Risk Score Model For Prediction Research Articles

#959 Developing and validating a risk score model for prediction of AKI in non-ICU hospitalized patients

Abstract Background and aim : Acute kidney injury (AKI) is an increasingly common and potentially catastrophic complication in hospitalized patients. Several risk score models have been designed for intensive care unit (ICU) patients. Aim in this work was to establish a new risk prediction score for AKI patients in general wards. Methods All hospitalized ward patients from the first of July 2019 to 31st of December 2019 who developed AKI 24 hours after admission were included. To create a new prediction score model of AKI in hospitalized patients we used the data collected from 107 patients who developed AKI. We used our prospective cohort to develop and validate this prediction score model of AKI in patients admitted to general wards. Results Only 107 patients of 10,243 (1%) developed AKI 24 hours after admission to the general wards. Mortality rate was 26.2%. A score model of 15 points, based on a combination of clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 out of 15 was a predictor of Acute Kidney Injury in non-ICU hospitalized patients. The area under the receiver operating characteristic (AUC ROC) value of the score model was 0.950, 95% CI (confidence interval) 0.922–0.978 and the P-value <0.001 with sensitivity of 94.39 and specificity of 81.43. Applying the score model in another group of patients (validation group n = 122) showed that the AUC ROC value in the validation group was 0.826. Conclusion We developed and validated a new risk score model with a cutoff value ≥ 4 out of 15 for prediction of Acute Kidney Injury in non-ICU hospitalized patients with a good sensitivity and specificity. It will help the early prediction of Acute Kidney Injury in non-ICU patients.

Risk score prediction for bacteriologically confirmed tuberculosis among HIV-infected adults on antiretroviral therapy in Ethiopia: prognostic model development.

This study was aimed at developing a risk score prediction model for bacteriologically confirmed TB among HIV-infected adults receiving antiretroviral therapy in Ethiopia. An institutional-based retrospective follow-up study was conducted among 569 HIV-infected adults on ART. We used demographic and clinical prognostic factors to develop a risk prediction model. Model performance was evaluated by discrimination and calibration using the area under the receiver operating characteristic (AUROC) curve and calibration plot. Bootstrapping was used for internal validation. A decision curve analysis was used to evaluate the clinical utility. Opportunistic infection, functional status, anemia, isoniazid preventive therapy, and WHO clinical stages were used to develop risk prediction. The AUROC curve of the original model was 87.53% (95% CI: 83.88-91.25) and the calibration plot (p-value = 0.51). After internal validation, the AUROC curve of 86.61% (95% CI: 82.92-90.29%) was comparable with the original model, with an optimism coefficient of 0.0096 and good calibration (p-value = 0.10). Our model revealed excellent sensitivity (92.65%) and negative predictive value (NPV) (98.60%) with very good specificity (70.06%) and accuracy (72.76%). After validation, accuracy (74.85%) and specificity (76.27%) were improved, but sensitivity (86.76%) and NPV (97.66%) were relatively reduced. The risk prediction model had a net benefit up to 7.5 threshold probabilities. This prognostic model had very good performance. Moreover, it had very good sensitivity and excellent NPV. The model could help clinicians use risk estimation and stratification for early diagnosis and treatment to improve patient outcomes and quality of life.

Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma

Medulloblastoma (MB) is a malignant brain tumour that is highly common in children and has a tendency to spread to the brain and spinal cord. MB is thought to be a metabolically driven brain tumour. Understanding tumour cell metabolic patterns and characteristics can provide a promising foundation for understanding MB pathogenesis and developing treatments. Here, by analysing RNA-seq data of MB samples from the Gene Expression Omnibus (GEO) database, 12 differentially expressed metabolic-related genes (DE-MRGs) were chosen for the construction of a predictive risk score model for MB. This model demonstrated outstanding accuracy in predicting the outcomes of MB patients and served as a standalone predictor. An evaluation of functional enrichment revealed that the risk score showed enrichment in pathways related to cancer promotion and the immune response. In addition, a high risk score was an independent poor prognostic factor for MB in patients with different ages, sexes, metastasis stages and subgroups (SHH and Group 4). Consistently, the metabolic enzyme ornithine decarboxylase (ODC1) was upregulated in MB patients with poor survival time. Inhibition of ODC1 in primary and metastatic MB cell lines decreased cell proliferation, migration and invasion but increased immune infiltration. This study could aid in identifying metabolic targets for MB as well as optimizing risk stratification systems and individual treatment plans for MB patients via the use of a metabolism-related gene prognostic risk score signature.

RISK SCORE PREDICTION MODEL FOR DEMENTIA IN PATIENTS WITH TYPE 2 DIABETES MELLITUS : AN UPDATE SYSTEMATIC REVIEW

Background: Dementia is a substantial global health issue. Reduction in future numbers of dementia cases through effective preventive strategies could significantly affect the personal and socioeconomic burdens of dementia. WHO has recommended that countries urgently develop national public health programmes to reduce the impact of dementia. The aim: The aim of this study to show about risk score prediction model for dementia in patients with type 2 diabetes mellitus. Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Google Scholar were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search get 67 articles, whereas the results of our search on SagePub get 210 articles, on Google Scholar 1350 articles. Records remove before screening are 667, so we get 960 articles fos screening. After we screened based on record exclude, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: RxDx-Dementia risk index to predict dementia in patients with type 2 diabetes and hypertension. The RxDx-Dementia risk index which incorporated diagnosis and prescription-based information in a single summary score performed better than diagnosis- or prescription-based comorbidity scores or its combinations. The RxDx-Dementia risk index can be used for prognostic purpose or to control confounding in epidemiological studies.

Construction of a 12-gene prognostic model for colorectal cancer based on heat shock protein-related genes

Some heat shock proteins (HSPs) have been shown to influence tumor prognosis, but their prognostic significance in colorectal cancer (CRC) remains unclear. This study explored the prognostic significance of HSP-related genes in CRC. Transcriptional data and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, and a literature search was conducted to identify HSP-related genes. Using Least Absolute Selection and Shrinkage Operator (LASSO) regression and univariate/multivariate Cox regression analyses, 12 HSP-related genes demonstrating significant associations with CRC survival were successfully identified and employed to formulate a predictive risk score model. The efficacy and precision of this model were validated utilizing TCGA and Gene Expression Omnibus (GEO) datasets, demonstrating its reliability in CRC prognosis prediction. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant disparities between high- and low-risk groups in chromatin remodeling biological functions and neutrophil extracellular trap formation pathways. Single sample gene set enrichment analysis (ssGSEA) further revealed differences in immune cell types and immune functional status between the two risk groups. Differential analysis showed higher expression of immune checkpoints within the low-risk group, while the high-risk group exhibited notably higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Additionally, we predicted the sensitivity of different prognosis risk patients to various drugs, providing potential drug choices for tailored treatment. Combined, our study successfully crafted a novel CRC prognostic model that can effectively predict patient survival, immune landscape, and treatment response, providing important support and guidance for CRC patient prognosis.

Retracted: Identification of an m6A RNA Methylation Regulator Risk Score Model for Prediction of Clinical Prognosis in Astrocytoma

Retracted: Identification of an m6A RNA Methylation Regulator Risk Score Model for Prediction of Clinical Prognosis in Astrocytoma

Comprehensive assessment of base excision repair (BER)-related lncRNAs as prognostic and functional biomarkers in lung adenocarcinoma: implications for personalized therapeutics and immunomodulation.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, and comprehending its molecular mechanisms is pivotal for advancing treatment efficacy. This study aims to explore the prognostic and functional significance of base excision repair (BER)-related long non-coding RNAs (BERLncs) in LUAD. A risk score model for BERLncs was developed using the least absolute shrinkage and selection operator regression and Cox regression analysis. Model validation and prognostic evaluation were performed using Kaplan-Meier and receiver-operating characteristic curve analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential biological functions of BERLncs. Comparative analyses were carried out to investigate disparities in tumor mutation burden (TMB), immune infiltration, tumor immune dysfunction and exclusion (TIDE) score, chemosensitivity, and immune checkpoint gene expression between the two risk groups. A predictive risk score model comprising 19 BERLncs was successfully developed. Patients were divided into high-risk and low-risk groups according to the median risk score. The high-risk subgroup exhibited significantly inferior overall survival. Functional enrichment analysis revealed pathways associated with lung cancer development, notably the neuroactive ligand-receptor interaction pathway. High-risk patients demonstrated elevated TMB, diminished TIDE scores, and an immunosuppressive tumor microenvironment, while low-risk patients displayed potential benefits from immunotherapy. Additionally, the risk model identified potential anticancer agents. The risk score model based on BERLncs shows promise as a prognostic biomarker for LUAD patients, providing valuable insights for clinical decision-making, therapeutic strategies, and understanding of underlying biological mechanisms.

Developing a Risk Stratification Model Based on Machine Learning for Targeted Screening of Diabetic Retinopathy in the Indian Population.

This study aimed to develop a predictive risk score model based on deep learning (DL) independent of fundus photography, totally reliant on systemic data through targeted screening from a population-based study to diagnose diabetic retinopathy (DR) in the Indian population. It involved machine learning application on datasets of a cross-sectional population-based study. A total of 1425 subjects (1175 subjects with known diabetes and 250 with newly diagnosed diabetes) were included in the study. We applied five machine learning algorithms, random forest (RF), logistic regression (LR), support vector machines (SVM), artificial neural networks (ANN), and decision trees (DT), to predict diabetic retinopathy in our datasets. We incorporated a percentage split in the first experiment and randomly divided our data set into 80% as a training set and 20% as a test set. We performed a three-way data split in the second experiment to prevent overestimating predictive performance. We randomly divided our data set into 60% as a training set, 20% as a validation set, and 20% as the test set. Furthermore, we integrated five-fold cross-validation to split the percentage to evaluate our method. We judged the predictive performance based on the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy (Acc), sensitivity, and specificity. The RF classifier achieved the best prediction performance with AUC, Acc, and sensitivity values of 0.91, 0.89, and 0.90, respectively, in the percentage split. Similarly, a three-way data split attained an outcome of 0.86 and 0.85 in AUC and Acc. Likewise, the five-fold cross-validation performed the best with results of 0.90, 0.97, 0.91, and 0.75 in AUC, Acc, sensitivity, and specificity, respectively. Since the RF classifier achieved the best performance,we propose it to identify diabetic retinopathy for targeted screening in the general population.

Systematic Review of <14 weeks risk score prediction models using maternal characteristics with and without biomarkers for the prediction of GDM

Systematic Review of <14 weeks risk score prediction models using maternal characteristics with and without biomarkers for the prediction of GDM

Necroptosis Related Genes Predict Prognosis and Therapeutic Potential in Gastric Cancer.

The clinical significance of necroptosis in gastric cancer (GC) has yet to be fully elucidated. The purpose of our study was to identify a necroptosis-relevant gene and to establish a prediction model to estimate the prognosis and therapeutic potential in GC. Here, we explored the expression profile of 76 necroptosis-related genes in TCGA-STAD patients. A six-gene risk score prediction model was established via regression analysis of the least absolute shrinkage and selection operator (LASSO) and validated in a separate cohort. Patients were separated into low- or high-risk groups according to the median risk score. We then compared and analyzed the biological process characteristics of two risk groups. Additionally, cell-to-cell communications and metabolic activity were analyzed in a single-cell solution. The in vitro experiments were conducted to explore the biological functions and drug sensitivity of necroptosis-related genes in gastric cancer. Our results identified that compared with the low-risk group, the high-risk group was associated with a higher clinical stage or grade and a worse prognosis. In addition, the low-risk group had higher levels of immunity and immune cell infiltration. Necroptosis was triggered by the TNF pathway in myeloid cells and the glycolysis pathway was altered. Necroptosis-related genes modulated the cell function, including proliferation and migration in vitro. Furthermore, the potential drugs' sensitivity was higher in the low-risk subgroup. These findings could facilitate a better understanding and improve the treatment potential and prognosis of GC patients.

Development of a Clinical Risk Score Prediction Tool for 5-, 9-, and 13-Year Risk of Dementia

Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression. To develop a point risk score prediction model of dementia. This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model. Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk. A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women. In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.

380. UTILITY OF PET/CT FOR LYMPH NODE METASTASIS DETECTION IN PT1 EARLY ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Nodal staging for early esophageal squamous cell carcinoma (ESCC) was mandatory to decide therapy strategy. Utility of preoperative Positron Emission Tomography/Computed Tomography (PET/CT) for lymph node metastasis (LNM) detection in early ESCC was evaluated in the study. 186 consecutive patients with pathologically confirmed T1 tumors undergoing preoperative PET/CT at Zhongshan Hospital between 2015 August to 2020 December were retrospectively enrolled. Clinical characteristics and PET/CT results were collected and compared between patients with or without LNM. A PET integrated risk score model for prediction of LNM was established and validated. Survivals were compared between high risk group and low risk group by Log Rank test. 40 out of 186 patients were diagnosed with LNM on PET-CT. PET/CT alone for LNM detection was with a 53.3% sensitivity, 84.65% specificity, and 90.4% negative predictive value. Tumor size >2.5 cm (P = 0.015), tumor depth (P = 0.012), LVI (P < 0.001), differentiation (P = 0.066) and PET LN (+) (P < 0.001) were included in the prediction model for LNM. The AUC of ROC in derivation cohort and validation cohort was 0.860 and 0.846 respectively. High risk group scoring >3 points showed poorer 5-year recurrence free survival (62.6% VS. 74.7%, P = 0.046) and similar overall survival (87.6% VS. 86.9%, P = 0.942) as compared to low risk group. PET/CT for LNM detection in early ESCC was of 53.3% sensitivity and 84.65% specificity. PET integrated risk score model for LNM was of fair property with 0.860 AUC and was predictive of recurrence.

Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach

BackgroundThe malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS).ResultsWe proposed an SNP-based feature extraction algorithm that incorporates the susceptibility information of an individual to malaria to generate the feature set. However, it can become computationally expensive for a machine learning model to learn from many SNPs. Therefore, we reduced the feature set by employing the Logistic Regression and Recursive Feature Elimination (LR-RFE) method to select SNPs that improve the efficacy of our model. Next, we calculated the wGRS of the selected feature set, which is used as the model's target variables. Moreover, to compare the performance of the wGRS-only model, we calculated and evaluated the combination of wGRS with genotype frequency (wGRS + GF). Finally, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression algorithms are utilized to establish the machine learning models for malaria risk prediction.ConclusionsOur proposed approach identified SNP rs334 as the most contributing feature with an importance score of 6.224 compared to the baseline, with an importance score of 1.1314. This is an important result as prior studies have proven that rs334 is a major genetic risk factor for malaria. The analysis and comparison of the three machine learning models demonstrated that LightGBM achieves the highest model performance with a Mean Absolute Error (MAE) score of 0.0373. Furthermore, based on wGRS + GF, all models performed significantly better than wGRS alone, in which LightGBM obtained the best performance (0.0033 MAE score).

POS-031 Developing and validating a Risk Score Model for prediction of Acute Kidney Injury (AKI) in Non-ICU Hospitalized Patients

IntroductionAbstractBackground: AKI is a common complication in hospitalized patients. Several risk score models have been designed for ICU patients. Our aim was to establish risk prediction score for AKI in general wards.MethodsAll hospitalized patients from 1/7/ 2019 to 31/12/2019 who developed AKI were included. We used our prospective cohort to develop and validate this prediction score model of AKI in general wards patients.ResultsA score model of 15 points, based on clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 was a predictor of AKI. The AUC ROC value of score model was 0.950, 95% and P-value <0.001 with sensitivity of 94.39 and specificity of 81.43. By applying this score on validation group (n=122) showed AUC ROC value of 0.826.ConclusionsWe developed and validated a new risk score model with cutoff ≥ 4 for prediction of AKI in non-ICU hospitalized patients with a good sensitivity and specificity.No conflict of interest IntroductionAbstractBackground: AKI is a common complication in hospitalized patients. Several risk score models have been designed for ICU patients. Our aim was to establish risk prediction score for AKI in general wards. Abstract Background: AKI is a common complication in hospitalized patients. Several risk score models have been designed for ICU patients. Our aim was to establish risk prediction score for AKI in general wards. MethodsAll hospitalized patients from 1/7/ 2019 to 31/12/2019 who developed AKI were included. We used our prospective cohort to develop and validate this prediction score model of AKI in general wards patients. All hospitalized patients from 1/7/ 2019 to 31/12/2019 who developed AKI were included. We used our prospective cohort to develop and validate this prediction score model of AKI in general wards patients. ResultsA score model of 15 points, based on clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 was a predictor of AKI. The AUC ROC value of score model was 0.950, 95% and P-value <0.001 with sensitivity of 94.39 and specificity of 81.43. By applying this score on validation group (n=122) showed AUC ROC value of 0.826. A score model of 15 points, based on clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 was a predictor of AKI. The AUC ROC value of score model was 0.950, 95% and P-value <0.001 with sensitivity of 94.39 and specificity of 81.43. By applying this score on validation group (n=122) showed AUC ROC value of 0.826. ConclusionsWe developed and validated a new risk score model with cutoff ≥ 4 for prediction of AKI in non-ICU hospitalized patients with a good sensitivity and specificity.No conflict of interest We developed and validated a new risk score model with cutoff ≥ 4 for prediction of AKI in non-ICU hospitalized patients with a good sensitivity and specificity.

CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

ObjectiveTo investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model.MethodsFifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model.ResultsUnivariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05).ConclusionCompared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.

Identification of Risk Factors and Development of Predictive Risk Score Model for Mortality after Open Ruptured Abdominal Aortic Aneurysm Repair.

Background and Objectives: Despite the relatively large number of publications concerning the validation of these models, there is currently no solid evidence that they can be used with absolute precision to predict survival. The goal of this study is to identify preoperative factors that influenced 30-day mortality and to create a predictive model after open ruptured abdominal aortic aneurysm (RAAA) repair. Materials and Methods: This was a retrospective single-center cohort study derived from a prospective collected database, between 1 January 2009 and 2016. Multivariate logistic regression analysis was used to identify all significant predictive factors. Variables that were identified in the multivariate analysis were dichotomized at standard levels, and logistic regression was used for the analysis. To ensure that dichotomized variables were not overly simplistic, the C statistic was evaluated for both dichotomized and continuous models. Results: There were 500 patients with complete medical data included in the analysis during the study period. Of them, 37.6% were older than 74 years, and 83.8% were males. Multivariable logistic regression showed five variables that were predictive of mortality: age > 74 years (OR = 4.01, 95%CI 2.43–6.26), loss of consciousness (OR = 2.21, 95%CI 1.11–4.40), previous myocardial infarction (OR = 2.35, 95%CI 1.19–4.63), development of ventricular arrhythmia (OR = 4.54, 95%CI 1.75–11.78), and DAP < 60 mmHg (OR = 2.32, 95%CI 1.17–4.62). Assigning 1 point for each variable, patients were stratified according to the preoperative RAAA mortality risk score (range 0–5). Patients with 1 point suffered 15.3% mortality and 3 points 68.2% mortality, while all patients with 5 points died. Conclusions: This preoperative RAAA score identified risk factors readily assessed at the bedside and provides an accurate prediction of 30-day mortality after open repair of RAAA.

Risk Score Prediction Model of Prognosis in GC Patients by Age and Gender Combined With m6A Modification Genes FTO and RBM15.

Background: Gastric cancer (GC) has a high mortality rate. N6-methyladenosine (m6A) is involved in the development of GC. Age and gender are associated with GC incidence and survival. This study aimed to explore the risk score prediction model of prognosis in GC patients by age and gender combined with m6A modification genes. Methods: Data on m6A modification gene expression and clinical information downloaded from the Cancer Genome Atlas (TCGA) database were used to construct the risk score prediction model. Cox and least absolute shrinkage and selection operator (LASSO) regression were performed to identify clinical characteristics and m6A modification genes associated with prognosis. A risk score prediction model was established based on multivariate Cox regression analysis. The Gene Expression Omnibus (GEO) database was used to validate this model. Results: Most of the m6A modification genes were upregulated in GC tumor tissues compared with that in normal tissues and were correlated with clinical characteristics including grade, stage status, and T status. The risk score prediction model was established based on age, gender, FTO, and RBM15. GC patients were divided into high- or low-risk groups based on the median risk score. Patients with a high risk score had poor prognosis. Multivariate Cox regression indicated that risk score was an independent prognostic factor for GC patients. The data from GSE84437 verified the predictive value of this model. Conclusion: The risk score prediction model based on age and gender combined with m6A modification genes FTO and RBM15 was an independent prognostic factor for GC patients.

A long-term ischemic stroke risk score model in patients aged 60 years and older with obstructive sleep apnea: a multicenter prospective cohort study

To analyze the independent risk factors of long-term ischemic stroke and establish a nomogram for predicting the long-term risks in elderly patients with obstructive sleep apnea (OSA). This multicenter prospective cohort study was conducted from January, 2015 to October, 2017 among consecutive elderly patients (≥60 years) with newly diagnosed OSA without a history of cardio-cerebrovascular diseases and loss of important clinical indicators. The follow-up outcome was the occurrence of ischemic stroke. The baseline demographic and clinical data, sleep parameters, laboratory and ultrasound results were collected from all the patients, who were randomized into the modeling group (n=856) and validation group (n=258) at a 3∶1 ratio. LASSO regression was used for variable reduction and dimension screening, and the risk score prediction model of ischemic stroke was established based on Cox proportional hazard regression. In the total of 1141 patients enrolled in this study, 58 (5.08%) patients experienced ischemic stroke during the median follow-up of 42 months (range 41-54 months). The cumulative incidence of ischemic stroke was 5.14% in the model group and 4.91% in the verification group (P < 0.05). Age (HR=3.44, 95% CI: 2.38- 7.77), fasting blood glucose (FPG) (HR=2.13, 95% CI: 1.22-3.72), internal diameter of the ascending aorta (HR=2.60, 95% CI: 1.0- 4.47), left atrial anteroposterior diameter (HR=1.98, 95% CI: 1.75-2.25) and minimum oxygen saturation (LSpO2) (HR=1.57, 95% CI: 1.20-1.93) were identified as independent risk factors for ischemic stroke (P < 0.05 or 0.01). A long-term ischemic stroke risk score model was constructed based the regression coefficient ratios of these 5 risk variables. Before and after the application of the Bootstrap method, the AUC of the cohort risk score model was 0.84 (95% CI: 0.78- 0.90) and 0.85 (95% CI: 0.78- 0.89) in the model group and was 0.83 (95% CI: 0.73-0.93) and 0.82 (95%CI: 0.72-0.90) in the verification group, respectively, suggesting a good prediction efficiency and high robustness of the model. At the best clinical cutoff point, the cumulative incidence of ischemic stroke was significantly higher in the high-risk group than in the low-risk group (P=0.021). This model can help to identify high-risk OSA patients for early interventions of the risks of ischemic stroke associated with OSA.

A Novel Multiple Risk Score Model for Prediction of Long-Term Ischemic Risk in Patients With Coronary Artery Disease Undergoing Percutaneous Coronary Intervention: Insights From the I-LOVE-IT 2 Trial.

Backgrounds: A plug-and-play standardized algorithm to identify the ischemic risk in patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI) could play a valuable step to help a wide spectrum of clinic workers. This study intended to investigate the ability to use the accumulation of multiple clinical routine risk scores to predict long-term ischemic events in patients with CAD undergoing PCI.Methods: This was a secondary analysis of the I-LOVE-IT 2 (Evaluate Safety and Effectiveness of the Tivoli drug-eluting stent (DES) and the Firebird DES for Treatment of Coronary Revascularization) trial, which was a prospective, multicenter, and randomized study. The Global Registry for Acute Coronary Events (GRACE), baseline Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX), residual SYNTAX, and age, creatinine, and ejection fraction (ACEF) score were calculated in all patients. Risk stratification was based on the number of these four scores that met the established thresholds for the ischemic risk. The primary end point was ischemic events at 48 months, defined as the composite of cardiac death, nonfatal myocardial infarction, stroke, or definite/probable stent thrombosis (ST).Results: The 48-month ischemic events had a significant trend for higher event rates (from 6.61 to 16.93%) with an incremental number of risk scores presenting the higher ischemic risk from 0 to ≥3 (p trend < 0.001). In addition, the categories were associated with increased risk for all components of ischemic events, including cardiac death (from 1.36 to 3.15%), myocardial infarction (MI) (from 3.31 to 9.84%), stroke (3.31 to 6.10%), definite/probable ST (from 0.58 to 1.97%), and all-cause mortality (from 2.14 to 6.30%) (all p trend < 0.05). The net reclassification index after combined with four risk scores was 12.5% (5.3–20.0%), 9.4% (2.0–16.8%), 12.1% (4.5–19.7%), and 10.7% (3.3–18.1%), which offered statistically significant improvement in the performance, compared with SYNTAX, residual SYNTAX, ACEF, and GRACE score, respectively.Conclusion: The novel multiple risk score model was significantly associated with the risk of long-term ischemic events in these patients with an increment of scores. A meaningful improvement to predict adverse outcomes when multiple risk scores were applied to risk stratification.

Identification of an m6A RNA Methylation Regulator Risk Score Model for Prediction of Clinical Prognosis in Astrocytoma.

Astrocytoma (AS) is the most ubiquitous primary malignancy of the central nervous system (CNS). The vital involvement of the N6-methyladenosine (m6A) RNA modification in the growth of multiple human tumors is known. This study entailed probing m6A regulators with AS prognosis to construct a risk prediction model (RS) for potential clinical use. A total of 579 AS patients' (of the Chinese Glioma Genome Atlas,CGGA) data and the expression of 12 published m6A-related genes were included in this study. Cox and selection operator (LASSO) regression analyses for independent prognostic factors and multifactor Cox analysis established an R.S. model to predict the AS patient prognosis. This was subject to verification employing 331 samples from the TCGA data set followed by gene ontology and pathway enrichment study with gene set enrichment analysis (GSEA). The R.S. constructed with three m6A genes inclusive of WTAP, RBM15, and YTHDF2 emerged as independent prognostic factors in AS patients with vital involvement in the advancement and development of the malignancy. In a nutshell, this work reported an m6A-related gene risk model to predict the prognosis of AS patients to pave the way for discerning diagnostic and prognostic biomarkers. Further corroboration employing relevant wet-lab assays of this model is warranted.