Predicting Atrial Fibrillation in Primary Care Using Machine Learning

Inhaled Treprostinil and FVC Change in Patients With Interstitial Lung Disease and Associated Pulmonary Hypertension

Safety of biweekly α1-antitrypsin treatment in the RAPID programme

Increasing bronchiectasis prevalence in Germany, 2009-2017: a population-based cohort study.

Prognostic Impact of Immunohistochemistry and MRD Results in Bone Marrow after Frontline Chemoimmunotherapy of Chronic Lymphocytic Leukemia (CLL): Results of Central Laboratory Analyses of the CLL10 Trial of the German CLL Study Group (GCLLSG)

FJ Martinez reports financial support from AstraZeneca, Afferent, Boehringer Ingelheim, ConCert, Genentech, GlaxoSmithKline, Kadmon, Mero, Pearl, Unity, Novartis, Takeda, Sunovion, Theravance and Veracyte for attendance at advisory boards, research support...

Directing Therapy with Circulating Tumor DNA Analysis in Advanced Breast Cancer: The PlasmaMATCH Trial

Effects of Sodium Glucose Co-Transporter 2 Inhibitor Canagliflozin in Patients with Type 2 Diabetes and Chronic Heart Failure (CANDLE): An Open-Label, Randomized Controlled Trial

Establishment of CORONET: COVID-19 Risk in Oncology Evaluation Tool to Identify Cancer Patients at Low Versus High Risk of Severe Complications of COVID-19 Infection Upon Presentation to Hospital

ObjectiveTo investigate whether opportunistic screening in primary care increases the detection of atrial fibrillation compared with usual care.DesignCluster randomised controlled trial.Setting47 intention-to-screen and 49 usual care primary care practices in...

Dramatic response of refractory sarcoidosis under ruxolitinib in a patient with associated JAK2-mutated polycythemia.

Development of a Machine Learning Algorithm for Rapid, Point-of-Care Prediction of Serum Monoclonal Proteins in Multiple Myeloma

Pembrolizumab Plus Chemotherapy as Neoadjuvant Treatment for Locally Advanced Triple-Negative Breast Cancer: Results from the Phase 1b Open-Label, Multicohort KEYNOTE-173 Study

T Troosters has received grants from Innovative Medicines Initiative Joint Undertaking and speaker fees from Boehringer Ingelheim and GlaxoSmithKline.J Bourbeau has received grants from the Canadian Institute of Health Research...

HomeStrokeVol. 52, No. 8Age At Menopause: A Female Risk Factor of Stroke? Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessEditorialPDF/EPUBAge At Menopause: A Female Risk Factor of Stroke? Hanne Christensen, MD, DMSci Charlotte CordonnierMD, PhD Hanne ChristensenHanne Christensen Correspondence to: Hanne Christensen, MD, DMSci, Department of Neurology, University of Copenhagen, Bispebjerg Hospital, Bispebjerg Bakke 23, Copenhagen NV 2400, Denmark. Email E-mail Address: [email protected] https://orcid.org/0000-0002-7472-3194 Department of Neurology, Bispebjerg Hospital and University. Copenhagen, Denmark (H.C.). Search for more papers by this author and Charlotte CordonnierCharlotte Cordonnier https://orcid.org/0000-0002-5697-6892 Université de Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000, France (C.C.). Search for more papers by this author Originally published3 Jun 2021https://doi.org/10.1161/STROKEAHA.121.034828Stroke. 2021;52:2592–2593This article is a commentary on the followingAge at Menopause and Risk of Ischemic and Hemorrhagic StrokeSee related article, p 2583Stroke is not only a leading cause of disability but is also a leading cause of death worldwide—particularly in women. The anticipated increase in the average age of the female population worldwide will lead to an increase in the prevalence of stroke in women and will challenge our healthcare systems. Women differ from men in a multitude of ways, including anatomy, vascular biology, coagulation, lifestyle, and societal roles. Among those differences, hormonal profiles probably constitute more prominent differences. Women are not men like others. Although risk of stroke differs throughout the life course, 3 stages of women’s lives have specificities in terms of stroke risk: child-bearing age, postmenopause, and over 80 years of age.1 In the era of personalized and precision medicine, our knowledge of women’s specificities is still incomplete.In this issue of Stroke, Welten et al2 followed up 16 244 postmenopausal Dutch women during a mean of 15 years. They showed that the age of menopause matters in the risk of future ischemic stroke: there is a 2% risk reduction of stroke for each year the menopause occurs later. The impact of a later menopause was observed in ischemic stroke but failed to reach statistical significance in hemorrhagic stroke, likely due to low numbers of hemorrhagic events. The authors evaluated both natural and surgical menopause. Interestingly, the association appeared stronger in natural than in surgical menopause.On this topic, data are controversial, and Welten et al2 raise important methodological issues that could explain the heterogeneity of data. For example, the methodological notion of immortal time bias is an important one.After menopause, the burden of vascular risk factors increase,3 and 1 in 5 women versus 1 in 6 men at the age of 55 years will suffer a stroke event during their remaining lifetime.4Overall, the epidemiological association of age at menopause is confirmed by Welten et al,2 but the basis of this association remains unclear. Several potential explanations are present and especially the role of estrogen has been explored: estrogen has vasoactive properties assumed to support vascular health, but hormone replacement therapy was shown to increase risk of stroke in several trials,5 although it has been hypothesized that this effect related to initiation of hormone replacement therapy years after menopause. It is also possible that menopause is at least partly an epiphenomenon to other risk factors which covariates with both risk of stroke and age of menopause including socioeconomic status6,7 or smoking.Many questions remain unanswered. First, is early menopause associated with specific vascular risk profiles, such as hypertension, hyperlipidemia, or does it trigger some risk factors such as atrial fibrillation? Furthermore, is there a pattern in stroke types (large vessel disease/small vessel disease/cardioembolic stroke) in women with stroke and early menopause? A translational approach will be of interest here. Although epidemiological data will bring large, observational data, refined animal models could also contribute to understanding a hypothetical causal relation.Exploring this topic would be of potential clinical value as age at menopause could be an element contributing to a personalized risk assessment for primary stroke. While existing data does not support therapeutic decisions on drug therapy influenced by age at menopause, nonpharmacological interventions (targeting smoking, alcohol consumption, diet, or physical activity) could be promoted in consultations related to menopause.Sources of FundingNone.Disclosures Dr Christensen reports Consulting Editor (Stroke). Dr Cordonnier reports personal fees from Boehringer-Ingelheim, personal fees from Bristol Myers Squibb (BMS), personal fees from Biogen, personal fees from AstraZeneca, and grants from French ministry of health outside the submitted work; and Associate editor (Stroke).FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.For Disclosures, see page 2593.Correspondence to: Hanne Christensen, MD, DMSci, Department of Neurology, University of Copenhagen, Bispebjerg Hospital, Bispebjerg Bakke 23, Copenhagen NV 2400, Denmark. Email hanne.krarup.[email protected]dk

There is a paucity of studies validating budget impact models. The lack of such studies may contribute to the underuse of budget impact models by payers in formulary decision making. To assess the face validity, internal verification, and predictive validity of a previously published model that assessed the budgetary impact of antidiabetic formulary changes. 4 experts with diverse backgrounds were selected and asked questions regarding the face validity of the structure/conceptual model, input data, and results from the budget impact model. To assess internal verification, structured "walk-throughs," unit tests, extreme condition tests, traces, replication tests, and double programming techniques were used. The predictive validity of the model was evaluated by comparing the predicted and realized budget using mean absolute scaled error. "Realized" budgetary impact of the formulary changes was calculated by taking the difference between realized budget in the year after the formulary changes and the budget had there been no formulary changes (i.e., the counterfactual). The counterfactual budget was modeled using the best fit autoregressive integrated moving average model. When assessing the face validity of the model, the 4 experts brought up issues such as how to incorporate other health insurance, recent policy changes, cost inflation, and potential impacts on insulin use. The 6 internal verification techniques caught mistakes in equations, missing data, and misclassified data. The realized budget was found to be lower than the predicted budget, with 13% error and an absolute scaled error of 2.60. After removing the model assumption that past utilization trends would continue, the model's predictive accuracy improved (the absolute scaled error dropped below 1 to 0.48). The "realized" budgetary impact was found to be greater than the predicted budgetary impact, largely because of lower-than-expected utilization. The budget impact model overpredicted utilization in the year after the formulary changes. Discoveries through the validation process improved the accuracy and transparency of the model. This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. AHRQ had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer Ingelheim, Janssen/J&J, Regeneron, and Sanofi-Aventis, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.