Ischemic heart disease: risk stratification and intervention. Risk of poor quality of life.
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Anaerobic Threshold↗
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Ischaemic heart disease: risk stratification and intervention. Risk of progression of coronary artery disease.
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Adult↗
Risk stratification in aortic stenosis: exercise haemodynamics to refine risk in early cardiac damage stages.
AIMS: To describe exercise haemodynamics across cardiac damage stages and evaluate the incremental prognostic impact of cardiac damage stage and exercise-induced pulmonary hypertension (exPHT) in patients with symptomatic moderate aortic stenosis (AS) and asymptomatic severe AS. METHODS AND RESULTS: A total of 436 consecutive patients with ≥ moderate AS (74 ± 10 years, 32% women, 56% severe AS) underwent cardiopulmonary exercise testing with echocardiography. The primary endpoint was heart failure (HF) death and HF hospitalizations. Cardiac damage stage was 0 in 93 patients, 1 (LV damage) in 135, 2 (LA/mitral damage) in 135, and 3-4 (pulmonary vasculature/tricuspid or RV damage) in 73. Higher stages were associated with worse exercise capacity and haemodynamics. Over a median follow-up of 37 months, 65 patients met the primary endpoint. After adjustment for age, AS severity, and aortic valve replacement, cardiac damage stage and exPHT were independently associated with HF outcomes [HR per stage increase 1.51 (1.26-1.82); P < 0.001; exPHT HR 2.36 (1.10-5.07); P = 0.03]. exPHT improved risk stratification in early-stage disease (stages 1-2), conferring an approximately five-fold higher risk of HF events in patients with exPHT [HR 4.45 (1.58-12.59); P < 0.01]. CONCLUSION: In patients with ≥ moderate AS and discordant symptoms, cardiac damage stage and exPHT independently refined HF risk stratification. ExPHT provides incremental prognostic value in early damage stages (1-2), representing over half of the cohort, supporting a stepwise approach of routine damage staging with selective with exPHT assessment with exercise echocardiography in this subgroup to guide more personalized management and potentially optimize AVR timing.
Humans↗
Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer.
Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential for guiding the use of postoperative adjuvant chemotherapy (ACT). In this study, we identified novel prognostic gene expression biomarkers in patients with pStage II CRC and developed a new risk stratification framework for ACT decision-making. First, genome-wide biomarker discovery was conducted to identify prognostic gene expression biomarkers associated with recurrence risk in pStage II CRC. This analysis identified 10 differentially expressed genes as potential biomarkers for recurrence. The efficacy of these biomarkers was then tested using 188 clinical surgical specimens obtained from patients with pStage II CRC. A predictive panel was developed using qRT-PCR and used to assess 93 clinical specimens with an area under the curve (AUC) of 0.82, and its performance was further validated in an independent cohort (n = 95). By incorporating key clinicopathological features, a Gene expression-based Prediction of Recurrence in pStage II CRC (GPRSC) signature was developed, which robustly predicted postoperative recurrence (AUC: 0.80). Finally, combining the GPRSC signature, microsatellite instability status, and conventional criteria, we developed a novel risk stratification system for postoperative ACT decision-making in pStage II CRC. Overall, we identified novel gene expression biomarkers and developed a prognostic signature that informs clinical decision-making regarding postoperative ACT in patients with pStage II CRC.
Humans↗
Risk stratification in angina pectoris.
Risk stratification of the patient with angina pectoris can be accomplished initially by the history, physical examination, and resting electrocardiogram. Noninvasive testing can add important prognostic information and identify patients who might benefit from revascularization procedures. An exercise electrocardiographic test usually is the initial test to determine risk, but exercise 201Th scintigraphy and exercise radionuclide angiography can provide important additional information regarding outcome in selected subsets of patients.
Angina Pectoris↗
Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.
Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.
Humans↗
Assessment of post-infarction jeopardized myocardium by vasodilation--thallium-201 tomography: impact on risk stratification.
For the purpose of risk stratification 80 consecutive patients (mean age 58 +/- 7 years) with a chest pain syndrome after documented myocardial infarction underwent tomographic vasodilation-redistribution thallium-201 perfusion imaging, using 0.56 mg kg-1 intravenous dipyridamole. Tomograms were analysed for size and location of reversible and fixed perfusion defects and correlated to angiographic characteristics, left ventricular ejection fraction and wall motion, collateral status and 1-year prognosis, as measured by cardiac events within 12 months. No serious side-effects were noted with the diagnostic use of intravenous dipyridamole. According to the perfusion pattern three subgroups of post-infarction patients were identified: (1) by ischaemia at a distance with redistribution in non-infarct related territories (n = 48); (2) by peri-infarctional ischaemia with redistribution in the territory of the 'infarct artery' (n = 9); and (3) by exclusively fixed defects without redistribution (n = 23). Ischaemia at a distance was associated with a larger reversible defect than peri-infarctional ischaemia (P less than 0.05) and the pattern without redistribution (P less than 0.005); the fixed defect size, however, was similar in all three subgroups. In addition, the severity of coronary artery disease (Gensini score and number of diseased vessels) and the degree of collateralization was higher in the presence of a redistribution pattern (P less than 0.05), although no significant differences in global and regional function were noted as a function of thallium-201 redistribution.(ABSTRACT TRUNCATED AT 250 WORDS)
Adult↗
[Risk stratification in patients recuperating from acute myocardial infarct. How useful is it?].
The author intended to discuss the individual prognostic value of some best known risk stratification variables after Acute Myocardial Infarction (AMI), whose tests have a low diagnostic accuracy due to high risk individual variations, emphasizing the statistic errors of using one single risk variable on post AMI risk stratification schemes. Some epidemiologic data about AMI survivors are reviewed and the author concludes by emphasizing the importance of using more AMI population characteristics on post AMI risk stratification schemes.
Arrhythmias, Cardiac↗
Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy.
Breast cancer risk stratification models identify individuals at increased risk, allowing earlier screening than for those at average risk and potentially improving health outcomes. Due to the increasing rates of breast cancer in individuals aged <40 years, especially among Black females, the American College of Radiology now recommends all females initiate breast cancer risk assessment by age 25 years. Several breast cancer risk prediction models are readily available, including the Gail Model, Breast Cancer Surveillance Consortium Risk Calculator, BOADICEA, and Tyrer-Cuzick Model. However, because these models were primarily developed using data from White women of European ancestry, they may underestimate risk in Black women. Indeed, current evidence suggests that these models underpredict breast cancer risk among Black women, particularly those of African ancestry. Although cancer risk prediction models typically incorporate personal characteristics, family history of cancer, and hormonal and lifestyle factors, inherited breast cancer genes can also increase risk for breast cancer. Beyond monogenic inherited breast cancer genes that increase breast cancer risk, emerging data suggest that single nucleotide polymorphisms identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores, which may further refine breast cancer risk. However, GWAS data are also primarily gathered from European ancestry females, further reducing the ability to accurately stratify breast cancer risk in non-European ancestry populations. Current data highlight the importance of ensuring representation from all populations in developing cancer risk prediction models, conducting genomics research, and designing effective implementation strategies to enhance the use of these models in routine clinical care. Although new analytic methods and models are being developed to improve breast cancer risk stratification across populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations.
Humans↗
International Liver Cancer Association (ILCA) white paper on hepatocellular carcinoma risk stratification and surveillance.
Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies.
Humans↗
Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.
PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.
Humans↗
Circulating IGF2BP3 enables risk stratification and predicts treatment response in Ewing sarcoma.
Ewing sarcoma (EWS), the second most common pediatric bone tumor, presents with a markedly heterogeneous clinical spectrum and optimal risk stratification is therefore crucial for improving treatment outcomes. The RNA-binding protein IGF2BP3 is a critical oncogenic driver of EWS malignancy. This study evaluates the clinical utility of circulating IGF2BP3 as a biomarker to predict treatment response and risk of disease progression in patients with EWS. Plasma samples from 60 patients with EWS diagnosed and treated at the IRCCS Rizzoli Orthopedic Institute (Bologna, Italy) were collected at diagnosis before treatment initiation and/or after induction chemotherapy. For 51 of these patients, blood was collected at diagnosis, prior to any treatments. For 25 patients, blood samples were available at diagnosis and before surgical intervention, allowing longitudinal analysis in the same patient. For 9 patients, blood was collected only after preoperative chemotherapy, before surgical intervention. Circulating IGF2BP3 levels were quantified using a highly specific and sensitive ELISA assay. Plasma samples from healthy donors served as controls. IGF2BP3 plasma levels were correlated with IGF2BP3 tumor tissue expression, established clinical risk factors, and cumulative incidence of relapse using univariable and multivariable analyses. Plasma IGF2BP3 levels were significantly elevated in patients with EWS compared with healthy controls, with a subset of patients (22/51, 43.2%) exhibiting clinically relevant concentrations. Circulating IGF2BP3 levels reflected tumor expression of the molecule and provided additional prognostic information beyond standard clinicopathologic features. The prognostic impact of circulating IGF2BP3 was primarily observed in patients with localized disease, in whom elevated levels were identified as a significant adverse prognostic factor for disease-specific survival (hazard ratio, 10.63; 95% CI, 1.27-88.62; P = 0.029). Longitudinal monitoring demonstrated that persistence of IGF2BP3 in plasma after induction chemotherapy was a strong predictor of poor clinical outcomes. Circulating IGF2BP3 represents a valuable biomarker for early risk stratification in EWS, particularly in patients with localized disease. Although this is single-marker assay, the expression of the molecule may impact on the fate of many mRNAs. We present an accurate, simple, cost-effective and easy clinical applicable tool to support risk-adapted therapeutic interventions. The limited number of employed patients warrants the need of larger cohorts for validation.
Humans↗
Perioperative myocardial infarction after coronary artery bypass surgery. Clinical significance and approach to risk stratification.
The clinical significance of perioperative myocardial infarction (MI) after coronary artery bypass surgery is not known. Therefore, strategies for the risk stratification of these patients do not exist. This study was undertaken to define the effect of perioperative MI on prognosis after discharge from the hospital and to develop an approach to the risk stratification of these patients. Fifty-nine patients with and 115 patients without perioperative MI were observed for 30 months for the development of cardiac events (death, nonfatal MI, and admission to hospital for unstable angina or congestive heart failure). Patients with perioperative MI were significantly more likely than patients without to have a cardiac event (31% versus 12%, p less than 0.01) and multiple events (19% versus 1%, p less than 0.001). Cox regression analysis identified two independent predictors of cardiac events other than perioperative MI (relative risk, 2.7): inadequate revascularization (relative risk, 3.5) and depressed (less than 40%) postoperative ejection fraction (EF) (relative risk, 2.1). Event-free survival rate of patients with perioperative MI varied markedly depending on the number of other negative prognostic variables present. Patients with perioperative MI who were adequately revascularized and had a postoperative EF greater than 40% had an event-free survival rate similar to patients without a perioperative MI (92% versus 87%, p = NS). Patients with perioperative MI who were inadequately revascularized and had depressed postoperative EF had an event-free survival rate of 13% (p less than 0.001 versus all other subsets). Event-free survival rate was intermediate (68%) in patients with perioperative MI and with only one of the other two variables (p less than 0.001 versus other subsets). In conclusion, perioperative MI adversely affects prognosis. Patients can be stratified into low, high, and intermediate risk subsets based on a simple assessment of the adequacy of revascularization and a determination of residual left ventricular function.
Coronary Artery Bypass↗
A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.
BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited. METHODS: We conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated. RESULTS: Women who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort. CONCLUSION: Early microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.
Humans↗
Genome-wide association, polygenic risk scores, and machine learning for chronic post-surgical pain risk stratification: A UK biobank study.
Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established heritability of pain phenotypes, large-scale genetic studies remain limited. This study aimed to identify genetic variants associated with chronic post-surgical pain, develop polygenic risk scores, and integrate these with clinical features for risk prediction. UK Biobank data from 47,836 participants (2490 cases and 45,346 controls) were split into training (80%; n = 38,268) and validation (20%; n = 9568) sets prior to analysis. A genome-wide association study was conducted on the training set only, across 19 million variants, and polygenic risk scores were constructed and integrated with clinical features in a logistic regression framework. Two close, rare, imputed signals crossed the genome-wide significance threshold but lacked local linkage-disequilibrium support, while 220 variants crossed the suggestive threshold. In the held-out validation set, cases had higher mean polygenic risk scores than controls (0.138 vs. -0.021; Cohen's d = 0.16, p < 0.001). A logistic regression model integrating clinical features and polygenic risk scores achieved an area under the curve of 0.639 (95% CI: 0.583-0.693), higher than models using either feature set alone. The polygenic risk score for chronic post-surgical pain was among the most important predictors. Risk stratification revealed the top quartile had 3.84-fold higher odds of chronic post-surgical pain than the bottom quartile (95% CI: 2.00-7.37). These findings suggest a possible modest genetic contribution to chronic post-surgical pain. Polygenic risk scores may complement clinical factors in surgical risk stratification. PERSPECTIVE: Chronic post-surgical pain may have a modest genetic contribution. This UK Biobank study identified over 220 variants at suggestive significance and constructed a polygenic risk score that was significantly elevated in cases. A combined clinical-genomic model achieved a 3.84-fold difference in odds across predicted-risk quartiles.
Chronic post-surgical pain↗
Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.
BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P < 0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P < 0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.
cross-attention fusion↗
Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.
BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.
Humans↗
The role of risk stratification in the early management of a myocardial infarction.
OBJECTIVE: To review the literature on early management of myocardial infarction. DATA SOURCES: Papers published or referenced in major English-language cardiology journals for the last 15 years. STUDY SELECTION: Large recent multicenter studies and the guidelines for early management of patients with acute myocardial infarction (American College of Cardiology/American Heart Association Task Force) were emphasized. DATA SYNTHESIS: A strategy for risk stratification was developed from information available in the emergency department, from the first days of the hospitalization, and before discharge to identify patients in whom intervention might improve prognosis. CONCLUSIONS: Treatment of myocardial infarction requires establishing patency of the infarct-related artery, usually with thrombolysis. Risk stratification begins in the emergency department (phase 1) using clinical (primarily the electrocardiogram) and historical data to identify patients at risk for massive infarction. For patients at highest risk, the efficacy of thrombolytic therapy must be assured and, if not effective, emergency angiography and mechanical reperfusion should be considered. During days 2 to 5, (phase 2), patients with large amounts of ischemic myocardium, postinfarction angina, "flash" pulmonary edema, or anterior non-Q infarctions are identified and studied. Predischarge (phase 3) stratification identifies those patients at risk for early death. A previous infarction, an ejection fraction less than 0.40, pulmonary congestion during the hospitalization, delayed afterpotentials on signal-averaged electrocardiography, symptomatic ventricular ectopic beats, decreased heart rate variability, limited exercise tolerance, or ischemia on exercise testing identifies patients at high risk. Patients with jeopardized myocardium must be identified for revascularization to try to improve survival. More data are needed to determine whether angioplasty or bypass surgery will improve prognosis in these patients.
Algorithms↗