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.

Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches.

BACKGROUND: Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects. METHODS: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. RESULTS: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. CONCLUSIONS: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.

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.

Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course.

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability.

BACKGROUND: Prognostic models that are accurate could help aid medical decision making. Large observational databases often contain temporal medical data for large and diverse populations of patients. It may be possible to learn prognostic models using the large observational data. Often the performance of a prognostic model undesirably worsens when transported to a different database (or into a clinical setting). In this study we investigate different ensemble approaches that combine prognostic models independently developed using different databases (a simple federated learning approach) to determine whether ensembles that combine models developed across databases can improve model transportability (perform better in new data than single database models)? METHODS: For a given prediction question we independently trained five single database models each using a different observational healthcare database. We then developed and investigated numerous ensemble models (fusion, stacking and mixture of experts) that combined the different database models. Performance of each model was investigated via discrimination and calibration using a leave one dataset out technique, i.e., hold out one database to use for validation and use the remaining four datasets for model development. The internal validation of a model developed using the hold out database was calculated and presented as the 'internal benchmark' for comparison. RESULTS: In this study the fusion ensembles generally outperformed the single database models when transported to a previously unseen database and the performances were more consistent across unseen databases. Stacking ensembles performed poorly in terms of discrimination when the labels in the unseen database were limited. Calibration was consistently poor when both ensembles and single database models were applied to previously unseen databases. CONCLUSION: A simple federated learning approach that implements ensemble techniques to combine models independently developed across different databases for the same prediction question may improve the discriminative performance in new data (new database or clinical setting) but will need to be recalibrated using the new data. This could help medical decision making by improving prognostic model performance.

90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model.

PURPOSE: The purpose of this study was to develop and validate a prediction model for 90-day mortality following a total knee replacement (TKR). TKR is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify high-risk patients who could be targeted with preventative interventions. The aim was to develop and validate a simple model to help inform treatment choices. METHODS: A mortality prediction model for knee OA patients following TKR was developed and externally validated using a US claims database and a UK general practice database. The target population consisted of patients undergoing a primary TKR for knee OA, aged ≥ 40 years and registered for ≥ 1 year before surgery. LASSO logistic regression models were developed for post-operative (90-day) mortality. A second mortality model was developed with a reduced feature set to increase interpretability and usability. RESULTS: A total of 193,615 patients were included, with 40,950 in The Health Improvement Network (THIN) database and 152,665 in Optum. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and 0.70 when externally validated on THIN. The 12 variable model achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. CONCLUSIONS: A simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR was developed that demonstrated good, robust performance. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and targeting prophylaxis for those at high risk. LEVEL OF EVIDENCE: III.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

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).

Predictive approaches to heterogeneous treatment effects: a scoping review.

BACKGROUND: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. METHODS: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. RESULTS: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). CONCLUSIONS: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status.

OBJECTIVES: Smoking status is poorly record in US claims data. IBM MarketScan Commercial is a claims database that can be linked to an additional health risk assessment with self-reported smoking status for a subset of 1,966,174 patients. We investigate whether this subset could be used to learn a smoking status phenotype model generalizable to all US claims data that calculates the probability of being a current smoker. METHODS: 251,643 (12.8%) had self-reported their smoking status as 'current smoker'. A regularized logistic regression model, the Current Risk of Smoking Status (CROSS), was trained using the subset of patients with self-reported smoking status. CROSS considered 53,027 candidate covariates including demographics and conditions/drugs/measurements/procedures/observations recorded in the prior 365 days, The CROSS phenotype model was validated across multiple other claims data. RESULTS: The internal validation showed the CROSS model achieved an area under the receiver operating characteristic curve (AUC) of 0.76 and the calibration plots indicated it was well calibrated. The external validation across three US claims databases obtained AUCs ranging between 0.82 and 0.87 showing the model appears to be transportable across Claims data. CONCLUSION: CROSS predicts current smoking status based on the claims records in the prior year. CROSS can be readily implemented to any US insurance claims mapped to the OMOP common data model and will be a useful way to impute smoking status when conducting epidemiology studies where smoking is a known confounder but smoking status is not recorded. CROSS is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/SmokingModel.

Thyroid Function and Sudden Cardiac Death: A Prospective Population-Based Cohort Study.

BACKGROUND: The association between thyroid function and cardiovascular disease is well established, but no study to date has assessed whether it is a risk factor for sudden cardiac death (SCD). Therefore, we studied the association of thyroid function with SCD in a prospective population-based cohort. METHODS: Participants from the Rotterdam Study ≥45 years with thyroid-stimulating hormone or free thyroxine (FT4) measurements and clinical follow-up were eligible. We assessed the association of thyroid-stimulating hormone and FT4 with the risk of SCD by using an age- and sex-adjusted Cox proportional-hazards model, in all participants and also after restricting the analysis to euthyroid participants (defined by thyroid-stimulating hormone 0.4-4.0 mIU/L). Additional adjustment included cardiovascular risk factors, notably hypertension, serum cholesterol, and smoking. We stratified by age and sex and performed sensitivity analyses by excluding participants with abnormal FT4 values (reference range of 0.85-1.95 ng/dL) and including only witnessed SCDs as outcome. Absolute risks were calculated in a competing risk model by taking death by other causes into account. RESULTS: We included 10 318 participants with 261 incident SCDs (median follow-up, 9.1 years). Higher levels of FT4 were associated with an increased SCD risk, even in the normal range of thyroid function (hazard ratio, 2.28 per 1 ng/dL FT4; 95% confidence interval, 1.31-3.97). Stratification by age or sex and sensitivity analyses did not change the risk estimates substantially. The absolute 10-year risk of SCD increased in euthyroid participants from 1% to 4% with increasing FT4 levels. CONCLUSIONS: Higher FT4 levels are associated with an increased risk of SCD, even in euthyroid participants.

Subclinical Abnormalities in Echocardiographic Parameters and Risk of Sudden Cardiac Death in a General Population: The Rotterdam Study.

BACKGROUND: Subclinical cardiac dysfunction has been associated with increased mortality, and heart failure increases the risk of sudden cardiac death (SCD). Less well known is whether subclinical cardiac dysfunction is also a risk factor for SCD. Our objective was to assess the association between echocardiographic parameters and SCD in a community-dwelling population free of heart failure. METHODS AND RESULTS: We computed hazard ratios (HRs) for left atrium diameter, left ventricular (LV) end-diastolic dimension, LV end-systolic dimension, LV mass, qualitative LV systolic function, LV fractional shortening, and diastolic function. During a median follow-up of 6.3 years in 4,686 participants, 68 participants died because of SCD. Significant associations with SCD were observed for qualitative LV systolic function and LV fractional shortening. For moderate/poor qualitative LV systolic function, the HR for SCD was 2.54 (95% confidence interval [CI] 1.10-5.87). Each standard deviation decrease in LV fractional shortening was associated with an HR of 1.36 (95% CI 1.09-1.70). CONCLUSIONS: Subclinical abnormalities in LV systolic function were associated with SCD risk in this general population. Although prediction of SCD remains difficult and traditional cardiovascular risk factors are of greatest importance, this knowledge might guide future directions to prevent SCD in persons with subclinical cardiac dysfunction.

Chronic obstructive pulmonary disease and sudden cardiac death: the Rotterdam study.

AIMS: Both sudden cardiac death (SCD) and chronic obstructive pulmonary disease (COPD) are common conditions in the elderly. Previous studies have identified an association between COPD and cardiovascular disease, and with SCD in specific patient groups. Our aim was to investigate whether there is an association between COPD and SCD in the general population. METHODS AND RESULTS: The Rotterdam study is a population-based cohort study among 14 926 subjects aged 45 years and older with up to 24 years of follow-up. Analyses were performed with a (time dependent) Cox proportional hazard model adjusted for age, sex, and smoking. Of the 13 471 persons included in the analysis; 1615 had a diagnosis of COPD and there were 551 cases of SCD. Chronic obstructive pulmonary disease was associated with an increased risk of SCD (age- and sex-adjusted hazard ratio, HR, 1.34, 95% CI 1.06-1.70). The risk particularly increased in the period 2000 days (5.48 years) after the diagnosis of COPD (age- and sex-adjusted HR 2.12, 95% CI 1.60-2.82) and increased further to a more than three-fold higher risk in COPD subjects with frequent exacerbations during this period (age- and sex-adjusted HR 3.58, 95% CI 2.35-5.44). Analyses restricted to persons without prevalent myocardial infarction or heart failure yielded similar results. CONCLUSION: Chronic obstructive pulmonary disease is associated with an increased risk for SCD. The risk especially increases in persons with frequent exacerbations 5 years after the diagnosis of COPD. This risk indicator could provide new directions for better-targeted actions to prevent SCD.

Use of oral contraceptives in three European countries: a population-based multi-database study.

OBJECTIVES: The aim of the study was to assess the prevalence of oral contraceptive (OC) use, user characteristics and prescribing patterns by accessing health care databases of three European countries. METHODS: A retrospective study was performed from 2009 to 2010 in three general practice (GP) databases from the Netherlands, UK and Italy and in one database of linked pharmacy and hospitalisation data in the Netherlands. The presence of selected chronic conditions and diagnoses of diseases associated with OC use were assessed, as were switches, discontinuations and types of OC used during the study period. RESULTS: Among 2.16 million women aged 15 to 49 years, 16.0% were using an OC on 1 January 2010. The prevalence ranged from 19.7% in a Dutch database to 2.6% in the Italian database. During 2009 and 2010, mainly second-generation progestogens were prescribed in the Netherlands (79.4% and 78.3% of users), both second- (57.9%) and third-generation progestogens (43.6%) were prescribed in the UK, and mainly third-generation progestogens in Italy (61.8%). Most switches were to third- or fourth-generation pills. The prevalence of chronic diseases tended to be higher among OC users, and the proportions of women with a history of disease associated with OC use tended to be lower than among non-users. CONCLUSIONS: Second-generation OCs were most frequently prescribed in the Netherlands. In the UK, and even more so in Italy, many women used third- or fourth-generation OCs. Preparation switches were mainly to third- or fourth-generation OCs. Among OC users, a somewhat higher prevalence of chronic diseases was observed; however, information bias cannot be ruled out.

Assessing Prolongation of the Heart Rate Corrected QT Interval in Users of Tricyclic Antidepressants: Advice to Use Fridericia Rather Than Bazett's Correction.

A prolonged heart rate corrected QT interval (QTc) increases the risk of sudden cardiac death. Some methods of heart rate correction (notably Bazett) overestimate QTc in people with high heart rates. Studies suggest that tricyclic antidepressants (TCAs) can prolong the QTc and increase heart rate. Therefore, we aimed to study whether TCA-induced QTc prolongation is a false-positive observation due to overestimation at high heart rates. For this, we included 12,734 participants from the prospective population-based Rotterdam Study, with a total of 27,068 electrocardiograms (ECGs), of which, 331 during TCA use. Associations between use of TCAs, QTc, and heart rate were studied with linear repeated measurement analyses. QT was corrected for heart rate according to Bazett (QTcBazett), Fridericia (QTcFridericia), or a correction based on regression coefficients obtained from the Rotterdam Study data (QTcStatistical). On ECGs recorded during TCA use, QTcBazett was 6.5 milliseconds (95% confidence interval, 4.0-9.0) longer, and heart rate was 5.8 beats per minute (95% confidence interval, 4.7-6.9) faster than during nonuse. QTcFridericia and QTcStatistical were not statistically significantly longer during TCA use than during nonuse. Furthermore, QTcBazett was similar for ECGs recorded during TCA use and nonuse after statistical adjustment for heart rate. According to our results, TCA use does not seem to be associated with QTc prolongation. Therefore, the current advice of regulatory authorities to restrict the use of these drugs and to do regular checkups of the QTc may need to be revised. Other formulas, like Fridericia's, might be preferred.

Drugs and ventricular repolarization in a general population: the Rotterdam Study.

PURPOSE: Prolonged ventricular repolarization (measured as heart-rate corrected QT (QTc) prolongation or JT-interval prolongation) is a risk factor for ventricular arrhythmias and can be drug-induced. Drugs can be classified as having known or possible QTc-prolonging properties. Regulatory agencies recommend avoiding concomitant use of multiple QTc-prolonging drugs, but evidence is lacking to what degree ventricular repolarization is influenced by concomitant use of these drugs. METHODS: Within a population-based cohort of persons aged 45 years and older, with up to five electrocardiograms recorded per participant between 1991 and 2010, we used generalised estimating equations to study the association between concomitant use of multiple QTc-prolonging drugs and repolarization duration. RESULTS: The study population consisted of 13 009 participants with 26 908 electrocardiograms. With the addition of a second or third QTc-prolonging drug there was no substantial increase in QTc and JT interval and no increased risk of a prolonged QTc interval, compared to use of one QTc-prolonging drug. There was a large difference between the effect of one known or one possible QTc-prolonging drugs on QTc interval: 15 ms for known, and 3 ms for possible QTc-prolonging drugs. CONCLUSIONS: In this study, the added prolongation in users of two or three QTc-prolonging drugs on QTc was small. There was a large difference in QTc prolongation between known and possible QTc-prolonging drugs. Further research in larger or high-risk populations is needed to establish whether it is safe to use multiple QTc-prolonging drugs concomitantly to prevent that the current advice might unnecessarily withhold beneficial drugs from patients.

Normal values of the electrocardiogram for ages 16-90 years.

INTRODUCTION: To establish an up-to-date and comprehensive set of normal values for the clinically current measurements in the adult ECG, covering all ages for both sexes. METHODS: The study population included 13,354 individuals, taken from four population studies in The Netherlands, ranging in age from 16 to 90 years (55% men) and cardiologically healthy by commonly accepted criteria. Standard 12-lead ECGs were available for all participants. The ECGs were processed by a well-validated computer program. Normal limits were taken as the 2nd and 98th percentiles of the measurement distribution per age group. RESULTS: Our study corroborates many findings of previous studies, but also provides more differentiated results, in particular for the older age groups. Age trends were apparent for the QTc interval, QRS axis, and indices of left ventricular hypertrophy. Amplitudes in the left precordial leads showed a substantial increase in the older age groups for women, but not for men. Sex-dependent differences were apparent for most ECG parameters. All results are available on the Website www.normalecg.org, both in tabular and in graphical format. CONCLUSIONS: We determined age- and sex-dependent normal values of the adult ECG. Our study distinguishes itself from other studies by the large size of the study population, comprising both sexes, the broad range of ages, and the exhaustive set of measurements. Our results emphasize that most diagnostic ECG criteria should be age- and sex-specific.

Annotation-preserving machine translation of English corpora to validate Dutch clinical concept extraction tools.

OBJECTIVE: To explore the feasibility of validating Dutch concept extraction tools using annotated corpora translated from English, focusing on preserving annotations during translation and addressing the scarcity of non-English annotated clinical corpora. MATERIALS AND METHODS: Three annotated corpora were standardized and translated from English to Dutch using 2 machine translation services, Google Translate and OpenAI GPT-4, with annotations preserved through a proposed method of embedding annotations in the text before translation. The performance of 2 concept extraction tools, MedSpaCy and MedCAT, was assessed across the corpora in both Dutch and English. RESULTS: The translation process effectively generated Dutch annotated corpora and the concept extraction tools performed similarly in both English and Dutch. Although there were some differences in how annotations were preserved across translations, these did not affect extraction accuracy. Supervised MedCAT models consistently outperformed unsupervised models, whereas MedSpaCy demonstrated high recall but lower precision. DISCUSSION: Our validation of Dutch concept extraction tools on corpora translated from English was successful, highlighting the efficacy of our annotation preservation method and the potential for efficiently creating multilingual corpora. Further improvements and comparisons of annotation preservation techniques and strategies for corpus synthesis could lead to more efficient development of multilingual corpora and accurate non-English concept extraction tools. CONCLUSION: This study has demonstrated that translated English corpora can be used to validate non-English concept extraction tools. The annotation preservation method used during translation proved effective, and future research can apply this corpus translation method to additional languages and clinical settings.

Blood pressure measurements for diagnosing hypertension in primary care: room for improvement.

BACKGROUND: In the adult population, about 50% have hypertension, a risk factor for cardiovascular disease and subsequent premature death. Little is known about the quality of the methods used to diagnose hypertension in primary care. OBJECTIVES: The objective was to assess the frequency of use of recognized methods to establish a diagnosis of hypertension, and specifically for OBPM, whether three distinct measurements were taken, and how correctly the blood pressure levels were interpreted. METHODS: A retrospective population-based cohort study using electronic medical records of patients aged between 40 and 70 years, who visited their general practitioner (GP) with a new-onset of hypertension in the years 2012, 2016, 2019, and 2020. A visual chart review of the electronic medical records was used to assess the methods employed to diagnose hypertension in a random sample of 500 patients. The blood pressure measurement method was considered complete if three or more valid office blood pressure measurements (OBPM) were performed, or home-based blood pressure measurements (HBPM), the office- based 30-minute method (OBP30), or 24-hour ambulatory blood pressure measurements (24 H-ABPM) were used. RESULTS: In all study years, OBPM was the most frequently used method to diagnose new-onset hypertension in patients. The OBP-30 method was used in 0.4% (2012), 4.2% (2016), 10.6% (2019), and 9.8% (2020) of patients respectively, 24 H-ABPM in 16.0%, 22.2%, 17.2%, and 19.0% of patients and HBPM measurements in 5.4%, 8.4%, 7.6%, and 7.8% of patients, respectively. A diagnosis of hypertension based on only one or two office measurements occurred in 85.2% (2012), 87.9% (2016), 94.4% (2019), and 96.8% (2020) of all patients with OBPM. In cases of incomplete measurement and incorrect interpretation, medication was still started in 64% of cases in 2012, 56% (2016), 60% (2019), and 73% (2020). CONCLUSION: OBPM is still the most often used method to diagnose hypertension in primary care. The diagnosis was often incomplete or misinterpreted using incorrect cut-off levels. A small improvement occurred between 2012 and 2016 but no further progress was seen in 2019 or 2020. If hypertension is inappropriately diagnosed, it may result in under treatment or in prolonged, unnecessary treatment of patients. There is room for improvement in the general practice setting.