Real-world treatment trajectories of adults with newly diagnosed asthma or COPD

BACKGROUND

There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice.

METHODS

This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.3 million patients with asthma or COPD. We analysed treatment trajectories at drug class level from first diagnosis and visualised these in sunburst plots.

RESULTS

In four countries (USA, UK, Spain and the Netherlands), most adults with asthma initiate treatment with short-acting ß2 agonists monotherapy (20.8%-47.4% of first-line treatments). For COPD, the most frequent first-line treatment varies by country. The largest percentages of untreated patients (for asthma and COPD) were found in claims databases (14.5%-33.2% for asthma and 27.0%-52.2% for COPD) from the USA as compared with EHR databases (6.9%-15.2% for asthma and 4.4%-17.5% for COPD) from European countries. The treatment trajectories showed step-up as well as step-down in treatments.

CONCLUSION

Real-world data from claims and EHRs indicate that first-line treatments of asthma and COPD vary widely across countries. We found evidence of a stepwise approach in the pharmacological treatment of asthma and COPD, suggesting that treatments may be tailored to patients' needs.

Heart rate: an accessible risk indicator in adult congenital heart disease

BACKGROUND

Higher resting heart rate has been described as a risk factor for adverse outcome in healthy individuals and cardiovascular patients. The aim of this study was to evaluate resting heart rate as risk factor in adult congenital heart disease (ACHD).

METHODS

In this prospective observational cohort study, patients with moderate or complex ACHD were included at routine outpatient visit. Standard 12-lead ECGs were obtained in rest. Heart rate was obtained from the ECG automatically by the Modular ECG Analysis System (MEANS). The primary endpoint was all-cause mortality and the secondary endpoint was a composite of all-cause mortality and heart failure. Survival was derived using the Kaplan-Meier estimator. Subgroups based on heart rate tertiles were compared by the log-rank test. Cox proportional hazards models were adjusted for clinical factors including age, sex and diagnosis (moderate vs complex ACHD).

RESULTS

A total of 556 patients were included (median age 32 years (IQR 24-41), 57.6% male). Mean heart rate was 69±13 bpm. Negative chronotropic medication was used by 74 (13.3%) patients. During a median follow-up of 10.1 (IQR 9.6-10.5) years, 36 patients (6.5%) died and 83 (14.9%) reached the secondary endpoint. Patients with higher heart rates had significantly lower survival and heart failure-free survival. After adjusting for clinical factors, heart rate remained associated with mortality (HR 1.57 per 10 bpm, 95% CI 1.26 to 1.96) and mortality or heart failure (HR 1.33 per 10 bpm, 95% CI 1.13 to 1.57).

CONCLUSION

Higher heart rate is associated with lower survival and heart failure-free survival in ACHD.

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.

The added value of text from Dutch general practitioner notes in predictive modeling

OBJECTIVE

This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

MATERIALS AND METHODS

We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.

RESULTS

On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms.

DISCUSSION

Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems.

CONCLUSION

Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care.

Parasitic infections related to anti-type 2 immunity monoclonal antibodies: a disproportionality analysis in the food and drug administration's adverse event reporting system (FAERS)

Introduction: Monoclonal antibodies (mAbs) targeting immunoglobulin E (IgE) [omalizumab], type 2 (T2) cytokine interleukin (IL) 5 [mepolizumab, reslizumab], IL-4 Receptor (R) α [dupilumab], and IL-5R [benralizumab]), improve quality of life in patients with T2-driven inflammatory diseases. However, there is a concern for an increased risk of helminth infections. The aim was to explore safety signals of parasitic infections for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab. Methods: Spontaneous reports were used from the Food and Drug Administration's Adverse Event Reporting System (FAERS) database from 2004 to 2021. Parasitic infections were defined as any type of parasitic infection term obtained from the Standardised Medical Dictionary for Regulatory Activities® (MedDRA®). Safety signal strength was assessed by the Reporting Odds Ratio (ROR). Results: 15,502,908 reports were eligible for analysis. Amongst 175,888 reports for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab, there were 79 reports on parasitic infections. Median age was 55 years (interquartile range 24-63 years) and 59.5% were female. Indications were known in 26 (32.9%) reports; 14 (53.8%) biologicals were reportedly prescribed for asthma, 8 (30.7%) for various types of dermatitis, and 2 (7.6%) for urticaria. A safety signal was observed for each biological, except for reslizumab (due to lack of power), with the strongest signal attributed to benralizumab (ROR = 15.7, 95% Confidence Interval: 8.4-29.3). Conclusion: Parasitic infections were disproportionately reported for mAbs targeting IgE, T2 cytokines, or T2 cytokine receptors. While the number of adverse event reports on parasitic infections in the database was relatively low, resulting safety signals were disproportionate and warrant further investigation.

Heart Rate Variability and Incident Type 2 Diabetes in General Population

CONTEXT

Hyperglycemia and autonomic dysfunction are bidirectionally related.

OBJECTIVE

We investigated the association of longitudinal evolution of heart rate variability (HRV) with incident type 2 diabetes (T2D) among the general population.

METHODS

We included 7630 participants (mean age 63.7 years, 58% women) from the population-based Rotterdam Study who had no history of T2D and atrial fibrillation at baseline and had repeated HRV assessments at baseline and during follow-up. We used joint models to assess the association between longitudinal evolution of heart rate and different HRV metrics (including the heart rate-corrected SD of the normal-to-normal RR intervals [SDNNc], and root mean square of successive RR-interval differences [RMSSDc]) with incident T2D. Models were adjusted for cardiovascular risk factors. Bidirectional Mendelian randomization (MR) using summary-level data was also performed.

RESULTS

During a median follow-up of 8.6 years, 871 individuals developed incident T2D. One SD increase in heart rate (hazard ratio [HR] 1.20; 95% CI, 1.09-1.33), and log(RMSSDc) (HR 1.16; 95% CI, 1.01-1.33) were independently associated with incident T2D. The HRs were 1.54 (95% CI, 1.08-2.06) for participants younger than 62 years and 1.15 (95% CI, 1.01-1.31) for those older than 62 years for heart rate (P for interaction <.001). Results from bidirectional MR analyses suggested that HRV and T2D were not significantly related to each other.

CONCLUSION

Autonomic dysfunction precedes development of T2D, especially among younger individuals, while MR analysis suggests no causal relationship. More studies are needed to further validate our findings.

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.

Challenges of Estimating Global Feature Importance in Real-World Health Care Data

Feature importance is often used to explain clinical prediction models. In this work, we examine three challenges using experiments with electronic health record data: computational feasibility, choosing between methods, and interpretation of the resulting explanation. This work aims to create awareness of the disagreement between feature importance methods and underscores the need for guidance to practitioners how to deal with these discrepancies.

Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?

We investigated a stacking ensemble method that combines multiple base learners within a database. The results on external validation across four large databases suggest a stacking ensemble could improve model transportability.

The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study

BACKGROUND

Neuropsychiatric symptoms (NPS) are prevalent in the early clinical stages of Alzheimer's disease (AD) according to proxy-based instruments. Little is known about which NPS clinicians report and whether their judgment aligns with proxy-based instruments. We used natural language processing (NLP) to classify NPS in electronic health records (EHRs) to estimate the reporting of NPS in symptomatic AD at the memory clinic according to clinicians. Next, we compared NPS as reported in EHRs and NPS reported by caregivers on the Neuropsychiatric Inventory (NPI).

METHODS

Two academic memory clinic cohorts were used: the Amsterdam UMC (n = 3001) and the Erasmus MC (n = 646). Patients included in these cohorts had MCI, AD dementia, or mixed AD/VaD dementia. Ten trained clinicians annotated 13 types of NPS in a randomly selected training set of n = 500 EHRs from the Amsterdam UMC cohort and in a test set of n = 250 EHRs from the Erasmus MC cohort. For each NPS, a generalized linear classifier was trained and internally and externally validated. Prevalence estimates of NPS were adjusted for the imperfect sensitivity and specificity of each classifier. Intra-individual comparison of the NPS classified in EHRs and NPS reported on the NPI were conducted in a subsample (59%).

RESULTS

Internal validation performance of the classifiers was excellent (AUC range: 0.81-0.91), but external validation performance decreased (AUC range: 0.51-0.93). NPS were prevalent in EHRs from the Amsterdam UMC, especially apathy (adjusted prevalence = 69.4%), anxiety (adjusted prevalence = 53.7%), aberrant motor behavior (adjusted prevalence = 47.5%), irritability (adjusted prevalence = 42.6%), and depression (adjusted prevalence = 38.5%). The ranking of NPS was similar for EHRs from the Erasmus MC, although not all classifiers obtained valid prevalence estimates due to low specificity. In both cohorts, there was minimal agreement between NPS classified in the EHRs and NPS reported on the NPI (all kappa coefficients < 0.28), with substantially more reports of NPS in EHRs than on NPI assessments.

CONCLUSIONS

NLP classifiers performed well in detecting a wide range of NPS in EHRs of patients with symptomatic AD visiting the memory clinic and showed that clinicians frequently reported NPS in these EHRs. Clinicians generally reported more NPS in EHRs than caregivers reported on the NPI.

Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

ECG Changes during Adult Life in Fabry Disease: Results from a Large Longitudinal Cohort Study

Background: Fabry disease (FD) is an X-linked, lysosomal storage disorder leading to severe cardiomyopathy in a significant proportion of patients. To identify ECG markers that reflect early cardiac involvement and disease progression, we conducted a long term retrospective study in a large cohort of FD patients. Methods: A total of 1995 ECGs from 133 patients with classical FD (64% females, 80% treated with enzyme replacement therapy), spanning 20 years of follow-up, were compared to ECGs from 3893 apparently healthy individuals. Generalized linear mixed models were used to evaluate the effect of age, FD and sex on: P-wave duration, PR-interval, QRS-duration, QTc, Cornell index, spatial QRS-T angle and frontal QRS-axis. Regression slopes and absolute values for each parameter were compared between FD patients and control subjects. Results: At a younger age (<40 years), the Cornell index was higher and frontal QRS-axis more negative in FD patients compared to controls (p < 0.05). For the other ECG parameters, the rate of change, more than the absolute value, was greater in FD patients compared to controls (p < 0.05). From the fifth decade (men) or sixth (women) onwards, absolute values for P-wave duration, QRS-duration, QTc and spatial QRS-T angle were longer and higher in FD patients compared to control subjects. Conclusions: ECG abnormalities indicative of FD are age and sex dependent. Tracking the rate of change in ECG parameters could be a good way to detect disease progression, guiding treatment initiation. Moreover, monitoring ECG changes in FD can be used to evaluate the effectiveness of treatment.

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.

Alignment of vaccine codes using an ontology of vaccine descriptions

BACKGROUND

Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines.

METHODS

We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors.

RESULTS

The best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96).

CONCLUSION

The VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies.

TreatmentPatterns: An R package to facilitate the standardized development and analysis of treatment patterns across disease domains

BACKGROUND AND OBJECTIVES

There is an increasing interest to use real-world data to illustrate how patients with specific medical conditions are treated in real life. Insight in the current treatment practices helps to improve and tailor patient care, but is often held back by a lack of data interoperability and a high-level of required resources. We aimed to provide an easy tool that overcomes these barriers to support the standardized development and analysis of treatment patterns for a wide variety of medical conditions.

METHODS

We formally defined the process of constructing treatment pathways and implemented this in an open-source R package TreatmentPatterns (https://github.com/mi-erasmusmc/TreatmentPatterns) to enable a reproducible and timely analysis of treatment patterns.

RESULTS

The developed package supports the analysis of treatment patterns of a study population of interest. We demonstrate the functionality of the package by analyzing the treatment patterns of three common chronic diseases (type II diabetes mellitus, hypertension, and depression) in the Dutch Integrated Primary Care Information (IPCI) database.

CONCLUSION

TreatmentPatterns is a tool to make the analysis of treatment patterns more accessible, more standardized, and more interpretation friendly. We hope it thereby contributes to the accumulation of knowledge on real-world treatment patterns across disease domains. We encourage researchers to further adjust and add custom analysis to the R package based on their research needs.

Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis

BACKGROUND

Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy.

METHODS

Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots.

FINDINGS

Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated.

INTERPRETATION

We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use.

FUNDING

This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

Atrial fibrillation patterns and their cardiovascular risk profiles in the general population: the Rotterdam study

BACKGROUND

Clinical guidelines categorize atrial fibrillation (AF) based on the temporality of AF events. Due to its dependence on event duration, this classification is not applicable to population-based cohort settings. We aimed to develop a simple and standardized method to classify AF patterns at population level. Additionally, we compared the longitudinal trajectories of cardiovascular risk factors preceding the AF patterns, and between men and women.

METHODS

Between 1990 and 2014, participants from the population-based Rotterdam study were followed for AF status, and categorized into 'single-documented AF episode', 'multiple-documented AF episodes', or 'long-standing persistent AF'. Using repeated measurements we created linear mixed-effects models to assess the longitudinal evolution of risk factors prior to AF diagnosis.

RESULTS

We included 14,061 participants (59.1% women, mean age 65.4 ± 10.2 years). After a median follow-up of 9.4 years (interquartile range 8.27), 1,137 (8.1%) participants were categorized as 'single-documented AF episode', 208 (1.5%) as 'multiple-documented AF episodes', and 57 (0.4%) as 'long-standing persistent AF'. In men, we found poorer trajectories of weight and waist circumference preceding 'long-standing persistent AF' as compared to the other patterns. In women, we found worse trajectories of all risk factors between 'long-standing persistent AF' and the other patterns.

CONCLUSION

We developed a standardized method to classify AF patterns in the general population. Participants categorized as 'long-standing persistent AF' showed poorer trajectories of cardiovascular risk factors prior to AF diagnosis, as compared to the other patterns. Our findings highlight sex differences in AF pathophysiology and provide insight into possible risk factors of AF patterns.

Electrocardiographic parameters and the risk of new-onset atrial fibrillation in the general population

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This study is further supported by the Senior Scientist Grant from Dutch Heart Foundation (03-004-2021-T050).

Background

The (shape of the) association and sex-differences between electrocardiographic parameters and new-onset atrial fibrillation (AF) remain incompletely understood.

Purpose

To investigate the association between electrocardiographic parameters and new-onset atrial fibrillation among men and women in the general population.

Methods

12,212 participants free of AF from a large population-based cohort 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 models and joint models, both adjusted for cardiovascular risk factors, were used to determine the (shape of) association between baseline and longitudinal electrocardiographic parameters with new-onset AF. Additionally, we evaluated potential sex-differences.

Results

During a median follow-up of 9.3 years, 1,282 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-shaped (Figure 1). Sex-differences in terms of the shape of the various associations were most apparent for baseline PR, QT, QTc, RR, and heart rate in relation to new-onset AF. Longitudinal measures of PR (hazard ratio (HR), 95% confidence interval (CI), 1.43, 1.02-2.04, p=0.0393), and QTc interval (HR, 95% CI, 5.23, 2.18-12.45, p=0.0002) were significantly associated with new-onset AF. Sex-stratified analyses showed that the longitudinal associations were more prominent among men.

Conclusions

Baseline electrocardiographic measures and risk of new-onset AF were generally U-shaped. Longitudinal electrocardiographic measures of PR, and QTc interval were significantly associated with new-onset AF, more pronounced in men. Our findings imply that different thresholds of electrocardiographic parameters might translate to a differential risk of AF among men and women, and that treatment options targeting specific electrocardiographic parameters might prevent AF in the general population, in particular in men.

Heart rate variability and atrial fibrillation in the general population: a longitudinal and mendelian randomization study

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This study is further supported by the Gender and prevention grant (555003017) from ZonMw.

Background

Sex-differences and the causality of the association between heart rate variability (HRV) and atrial fibrillation (AF) remain unclear.

Purpose

To investigate the sex-differences and the causality of the association between heart rate variability and atrial fibrillation.

Methods

12,334 participants free of AF from a large population-based cohort study were included. Measures of HRV including the standard deviation of normal RR-intervals (SDNN), SDNN corrected for heart rate (SDNNc), RR-interval differences (RMSSD), RMSSD corrected for heart rate (RMSSDc), and heart rate were assessed at baseline and follow-up examinations. Joint models, adjusted for cardiovascular risk factors, were used to determine the association between longitudinal measures of HRV with new-onset AF. Additionally, we evaluated sex-differences. Genetic variants for HRV were used as instrumental variables in a Mendelian randomization (MR) analysis using GWAS summary-level data.

Results

During a median follow-up of 9.4 years, 1,302 incident AF cases occurred. In joint models, higher SDNN (hazard ratio (HR), 95% confidence interval (CI), 1.24, 1.04-1.47, p=0.0213), and higher RMSSD (HR, 95% CI, 1.33, 1.13-1.54, p=0.0010) were significantly associated with new-onset AF. Sex-stratified analyses showed that the associations were mostly prominent among women. In MR analyses, genetically determined decreases in SDNN (odds ratio (OR), 95% CI, 1.60, 1.27-2.02, p=8.36x10-05), and RMSSD (OR, 95% CI, 1.56, 1.31-1.86, p= 6.32x10-07) were significantly associated with increased AF risk.

Conclusions

Longitudinal measures of uncorrected HRV were significantly associated with new-onset AF, in particular among women. MR analyses supported the causal relationship between uncorrected measures of HRV with AF. Our findings indicate that measures to modulate HRV might prevent AF in the general population, especially among women.

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.

QTc-interval prolongation and increased risk of sudden cardiac death associated with hydroxychloroquine

AIMS:

Hydroxychloroquine and chloroquine ([hydroxy]chloroquine) are drugs used to treat malaria and rheumatological disorders and were recently suggested as beneficial for prevention and treatment of patients with coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 infection. However, longitudinal studies to assess the electrocardiographic and cardiotoxic effects of these drugs are limited. In this study, we aimed to investigate the effect of these drugs on QTc-interval and incidence of sudden cardiac death (SCD).

METHODS

We designed a longitudinal follow-up study of individuals within the prospective population-based Rotterdam Study. Eligible individuals had available data on medication and repeated ECG measurements. The study period was between 1 January 1991 and 1 January 2014. We studied on current and past use of [hydroxy]chloroquine as a time-varying exposure; high versus low daily dose of [hydroxy]chloroquine. QTc-interval duration, and the occurrence of SCD were the main outcomes. SCD was defined as an unexpected and sudden death due to cardiac arrhythmia within one hour of the onset of acute symptoms, and in patients without cardiac symptoms within 24 hours before death.

RESULTS

Among the study population of 14 594 individuals (58.8% women) with an average age of 65 years, 346 patients used [hydroxy]chloroquine at any time during follow-up. The total number of SCD cases was 609. In a multiple linear mixed model analysis, the current use of [hydroxy]chloroquine was associated with a significantly increased duration of the QTc-interval of 8.1 ms (95% CI: 3.6; 12.6) compared with non-users. The association was stronger among current-high daily dosage [15.3 (95%CI: 7.0; 23.6)] compared with current-low daily dosage [5.5 (95%CI: 0.4; 10.7)] users. In a Cox proportional hazard regression analysis, the risk of SCD was significantly higher in participants who were current users of [hydroxy]chloroquine than in non-users [adjusted hazard ratio; 3.7 (95%CI: 1.1; 12.6)].

CONCLUSIONS

In this longitudinal study, persons who received [hydroxy]chloroquine had an increased QTc-interval duration and the association was dose-dependent. [Hydroxy]chloroquine was associated with a significantly increased risk of SCD. As long as their activity against COVID-19 is controversial, cardiotoxicity is a strong argument against using these drugs to treat COVID-19 infections.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01505-z.

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

Objectives

This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators.

Materials and Methods

We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009–2019.

Results

We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009–2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented.

Discussion

Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented.

Conclusion

Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.

OUP accepted manuscript

Objective

This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance.

Materials and Methods

We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.

Results

We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited.

Conclusion

The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources

INTRODUCTION

External validation of prediction models is increasingly being seen as a minimum requirement for acceptance in clinical practice. However, the lack of interoperability of healthcare databases has been the biggest barrier to this occurring on a large scale. Recent improvements in database interoperability enable a standardized analytical framework for model development and external validation. External validation of a model in a new database lacks context, whereby the external validation can be compared with a benchmark in this database. Iterative pairwise external validation (IPEV) is a framework that uses a rotating model development and validation approach to contextualize the assessment of performance across a network of databases. As a use case, we predicted 1-year risk of heart failure in patients with type 2 diabetes mellitus.

METHODS

The method follows a two-step process involving (1) development of baseline and data-driven models in each database according to best practices and (2) validation of these models across the remaining databases. We introduce a heatmap visualization that supports the assessment of the internal and external model performance in all available databases. As a use case, we developed and validated models to predict 1-year risk of heart failure in patients initializing a second pharmacological intervention for type 2 diabetes mellitus. We leveraged the power of the Observational Medical Outcomes Partnership common data model to create an open-source software package to increase the consistency, speed, and transparency of this process.

RESULTS

A total of 403,187 patients from five databases were included in the study. We developed five models that, when assessed internally, had a discriminative performance ranging from 0.73 to 0.81 area under the receiver operating characteristic curve with acceptable calibration. When we externally validated these models in a new database, three models achieved consistent performance and in context often performed similarly to models developed in the database itself. The visualization of IPEV provided valuable insights. From this, we identified the model developed in the Commercial Claims and Encounters (CCAE) database as the best performing model overall.

CONCLUSION

Using IPEV lends weight to the model development process. The rotation of development through multiple databases provides context to model assessment, leading to improved understanding of transportability and generalizability. The inclusion of a baseline model in all modelling steps provides further context to the performance gains of increasing model complexity. The CCAE model was identified as a candidate for clinical use. The use case demonstrates that IPEV provides a huge opportunity in a new era of standardised data and analytics to improve insight into and trust in prediction models at an unprecedented scale.

The β-angle can help guide clinical decisions in the diagnostic work-up of patients suspected of Brugada syndrome: a validation study of the β-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 β-angle. Previous studies suggested that the β-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 β-angle in patients suspected of BrS.

METHODS AND RESULTS

A large cohort (n = 1430) of consecutive patients who underwent provocation testing was evaluated. β-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 β-angle cut-offs were calculated and evaluated. The importance of the β-angle for predicting the provocation test outcome was determined using a prediction model constructed with logistic regression. The optimum β-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 β-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 β-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 β-angle alone does not demonstrate strong diagnostic characteristics. However, the β-angle is an important variable to predict provocation test outcome and thus has added value.

Disentangling the association between kidney function and atrial fibrillation: a bidirectional Mendelian randomization study

Background

Observational studies suggest that kidney function and atrial fibrillation (AF) are bidirectionally associated. Whether this bidirectional association is causal remains unclear.

Purpose

To investigate the causality of the bidirectional association between kidney function and AF.

Methods

Genetic variants associated with different measures of kidney function including estimated glomerular filtration rate (eGFR) based on creatinine (eGFRcreat), blood urea nitrogen (BUN), chronic kidney disease (CKD, eGFR &lt;60ml/min/1.73m2), eGFR based on cystatin (eGFRcys), urine albumin-to-creatinine ratio (UACR) and microalbuminuria (MA, UACR &gt;30mg/g) were retrieved from multiple Genome-Wide Association Studies (GWAS). These GWAS were all part of the Chronic Kidney Disease Genetics (CKDGen) Consortium (n=24,063–1,040,070). Genetic variants associated with AF were retrieved from a GWAS on AF (n=1,030,836). We used two-sample MR analyses to assess the potential causality of the bidirectional association between kidney function and AF.

Results

MR analyses supported a causal effect of genetically predicted BUN, CKD and MA on AF risk (for BUN: n=18 SNPs, outlier corrected odds ratio (OR): 2.05, per 1 unit increase of BUN (mg/dL), 95% CI: 1.30–3.25, p-value = 2.13E-03. For CKD: n=9 SNPs, outlier corrected OR: 1.10, 95% CI: 1.04–1.17, p-value = 1.97E-03. For MA: n=5 SNPs, outlier corrected OR: 1.26, 95% CI: 1.10–1.46, p-value = 1.38E-03). MR analyses also supported a causal effect of genetically predicted AF on eGFRcreat (n=97 SNPs, outlier corrected OR: 0.998, per 1 unit increase of log transformed eGFRcreat (ml/min/1.73m2), 95% CI: 0.997–0.999, p-value = 6.78E-03), CKD risk (n=107 SNPs, outlier corrected OR: 1.06, 95% CI: 1.03–1.09, p-value = 2.97E-04) and MA risk (n=83 SNPs, outlier corrected OR: 1.07, 95% CI: 1.04–1.09, p-value = 2.49E-08). A suggestive causal effect of genetically predicted AF on eGFRcys was found (n=103 SNPs, outlier corrected OR: 0.993, per 1 unit increase of log transformed eGFRcys (ml/min/1.73m2), 95% CI: 0.986–0.999, p-value = 4.60E-02). MR analyses did not support a significant causal effect of the other kidney function measures on AF risk and vice versa. Moreover, sensitivity analyses, including weighted median estimator (WME), MR-Egger and the MR pleiotropy residual sum and outlier test (MR-PRESSO) indicated that these findings were robust. Furthermore, the associations did not change when genetic variants associated with coronary artery disease and heart failure were excluded.

Conclusions

MR analyses supported a bidirectional causal association between kidney function and AF. Our findings carry the potential for identification of important therapeutic targets for both conditions with implications for secondary prevention.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Erasmus Medical Center and Erasmus University, Rotterdam Forest plot with the MR effect estimatesBidirectional MR: Kidney function and AF

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

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).

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 (PRS_PR), QRS duration (PRS_QRS), and Brugada syndrome (PRS_BrS) 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 PRS_PR, baseline PR, and female sex are associated with more pronounced PR slope, while PRS_QRS and age are positively associated with QRS slope (P < 0.01 for all). PRS_BrS, 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. PRS_BrS, 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.

Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation

BACKGROUND

The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD.

METHODS

Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies.

RESULTS

We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci (TTN, CAND2, SCN10A, PITX2, CAV1, SYNPO2L, SOX5, TBX5, MYH6, RPL3L). The top variants at known sarcomere genes (TTN, MYH6) were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, PITX2 and SCN10A) were associated with longer PWD but lower AF risk.

CONCLUSIONS

Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.

Do sex differences in the prevalence of ECG abnormalities vary across ethnic groups living in the Netherlands? A cross-sectional analysis of the population-based HELIUS study

OBJECTIVES

Major ECG abnormalities have been associated with increased risk of cardiovascular disease (CVD) burden in asymptomatic populations. However, sex differences in occurrence of major ECG abnormalities have been poorly studied, particularly across ethnic groups. The objectives were to investigate (1) sex differences in the prevalence of major and, as a secondary outcome, minor ECG abnormalities, (2) whether patterns of sex differences varied across ethnic groups, by age and (3) to what extent conventional cardiovascular risk factors contributed to observed sex differences.

DESIGN

Cross-sectional analysis of population-based study.

SETTING

Multi-ethnic, population-based Healthy Life in an Urban Setting cohort, Amsterdam, the Netherlands.

PARTICIPANTS

8089 men and 11 369 women of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin aged 18-70 years without CVD.

OUTCOME MEASURES

Age-adjusted and multivariable logistic regression analyses were performed to study sex differences in prevalence of major and, as secondary outcome, minor ECG abnormalities in the overall population, across ethnic groups and by age-groups (18-35, 36-50 and >50 years).

RESULTS

Major and minor ECG abnormalities were less prevalent in women than men (4.6% vs 6.6% and 23.8% vs 39.8%, respectively). After adjustment for conventional risk factors, sex differences in major abnormalities were smaller in ethnic minority groups (OR ranged from 0.61 in Moroccans to 1.32 in South-Asian Surinamese) than in the Dutch (OR 0.49; 95% CI 0.36 to 0.65). Only in South-Asian Surinamese, women did not have a lower odds than men (OR 1.32; 95% CI 0.96 to 1.84). The pattern of smaller sex differences in ethnic minority groups was more pronounced in older than in younger age-groups.

CONCLUSIONS

The prevalence of major ECG abnormalities was lower in women than men. However, sex differences were less apparent in ethnic minority groups. Conventional risk factors did not contribute substantially to observed sex differences.

Identifying disease trajectories with predicate information from a knowledge graph

BACKGROUND

Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often diagnosed in patients in specific temporal sequences, which are referred to as disease trajectories. Here, we determine whether a sequence of two diseases forms a trajectory by leveraging the predicate information from paths between (disease) proteins in a knowledge graph. Furthermore, we determine the added value of directional information of predicates for this task. To do so, we create four feature sets, based on two methods for representing indirect paths, and both with and without directional information of predicates (i.e., which protein is considered subject and which object). The added value of the directional information of predicates is quantified by comparing the classification performance of the feature sets that include or exclude it.

RESULTS

Our method achieved a maximum area under the ROC curve of 89.8% and 74.5% when evaluated with two different reference sets. Use of directional information of predicates significantly improved performance by 6.5 and 2.0 percentage points respectively.

CONCLUSIONS

Our work demonstrates that predicates between proteins can be used to identify disease trajectories. Using the directional information of predicates significantly improved performance over not using this information.

Prevalence of ECGs Exceeding Thresholds for ST-Segment-Elevation Myocardial Infarction in Apparently Healthy Individuals: The Role of Ethnicity

Background

Early prehospital recognition of critical conditions such as ST‐segment–elevation myocardial infarction (STEMI) has prognostic relevance. Current international electrocardiographic STEMI thresholds are predominantly based on individuals of Western European descent. However, because of ethnic electrocardiographic variability both in health and disease, there is a need to reevaluate diagnostic ST‐segment elevation thresholds for different populations. We hypothesized that fulfillment of ST‐segment elevation thresholds of STEMI criteria (STE‐ECGs) in apparently healthy individuals is ethnicity dependent.

Methods and Results

HELIUS (Healthy Life in an Urban Setting) is a multiethnic cohort study including 10 783 apparently healthy subjects of 6 different ethnicities (African Surinamese, Dutch, Ghanaian, Moroccan, South Asian Surinamese, and Turkish). Prevalence of STE‐ECGs across ethnicities, sexes, and age groups was assessed with respect to the 2 international STEMI thresholds: sex and age specific versus sex specific. Mean prevalence of STE‐ECGs was 2.8% to 3.4% (age/sex‐specific and sex‐specific thresholds, respectively), although with large ethnicity‐dependent variability. Prevalences in Western European Dutch were 2.3% to 3.0%, but excessively higher in young (<40 years) Ghanaian males (21.7%–27.5%) and lowest in older (≥40 years) Turkish females (0.0%). Ethnicity (sub‐Saharan African origin) and other variables (eg, younger age, male sex, high QRS voltages, or anterolateral early repolarization pattern) were positively associated with STE‐ECG occurrence, resulting in subgroups with >45% STE‐ECGs.

Conclusions

The accuracy of diagnostic tests partly relies on background prevalence in healthy individuals. In apparently healthy subjects, there is a highly variable ethnicity‐dependent prevalence of ECGs with ST‐segment elevations exceeding STEMI thresholds. This has potential consequences for STEMI evaluations in individuals who are not of Western European descent, putatively resulting in adverse outcomes with both over‐ and underdiagnosis of STEMI.

Assessment of actionable findings in radiology reports

PURPOSE

The American College of Radiology (ACR) Actionable Reporting Work Group defined three categories of imaging findings that require additional, nonroutine communication with the referring physician because of their urgency or unexpectedness. The objective of this study was to determine the prevalence of actionable findings in radiology reports, and to assess how well radiologists agree on the categorisation of actionable findings.

METHOD

From 124,909 consecutive radiology reports stored in the electronic health record system of a large university hospital, 1000 reports were randomly selected. Two radiologists independently annotated all actionable findings according to the three categories of urgency defined by the ACR Work Group. Annotation differences were resolved in a consensus meeting and a final category was established for each report. Interannotator agreement was measured by accuracy and the kappa coefficient.

RESULTS

The prevalence of the three categories of actionable findings together was 32.5 %. Of all reports, 10.9 % were from patients seen in the emergency department. Prevalence of actionable findings for these patients (45.9 %) was considerably higher than for patients in routine clinical care (30.9 %). Interannotator agreement scores on the categorisation of actionable findings were 0.812 for accuracy and 0.616 for kappa coefficient.

CONCLUSIONS

The prevalence of actionable findings in radiology reports is high. The interannotator agreement scores are moderate, indicating that categorisation of actionable findings is a difficult task. To avoid unneeded increase in the workload of radiologists, in particular in routine practice, clinical context may need to be considered in deciding whether a finding is actionable.

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

KCND3 potassium channel gene variant confers susceptibility to electrocardiographic early repolarization pattern

BACKGROUNDThe presence of an early repolarization pattern (ERP) on the surface ECG is associated with risk of ventricular fibrillation and sudden cardiac death. Family studies have shown that ERP is a highly heritable trait, but molecular genetic determinants are unknown.METHODSTo identify genetic susceptibility loci for ERP, we performed a GWAS and meta-analysis in 2,181 cases and 23,641 controls of European ancestry.RESULTSWe identified a genome-wide significant (P < 5 × 10-8) locus in the potassium voltage-gated channel subfamily D member 3 (KCND3) gene that was successfully replicated in additional 1,124 cases and 12,510 controls. A subsequent joint meta-analysis of the discovery and replication cohorts identified rs1545300 as the lead SNP at the KCND3 locus (OR 0.82 per minor T allele, P = 7.7 × 10-12) but did not reveal additional loci. Colocalization analyses indicate causal effects of KCND3 gene expression levels on ERP in both cardiac left ventricle and tibial artery.CONCLUSIONSIn this study, we identified for the first time to our knowledge a genome-wide significant association of a genetic variant with ERP. Our findings of a locus in the KCND3 gene provide insights not only into the genetic determinants but also into the pathophysiological mechanism of ERP, discovering a promising candidate for functional studies.FUNDINGThis project was funded by the German Center for Cardiovascular Research (DZHK Shared Expertise SE081 - STATS). For detailed funding information per study, see the Supplemental Acknowledgments.

Normal Values of QT Variability in 10-s Electrocardiograms for all Ages

Aims: QT variability is a promising electrocardiographic marker. It has been studied as a screening tool for coronary artery disease and left ventricular hypertrophy, and increased QT variability is a known risk factor for sudden cardiac death. Considering that comprehensive normal values for QT variability were lacking, we set out to establish these in standard 10-s electrocardiograms (ECGs) covering both sexes and all ages.

Methods: Ten-second, 12-lead ECGs were provided by five Dutch population studies (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study). ECGs were recorded digitally and processed by well-validated analysis software. We selected cardiologically healthy participants, 46% being women. Ages ranged from 11 days to 91 years. After quality control, 13,828 ECGs were available. We assessed three markers: standard deviation of QT intervals (SDqt), short-term QT variability (STVqt), and QT variability index (QTVI).

Results: For SDqt and STVqt, the median and the lower limit of normal remained stable with age. The upper limit of normal declined until around age 45, and increased strongly in the elderly, notably so in women. This implies that a subset of the population, small enough not to have appreciable effect on the median, shows a high degree of QT variability with a possible risk of arrhythmias or worse, especially in women. Otherwise, sex differences were negligible in all three measurements. For QTVI, median, and normal limits decreased until age 20, and steadily went up afterwards except for the lower limit of normal, which flattens off after age 65.

Conclusion: We report the first set of normal values for QT variability based on 10-s ECGs, for all ages and both sexes.

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.

Generating and evaluating a propensity model using textual features from electronic medical records

Background

Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding.

Methods

In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users.

Results

The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18–1.36). Matching only on age resulted in an HR of 0.36 (0.11–1.16), and of 0.35 (0.11–1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13–1.38), which reduced to 0.35 (0.96–1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13–1.39), which dropped to 0.31 (0.09–1.08) after adjustment for age and sex.

Conclusions

PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates.

Automatic identification of relevant chemical compounds from patents

In commercial research and development projects, public disclosure of new chemical compounds often takes place in patents. Only a small proportion of these compounds are published in journals, usually a few years after the patent. Patent authorities make available the patents but do not provide systematic continuous chemical annotations. Content databases such as Elsevier's Reaxys provide such services mostly based on manual excerptions, which are time-consuming and costly. Automatic text-mining approaches help overcome some of the limitations of the manual process. Different text-mining approaches exist to extract chemical entities from patents. The majority of them have been developed using sub-sections of patent documents and focus on mentions of compounds. Less attention has been given to relevancy of a compound in a patent. Relevancy of a compound to a patent is based on the patent's context. A relevant compound plays a major role within a patent. Identification of relevant compounds reduces the size of the extracted data and improves the usefulness of patent resources (e.g. supports identifying the main compounds). Annotators of databases like Reaxys only annotate relevant compounds. In this study, we design an automated system that extracts chemical entities from patents and classifies their relevance. The gold-standard set contained 18 789 chemical entity annotations. Of these, 10% were relevant compounds, 88% were irrelevant and 2% were equivocal. Our compound recognition system was based on proprietary tools. The performance (F-score) of the system on compound recognition was 84% on the development set and 86% on the test set. The relevancy classification system had an F-score of 86% on the development set and 82% on the test set. Our system can extract chemical compounds from patents and classify their relevance with high performance. This enables the extension of the Reaxys database by means of automation.

Using predicate and provenance information from a knowledge graph for drug efficacy screening

Background

Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins.

Results

Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best.

Conclusion

We conclude that both predicate and provenance information provide added value for drug efficacy screening.

Electronic supplementary material

The online version of this article (10.1186/s13326-018-0189-6) contains supplementary material, which is available to authorized users.

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.

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.

Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6

BACKGROUND

Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

RESULTS

Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

CONCLUSIONS

Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.

Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval

BACKGROUND

Electrical conduction from the cardiac sinoatrial node to the ventricles is critical for normal heart function. Genome-wide association studies have identified more than a dozen common genetic loci that are associated with PR interval. However, it is unclear whether rare and low-frequency variants also contribute to PR interval heritability.

METHODS

We performed large-scale meta-analyses of the PR interval that included 83 367 participants of European ancestry and 9436 of African ancestry. We examined both common and rare variants associated with the PR interval.

RESULTS

We identified 31 genetic loci that were significantly associated with PR interval after Bonferroni correction (P<1.2×10-6), including 11 novel loci that have not been reported previously. Many of these loci are involved in heart morphogenesis. In gene-based analysis, we found that multiple rare variants at MYH6 (P=5.9×10-11) and SCN5A (P=1.1×10-7) were associated with PR interval. SCN5A locus also was implicated in the common variant analysis, whereas MYH6 was a novel locus.

CONCLUSIONS

We identified common variants at 11 novel loci and rare variants within 2 gene regions that were significantly associated with PR interval. Our findings provide novel insights to the current understanding of atrioventricular conduction, which is critical for cardiac activity and an important determinant of health.