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)
Background : Venous thromboembolism (VTE) remains a significant complication of severe COVID-19 infection in spite of pharmacological thromboprophylaxis. Various coaguopathies including thrombocytopenia, thrombocytosis, DIC and fibrinolytic shutdown have been reported. Interpretation of isolated coagulation and fibrinolysis biomarkers can be complicated by septic states, disease severity and thrombo-inflammation. Thromboelastography (TEG) and Rotational Thromboelastography (ROTEM) are whole blood viscoelastic (VE) tests that provide rapid and full assessment of haemostasis process. Aims : A systematic review and meta-analysis, to evaluate if TEG/ ROTEM can help detect hypercoagulability and/or predict VTE risk in COVID-19 patients. Methods : We searched MEDLINE, EMBASE, and EPUB Ahead of Print & Other Non-Indexed Citations (from inception to 18 th October 2020). Studies included were observational or RCTs evaluating VE tests with COVID-19 infection. Review was registered with PROSPERO (CRD42021229814). 2 reviewers (SK, MO) reviewed all full-text versions of all eligible studies. Data abstracted on study design, demographic characteristics and clinical outcomes-VTE, mortality and laboratory tests and VE parameters. The effect estimates were expressed as odds ratio (OR) or standardized mean difference (SMD) with 95% confidence interval. Results : We identified 153 articles (PRISMA in Figure 1). A total of 841 patients from 20 selected studies (3 prospective,17 retrospective) were identified. 841 COVID-19 patients with 589 (70%)who had viscoelastic tests (10 ROTEM,10 TEG). 431 (73%) had elevated D-dimer. 293 (50%) patients were hypercoagulable on TEG/ROTEM and 245 (41%) normal. 3 studies reported fibrinolytic shutdown. 241 (29%) patients had VTE events. ROTEM showed a higher sensitivity (93%) and specificity (87%) compared to TEG with a sensitivity of 51% and specificity of 57%. The OR was 1.77 (95% CI: 0.62-to 5.04, I 2 = 57%, P = 0.29). Conclusions : TEG ® /ROTEM ® can detect hypercoagulability in association with COVID19 coagulopathy but may not be sufficient to predict VTE. Clot mass and viscoelasticity (MCF in ROTEM or MA in TEG) appear to be the most critical parameters.
With a growing interest in the use of real-world evidence for regulatory decision-making, it is important to understand whether real-world data can be used to emulate the results of randomized clinical trials.
Objective
To use electronic health record and administrative claims data to emulate the ongoing PRONOUNCE trial (A Trial Comparing Cardiovascular Safety of Degarelix Versus Leuprolide in Patients With Advanced Prostate Cancer and Cardiovascular Disease).
Design, Setting, and Participants
This retrospective, propensity-matched cohort study included adult men with a diagnosis of prostate cancer and cardiovascular disease who initiated either degarelix or leuprolide between December 24, 2008, and June 30, 2019. Participants were commercially insured individuals and Medicare Advantage beneficiaries included in a large US administrative claims database.
Exposures
Degarelix or leuprolide.
Main Outcomes and Measures
The primary end point was time to first occurrence of a major adverse cardiovascular event (MACE), defined as death due to any cause, myocardial infarction, or stroke, analogous to the PRONOUNCE trial. Secondary end points were time to death due to any cause, myocardial infarction, stroke, and angina. Cox proportional hazards regression was used to evaluate primary and secondary end points.
Results
A total of 32 172 men initiated degarelix or leuprolide for prostate cancer; of them, 9490 (29.5%) had cardiovascular disease, and 7800 (24.2%) met the PRONOUNCE trial eligibility criteria and were included in this study. Overall, 165 participants (2.1%) were Asian, 1390 (17.8%) were Black, 663 (8.5%) were Hispanic, and 5258 (67.4%) were White. The mean (SD) age was 74.4 (7.4) years. Among 2226 propensity score–matched patients, no significant difference was observed in the risk of MACE for patients taking degarelix vs those taking leuprolide (10.18 vs 8.60 events per 100 person-years; hazard ratio [HR], 1.18; 95% CI, 0.86-1.61). Degarelix was associated with a higher risk of death from any cause (HR, 1.48; 95% CI, 1.01-2.18) but not of myocardial infarction (HR, 1.16; 95% CI, 0.60-2.25), stroke (HR, 0.92; 95% CI, 0.45-1.85), or angina (HR, 1.36; 95% CI, 0.43-4.27).
Conclusions and Relevance
In this emulation of a clinical trial of men with cardiovascular disease undergoing treatment for prostate cancer, degarelix was not associated with a lower risk of cardiovascular events than leuprolide. Comparison of these data with PRONOUNCE trial results, when published, will help enhance our understanding of the appropriate role of using real-world data to emulate clinical trials.
To evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program.This retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints.Among 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged.High-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.
Background/aims There has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials. Methods This study compared participants’ demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials’ using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences <0.20) and results of the primary and secondary endpoints (concordance determined by direction of effect estimates and statistical significance). Results The PRONOUNCE trial enrolled 544 participants, and the emulated trial included 2226 propensity score-matched participants. In the PRONOUNCE trial publication, one of the 32 baseline participant characteristics was listed as an exclusion criterion on ClinicalTrials.gov but was ultimately not used. Among the remaining 31 characteristics, 9 (29.0%) were ascertainable, 11 (35.5%) were partially ascertainable, and 10 (32.2%) were not ascertainable using structured data from OptumLabs. For one additional variable, the PRONOUNCE trial did not provide sufficient detail to allow its ascertainment. Of the nine variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 6 (66.7%). The primary endpoint of time from randomization to the first major adverse cardiovascular event and secondary endpoints of nonfatal myocardial infarction and stroke were concordant between the emulated trial and published randomized controlled trial. The GRADE trial enrolled 5047 participants, and the emulated trial included 7540 participants. In the GRADE trial publication, 8 of 34 (23.5%) baseline participant characteristics were ascertainable, 14 (41.2%) were partially ascertainable, and 11 (32.4%) were not ascertainable using structured data from OptumLabs. For one variable, the GRADE trial did not provide sufficient detail to allow for ascertainment. Of the eight variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 4 (50.0%). The primary endpoint of time to hemoglobin A1c ≥7.0% was mostly concordant between the emulated trial and the published randomized controlled trial. Conclusion Despite challenges, observational methods and real-world data can be leveraged in certain important situations for a more timely evaluation of drug effectiveness and safety in more diverse and representative patient populations.
Abstract Objective To emulate the GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) trial using real world data before its publication. GRADE directly compared second line glucose lowering drugs for their ability to lower glycated hemoglobin A 1c (HbA 1c ). Design Observational study. Setting OptumLabs® Data Warehouse (OLDW), a nationwide claims database in the US, 25 January 2010 to 30 June 2019. Participants Adults with type 2 diabetes and HbA 1c 6.8-8.5% while using metformin monotherapy, identified according to the GRADE trial specifications, who also used glimepiride, liraglutide, sitagliptin, or insulin glargine. Main outcome measures The primary outcome was time to HbA 1c ≥7.0%. Secondary outcomes were time to HbA 1c >7.5%, incident microvascular complications, incident macrovascular complications, adverse events, all cause hospital admissions, and all cause mortality. Propensity scores were estimated using the gradient boosting machine method, and inverse propensity score weighting was used to emulate randomization of the treatment groups, which were then compared using Cox proportional hazards regression. Results 8252 people were identified (19.7% of adults starting the study drugs in OLDW) who met eligibility criteria for the GRADE trial (glimepiride arm=4318, liraglutide arm=690, sitagliptin arm=2993, glargine arm=251). The glargine arm was excluded from analyses owing to small sample size. Median times to HbA 1c ≥7.0% were 442 days (95% confidence interval 394 to 480 days) for glimepiride, 764 (741 to not calculable) days for liraglutide, and 427 (380 to 483) days for sitagliptin. Liraglutide was associated with lower risk of reaching HbA 1c ≥7.0% compared with glimepiride (hazard ratio 0.57, 95% confidence interval 0.43 to 0.75) and sitagliptin (0.55, 0.41 to 0.73). Results were consistent for the secondary outcome of time to HbA 1c >7.5%. No significant differences were observed among treatment groups for the remaining secondary outcomes. Conclusions In this emulation of the GRADE trial, liraglutide was statistically significantly more effective at maintaining glycemic control than glimepiride or sitagliptin when added to metformin monotherapy. Generating timely evidence on medical treatments using real world data as a complement to prospective trials is of value.
Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).
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