Development and validation of a patient-level model to predict dementia across a network of observational databases.

BACKGROUND: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. METHODS: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. RESULTS: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. CONCLUSIONS: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

The NOS1AP gene rs10494366 common genetic variant does not modify the risk of sudden cardiac death in users of digoxin.

AIMS: Common genetic variations in the nitric oxide synthase-1 adaptor protein (NOS1AP) gene are associated with QT-interval prolongation. In a previous study, we observed an association between the rs10494366 variant of this gene and an increased QT-interval shortening in digoxin users. As QT-interval shortening is a risk factor for sudden cardiac death (SCD), in this study, we investigated whether the association between digoxin use and risk of SCD differs in participants with different NOS1AP rs10494366 genotypes. METHODS: We included 11 377 individuals from the prospective population-based cohort of the Rotterdam Study. We used Cox proportional hazard regression analysis with digoxin as time-dependent exposure to estimate the associations between current digoxin use and the risk of SCD among different rs10494366 genotype groups in the adjusted models. We also studied whether such an association was dose-dependent, comparing high dosage (≥ 0.250 mg), moderate dosage (0.125 mg ≤ dose< 0.250 mg) and low dosage (< 0.125 mg) digoxin users with non-users. RESULTS: The median baseline age of the total study population was 62 (interquartile range [IQR] 58-71) years. The cumulative incidence of SCD was 4.1% (469 cases), and among them, 74 (15.7%) individuals were current digoxin users at the time of death, during a median follow-up of 11.5 (IQR 6.5-17) years. Current digoxin users had an increased risk of SCD (multivariable adjusted model hazard ratio [HR]: 3.07; 95% confidence interval [CI]: 2.38-3.98), with no significant differences between the three genotype groups. The adjusted HRs were 4.03 [95% CI: 1.98-8.21] in the minor homozygous GG, 3.46 [95% CI: 2.37-5.04] in the heterozygous TG and 2.56 [95%CI: 1.70-3.86] in the homozygous TT genotype groups. Compared to low- and moderate-dose, high-dose digoxin users with GG genotype had the highest risk of SCD (HR: 5.61 [95% CI: 1.34-23.47]). CONCLUSIONS: Current use of digoxin is associated with a significantly increased risk of SCD. The NOS1AP gene rs10494366 variant did not modify the digoxin-associated risk of SCD in a population of European ancestry.

Descriptive analysis on disproportionate medication errors and associated patient characteristics in the Food and Drug Administration's Adverse Event Reporting System.

BACKGROUND: Medication errors (MEs) are a major public health concern which can cause harm and financial burden within the healthcare system. Characterizing MEs is crucial to develop strategies to mitigate MEs in the future. OBJECTIVES: To characterize ME-associated reports, and investigate signals of disproportionate reporting (SDRs) on MEs in the Food and Drug Administration's Adverse Event Reporting System (FAERS). METHODS: FAERS data from 2004 to 2020 was used. ME reports were identified with the narrow Standardised Medical Dictionary for Regulatory Activities® (MedDRA®) Query (SMQ) for MEs. Drug names were converted to the Anatomical Therapeutic Chemical (ATC) classification. SDRs were investigated using the reporting odds ratio (ROR). RESULTS: In total 488 470 ME reports were identified, mostly (59%) submitted by consumers and mainly (55%) associated with females. Median age at time of ME was 57 years (interquartile range: 37-70 years). Approximately 1 out of 3 reports stated a serious health outcome. The most prevalent reported drug class was "antineoplastic and immunomodulating agents" (25%). The most common ME type was "incorrect dose administered" (9%). Of the 1659 SDRs obtained, adalimumab was the most common drug associated with MEs, noting a ROR of 1.22 (95% confidence interval: 1.21-1.24). CONCLUSION: This study offers a first of its kind characterization of MEs as reported to FAERS. Reported MEs are frequent and may be associated with serious health outcomes. This FAERS data provides insights on ME prevention and offers possibilities for additional in-depth analyses.

Electrocardiographic parameters and the risk of new-onset atrial fibrillation in the general population: the Rotterdam Study.

AIMS: We aimed to assess the (shape of the) association and sex differences in the link between electrocardiographic parameters and new-onset atrial fibrillation (AF). METHODS AND RESULTS: A total of 12 212 participants free of AF at baseline from the population-based Rotterdam Study were included. Up to five repeated measurements of electrocardiographic parameters including PR, QRS, QT, QT corrected for heart rate (QTc), JT, RR interval, and heart rate were assessed at baseline and follow-up examinations. Cox proportional hazards- and joint models, adjusted for cardiovascular risk factors, were used to determine the (shape of the) association between baseline and longitudinal electrocardiographic parameters with new-onset AF. Additionally, we evaluated potential sex differences. During a median follow-up of 9.3 years, 1282 incident AF cases occurred among 12 212 participants (mean age 64.9 years, 58.2% women). Penalized cubic splines revealed that associations between baseline electrocardiographic measures and risk of new-onset AF were generally U- and N-shaped. Sex differences in terms of the shape of the various associations were most apparent for baseline PR, QT, QTc, RR interval, and heart rate in relation to new-onset AF. Longitudinal measures of higher PR interval [fully adjusted hazard ratio (HR), 95% confidence interval (CI), 1.43, 1.02-2.04, P = 0.0393] and higher QTc interval (fully adjusted HR, 95% CI, 5.23, 2.18-12.45, P = 0.0002) were significantly associated with new-onset AF, in particular in men. CONCLUSION: Associations of baseline electrocardiographic measures and risk of new-onset AF were mostly U- and N-shaped. Longitudinal electrocardiographic measures of PR and QTc interval were significantly associated with new-onset AF, in particular among men.

Heart rate variability is associated with left ventricular systolic, diastolic function and incident heart failure in the general population.

BACKGROUND: HRV has mostly shown associations with systolic dysfunction and more recently, with diastolic dysfunction in Heart failure (HF) patients. But the role of sympathetic nervous system in changes of left ventricular (LV) systolic and diastolic function and new-onset HF has not been extensively studied. METHODS: Among 3157 men and 4405 women free of HF and atrial fibrillation retrospectively included from the population-based Rotterdam Study, we used linear mixed models to examine associations of RR-interval differences and standard deviation of RR-intervals corrected for heart rate (RMSSDc and SDNNc) with longitudinal changes of LV ejection fraction (LVEF), E/A ratio, left atrial (LA) diameter, E/e' ratio. Afterwards, using cox regressions, we examined their association with new-onset HF. RESULTS: Mean (SD) age was 65 (9.95) in men and 65.7 (10.2) in women. Every unit increase in log RMSSDc was accompanied by 0.75% (95%CI:-1.11%;-0.39%) and 0.31% (- 0.60%;-0.01%) lower LVEF among men and women each year, respectively. Higher log RMSSDc was linked to 0.03 (- 0.04;-0.01) and 0.02 (- 0.03;-0.003) lower E/A and also - 1.76 (- 2.77;- 0.75) and - 1.18 (- 1.99;-0.38) lower LVM index in both sexes and 0.72 mm (95% CI: - 1.20;-0.25) smaller LA diameters in women. The associations with LVEF in women diminished after excluding HF cases during the first 3 years of follow-up. During a median follow-up of 8.7 years, hazard ratios (95%CI) for incident HF were 1.34 (1.08;1.65) for log RMSSDc in men and 1.15 (0.93;1.42) in women. SDNNc showed similar associations. CONCLUSIONS: Indices of HRV were associated with worse systolic function in men but mainly with improvement in LA size in women. Higher HRV was associated with higher risk of new-onset HF in men. Our findings highlight potential sex differences in autonomic function underlying cardiac dysfunction and heart failure in the general population.

A NOS1AP gene variant is associated with a paradoxical increase of the QT-interval shortening effect of digoxin.

Digoxin is characterized by a small therapeutic window and a QT-interval shortening effect. Moreover, it has been shown that the genetic variants of the nitric oxide synthase-1 adaptor protein (NOS1AP) gene are associated with QT-interval prolongation. We investigated whether the rs10494366 variant of the NOS1AP gene decreases the QT-interval shortening effect of digoxin in patients using this drug. We included 10,057 individuals from the prospective population-based cohort of the Rotterdam Study during a median of 12.2 (interquartile range (IQR) 6.7-18.1) years of follow-up. At study entry, the mean age was 64 years and almost 59% of participants were women. A total of 23,179 ECGs were longitudinally recorded, of which 334 ECGs were from 249 individuals on digoxin therapy. The linear mixed model analysis was used to estimate the effect of the rs10494366 variant on the association between digoxin use and QT-interval duration, adjusted for age, sex, RR interval, diabetes, heart failure, and history of myocardial infarction. In non-users of digoxin, the GG genotype was associated with a significant 6.5 ms [95% confidence interval (CI) 5.5; 7.5] longer QT-interval duration than the TT variant. In current digoxin users, however, the GG variant was associated with a significantly -23.9 [95%CI -29.5; -18.5] ms shorter mean QT-interval duration than in those with the TT variant with -15.9 [95%CI -18.7; -13.1]. This reduction was strongest in the high digoxin dose category [≥0.250 mg/day] with the GG genotype group, with -40.8 [95%CI -52.5; -29.2] ms changes compared to non-users. Our study suggests that the minor homozygous GG genotype group of the NOS1AP gene rs10494366 variant is associated with a paradoxical increase of the QT-interval shortening effect of digoxin in a population of European ancestry.

Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing.

OBJECTIVES: To develop and validate classifiers for automatic detection of actionable findings and documentation of nonroutine communication in routinely delivered radiology reports. METHODS: Two radiologists annotated all actionable findings and communication mentions in a training set of 1,306 radiology reports and a test set of 1,000 reports randomly selected from the electronic health record system of a large tertiary hospital. Various feature sets were constructed based on the impression section of the reports using different preprocessing steps (stemming, removal of stop words, negations, and previously known or stable findings) and n-grams. Random forest classifiers were trained to detect actionable findings, and a decision-rule classifier was trained to find communication mentions. Classifier performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: On the training set, the actionable finding classifier with the highest cross-validated performance was obtained for a feature set of unigrams, after stemming and removal of negated, known, and stable findings. On the test set, this classifier achieved an AUC of 0.876 (95% CI 0.854-0.898). The classifier for communication detection was trained after negation removal, using unigrams as features. The resultant decision rule had a sensitivity of 0.841 (95% CI 0.706-0.921) and specificity of 0.990 (95% CI 0.981-0.994) on the test set. CONCLUSIONS: Automatic detection of actionable findings and subsequent communication in routinely delivered radiology reports is possible. This can serve quality control purposes and may alert radiologists to the presence of actionable findings during reporting. KEY POINTS: • Classifiers were developed for automatic detection of the broad spectrum of actionable findings and subsequent communication mentions in routinely delivered radiology reports. • Straightforward report preprocessing and simple feature sets can produce well-performing classifiers. • The resultant classifiers show good performance for detection of actionable findings and excellent performance for detection of communication mentions.

External validation of existing dementia prediction models on observational health data.

BACKGROUND: Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records. METHODS: We identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models. RESULTS: We reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard. CONCLUSION: We observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

The ß-angle can help guide clinical decisions in the diagnostic work-up of patients suspected of Brugada syndrome: a validation study of the ß-angle in determining the outcome of a sodium channel provocation test.

AIMS: In patients with Brugada syndrome (BrS) but without spontaneous Type-1 electrocardiogram, several electrocardiographic characteristics have been studied, including the ß-angle. Previous studies suggested that the ß-angle might be useful in distinguishing BrS-patients from patients with only suggestive repolarization patterns without performing sodium channel blocker provocation testing. In this study, we aimed to determine the diagnostic value of the ß-angle in patients suspected of BrS. METHODS AND RESULTS: A large cohort (n = 1430) of consecutive patients who underwent provocation testing was evaluated. ß-angles were measured in leads V1, V2, and their corresponding positions over the second and third intercostal space. Receiver-operating characteristic curves were constructed and the diagnostic accuracy of previously reported ß-angle cut-offs were calculated and evaluated. The importance of the ß-angle for predicting the provocation test outcome was determined using a prediction model constructed with logistic regression. The optimum ß-angle cut-off in our cohort for ruling out a positive provocation test was 15°; sensitivities were 80-98% and negative predictive values were 79-96% among the right precordial leads. Previously reported ß-angle cut-offs performed less well, indicated by lower Youden indices. In the optimism-corrected prediction model [C-statistic: 0.78 (95% CI: 0.75-0.81)], the ß-angle had large value (Z-score: 2.1-10.3) and aided construction of a nomogram to predict test outcome. CONCLUSION: To predict the outcome of provocation testing for BrS, the ß-angle alone does not demonstrate strong diagnostic characteristics. However, the ß-angle is an important variable to predict provocation test outcome and thus has added value.

The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies.

Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this determines the relative importance of the properties of explainability (i.e. interpretability and fidelity). Based on this, we propose a framework to guide the choice between classes of explainable AI methods (explainable modelling versus post-hoc explanation; model-based, attribution-based, or example-based explanations; global and local explanations). Furthermore, we find that quantitative evaluation metrics, which are important for objective standardized evaluation, are still lacking for some properties (e.g. clarity) and types of explanations (e.g. example-based methods). We conclude that explainable modelling can contribute to trustworthy AI, but the benefits of explainability still need to be proven in practice and complementary measures might be needed to create trustworthy AI in health care (e.g. reporting data quality, performing extensive (external) validation, and regulation).

Left Axis Deviation in Brugada Syndrome: Vectorcardiographic Evaluation during Ajmaline Provocation Testing Reveals Additional Depolarization Abnormalities.

Patients with Brugada syndrome (BrS) can show a leftward deviation of the frontal QRS-axis upon provocation with sodium channel blockers. The cause of this axis change is unclear. In this study, we aimed to determine (1) the prevalence of this left axis deviation and (2) to evaluate its cause, using the insights that could be derived from vectorcardiograms. Hence, from a large cohort of patients who underwent ajmaline provocation testing (n = 1430), we selected patients in whom a type-1 BrS-ECG was evoked (n = 345). Depolarization and repolarization parameters were analyzed for reconstructed vectorcardiograms and were compared between patients with and without a >30° leftward axis shift. We found (1) that the prevalence of a left axis deviation during provocation testing was 18% and (2) that this left axis deviation was not explained by terminal conduction slowing in the right ventricular outflow tract (4th QRS-loop quartile: +17 ± 14 ms versus +13 ± 15 ms, nonsignificant) but was associated with a more proximal conduction slowing (1st QRS-loop quartile: +12[8;18] ms versus +8[4;12] ms, p < 0.001 and 3rd QRS-loop quartile: +12 ± 10 ms versus +5 ± 7 ms, p < 0.001). There was no important heterogeneity of the action potential morphology (no difference in the ventricular gradient), but a left axis deviation did result in a discordant repolarization (spatial QRS-T angle: 122[59;147]° versus 44[25;91]°, p < 0.001). Thus, although the development of the type-1 BrS-ECG is characterized by a terminal conduction delay in the right ventricle, BrS-patients with a left axis deviation upon sodium channel blocker provocation have an additional proximal conduction slowing, which is associated with a subsequent discordant repolarization. Whether this has implications for risk stratification is still undetermined.

Predicting cardiac electrical response to sodium-channel blockade and Brugada syndrome using polygenic risk scores.

AIMS: Sodium-channel blockers (SCBs) are associated with arrhythmia, but variability of cardiac electrical response remains unexplained. We sought to identify predictors of ajmaline-induced PR and QRS changes and Type I Brugada syndrome (BrS) electrocardiogram (ECG). METHODS AND RESULTS: In 1368 patients that underwent ajmaline infusion for suspected BrS, we performed measurements of 26 721 ECGs, dose-response mixed modelling and genotyping. We calculated polygenic risk scores (PRS) for PR interval (PRSPR), QRS duration (PRSQRS), and Brugada syndrome (PRSBrS) derived from published genome-wide association studies and used regression analysis to identify predictors of ajmaline dose related PR change (slope) and QRS slope. We derived and validated using bootstrapping a predictive model for ajmaline-induced Type I BrS ECG. Higher PRSPR, baseline PR, and female sex are associated with more pronounced PR slope, while PRSQRS and age are positively associated with QRS slope (P < 0.01 for all). PRSBrS, baseline QRS duration, presence of Type II or III BrS ECG at baseline, and family history of BrS are independently associated with the occurrence of a Type I BrS ECG, with good predictive accuracy (optimism-corrected C-statistic 0.74). CONCLUSION: We show for the first time that genetic factors underlie the variability of cardiac electrical response to SCB. PRSBrS, family history, and a baseline ECG can predict the development of a diagnostic drug-induced Type I BrS ECG with clinically relevant accuracy. These findings could lead to the use of PRS in the diagnosis of BrS and, if confirmed in population studies, to identify patients at risk for toxicity when given SCB.

Discovery of novel heart rate-associated loci using the Exome Chip.

Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.

Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram.

The ST-segment and adjacent T-wave (ST-T wave) amplitudes of the electrocardiogram are quantitative characteristics of cardiac repolarization. Repolarization abnormalities have been linked to ventricular arrhythmias and sudden cardiac death. We performed the first genome-wide association meta-analysis of ST-T-wave amplitudes in up to 37 977 individuals identifying 71 robust genotype-phenotype associations clustered within 28 independent loci. Fifty-four genes were prioritized as candidates underlying the phenotypes, including genes with established roles in the cardiac repolarization phase (SCN5A/SCN10A, KCND3, KCNB1, NOS1AP and HEY2) and others with as yet undefined cardiac function. These associations may provide insights in the spatiotemporal contribution of genetic variation influencing cardiac repolarization and provide novel leads for future functional follow-up.

Masking by vaccines in pediatric drug safety signal detection in the EudraVigilance database.

PURPOSE: Postmarketing drug safety surveillance relies upon measures of disproportionate reporting in spontaneous reporting systems. It has been hypothesized that products or events reported frequently may "mask" signals. METHODS: We analyzed the masking effect of vaccines in pediatrics in the EudraVigilance database by conducting disproportionality analysis in the full database (containing vaccine exposures) and in a restricted set (excluding vaccine exposures). We measured performance of the reporting odds ratio (ROR) in both data sets using a pediatric-specific drug reference set and in the absence of a reference set. We assessed masking effects across age groups and conducted a classification tree (CART) analysis. RESULTS: Removal of vaccines decreased the ROR values both in negative and positive controls. Exceptions were drug-event combinations including outcomes frequent in vaccine reports. When restricted to positive control associations, removal of vaccine-related events resulted in increased ROR values for events commonly reported following vaccination. For events rarely associated with vaccination, ROR values decreased for all age groups, especially infants. Analysis in the absence of a reference set showed decrease in ROR following vaccine removal and CART revealed that change in ROR with vaccine removal depended upon age and proportion of reports including a vaccine. CONCLUSIONS: Removal of vaccines for signal detection in a pediatric population has an impact on ROR, dependent upon the reporting frequency of the event of interest in combination with vaccines. We recommend stratification by age and removal of vaccine exposures if the investigated adverse drug reactions include those typically reported in association with vaccines for the age strata.

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

BACKGROUND: Increased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals. METHODS AND RESULTS: We conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in BRE (ß=56.3, pinteraction=3.9e-9) and rs9830388 in UBE2E2 (ß=25.2, pinteraction=1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3 significantly modified the association between TCAs and QT intervals (ß=9.3, pinteraction=2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (pinteraction>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries. CONCLUSIONS: Among Europeans, TCA interactions with variants in BRE and UBE2E2 were identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.

Prognostic relevance of the interaction between short-term, metronome-paced heart rate variability, and inflammation: results from the population-based CARLA cohort study.

AIMS: To determine the interaction between HRV and inflammation and their association with cardiovascular/all-cause mortality in the general population. METHODS AND RESULTS: Subjects of the CARLA study (n = 1671; 778 women, 893 men, 45-83 years of age) were observed for an average follow-up period of 8.8 years (226 deaths, 70 cardiovascular deaths). Heart rate variability parameters were calculated from 5-min segments of 20-min resting electrocardiograms. High-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and soluble tumour necrosis factor-alpha receptor type 1 (sTNF-R1) were measured as inflammation parameters. The HRV parameters determined included the standard deviation of normal-to-normal intervals (SDNN), the root-mean-square of successive normal-interval differences (RMSSD), the low- and high-frequency (HF) power, the ratio of both, and non-linear parameters [Poincaré plot (SD1, SD2, SD1/SD2), short-term detrended fluctuation analysis]. We estimated hazard ratios by using covariate-adjusted Cox regression for cardiovascular and all-cause mortality incorporating an interaction term of HRV/inflammation parameters. Relative excess risk due to interactions (RERIs) were computed. We found an interaction effect of sTNF-R1 with SDNN (RERI: 0.5; 99% confidence interval (CI): 0.1-1.0), and a weaker effect with RMSSD (RERI: 0.4; 99% CI: 0.0-0.9) and HF (RERI: 0.4; 99% CI: 0.0-0.9) with respect to cardiovascular mortality on an additive scale after covariate adjustment. Neither IL-6 nor hsCRP showed a significant interaction with the HRV parameters. CONCLUSION: A change in TNF-α levels or the autonomic nervous system influences the mortality risk through both entities simultaneously. Thus, TNF-α and HRV need to be considered when predicating mortality.

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

AIMS: To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters. METHODS AND RESULTS: Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up. Linear regression models with a quadratic term were used. Crude and covariate adjusted estimates were calculated. Missing values were imputed by means of multiple imputation. Heart rate variability measures taken into account consisted of linear time and frequency domain [standard deviation of normal-to-normal intervals (SDNN), high-frequency power (HF), low-frequency power (LF), LF/HF ratio] and non-linear measures [detrended fluctuation analysis (DFA1), SD1, SD2, SD1/SD2 ratio]. Echocardiographic parameters considered were ventricular mass index, diastolic interventricular septum thickness, left ventricular diastolic dimension, left atrial dimension systolic (LADS), and ejection fraction (Teichholz). A negative quadratic relation between baseline LADS and change in SDNN and HF was observed. The maximum HF and SDNN change (an increase of roughly 0.02%) was predicted at LADS of 3.72 and 3.57 cm, respectively, while the majority of subjects experienced a decrease in HRV. There was no association between further echocardiographic parameters and change in HRV, and there was no evidence of a predictive value of HRV in the prediction of changes in cardiac structure. CONCLUSION: In the general population, LADS predicts 4-year alteration in SDNN and HF non-linearly. Because of the novelty of the result, analyses should be replicated in other populations.

Automated extraction of potential migraine biomarkers using a semantic graph.

PROBLEM: Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers. METHOD: We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance. RESULTS: Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974. DISCUSSION: Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers.