scholarly journals Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

2021 ◽  
Author(s):  
Bo Liu ◽  
Tingting Fu ◽  
Ping He ◽  
Chenyou Du ◽  
Ke Xu
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Group Versus ◽  
Low Risk ◽  
Risk Patients ◽  
Validation Set ◽  
Immune Related Genes

Purpose: To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC). Methods: Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and ImmPort were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452. Results: In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group.  Conclusions: The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

scholarly journals Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Peijie Chen ◽  
Yuting Gao ◽  
Si Ouyang ◽  
Li Wei ◽  
Min Zhou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Non Coding Rnas ◽  
Validation Set ◽  
Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p &lt; 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

scholarly journals A Novel Prognostic Signature Based on Cell-In-Cell-Related Genes for Predicting Survival and Tumor Microenvironment in Pancreatic Cancer

2021 ◽  
Author(s):  
Jianlu Song ◽  
Rexiati Ruze ◽  
Yuan Chen ◽  
Ruiyuan Xu ◽  
Xinpeng Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
High Risk ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Cancer Genome ◽  
Low Risk ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Abstract Background: Pancreatic cancer (PC) is a highly malignant tumor featured with high intra-tumoral heterogeneity and poor prognosis. Cell-in-cell (CIC) structures have been reported in multiple tumor types, and their presence is thought to promote clonal selection and tumor evolution. Here, we aimed to establish a CIC-related gene signature for predicting the prognosis and evaluating immune microenvironment in PC. Methods: In this study, the gene expression data, as well as corresponding clinicopathological data of PC and normal pancreatic tissues were collected from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. Differential gene expression analysis, random forest screening, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed on 101 CIC-related genes to construct a prognostic gene signature. The effectiveness and robustness of the prognostic gene signature were evaluated by receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis and establishing the nomogram model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to annotate the biological functions of the differentially expressed genes (DEGs). Quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry (IHC) staining were validated the core gene expression in both mRNA and protein levels. Results: A 4-gene signature was constructed to stratify patients into the low-risk and high-risk groups with distinct survival outcomes, somatic mutation profiles and immune features. The high-risk group had poorer prognosis than did the low-risk group. This signature was found to be an independent prognostic factor for PC patients with favorable predictive efficiency. Functional enrichment analyses showed that numerous terms and pathways associated with invasion and metastasis were enriched in the high-risk group. Moreover, the high-risk group had a higher tumor mutation burdens and lower immune cell infiltrations. KRT7, as the most important risk gene, was significantly associated with the worse prognosis of PC. CIC formation assay performing in PC cell lines indicated that KRT7 expression was correlated with CIC frequency. Conclusions: The signature based on four CIC-related genes could be applicable for predicting the prognosis of PC, and targeting CIC processes may be a potential therapeutic option. Further studies are needed to reveal the underlying molecular mechanisms and biological implications of CIC in PC progression.

scholarly journals An Immune-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ziwei Wang ◽  
Yan Liu ◽  
Jun Zhang ◽  
Rong Zhao ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Figo Stage ◽  
Low Risk ◽  
Pathological Grade ◽  
Immune Related Genes

Background. Endometrial cancer is among the most common malignant tumors threatening the health of women. Recently, immunity and long noncoding RNA (lncRNA) have been widely examined in oncology and shown to play important roles in oncology. Here, we searched for immune-related lncRNAs as prognostic biomarkers to predict the outcome of patients with endometrial cancer. Methods. RNA sequencing data for 575 endometrial cancer samples and immune-related genes were downloaded from The Cancer Genome Atlas (TCGA) database and gene set enrichment analysis (GSEA) gene sets, respectively. Immune-related lncRNAs showing a coexpression relationship with immune-related genes were obtained, and Cox regression analysis was performed to construct the prognostic model. Survival, independent prognostic, and clinical correlation analyses were performed to evaluate the prognostic model. Immune infiltration of endometrial cancer samples was also evaluated. Functional annotation of 12 immune-related lncRNAs was performed using GSEA software. Prognostic nomogram and survival analysis for independent prognostic risk factors were performed to evaluate the prognostic model and calculate the survival time based on the prognostic model. Results. Twelve immune-related lncRNAs (ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084117.1, NRAV, SCARNA9, AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5) were obtained, and a prognostic model was constructed. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Patient age, pathological grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, and risk status were the risk factors. The 12 immune-related lncRNAs correlated with patient age, pathological grade, and FIGO stage. Principal component analysis and functional annotation showed that the high-risk and low-risk groups separated better, and the immune status of the high-risk and low-risk groups differed. Nomogram and receiver operating characteristic (ROC) curves effectively predicted the prognosis of endometrial cancer. Additionally, age, pathological grade, FIGO stage, and risk status were all related to patient survival. Conclusion. We identified 12 immune-related lncRNAs affecting the prognosis of endometrial cancer, which may be useful as therapeutic targets and molecular biomarkers.

A prognostic 10-miRNA risk score (10-miRNA RS) in predicting neoadjuvant chemotherapy sensitivity of luminal breast cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3139-3139
Author(s):  
Chang Gong ◽  
Luyuan Tan ◽  
Na You ◽  
Kai Chen ◽  
Weige Tan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Risk Patients

3139 Background: The 10-miRNA risk score is a prognostic 10-gene expression signature specifically developed in luminal breast cancer associated with relapse-free survival. Since high-risk patients identified by10-miRNA RS had worse prognosis but better outcome with chemotherapy than low-risk patients (Gong C et al, EBioMedicine. 2016), this model may facilitate personalized therapy-decision making for luminal breast cancer patients. Therefore, we seek to validate whether high-risk group are more sensitive to chemotherapy than low-risk group by assessing the predictive value of 10-miRNA RS for pathological complete response (pCR) in patients receiving neoadjuvant chemotherapy (NAC). Methods: The 10-miRNA gene expression and clinicopathological data were prospectively gathered from 251 pretreated biopsy-diagnosed luminal breast cancer patients from 4 breast cancer centers. Formalin-fixed paraffin-embedded tissues from basal line biopsy were used for the detection of 10-miRNA expression to calculate the RS. The correlation between pCR and the 10-miRNA RS classification were identified. Results: In this prospective, multicenter study, the overall pCR rate was 13.6% (34/251). The 10-miRNA RS of the pCR group was significantly higher than the non-pCR group ( P = 0.015). Fifty-one percent of patients were classified as low-risk according to the 10-miRNA RS classification and 49% as high-risk with a RS cut-off point of 2.144. The 10-miRNA RS classification was associated with a pCR rate of 9.4% in the low-risk group and 17.8% in the high-risk group ( P = 0.041). The correlation between the pCR and the 10-miRNA RS classification was significant in subgroup analysis stratified by molecular subtypes (8% vs. 13.2% in luminal B1; 14.7% vs. 30.1% in luminal B2; no pCR was observed in all 13 luminal A subtype). In multivariate analysis, the 10-miRNA RS remained significantly associated with pCR and independent from subtype, ki67 and other clinicopathological characteristics. Conclusions: 10-miRNA RS clearly defined that high-risk patients are more sensitive to chemotherapy which leads to a higher pCR rate in NAC patients. Thus, 10-miRNA RS is not only a prognostic factor but an effective method in determining whether a patient would undergo surgery or receive NAC prior to surgery. Clinical trial information: ChiCTR-DDD-17013651.

scholarly journals Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xinyu Gu ◽  
Jun Guan ◽  
Jia Xu ◽  
Qiuxian Zheng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Immune Related Genes

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

Enteropathy-Associated T-Cell Lymphoma: a Clinical Prognostic Model to Identify High Risk Patients

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3092-3092
Author(s):  
Laura R de Baaij ◽  
Jolanda MW van de Water ◽  
Wieke HM Verbeek ◽  
Otto J Visser ◽  
Dirk J Kuik ◽  
...  
Keyword(s):  
Risk Factors ◽  
T Cell ◽  
High Risk ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
T Cell Lymphoma ◽  
Risk Patients

Abstract Abstract 3092 Enteropathy-associated T-cell lymphoma (EATL) is a rare intestinal lymphoma that arises from intraepithelial lymphocytes. In Western countries EATL accounts for 5% of all gastrointestinal lymphomas and in 80–90% of all cases this lymphoma is associated with celiac disease (CD). Based on clinical presentation, EATL can be divided into two subtypes: primary and secondary EATL. Primary EATL develops without a preceding history of CD. The first presentation is often perforation or obstruction, which leads to diagnosis of both EATL and CD. Secondary EATL is diagnosed in patients with well-established CD or refractory CD. These patients deteriorate and eventually develop EATL. The current standard treatment for both types of EATL consists of surgery and chemotherapy, but overall survival (OS) is poor and new therapeutic strategies are urgently needed. For risk-based selection of patients for new therapies and clinical trials, prognostic models as the International Prognostic Index (IPI) are generally used. Since IPI is not predictive for EATL, we determined a prognostic model specifically for EATL, which can identify high-risk patients who need more aggressive therapy. Forty-one patients were diagnosed with EATL and retrospectively analyzed. Two- and 5-years OS were 18% and 10% respectively (range: 0 – 97 months). In multivariate analysis, 3 risk factors were predictive for survival: serum LDH > normal (P < 0.001; RR 6.65; 95% CI 1.96 to 9.89), presence of B-symptoms (P < 0.001; RR 4.41; 95% CI 2.73 to 16.18) and subtype secondary EATL (P = 0.036; RR 2.33; 95% CI 1.06 to 5.13). A weighted point score was assigned to each of these 3 factors and a prognostic model was constructed. Four risk groups were identified (P < 0.0001). Group I showed most favorable outcome: 2- and 5-years OS were 55% and 30% respectively. Although survival rates in groups II, III and IV were significantly different, in none of these groups 2-years survival was achieved. Therefore, the model was simplified to a low risk and a high risk group (P < 0.0001, Figure 1). The low risk group represented patients with no risk factors, i.e. primary EATL with no B-symptoms and normal LDH. In the high risk group, patients had 1 or more of the risk factors elevated serum LDH, B-symptoms or subtype secondary EATL. The new prognostic model showed superior predictive capacity as compared to IPI. In conclusion, our new prognostic model clearly identifies a high and a low risk group. Patients with one or more of the risk factors serum LDH > normal, B-symptoms or subtype secondary EATL are at high risk, and therefore new therapies for this group are urgently needed. Figure 1: Survival in EATL. Low risk group = no risk factors. High risk group = presence of 1 or more of the following risk factors: serum LDH > normal, B-symptoms or subtype secondary EATL. Figure 1:. Survival in EATL. Low risk group = no risk factors. High risk group = presence of 1 or more of the following risk factors: serum LDH > normal, B-symptoms or subtype secondary EATL. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Construction and Validation of an Immune-related Gene Pairs Prognostic Model for Breast Cancer

2021 ◽  
Author(s):  
Ying Zhong ◽  
Zhe Wang ◽  
Yidong Zhou ◽  
Feng Mao ◽  
Yan Lin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Model ◽  
Low Risk ◽  
Cancer Prognosis ◽  
Related Gene ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Risk Patients ◽  
Immune Related Genes

Abstract Background: Immunotherapy plays an increasingly important role in the treatment of advanced female breast cancer, which has the highest mortality rate among malignant tumors. The purpose of this study was to identify immune-related genes associated with breast cancer prognosis as possible targets of immunotherapy, and their related biological processes and signaling pathways.Methods: Clinical data and gene expression profiles of patients with breast cancer were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and divided into training (n = 1053) and verification (n = 508) groups. CIBERSORT was used to predict differences in immune cell infiltration in patient subsets stratified according to risk. Gene Ontology (GO) enrichment analysis was used to identify pathways associated with immune-related genes in patient subsets stratified according to risk.Results: The prognostic model composed of 27 immune-related gene pairs significantly distinguished between high- and low-risk patients. Univariate and multivariate analyses indicated that the model was an independent prognostic factor for breast cancer. Among the identified genes, APOBEC3G, PLXNB1, and C3AR1 had not been previously studied in breast cancer and warrant further exploration. CCR chemokine receptor binding, regulation of leukocyte-mediated cytotoxicity, T cell migration, T cell receptor complex, and other pathways were significantly enriched in low-risk patients. M2 and M0 macrophages were more highly expressed in high-risk than in low-risk patients. CD8+ T cells and naïve B cells were more abundant in low-risk than in high-risk patients.Conclusion: The immune-related gene pairs prognostic model developed in the current study can help assess breast cancer prognosis and provides a potential target and research direction for breast cancer immunotherapy in the future.

scholarly journals Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen-Dong Huang ◽  
Yang-Yang Yao ◽  
Ting-Yu Chen ◽  
Yi-Fan Zhao ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Training Set ◽  
Metabolic Genes ◽  
Validation Set

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P &lt; 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P &lt; 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

scholarly journals Echocardiography and EuroSCORE II for the stratification of low-gradient severe aortic stenosis and preserved left ventricular ejection fraction

2021 ◽  
Author(s):  
Yan Fan ◽  
Hong Shen ◽  
Brandon Stacey ◽  
David Zhao ◽  
Robert J. Applegate ◽  
...  
Keyword(s):  
High Risk ◽  
Left Ventricular Ejection Fraction ◽  
Risk Group ◽  
Severe Aortic Stenosis ◽  
Left Ventricular ◽  
High Risk Group ◽  
Low Risk ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Risk Patients

AbstractThe purpose of this study was to explore the utility of echocardiography and the EuroSCORE II in stratifying patients with low-gradient severe aortic stenosis (LG SAS) and preserved left ventricular ejection fraction (LVEF ≥ 50%) with or without aortic valve intervention (AVI). The study included 323 patients with LG SAS (aortic valve area ≤ 1.0 cm2 and mean pressure gradient < 40 mmHg). Patients were divided into two groups: a high-risk group (EuroSCORE II ≥ 4%, n = 115) and a low-risk group (EuroSCORE II < 4%, n = 208). Echocardiographic and clinical characteristics were analyzed. All-cause mortality was used as a clinical outcome during mean follow-up of 2 ± 1.3 years. Two-year cumulative survival was significantly lower in the high-risk group than the low-risk patients (62.3% vs. 81.7%, p = 0.001). AVI tended to reduce mortality in the high-risk patients (70% vs. 59%; p = 0.065). It did not significantly reduce mortality in the low-risk patients (82.8% with AVI vs. 81.2%, p = 0.68). Multivariable analysis identified heart failure, renal dysfunction and stroke volume index (SVi) as independent predictors for mortality. The study suggested that individualization of AVI based on risk stratification could be considered in a patient with LG SAS and preserved LVEF.

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