scholarly journals Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-41
Author(s):  
Feng Qiu ◽  
Yumei Zhu ◽  
Yafeng Shi ◽  
Jingjing Ji ◽  
Yingchao Jin
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Immune Cells ◽  
Gene Signature ◽  
Risk Scores ◽  
Training Set ◽  
Chemotherapy Drugs ◽  
Efficacy Assessment

Objective. Due to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer. Methods. An immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined. Results. Our results showed that high risk scores were indicative of undesirable survival outcomes both in the training set ( p < 0.0001 ) and the validation set ( p = 0.002 ). Moreover, this signature could independently predict patients’ survival (HR: 2.656 (1.919-3.676) and p < 0.001 ). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05 ). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways ( p < 0.05 ). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs ( p < 0.05 ). There was heterogeneity in immune cells between high- and low-risk samples ( p < 0.05 ). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score ( p < 0.05 ). Conclusion. This immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.

scholarly journals DNA Repair and Replication-Related Gene Signature Based on Tumor Mutation Burden Reveals Prognostic and Immunotherapy Response in Gastric Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Lei Zhang ◽  
Dahai Hu ◽  
Shuchen Huangfu ◽  
Jiaxin Zhou ◽  
Wei Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Tumor Mutation Burden ◽  
Mutation Burden

The genomic variant features (mutations, deletions, structural variants, etc.) within gastric cancer impact its evolution and immunogenicity. The tumor has developed several coping strategies to respond to these changes by DNA repair and replication (DRR). However, the intrinsic relationship between the associated DRR-related genes and gastric cancer progression remained unknown. This study selected DRR-related genes with tumor mutation burden based on the TCGA (The Cancer Genome Atlas) database of gastric cancer transcriptome and mutation data. The prognosis model of seven genes (LAMA2, CREB3L3, SELP, ABCC9, CYP1B1, CDH2, and GAMT) was constructed by a univariate and LASSO regression analysis and divided into high-risk and low-risk groups with the median risk score. Survival analysis showed that overall survival (OS) was lower in the high-risk group than that in the low-risk group. Moreover, patients with gastric cancer in the high-risk group have worse survival in different subgroups, including age, gender, histological grade, and TNM stage. The nomogram that included risk scores for DRR-related genes could accurately foresee OS of patients with gastric cancer. Interestingly, the tumor mutation burden score was higher in the low-risk group than that in the high-risk group, and the risk score for DRR-related genes was negatively correlated with tumor mutation burden in gastric cancer. Next, we further combined the risk score and tumor mutation burden to evaluate the prognosis of gastric cancer patients. The low-risk cohort had a better prognosis than the high-risk cohort in the high tumor mutation burden subgroup. The number of mutation types in the high-risk group was lower than that in the low-risk group. In the immune microenvironment of gastric cancer, more naïve B cells, memory resting CD4+ T cells, Treg cells, monocytes cells, and resting mast cells were infiltrated in the high-risk group. At last, PD-L1 and IAP expressions were negatively correlated with the risk scores; patients with gastric cancer in the low-risk group showed better immunotherapy outcomes than those in the high-risk group. Overall, the DRR-related gene signature based on tumor mutation burden is a novel biomarker for prognostic and immunotherapy response in patients with gastric cancer.

scholarly journals Prognostic Implications of Immune-Related Gene Pairs Signatures in Bladder Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Hui Xiong ◽  
Hui Gao ◽  
Jinding Hu ◽  
Yun Dai ◽  
Hanbo Wang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Related Gene ◽  
Gene Pairs ◽  
Immune Related Gene

Compelling evidence indicates that immune function is correlated with the prognosis of bladder cancer (BC). Here, we aimed to develop a clinically translatable immune-related gene pairs (IRGPs) prognostic signature to estimate the overall survival (OS) of bladder cancer. From the 251 prognostic-related IRGPs, 37 prognostic-related IRGPs were identified using LASSO regression. We identified IRGPs with the potential to be prognostic markers. The established risk scores divided BC patients into high and low risk score groups, and the survival analysis showed that risk score was related to OS in the TCGA-training set ( p < 0.001 ; HR = 7.5 [5.3, 10]). ROC curve analysis showed that the AUC for the 1-year, 3-year, and 5-year follow-up was 0.820, 0.883, and 0.879, respectively. The model was verified in the TCGA-testing set and external dataset GSE13507. Multivariate analysis showed that risk score was an independent prognostic predictor in patients with BC. In addition, significant differences were found in gene mutations, copy number variations, and gene expression levels in patients with BC between the high and low risk score groups. Gene set enrichment analysis showed that, in the high-risk score group, multiple immune-related pathways were inhibited, and multiple mesenchymal phenotype-related pathways were activated. Immune infiltration analysis revealed that immune cells associated with poor prognosis of BC were upregulated in the high-risk score group, whereas immune cells associated with a better prognosis of BC were downregulated in the high-risk score group. Other immunoregulatory genes were also differentially expressed between high and low risk score groups. A 37 IRGPs-based risk score signature is presented in this study. This signature can efficiently classify BC patients into high and low risk score groups. This signature can be exploited to select high-risk BC patients for more targeted treatment.

scholarly journals A Comprehensive Prognostic and Immunological Analysis of a Six-Gene Signature Associated With Glycolysis and Immune Response in Uveal Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Wenli Li
Keyword(s):  
Immune Response ◽  
Risk Stratification ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Prediction ◽  
Regression Analyses ◽  
Training Set ◽  
Kaplan Meier

Uveal melanoma (UM) is a subtype of melanoma with poor prognosis. This study aimed to construct a new prognostic gene signature that can be used for survival prediction and risk stratification of UM patients. In this work, transcriptome data from the Molecular Signatures Database were used to identify the cancer hallmarks most relevant to the prognosis of UM patients. Weighted gene co-expression network, univariate least absolute contraction and selection operator (LASSO), and multivariate Cox regression analyses were used to construct the prognostic gene characteristics. Kaplan–Meier and receiver operating characteristic (ROC) curves were used to evaluate the survival predictive ability of the gene signature. The results showed that glycolysis and immune response were the main risk factors for overall survival (OS) in UM patients. Using univariate Cox regression analysis, 238 candidates related to the prognosis of UM patients were identified (p &lt; 0.05). Using LASSO and multivariate Cox regression analyses, a six-gene signature including ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3, and SLC39A4 was constructed. Kaplan–Meier analysis of the UM cohort in the training set showed that patients with higher risk scores had worse OS (HR = 2.61, p &lt; 0.001). The time-dependent ROC (t-ROC) curve showed that the risk score had good predictive efficiency for UM patients in the training set (AUC &gt; 0.9). Besides, t-ROC analysis showed that the predictive ability of risk scores was significantly higher than that of other clinicopathological characteristics. Univariate and multivariate Cox regression analyses showed that risk score was an independent risk factor for OS in UM patients. The prognostic value of risk scores was further verified in two external UM cohorts (GSE22138 and GSE84976). Two-factor survival analysis showed that UM patients with high hypoxia or immune response scores and high risk scores had the worst prognosis. Moreover, a nomogram based on the six-gene signature was established for clinical practice. In addition, risk scores were related to the immune infiltration profiles. Taken together, this study identified a new prognostic six-gene signature related to glycolysis and immune response. This six-gene signature can not only be used for survival prediction and risk stratification but also may be a potential therapeutic target for UM patients.

scholarly journals Primary care practices’ ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
David A. Dorr ◽  
Rachel L. Ross ◽  
Deborah Cohen ◽  
Devan Kansagara ◽  
Katrina Ramsey ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Ed Visits ◽  
Care Practices ◽  
Primary Care Practices

Abstract Background Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. Methods Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. Results In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. Conclusions Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

scholarly journals Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoxiang Gong ◽  
Lingjuan Liu ◽  
Jie Xiong ◽  
Xingfang Li ◽  
Jie Xu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cells ◽  
Calcium Binding ◽  
Risk Group ◽  
Binding Protein ◽  
Gene Signature ◽  
High Risk Group ◽  
Training Set ◽  
Immune Infiltration

Background. Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning. Methods. We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA. Results. We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group. Conclusion. We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

scholarly journals A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Zong-xiu Yin ◽  
Chun-yan Xing ◽  
Guan-hua Li ◽  
Long-bin Pang ◽  
Jing Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Scores ◽  
Biological Pathway ◽  
Training Cohort ◽  
Therapeutic Recommendations

Abstract Background Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy. Methods We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8). Results Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels. Conclusions In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

scholarly journals The Diagnostic and Prognostic Value of BEN-Domain (BEND) Family Genes in Gastric Cancer

2022 ◽  
Author(s):  
Jiaxin Fan ◽  
Min Yang ◽  
Chaojie Liang ◽  
Chaowei Liang ◽  
Jiansheng Guo
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Malignant Tumors ◽  
Risk Groups ◽  
Risk Scores ◽  
Gastric Cancer Patients

Abstract BEND(BEN domain-containing protein)is a domain protein-coding gene, whose abnormal expression is related to the occurrence of malignant tumors. But studies on gastric cancer are rare. We attempted to investigate the role of BEND family genes in evaluating the prognosis of gastric cancer and guiding clinical treatment. We analyzed the BEND family genes expression, prognostic value, and drug sensitivity in pan-cancer, and the correlation between their expression and tumor microenvironment of gastric cancer, stemness index, immune subtypes, and clinicopathological characteristics were analyzed. We constructed a model using BEND3P1 and BEND6 to evaluate the prognosis of gastric cancer patients. Multivariate Cox proportional risk model analysis showed that risk score is an independent risk factor for gastric cancer patients. To assess the value of risk score for prognosis, patients were divided into high-risk and low-risk groups based on median risk scores, and survival analyses were performed. The results showed that the OS of patients with high-risk scores is significantly lower. We also constructed a nomogram to predict individual survival probability using the BEND risk score and clinical case characteristics. In conclusion, the BEND family genes can predict the prognosis and guide the treatment of gastric cancer patients.

scholarly journals Identification of Metastasis-Related Signature For Predicting Survival In Osteosarcoma

2021 ◽  
Author(s):  
Song Shi ◽  
Shuaijie Yang ◽  
Zhenyu Zhou ◽  
Kai Sun ◽  
Ran Tao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Scores ◽  
Activation Markers

Abstract BackgroundRNA sequencing has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. In this study, we aimed to develop a signature to improve the prognostic predictions of osteosarcoma.Materials and methodsBy comparing the expression profiles between metastatic and non-metastatic samples, we obtained 57 metastatic-related gene signatures. Then we constructed a 3‐gene signature to predict the prognostic risk of osteosarcoma patients by the Cox proportional hazards regression model. The risk score derived from this signature could successfully stratify osteosarcoma patients into subgroups with different survival outcomes.ResultsPatients in the low-risk group showed more prolonged overall survival than those in the high-risk group. And the performance was validated with another independent dataset. Multivariate cox regression revealed that the risk score served as an independent risk factor. Besides, we found that patients with low-risk scores had higher expression levels of immune-related signatures, suggesting an active immune status in those patients. Using the CIBERSORT database, we further systematically analyzed the relationships between the risk score and immune cell infiltration levels, as well as the immune activation markers. Higher infiltration of immune cells (CD8 T cells, monocytes, M2 macrophages, and memory B cells) and higher levels of immune cytotoxic markers (GZMA, GMZB, IFNG, and TNF) were observed in patients in the low-risk group.ConclusionsIn summary, this 3-gene signature could be a reliable marker for prognostic evaluation and help clinicians identify high‐risk patients.

scholarly journals Development and Validation of a Prognostic Classifier Based on Lipid Metabolism–Related Genes in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiao-Li Wei ◽  
Tian-Qi Luo ◽  
Jia-Ning Li ◽  
Zhi-Cheng Xue ◽  
Yun Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lipid Metabolism ◽  
High Risk ◽  
Risk Score ◽  
Roc Analysis ◽  
Immune Cell ◽  
Low Risk ◽  
Risk Scores ◽  
Predictive Score ◽  
Score Model

Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients.Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 (n = 300) and GSE26942 (n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors.Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high–risk and low–risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival probability of patients at 1, 3, and 5 years.Conclusion: A novel 19-gene risk predictive score model was developed based on the lipid metabolism–related genes, which could be a potential prognostic indicator and therapeutic target of GC.

Abstract 5585: Understanding Physicians’ Risk Stratification of Acute Coronary Syndromes: Insights from the Canadian ACS II Registry

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Andrew T Yan ◽  
Raymond T Yan ◽  
Thao Huynh ◽  
Amparo Casanova ◽  
F. E Raimondo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Acute Coronary Syndromes ◽  
Risk Scores ◽  
Risk Category ◽  
Patient Risk ◽  
Treatment Risk ◽  
Coronary Syndromes

An important treatment-risk paradox exists in the management of acute coronary syndromes (ACS). However, the process of risk stratification by physicians and its relationship to patient management have not been well studied. Our objective was to examine patient risk assessment by physician in relation to treatment and objective risk score evaluation, and the underlying patient characteristics that physicians consider to indicate high risk. The prospective Canadian ACS II Registry recruited 1956 patients admitted for non-ST elevation ACS in 36 hospitals in Oct 2002-Dec 2003. Patient risk assessment by the treating physician and management were recorded on standardized case report forms. We calculated the TIMI, PURSUIT and GRACE risk scores for each patient. Of the 1956 ACS patients, 347 (17.8%) patients were classified as low risk, 822 (42%) as intermediate risk, and 787 (40.2%) as high risk by their treating physicians. Patients considered as high risk were more likely to receive aggressive medical therapies and to undergo coronary angiography and revascularization. However, there were only weak correlations (Kendall’s tau-b correlation coefficients ranging from 0.08 to 0.14) between risk assessment by physicians and all 3 validated risk scores. Advanced age was an independent negative predictor. Furthermore, there was no significant association between the high risk category and several established prognosticators, such as history of heart failure, hemodynamic variables, and creatinine. Contemporary risk stratification of ACS appears suboptimal and may perpetuate the treatment-risk paradox. Physicians may not recognize and incorporate the most powerful adverse prognosticators into overall patient risk assessment. Routine use of validated risk score may enhance risk stratification and facilitate more appropriate tailoring of intensive therapies towards high-risk patients.

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