A simplified clinical electrocardiogram score for the prediction of cardiovascular mortality

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram

BACKGROUND

Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling.

METHODS

This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices.

RESULTS

The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]).

CONCLUSION

ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Electrocardiography score based on the Minnesota code classification system predicts cardiovascular mortality in an asymptomatic low-risk population

BACKGROUND

The use of a single abnormal finding on electrocardiography (ECG) is not recommended for stratifying the risk of cardiovascular (CV) events in low-risk general populations because of its low discriminative power. However, the value of a scoring system containing multiple abnormal ECG findings for predicting CV death has not been sufficiently evaluated.

METHODS

In a prospective community-based cohort study, 8417 participants without atherosclerotic CV diseases (ASCVDs) and any related symptoms were followed for 18 years. The standard 12-lead ECGs were recorded at baseline and the ECG findings were categorized using the Minnesota code classification. CV deaths were defined as death from myocardial infarction (MI), chronic ischemic heart disease, heart failure, fatal arrhythmia, cerebrovascular event, pulmonary thromboembolism, peripheral vascular disease and sudden cardiac arrest and identified using the Korean National Statistical Office (KOSTAT) database.

RESULTS

In a multivariate Cox proportional hazard (CPH) model, major and minor ST-T wave abnormalities, atrial fibrillation (AF), Q waves in the anterior leads, the lack of Q waves in the posterior leads, high amplitudes of the left and right precordial leads, left axis deviation and sinus tachycardia were associated with higher risks of CV deaths. The ECG score consisted of these findings showed modest predictive values represented by C-statistics that ranged from 0.632 to 760 during the follow-up and performed better in the early follow-up period. The ECG score independently predicted CV death after adjustment for relevant covariates in a multivariate model, and improved the predictive performance of the 10-year ASCVD risk estimator and a model of conventional risk factors including age, diabetes and current smoking. The combined ECG score (Harrell's C-index: 0.852, 95% confidence interval [CI], 0.828-0.876) composed of the ECG score and the conventional risk factors outperformed the 10-year ASCVD risk estimator (Harrell's C-index: 0.806; 95% CI, 0.780-0.833) and the model of the conventional risk factors (Harrell's C-index: 0.841, 95% CI, 0.817-0.865) and exhibited an excellent goodness of fit between the predicted and observed probabilities of CV death.

CONCLUSIONS

The ECG score could be useful to predict CV death independently and may add value to the conventional CV risk estimators regarding the risk stratification of CV death in asymptomatic low-risk general populations.

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

Background

Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.

Methods

We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (S_tr) and internal validation (S_v) sets [S_tr (1): S_v (1): 50:50; S_tr (2): S_v (2): 60:40; S_tr (3): S_v (3): 70:30; S_tr (4): S_v (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from S_tr to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (M_ecg), CAC alone (M_cac) and a combination of eRiS and CAC (M_ecg+cac) for MACE prediction. A nomogram (M_nom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of M_ecg, M_cac, M_ecg+cac and M_nom in predicting CV disease-free survival in ASCVD.

Findings

Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (S_tr). The M_ecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all S_v). The M_ecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that M_ecg+cac was superior to M_ecg (p = 1.8e-10) or M_cac (p < 2.2e-16) alone. The M_nom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, M_nom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.

Conclusion

The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Major adverse cardiovascular events in older emergency department patients presenting with non-cardiac medical complaints

OBJECTIVE

The risk of major adverse cardiovascular events (MACE) for older emergency department (ED) patients presenting with non-cardiac medical complaints is unknown. To apply preventive measures timely, early identification of high-risk patients is incredibly important. We aimed at investigating the incidence of MACE within one year after their ED visit and the predictive value of high-sensitivity cardiac troponin T (hs-cTnT) and N‑terminal pro-B-type natriuretic peptide (NT-proBNP) for subsequent MACE.

METHODS

This is a substudy of a Dutch prospective cohort study (RISE UP study) in older (≥ 65 years) medical ED patients who presented with non-cardiac complaints. Biomarkers were measured upon ED arrival. Cox-regression analysis was used to determine the predictive value of the biomarkers, when corrected for other possible predictors of MACE, and area under the curves (AUCs) were calculated.

RESULTS

Of 431 patients with a median age of 79 years, 86 (20.0%) developed MACE within 1 year. Both hs-cTnT and NT-proBNP were predictive of MACE with an AUC of 0.74 (95% CI 0.68-0.80) for both, and a hazard ratio (HR) of 2.00 (95% CI 1.68-2.39) and 1.82 (95% CI 1.57-2.11) respectively. Multivariate analysis correcting for other possible predictors of MACE revealed NT-proBNP as an independent predictor of MACE.

CONCLUSION

Older medical ED patients are at high risk of subsequent MACE within 1 year after their ED visit. While both hs-cTnT and NT-proBNP are predictive, only NT-proBNP is an independent predictor of MACE. It is likely that early identification of those at risk offers a window of opportunity for prevention.

Can the ECG be used to estimate age-related survival?

BACKGROUND

There are few reports of the relationship between electrocardiogram (ECG) findings and the age-related survival of acutely ill patients.

AIM

This study compared the 1-year survival curves of patients attending two Danish emergency departments (EDs) with normal and abnormal ECGs. Patients were divided into age groups from 20 to 90 years of age, and an abnormal ECG was defined as low QRS voltage (i.e. lead I + II <1.4 mV) or QTc interval prolongation >434 ms.

METHODS

A retrospective register-based observational study on 35 496 patients attending two Danish EDs, with 100% follow-up for 1 year.

RESULTS

ECG abnormality increases linearly with age, and between 30 and 70 years of age. Patients aged 20-29 years with ECG abnormalities are more than four times more likely to die within a year than patients of the same age with a normal ECG. An individual with an abnormal ECG has the same risk of dying within a year as an individual with a normal ECG who is 10 years older. After 70 years of age this tight relationship ends, but for younger individuals with an abnormal ECG the increase in mortality is even higher.

CONCLUSION

An ECG may be a simple practical estimate of age-related survival. For a patient under 70 years, an abnormal QRS voltage or a prolonged QTc interval may increase 1-year mortality to that of a patient ∼10 years older.

Electrocardiogram Risk Score and Prevalence of Subclinical Atherosclerosis: A Cross-Sectional Study

Integrated abnormal electrocardiogram (ECG) parameters predict the risk of cardiovascular disease (CVD); however, its relationship with subclinical CVD is unknown. We aimed to evaluate the association between the integrated ECG risk score and the prevalence of coronary artery calcium (CAC). A cross-sectional study comprised 134,802 participants with no known CVD who underwent ECG and CAC computed tomography. The ECG risk score was the sum of five ECG abnormalities: heart rate of >80 beats, QRS of >110 ms, left ventricular hypertrophy, T-wave inversion, and prolonged QTc. A multinomial regression model was used to estimate the prevalence ratios (PRs) and their 95% confidence intervals (CIs) for prevalent CAC. The prevalence of CAC progressively increased as the ECG risk score increased. After adjustment for conventional CVD risk factors and other confounders, the multivariable-adjusted PRs (95% CI) for a CAC of 1−100 in the 1, 2, and ≥3 ECG risk score groups were 1.06 (1.02−1.10), 1.12 (1.03−1.22), and 1.19 (1.00−1.42), respectively, while the corresponding PRs for a CAC of >100 were 1.03 (0.95−1.12), 1.44 (1.25−1.66), and 1.75 (1.33−2.29), respectively. Integrative ECG scoring may help identify individuals requiring lipid-lowering medications, even in young and asymptomatic populations.

ECG changes in hospitalised patients with COVID-19 infection

The coronavirus disease 2019 (COVID-19) commonly involves the respiratory system but increasingly cardiovascular involvement is recognised. We assessed electrocardiogram (ECG) abnormalities in patients with COVID-19. We performed retrospective analysis of the hospital's COVID-19 database from April to May 2020. Any ECG abnormality was defined as: 1) new sinus bradycardia; 2) new/worsening bundle-branch block; 3) new/worsening heart block; 4) new ventricular or atrial bigeminy/trigeminy; 5) new-onset atrial fibrillation (AF)/atrial flutter or ventricular tachycardia (VT); and 6) new-onset ischaemic changes. Patients with and without any ECG change were compared. There were 455 patients included of whom 59 patients (12.8%) met criteria for any ECG abnormality. Patients were older (any ECG abnormality 77.8 ± 12 years vs. no ECG abnormality 67.4 ± 18.2 years, p<0.001) and more likely to die in-hospital (any ECG abnormality 44.1% vs. no ECG abnormality 27.8%, p=0.011). Coxproportional hazard analysis demonstrated any ECG abnormality (hazard ratio [HR] 1.97, 95% confidence interval [CI] 1.12 to 3.47, p=0.019), age (HR 1.03, 95%CI 1.01 to 1.05, p=0.0009), raised high sensitivity troponin I (HR 2.22, 95%CI 1.27 to 3.90, p=0.006) and low estimated glomerular filtration rate (eGFR) (HR 1.73, 95%CI 1.04 to 2.88, p=0.036) were independent predictors of in-hospital mortality. In conclusion, any new ECG abnormality is a significant predictor of in-hospital mortality.

Prognostic performance of ECG abnormalities compared to vital signs in acutely ill patients in a resource-poor hospital in Uganda

Background

There are few reports of electrocardiogram (ECG) findings and their prognostic value in acutely ill patients admitted to low resource hospitals in sub-Saharan Africa.

Methods

We undertook an observational study of acutely ill medical patients admitted to a low-resource hospital in Uganda. Vital signs were used to calculate the National Early Warning Score (NEWS), and all ECGs were assessed using Tan et al.’s scoring system as described in Clin Cardiol 2009;32:82–86.

Results

There were 1361 ECGs performed, covering 68% of all acutely ill medical patients admitted to the hospital during the study. The most common ECG abnormality was a prolonged QTc interval (42% of all patients) and left ventricular hypertrophy (13.5%). Compared to the 519 patients (38%) with no Tan score abnormality, the 842 (62%) patients with one or more abnormalities were more likely to die in hospital (OR = 2.82; CI_95% = 1.50–5.36) and within 30 days of discharge (OR = 2.46; CI_95% = 1.50–4.08). There was no relationship between age and mortality; however, after adjustment by logistic regression, any NEWS ≥1 on admission, a Tan score of ≥1, and male sex all remained clinically significant predictors of both in-hospital and 30-day mortality.

Discussion

The majority of acutely ill medical patients admitted in a low-resource hospital in sub-Saharan Africa had ECG abnormalities, of which prolonged QTc and left ventricular hypertrophy were most common. Those with any Tan score abnormality were twice as likely to die as those without an abnormality.

QRS voltage is a predictor of in-hospital mortality of acutely ill medical patients

BACKGROUND

Low QRS voltage has been shown to be associated with increased mortality in the general population and in a small pilot study the combined QRS voltage of ECG leads I and II was found to be associated with in-hospital mortality.

HYPOTHESIS

Confirm that low QRS voltage predicts the in-hospital mortality of acutely ill patients, and compare QRS voltage with other predictors of mortality that can be easily, quickly and cheaply obtained at the bedside.

METHODS

Prospective observational study of vital signs, QRS voltage and simple tools used to assess mental, functional and nutritional status at the bedside in unselected acutely ill patients admitted to a resource-poor hospital in sub-Saharan Africa.

RESULTS

Out of 1486 patients, 77 died (5.2%) in hospital. A combined lead I + II voltage <1.8 mV was present in 789 (53.1%) of patients, and significantly associated with in-hospital mortality (odds ratio 3.6, 95% CI 2.0-6.5, χ² 21.2, P < 0.00001). On logistic regression impaired mobility, the National Early Warning Score, male gender and lead I + II voltage were the only independent predictors of mortality. None of the 445 patients who were mobile on admission with a lead I + II voltage ≥ 1.8 mV died in hospital.

CONCLUSIONS

Low QRS voltage, male gender, NEWS, and impaired mobility were independent predictors of in-hospital mortality in the study population. These four variables, which are easily obtained at the bedside, could potentially provide a rapid, easy, and cheap risk stratification system.

Prevalence and prognostic impact of electrocardiographic abnormalities in outpatients with extracardiac artery disease

Identifying cardiac disease in patients with extracardiac artery disease (ECAD) is essential for clinical decision-making. Electrocardiography (ECG) is an easily accessible tool to unmask subclinical cardiac disease and to risk stratify patient with or without manifest cardiovascular disease (CV). We aimed to examine the prevalence and prognostic impact of ECG changes in outpatients with ECAD. Outpatients with carotid or lower extremity artery disease (n = 435) and community-based controls (n = 397) underwent resting ECG. The patients were followed during a median of 4·8 years for CV events (hospitalization or death caused by ischaemic heart disease, cardiac arrest, heart failure, or stroke). ECG abnormalities were classified according to the Minnesota Code. Major (33% versus 15%, P<0·001) but not minor ECG abnormalities (23% versus 26%, P = 0·42) were significantly more common in patients versus controls. During the follow-up, 141 patients experienced CV events. Both major ECG abnormalities [hazard ratio (HR) 1·58, 95% confidence interval (CI) 1·11-2·25, P = 0·012] and any ECG abnormalities (HR 1·57, 95% CI 1·06-2·33, P = 0·024) were significantly associated with CV events after adjustment for potential risk factors. In conclusion, ECG abnormalities were common in these outpatients with ECAD. Major and any ECG abnormalities were independent predictors of CV events. Addition of easily accessible ECG information might be useful in risk stratification for such patients.

An Electrocardiogram-Based Risk Equation for Incident Cardiovascular Disease From the National Health and Nutrition Examination Survey

Importance

Electrocardiography (ECG) may detect subclinical cardiovascular disease (CVD) in asymptomatic individuals, but its role in assessing adverse events beyond traditional risk factors is not clear. Interval and vector data that are commonly available on modern ECGs may offer independent prognostic information that improves risk classification.

Objectives

To derive and validate a CVD risk equation based on ECG metrics and to determine its incremental benefit in addition to the Framingham risk score (FRS).

Design, Setting, and Participants

This study included 3640 randomly selected community-based adults aged 40 to 74 years without known CVD from the First National Health and Nutrition Examination Survey (NHANES I) cohort (1971-1975) and 6329 from the NHANES III cohort (1988-1994). Participants were sampled from across the United States. A risk score to assess incident nonfatal and fatal CVD events was derived based on computer-generated ECG data, including frontal P, R, and T axes; heart rate; and PR, QRS, and QT intervals from NHANES I. The most prognostic variables, along with age and sex, were incorporated into the NHANES ECG risk equation. The equation was evaluated in the NHANES III cohort for an independent validation. Follow-up in the NHANES III cohort was completed on December 31, 2006. Data for this study were analyzed from August 11, 2015, to May 20, 2016.

Main Outcomes and Measures

The primary end point was CVD death. Secondary outcomes included 10-year ischemic heart disease and all-cause death.

Results

The final study sample included 9969 participants (4714 men [47.3%]; 5255 women [52.7%]; mean [SD] age, 55.3 [10.1] years) from both cohorts. Frontal T axis, heart rate, and heart rate-corrected QT interval were the most significant ECG factors in the NHANES I cohort. In the validation cohort (NHANES III), the equation provided for prognostic information for fatal CVD with a hazard ratio (HR) of 3.23 (95% CI, 2.82-3.72); the C statistic was 0.79 (95% CI, 0.76-0.81). When added to the FRS in Cox proportional hazards regression models, the categorical (1%, 5%, and 10% cutoffs) net reclassification improvement was 24%. When the FRS and ECG scores were combined in a single model, the C statistic improved by 0.04 (95% CI, 0.02-0.06) to 0.80 (95% CI, 0.77-0.82). Similar improvements were noted when the ECG score was added to the pooled cohort equation. When the equation for prognostic information about ischemic heart disease and all-cause death was evaluated, the results were similar.

Conclusions and Relevance

An ECG risk score based on age, sex, heart rate, frontal T axis, and QT interval assesses the risk for CVD and compares favorably with the FRS alone in an independent cohort of asymptomatic individuals. Although the ECG risk equation is low cost, further research is needed to ascertain whether this additional step in risk stratification may improve prevention efforts and reduce CVD events.

Electrocardiographic predictors of sudden and non-sudden cardiac death in patients with ischemic cardiomyopathy

OBJECTIVE

This study evaluated the prognostic value of electrocardiogram (ECG)-based predictors in the primary prevention of sudden cardiac arrest (SCA) among ischemic cardiomyopathy patients with depressed left ventricular ejection fraction (LVEF ≤35%).

BACKGROUND

The prediction of cause-specific mortality in high-risk patients offers the potential for targeting specific therapies (i.e., implantable cardioverter-defibrillator [ICD]).

METHODS

Subjects were recruited from the Prediction of Arrhythmic Events with Positron Emission Tomography (PAREPET) study. Continuous Holter 12-lead ECG recordings were obtained at the start of study and used to compute 15 clinically-important ECG abnormalities (e.g., atrial fibrillation).

RESULTS

Among 197 patients (age 67 ± 11 years, 93% male, mean follow-up 4.1 years) enrolled, 30 (15%) were SCA cases and 35 (18%) cardiac non-sudden deaths (C/NS). In multivariate analysis, only heart-rate-corrected QT interval (QTc) predicted SCA (hazard ratio 2.9 [1.2-7.3]) and only depressed heart rate variability (HRV) predicted C/NS (hazard ratio 5.0 [1.5-17.1]) independent of demographic and clinical parameters.

CONCLUSIONS

Among patients with depressed LVEF, prolonged QTc suggests greater potential benefit from ICD therapy to prevent SCA; depressed HRV suggests potential benefit from bi-ventricular pacing to prevent C/NS.

Usefulness of QRS axis change to predict mortality in patients with left bundle branch block

QRS duration correlates with poor prognosis in patients with left bundle branch block (LBBB), but the importance of left-axis deviation (LAD) is not well established. To determine if LAD confers a mortality risk in patients with LBBB, a single-center, retrospective, population-based cohort study was conducted. Included were all patients at 1 hospital with LBBB on electrocardiography from 1995 to 2005 over a 17-year follow-up period (n = 2,794, median follow-up duration 20 months, interquartile range 6 to 64). Half of all patients with LBBB had LAD. The all-cause mortality rate in the entire cohort was 15%. LAD was not associated with mortality, either as a single outcome (odds ratio [OR] 1.1, 95% confidence interval [CI] 0.88 to 1.3, p = 0.50) or in time-to-event analysis (p = 0.40). Significant risk factors for mortality included high creatinine (OR 1.2, 95% CI 1.1 to 1.3), low hemoglobin (OR 1.2, 95% CI 1.1 to 1.3), history of atrial fibrillation (OR 1.6, 95% CI 1.3 to 2.1), electrocardiographic evidence of previous infarct (OR 1.5, 95% CI 1.2 to 1.9), and history of ventricular tachycardia (OR 1.4, 95% CI 1.0 to 1.9). On bivariate analysis, LAD was associated with atrial fibrillation, ventricular tachycardia, age, and congestive heart failure. Patients with LBBB who converted from normal axis to LAD had significantly higher mortality in time-to-event analysis (p = 0.02). In conclusion, in patients with LBBB, LAD does not confer significant mortality risk. However, those with normal axis who developed LAD during the study period had significantly higher mortality. Perhaps when LBBB and LAD develop concurrently, there is no increased risk over baseline LBBB development, but it may herald a worse prognosis if LAD develops against the background of previous LBBB, from an unknown mechanism.

High-risk electrocardiographic parameters are ubiquitous in patients with ischemic cardiomyopathy

BACKGROUND

The electrocardiogram (ECG) can be used to predict cardiovascular risk; however, like all risk factors with imperfect specificity, studies in low risk populations have been plagued by poor predictive accuracy. Although predictive accuracy might be improved among cohorts with a higher likelihood of cardiovascular events, this would also affect the prevalence of abnormal parameters and their exclusions.

METHOD

To determine the magnitude of these changes in a cohort with ischemic cardiomyopathy we analyzed 15 previously validated high-risk parameters from the resting and ambulatory ECG in subjects enrolled in the Prediction of Arrhythmic Events with Positron Emission Tomography (PAREPET) study (n = 198).

RESULTS

Using the published exclusion criteria from the validation studies (i.e., atrial fibrillation, persistent pacing, prolonged QRS), only 4 high-risk ECG parameters (27%) could be evaluated in all subjects and only 42% of subjects could have all 15 ECG parameters assessed. Nevertheless, almost every subject (97%) had at least one abnormal parameter. On average, there were 3.4 ± 1.8 (range, 0-8) high-risk ECG parameters per subject among the 11.7 ± 4.5 (range, 4-15) parameters that could be assessed.

CONCLUSIONS

Thus, 34% of all assessable parameters were abnormal. In conclusion, a significant proportion of ECG parameters cannot be assessed in patients with ischemic cardiomyopathy, but high-risk results are ubiquitous. The influence of these issues will be clarified when the results of the PAREPET study are available to actually determine the predictive value of these parameters on cause-specific mortality in a high-risk cohort.

Kawasaki disease: update on pathogenesis

PURPOSE OF REVIEW

This review will highlight recent advances in our understanding of the pathogenesis of Kawasaki disease, highlighting the molecular players involved in regulation of T-cell activation and their affect on disease incidence and outcome in both humans and mouse.

RECENT FINDINGS

Kawasaki disease is the most common cause of multisystem vasculitis in childhood. The vessels most commonly damaged are the coronary arteries, making Kawasaki disease the number one cause of acquired heart disease in children from the developed world. The contribution of genetics to disease predisposition is clearly implicated, but the mechanisms involved in regulating predisposition to disease susceptibility and outcome are not clearly understood. Two independent approaches have recently identified regulation of T-cell activation as the critical factor in determining susceptibility and severity of Kawasaki disease. Firstly, genetic analysis of affected Japanese children identified ITPKC, 1,4,5-triphosphate 3-kinase C, a kinase involved in regulation of T-cell activation, to be significantly associated with susceptibility to and increased severity of Kawasaki disease. A second independent approach using an animal model of Kawasaki disease has also identified regulation of T-cell activation, specifically costimulation, the second signal regulating optimal T-cell activation as the critical regulator of susceptibility to and severity of disease.

SUMMARY

Understanding the molecular players responsible for dysregulation of the immune response in Kawasaki disease will foster development of improved diagnostic/predictive tools and more rational use of therapeutic agents to improve outcome in affected children.