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This cross-sectional study examines the concordance between clinical studies posted as preprints and subsequently published in high-impact journals, including key study characteristics, reported results, and study interpretations.
Major adverse cardiovascular events (MACE) are a major cause of morbidity and mortality among adults with type 2 diabetes. Currently available risk prediction models estimate long-term risk (typically 10-year) and require clinical data not routinely available in real-world data sources, which limit their use on population level. Using de-identified claims data from OptumLabs® Data Warehouse for enrollees in private and Medicare Advantage health plans linked to 100% sample of Medicare fee-for-service beneficiaries between 2014 and 2021, we derived ACME to estimate annualized risk of non-fatal myocardial infarction, non-fatal stroke, or all-cause mortality in patients with type 2 diabetes aged ≥21 years (N=6,623,526). The Cox proportional hazards model, combined with an estimate of the baseline hazard using the Nelson-Aalen estimator to allow conditional survival predictions, included 28 covariates available in claims (age, sex, cardiovascular comorbidities, and cardiovascular medications). The study population had mean age 68.1 (SD, 10.6) years, 49.8% were women, and 73.0% were non-Hispanic White. ACME’s concordance index was 0.74 (se = 0.0002) and it performed comparably in all racial and ethnic groups. Strongest predictors of MACE were acute myocardial infarction and stroke within the past 3 months, heart failure hospitalization within the past year, and end-stage kidney disease. Overall, 4.2% of patients were predicted to have low (<1%) annualized MACE risk, 62.8% were predicted to have moderate (≥1 to <5%) annualized risk, and 33.0% were predicted to have high (≥5%) annualized risk. Other thresholds can be chosen for individualized applications. ACME can support population risk stratification and health management efforts at the health system and payer levels, participant identification for decentralized pragmatic clinical trials of cardiovascular disease and its prevention, and risk-stratified observational studies using real-world data. Disclosure R.G.Mccoy: Consultant; Emmi. G.Umpierrez: Research Support; Abbott, Dexcom, Inc., Baxter. R.J.Galindo: Consultant; Novo Nordisk, Eli Lilly and Company, Sanofi, Pfizer Inc., Bayer Inc., WW (Weight Watchers), Research Support; Novo Nordisk, Eli Lilly and Company, Dexcom, Inc. J.Brito: None. M.Mickelson: None. E.Polley: None. K.Swarna: None. Y.Deng: None. J.Herrin: Consultant; Johnson & Johnson Medical Devices Companies. J.Ross: Research Support; Johnson & Johnson. D.M.Kent: Research Support; W.L. Gore. B.Borah: Consultant; Boehringer Ingelheim Inc., Exact Sciences. W.Crown: Consultant; Janssen Scientific Affairs, LLC, UnitedHealth Group, Viatris Inc. V.M.Montori: None. Funding Patient-Centered Outcomes Research Institute (DB-2020C2-20306)
Randomized controlled trials have long been the gold standard for evidence in medical product evaluation, but there is growing support for the use of real-world evidence (RWE). The authors review the benefits and limitations of RWE and discuss the implications for P&T committees.
Background: Stroke prevention using warfarin is challenging in AF patients with CKD, due to high bleeding risk and difficulties in the INR control. NOACs provide alternative options, but all have greater degrees of renal clearance. This study aimed to compare the outcomes of apixaban, dabigatran, rivaroxaban, and warfarin across the range of kidney function in patients with AF. Methods: Using a US administrative database including private insurance or Medicare Advantage patients with linked claims and laboratory data, we identified 34,569 new users of oral anticoagulants with AF and eGFR ≥15 between 10/1/2010-11/29/2017. Stabilized IPTW balanced four treatment groups on 66 baseline characteristics. The primary outcomes included stroke, major bleeding, and mortality. Weighted Cox proportional hazards models compared treatments in the overall population and in each eGFR subgroup, with mortality as a competing risk for stroke and major bleeding. Results: The proportion of patients using warfarin increased as the kidney function declined - 26.5%, 30.4%, 34.6%, 40.5%, and 55.0% of patients were prescribed warfarin in eGFR ≥90, 60-90, 45-60, 30-45, 15-30 groups, respectively. In comparison to warfarin, apixaban was associated with a lower risk of stroke, major bleeding, and mortality; dabigatran was associated with a similar risk of stroke, and a lower risk of major bleeding and mortality; rivaroxaban was associated with a lower risk of stroke, major bleeding, and mortality (Figure). When comparing one NOAC to another NOAC, apixaban and dabigatran were associated with a lower risk of major bleeding than rivaroxaban (HR 0.61 [0.51-0.73], p<0.001 for apixaban versus rivaroxaban; HR 0.67 [0.50-0.90], p=0.007 for dabigatran versus rivaroxaban); dabigatran was associated with a higher risk of stroke than apixaban (HR 1.65 [1.11-2.46], p=0.01); there was no difference in mortality. There was no significant interaction between treatment and eGFR categories for any outcome, but the number of patients with low eGFR was small. Conclusions: In practice, relative to warfarin, NOACs are progressively less commonly used with increasing degree of renal dysfunction. However, each NOAC was consistently associated with at least equivalent effectiveness and safety compared with warfarin across the range of kidney function.
Cardiogenic shock (CS) is a deadly and complicated illness. Despite extensive research into its treatment, mortality remains high and has not decreased over time. Patients suffering from CS are highly heterogeneous. Developing an understanding of phenotypes among these patients is crucial for understanding this disease and appropriate treatments for individual patients. In this work, we develop a deep mixture of experts approach to jointly find phenotypes among patients with CS while simultaneously estimating their risk of in-hospital mortality. This model is applied to a cohort of 28,304 patients with CS, predicting in-hospital mortality with an AUROC of 0.85 ± 0.01 and discovering five phenotypes among the population. This approach allows for grouping patients in clinical clusters with different rates of device utilization and different risk of mortality. This approach jointly finds phenotypes within a clinical population and in modeling risk among that population.
The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.
Pharmaceutical marketing can lead to overdiagnosis, overtreatment, and overuse of medications. Digital advertising creates new pathways for reaching physicians, allowing delivery of marketing messages at the point of care, when clinical decisions are being made.
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