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Prediction of ischemic events on the basis of transcriptomic and genomic profiling in patients undergoing carotid endarterectomy.

ABSTRACT: Classic risk factors, including age, smoking, serum cholesterol, diabetes and blood pressure, constitute the basis of present risk prediction models but fail to identify all individuals at risk. The objective of this study was to investigate if genomic and transcriptional patterns improve prediction of ischemic events in patients with established carotid artery disease. Genotype and gene expression profiles were obtained from carotid plaque tissue (n = 126) and peripheral blood mononuclear cells (n = 97) of patients undergoing carotid endarterectomy. Patients were followed for an average of 44 months, and 25 ischemic events occurred (18 ischemic strokes and 7 myocardial infarctions). Blinded leave-one-out cross-validation on Cox regression coefficients was used to assign gene expression-based risk scores to each patient. When compared with classic risk factors, addition of carotid plaque gene expression-based risk score improved the prediction of future ischemic events from an area under the curve (AUC) of 0.66 to an AUC of 0.79. The inclusion of gene expression risk score from peripheral blood mononuclear cells or from 25 established myocardial infarction risk single nucleotide polymorphisms only exhibited marginal effects on the prediction of ischemic events. Prediction of ischemic events is improved by inclusion of gene expression profiling from carotid endarterectomy tissue compared with prediction on the basis of classic risk markers alone in patients with atherosclerosis. The method may be developed to identify subjects at very high risk of ischemic events.

SUBMITTER: Folkersen L

PROVIDER: S-EPMC3388132 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Prediction of ischemic events on the basis of transcriptomic and genomic profiling in patients undergoing carotid endarterectomy.

Folkersen Lasse L Persson Jonas J Ekstrand Johan J Agardh Hanna E HE Hansson Göran K GK Gabrielsen Anders A Hedin Ulf U Paulsson-Berne Gabrielle G

Molecular medicine (Cambridge, Mass.) 20120509

Classic risk factors, including age, smoking, serum cholesterol, diabetes and blood pressure, constitute the basis of present risk prediction models but fail to identify all individuals at risk. The objective of this study was to investigate if genomic and transcriptional patterns improve prediction of ischemic events in patients with established carotid artery disease. Genotype and gene expression profiles were obtained from carotid plaque tissue (n = 126) and peripheral blood mononuclear cells ...[more]

PMID: 22371308

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Project description:ObjectiveMost studies based on state and nationwide registries evaluating perioperative outcome after carotid endarterectomy (CEA) rely on hospital discharge data only. Therefore, the true 30-day complication risk after carotid revascularization may be underestimated.MethodsWe used the National Surgical Quality Improvement Program database 2005-2010 to assess the in-hospital and postdischarge rate of any stroke, death, cardiac event (new Q-wave myocardial infarction or cardiac arrest), and combined stroke/death and combined adverse outcome (S/D/CE) at 30 days following CEA. Multivariable analyses were used to identify predictors for in-hospital and postdischarge events separately, and in particular, those that predict postdischarge events distinctly.ResultsA total of 35,916 patients who underwent CEA during 2005-2010 were identified in the National Surgical Quality Improvement Program database; 59% were male, median age was 72 years, and 44% had a previous neurologic event. Thirty-day stroke rate was 1.6% (n = 591), death rate was 0.8% (n = 272), cardiac event rate was 1.0% (n = 350), stroke or death rate was 2.2% (n = 794), and combined S/D/CE rate was 2.9% (n = 1043); 33% of strokes, 53% of deaths, 32% of cardiac events, 40% of combined stroke/death, and 38% of combined S/D/CE took place after hospital discharge. Patients with a prior stroke or transient ischemic attack had similar proportions of postdischarge events compared with patients without prior symptoms. Independent predictors for postdischarge events, but not for in-hospital events were female sex (stroke [odds ratio (OR), 1.6; 95% confidence interval (CI), 1.2-2.1] and stroke/death [OR, 1.4; 95% CI, 1.1-1.7]), renal failure (stroke [OR, 3.0; 95% CI, 1.4-6.2]) and chronic obstructive pulmonary disease (death [OR, 2.5; 95% CI, 1.6-3.7], stroke/death [OR, 1.8; 95% CI, 1.4-2.4], and S/D/CE [OR 1.8, 95% CI 1.4-2.3]).ConclusionsWith 38% of perioperative adverse events after CEA happening posthospitalization, regardless of symptoms status, we need to be alert to the ongoing risks after discharge particularly in women, patients with renal failure, or chronic obstructive pulmonary disease. This emphasizes the need for reporting and comparing 30-day adverse event rates when evaluating outcomes for CEA, or comparing carotid stenting to CEA.

Project description:Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.

Project description:BACKGROUND AND PURPOSE:Techniques to stratify subgroups of patients with asymptomatic carotid artery disease are urgently needed to guide decisions on optimal treatment. Reliance on estimates of % luminal stenosis has not been effective, perhaps because that approach entirely disregards potentially important information on the pathological process in the wall of the artery. METHODS:Since plaque lipid is a key determinant of plaque behaviour we used a newly validated, high-sensitivity T2-mapping MR technique for a systematic survey of the quantity and distribution of plaque lipid in patients undergoing endarterectomy. Lipid percentage was quantified in 50 carotid endarterectomy patients. Lipid distribution was tested, using two imaging indices (contribution of the largest lipid deposit towards total lipid (LLD %) and a newly-developed LAI 'lipid aggregation index'). RESULTS:The bifurcation contained maximal lipid volume. Lipid percentage was higher in symptomatic vs. asymptomatic patients with degree of stenosis (DS ? 50%) and in the total cohort (P = 0.013 and P = 0.005, respectively). Both LLD % and LAI was higher in symptomatic patients (P = 0.028 and P = 0.018, respectively), suggesting that for a given plaque lipid volume, coalesced deposits were more likely to be associated with symptomatic events. There was no correlation between plaque volume or lipid content and degree of luminal stenosis measured on ultrasound duplex (r = -0.09, P = 0.53 and r = -0.05, P = 0.75), respectively. However, there was a strong correlation in lipid between left and right carotid arteries (r = 0.5, P <0.0001, respectively). CONCLUSIONS:Plaque lipid content and distribution is associated with symptomatic status of the carotid plaque. Importantly, plaque lipid content was not related to the degree of luminal stenosis assessed by ultrasound. Determination of plaque lipid content may prove useful for stratification of asymptomatic patients, including selection of optimal invasive treatments.

Project description:ImportanceRandomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years.ObjectiveTo develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis.Design, setting, and participantsIn this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples.Main outcome and measureFive-year mortality.ResultsAmong 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities.Conclusions and relevanceAccording to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis.

Dataset Information

Prediction of ischemic events on the basis of transcriptomic and genomic profiling in patients undergoing carotid endarterectomy.

Publications

Prediction of ischemic events on the basis of transcriptomic and genomic profiling in patients undergoing carotid endarterectomy.

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