Sudden Infant Death Syndrome Project : summary.

Mode of access: Internet.

Sudden infant death syndrome : identification and case management : conference proceedings.

Cover title.

Sudden infant death syndrome, a guide for funeral directors /

Reviewed and endorsed by the Illinois Funeral Directors Association and the Mortuary Science and Funeral Service Program, Southern Illinois University.

Sudden infant death syndrome : a guide for nurse family counselors /

"August, 1980."

Sudden infant death syndrome, general public education presentation.

Mode of access: Internet.

Sudden infant death syndrome : a guide for coroners /

"April, 1981."

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Letter: Brain natriuretic peptide is a potentially useful screening tool for the detection of cardiovascular disease in patients with rheumatoid arthritis

Patients with rheumatoid arthritis (RA) have a significantly higher risk of coronary heart disease, despite being less likely to report symptoms of angina, and are more likely to experience unrecognised myocardial infarction and sudden cardiac death than non-RA controls.1 Furthermore, left ventricular diastolic dysfunction has been described in up to 40% of patients with RA.2...

Genetics of Cardiovascular Disease: A Candidate Gene Study of USF1

Cardiovascular diseases (CVD) are major contributors to morbidity and mortality worldwide. Several interacting environmental, biochemical, and genetic risk factors can increase disease susceptibility. While some of the genes involved in the etiology of CVD are known, many are yet to be discovered. During the last few decades, scientists have searched for these genes with genome-wide linkage and association methods, and with more targeted candidate gene studies. This thesis investigates variation within the upstream transcription factor 1 (USF1) gene locus in relation to CVD risk factors, atherosclerosis, and incidence and prevalence of CVD. This candidate gene was first identified in Finnish families ascertained for familial combined hyperlipidemia, a common dyslipidemia predisposing to coronary heart disease. The gene is a ubiquitously expressed transcription factor regulating expression of several genes from lipid and glucose metabolism, inflammation, and endothelial function. First, we examined association between USF1 variants and several CVD risk factors, such as lipid phenotypes, body composition measures, and metabolic syndrome, in two prospective population cohorts. Our data suggested that USF1 contributes to these CVD risk factors at the population level. Notably, the associations with quantitative measurements were mostly detected among study subjects with CVD or metabolic syndrome, suggesting complex interactions between USF1 effects and the pathophysiological state of an individual. Second, we investigated how variation at the USF1 locus contributes to atherosclerotic lesions of the coronary arteries and abdominal aorta. For this, we used two study samples of middle-aged men with detailed measurements of atherosclerosis obtained in autopsy. USF1 variation significantly associated with areas of several types of lesions, especially with calcification of the arteries. Next, we tested what effect the USF1 risk variants have on sudden cardiac death and incidence of CVD. The atherosclerosis-associated risk variant increased the risk of sudden cardiac death of the same study subjects. Furthermore, USF1 alleles associated with incidence of CVD in the Finnish population follow-up cohorts. These associations were especially prominent among women, suggesting a sex specific effect, which has also been detected in subsequent studies. Finally, as some of the low-yield DNA samples of the Finnish follow-up study cohort needed to be whole-genome amplified (WGA) prior to genotyping, we evaluated whether the produced WGA genotypes were of good quality. Although the samples giving genotype discrepancies could not be detected before genotyping with standard laboratory quality control methods, our results suggested that enhanced quality control at the time of the genotyping could identify such samples. In addition, combining two WGA reactions into one pooled DNA sample for genotyping markedly reduced the number of discrepancies and samples showing them. In conclusion, USF1 seems to have a role in the etiology of CVD. Additional studies are warranted to identify functional variants and to study interactions between USF1 and other genetic or environmental factors. This USF1 study, and other studies with low DNA yield of some samples, can benefit from whole genome amplification of the low-yield samples prior to genotyping. Careful quality control procedures are, however, needed in WGA genotyping.

Ion channel gene variants predisposing to severe human ventricular arrhythmias

Congenital long QT syndrome (LQTS) with an estimated prevalence of 1:2000-1:10 000 manifests with prolonged QT interval on electrocardiogram and risk for ventricular arrhythmias and sudden death. Several ion channel genes and hundreds of mutations in these genes have been identified to underlie the disorder. In Finland, four LQTS founder mutations of potassium channel genes account for up to 40-70% of genetic spectrum of LQTS. Acquired LQTS has similar clinical manifestations, but often arises from usage of QT-prolonging medication or electrolyte disturbances. A prolonged QT interval is associated with increased morbidity and mortality not only in clinical LQTS but also in patients with ischemic heart disease and in the general population. The principal aim of this study was to estimate the actual prevalence of LQTS founder mutations in Finland and to calculate their effect on QT interval in the Finnish background population. Using a large population-based sample of over 6000 Finnish individuals from the Health 2000 Survey, we identified LQTS founder mutations KCNQ1 G589D (n=8), KCNQ1 IVS7-2A>G (n=1), KCNH2 L552S (n=2), and KCNH2 R176W (n=16) in 27 study participants. This resulted in a weighted prevalence estimate of 0.4% for LQTS in Finland. Using a linear regression model, the founder mutations resulted in a 22- to 50-ms prolongation of the age-, sex-, and heart rate-adjusted QT interval. Collectively, these data suggest that one of 250 individuals in Finland may be genetically predisposed to ventricular arrhythmias arising from the four LQTS founder mutations. A KCNE1 D85N minor allele with a frequency of 1.4% was associated with a 10-ms prolongation in adjusted QT interval and could thus identify individuals at increased risk of ventricular arrhythmias at the population level. In addition, the previously reported associations of KCNH2 K897T, KCNH2 rs3807375, and NOS1AP rs2880058 with QT interval duration were confirmed in the present study. In a separate study, LQTS founder mutations were identified in a subgroup of acquired LQTS, providing further evidence that congenital LQTS gene mutations may underlie acquired LQTS. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is characterized by exercise-induced ventricular arrhythmias in a structurally normal heart and results from defects in the cardiac Ca2+ signaling proteins, mainly ryanodine receptor type 2 (RyR2). In a patient population of typical CPVT, RyR2 mutations were identifiable in 25% (4/16) of patients, implying that noncoding variants or other genes are involved in CPVT pathogenesis. A 1.1 kb RyR2 exon 3 deletion was identified in two patients independently, suggesting that this region may provide a new target for RyR2-related molecular genetic studies. Two novel RyR2 mutations showing a gain-of-function defect in vitro were identified in three victims of sudden cardiac death. Extended pedigree analyses revealed some surviving mutation carriers with mild structural abnormalities of the heart and resting ventricular arrhythmias suggesting that not all RyR2 mutations lead to a typical CPVT phenotype, underscoring the relevance of tailored risk stratification of a RyR2 mutation carrier.

Modification of ventricular repolarization in silent long QT syndrome mutation carriers

Congenital long QT syndrome (LQTS) is a familial disorder characterized by ventricular repolarization that makes carriers vulnerable to malignant ventricular tachycardia and sudden cardiac death. The three main subtypes (LQT1, LQT2 and LQT3) constitute 95% of cases. The disorder is characterized by a prolonged QT interval in electrocardiograms (ECG), but a considerable portion are silent carriers presenting normal (QTc < 440 ms) or borderline (QTc < 470 ms) QT interval. Genetic testing is available only for 60-70% of patients. A number of pharmaceutical compounds also affect ventricular repolarization, causing a clinically similar disorder called acquired long QT syndrome. LQTS carriers - who already have impaired ventricular repolarization - are especially vulnerable. In this thesis, asymptomatic genotyped LQTS mutation carriers with non-diagnostic resting ECG were studied. The body surface potential mapping (BSPM) system was utilized for ECG recording, and signals were analyzed with an automated analysis program. QT interval length, and the end part of the T wave, the Tpe interval, was studied during exercise stress testing and an epinephrine bolus test. In the latter, T wave morphology was also analyzed. The effect of cetirizine was studied in LQTS carriers and also with supra- therapeutic dose in healthy volunteers. At rest, LQTS mutation carriers had a slightly longer heart rate adjusted QTc interval than healthy subjects (427 ± 31 ms and 379 ± 26 ms; p<0.001), but significant overlapping existed. LQT2 mutation carriers had a conspicuously long Tpe-interval (113 ± 24 ms; compared to 79 ± 11 ms in LQT1, 81 ± 17 ms in LQT3 and 78 ± 10 ms in controls; p<0.001). In exercise stress tests, LQT1 mutation carriers exhibit a long QT interval at high heart rates and during recovery, whereas LQT2 mutation carriers have a long Tpe interval at the beginning of exercise and at the end of recovery at low heart rates. LQT3 mutation carriers exhibit prominent shortening of both QT and Tpe intervals during exercise. A small epinephrine bolus revealed disturbed repolarization, especially in LQT2 mutation carriers, who developed prolonged Tpe intervals. A higher epinephrine bolus caused abnormal T waves with a different T wave profile in LQTS mutation carriers compared to healthy controls. These effects were seen in LQT3 as well, a group that may easily escape other provocative tests. In the cetirizine test, the QT and Tpe intervals were not prolonged in LQTS mutation carriers or in healthy controls. Subtype-specific findings in exercise test and epinephrine bolus test help to diagnose silent LQTS mutation carriers and to guide subtype-specific treatments. The Tpe interval, which signifies the repolarization process, seems to be a sensitive marker of disturbed repolarization along with the QT interval, which signifies the end of repolarization. This method may be used in studying compounds that are suspected to affect repolarization. Cetirizine did not adversely alter ventricular repolarization and would not be pro-arrhythmic in common LQT1 and LQT2 subtypes when used at its recommended doses.

A Study of Early Afterdepolarizations in a Model for Human Ventricular Tissue

Sudden cardiac death is often caused by cardiac arrhythmias. Recently, special attention has been given to a certain arrhythmogenic condition, the long-QT syndrome, which occurs as a result of genetic mutations or drug toxicity. The underlying mechanisms of arrhythmias, caused by the long-QT syndrome, are not fully understood. However, arrhythmias are often connected to special excitations of cardiac cells, called early afterdepolarizations (EADs), which are depolarizations during the repolarizing phase of the action potential. So far, EADs have been studied mainly in isolated cardiac cells. However, the question on how EADs at the single-cell level can result in fibrillation at the tissue level, especially in human cell models, has not been widely studied yet. In this paper, we study wave patterns that result from single-cell EAD dynamics in a mathematical model for human ventricular cardiac tissue. We induce EADs by modeling experimental conditions which have been shown to evoke EADs at a single-cell level: by an increase of L-type Ca currents and a decrease of the delayed rectifier potassium currents. We show that, at the tissue level and depending on these parameters, three types of abnormal wave patterns emerge. We classify them into two types of spiral fibrillation and one type of oscillatory dynamics. Moreover, we find that the emergent wave patterns can be driven by calcium or sodium currents and we find phase waves in the oscillatory excitation regime. From our simulations we predict that arrhythmias caused by EADs can occur during normal wave propagation and do not require tissue heterogeneities. Experimental verification of our results is possible for experiments at the cell-culture level, where EADs can be induced by an increase of the L-type calcium conductance and by the application of I-Kr blockers, and the properties of the emergent patterns can be studied by optical mapping of the voltage and calcium.