and based on the median value of risk scores, high and low-risk groups were distinguished. it was observed that UMP levels were higher in the low-risk group. Moreover, higher UMP levels were associated with elevated immune levels and decreased tumor purity. Analysis of targeted metabolomics datasets from various pan-cancer cell lines revealed that UMP expression level in tumor cells was lower than that in B and T cells. Furthermore, in different subtypes of LUAD, including AAH, AIS, MIA, and IAC, UMP expression in tissues gradually decreased as the severity of the subtype increased (P &lt; 0.05). Conclusions: The study revealed that UMP metabolites are expressed at higher levels in immune cells compared to tumor cells within LUAD. Moreover, UMP expression levels showed significant variations among different disease severities of LUAD. These findings indicate that UMP has the potential to serve as a molecular marker for distinguishing between different disease severities. Further research is needed to investigate the underlying mechanisms and validate the diagnostic usefulness of UMP in LUAD.

Predictive value of guideline-referred risk scores in estimating left atrial low voltage areas and recurrence of atrial fibrillation in repeat ablation procedures

Abstract Background Despite advances in mapping and ablation techniques, atrial fibrillation (AF) recurrence remains a challenge after pulmonary vein isolation. Low voltage areas (LVA) in the left atrium are associated with higher recurrence rates after left atrial ablation. Risk scores for evaluating post-ablation recurrences are not well established, although a better patient selection for repeat procedures would be of great help in clinical practice. Purpose Risk scores for prediction of AF recurrence have not been tested in a cohort of repeat ablations. Methods This single-center study includes consecutive patients from the prospective Bernau ablation registry undergoing ultra-high-density (UHD) mapping and repeat ablation for AF/AT recurrence between 2016 and 2020. The potential of seven guideline mentioned risk scores (APPLE, DR-FLASH, MB-LATER, ATLAS, CAAP-AF, BASE-AF 2, ALARMEc) to predict (1) AF/AT recurrence beyond a three-months blanking period after repeat left atrial ablation (Re-PVI ± further LA ablation) and (2) the percentage of left atrial LVA in UHD mapping was investigated. LVA were defined as sites with a bipolar peak-to-peak voltage of &amp;lt;0.5 mV with an extent of &amp;gt;1cm². Optimal cutoff for sensitivity and specificity for LVA and AF/AT recurrence as endpoint was chosen using C statistics with receiver-operator characteristics (ROC). Further ROCs were performed to illustrate the predictive ability of the scores. Pearson correlation was used to test associations between variables. Results 160 patients (mean age 67.9 ± 9.1 years, 60.6% persistent AF, mean AF duration 4.6 ± 3.8 years) with complete left atrial UHD mappings (mean EGMs 9754 ± 5808) were included. Overall recurrence rate over a mean follow-up time of 16 ± 11 months was 55.6%. The predictive value of the investigated risk scores on AF/AT recurrence in our cohort was low (Table 1), with the highest power for CAAP-AF (p = 0.015, AUC = 0.615) and DR-FLASH score (p = 0.040, AUC = 0.594), Figure 1. With respect to left atrial LVA we found a better predictive power for the CAAP-AF (p &amp;lt; 0.001, AUC 0.702), APPLE (p &amp;lt; 0.001, AUC 0.687), DR-FLASH (p &amp;lt; 0.001, AUC 0.688), ATLAS (p = 0.005, AUC 0.634) and ALARMEc (p = 0.007, AUC 0.608) score to predict low voltage based on a calculated cut-off of 22% of total left atrial surface (Figure 1). Conclusion The predictive value of guideline-referred risk scores in estimating AF/AT recurrence after repeat ablation is low and does not seem to be of relevant help in patient selection for further interventional treatment. Some scores demonstrate a fairly good prediction for the amount of left atrial LVA and therefore might help in choosing the right mapping and ablation regime beforehand.

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.

Bellmunt risk score as a survival predictor in patients with metastatic castration-resistant prostate cancer.

The prognosis of metastatic castration-resistant prostate cancer (mCRPC) is influenced by numerous individual factors. Despite various proposed prognostic models, the clinical application of these remains limited, probably due to complexity. Our study aimed to evaluate the predictive value of the Bellmunt risk score, which is well-known for urothelial carcinoma and easily assessed, in mCRPC patients. The Bellmunt risk score was calculated from three risk factors (Eastern Cooperative Oncology Group Performance Status (ECOG PS) ≥1, serum hemoglobin <10 g/dL, presence of liver metastases) in 125 patients who received first-line mCRPC treatment between 2005 and 2023. In addition, a modified score was established (one point each for hemoglobin <10 g/dL and the presence of liver metastases added to the ECOG PS). Associations with overall survival (OS) under first- and second-line therapy were tested using Cox regression analyzes, log-rank tests, concordance index (C-index) and time-dependent receiver operating characteristic. There is a significant correlation between the level of the Bellmunt risk score and shorter OS (hazard ratio: 3.23, 95% confidence interval: 2.06-5.05; log-rank p < 0.001; C-index: 0.724). The semi-quantitative modified risk score showed even better prognostic discrimination (log-rank p < 0.001, C-index: 0.764). The score and its dynamics were also predictive in the second-line setting (log-rank p < 0.001 and = 0.01; C-index: 0.742 and 0.595). The Bellmunt risk score is easy to assess and provides useful prognostic information in mCRPC, and can support physicians in their treatment decisions.