ObjectiveAtrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community.MethodsOvid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation.ResultsEleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526–0.815), CHA2DS2-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/thromboembolism (2 points), Vascular disease, Age 65–74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531–0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513–0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was ‘low’. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation.ConclusionsModels externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance.Systematic review registrationPROSPERO CRD42021245093.

Aims To investigate trends in the prescription of oral anticoagulants (OACs) and antiplatelet agents for atrial fibrillation (AF). Methods and results Prescription data for 450 518 patients with AF from 3352 General Practices in England, was obtained from the GRASP-AF registry, 2009–2018. Annualized temporal trends for OAC and antiplatelet prescription were reported according to eligibility based on stroke risk (CHADS2 or CHA2DS2-VASc scores ≥1 or >2, respectively). From 2009 to 2018, the prevalence of AF increased from 1.6% [95% confidence interval (CI) 1.5–1.7%] to 2.4% (2.3–2.5%), and for those with AF the proportion prescribed OAC increased from 47.6% to 75.0% (P-trend < 0.001; relative risk 1.57, 95% CI 1.55–1.60) and for antiplatelet decreased from 37.4% to 9.2% (P-trend < 0.001). In early-years (2009–2013), eligible patients aged ≥80 years were less likely to be prescribed OAC than patients aged <80 years [odds ratio (OR) 0.55, 95% CI 0.51–0.59 for CHADS2≥1 in 2009] (all P-trends < 0.001). This ‘OAC prescription gap’ reduced over the study period (OR 0.93, 0.90–0.96 in 2018). Whilst the prescription of direct oral anticoagulant (DOAC) as a proportion of all OAC increased from 0.1% (95% CI 0.0–0.2%) in 2011 to 58.8% (58.4–59.2%) in 2018, it was inversely associated with patient age (P-trend < 0.001) and their risk of stroke. Conclusion Between 2009 and 2018, in England, the use of OAC for stroke prophylaxis in AF increased, with DOAC accounting for over half of OAC uptake in 2018. Despite a reduction in the OAC-prescription gap, a new paradox exists relating to DOAC prescription for the elderly and those at higher risk of stroke.

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'Personalized' (or more specifically targeted) medicines promise aging populations important medium- to long-term benefits, including extended healthy life expectancies. But the time and resources needed to deliver such advances may be significantly greater than optimists expect. Given current methodologies, drugs for limited numbers of older patients are also much less likely to be judged cost effective than treatments for more common conditions in younger populations. There is a consequent danger that private and public funding for personalized medicine development will in the future fall to a level that is significantly below the global public's best interests. Policy-makers should be aware of this risk, and take actions to mitigate it. These include minimizing the costs and complexities associated with marketing personalized medicines and linked diagnostic tests, as well as strengthening intellectual property protection in ways that will allow 'rich world' pharmaceutical price moderation, increased innovator security and enhanced treatment access for poorer populations.

Background: Potentially inappropriate medications are major health concerns for patients aged ≥65 years. To investigate the prevalence of potentially inappropriate medications, Beer's criteria can be used. We estimated the prevalence of potentially inappropriate medications prescription among patients aged ≥65 years admitted to Kuwait's largest hospital and identified the predictors of prescribing a potentially inappropriate medication. Methods: A cross-sectional study was conducted retrospectively using inpatient records from the medical department at the Hospital in Kuwait from 1 January 2019 to 31 December 2019. The latest version of Beer's criteria was used to identify potentially inappropriate medications in patients' medical records. Data were analyzed descriptively to estimate the prevalence of potentially inappropriate medications and to describe participant characteristics. The predictors of potentially inappropriate medications prescribing were determined using binary logistic regression. Results: A total of 423 medical records of patients were collected. The mean age of the patients admitted was 76 ± 7 years, and 222 of them (52.5%) were women. Upon hospital admission, potentially inappropriate medication was prevalent in 58.4% of patients. The most prevalent potentially inappropriate medications identified were proton pump inhibitors (27.3%), diuretics (21.5%), antipsychotic agents (9%), selective serotonin reuptake inhibitors (5%), and methyldopa (4%). Polypharmacy, Alzheimer's disease, depression, irritable bowel syndrome, hypothyroidism, chronic kidney disease were predictors of potentially inappropriate medications prescription. Conclusion:A high prevalence of potentially inappropriate medication prescription was observed among patients aged ≥65 years admitted to a hospital in Kuwait. The most likely predictor of potentially inappropriate medication prescription was polypharmacy.