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Electronic medical records can be used to emulate target trials of sustained treatment strategies.

ABSTRACT: OBJECTIVE:To emulate three target trials: single treatment vs. no treatment, joint treatment vs. no treatment, and head-to-head comparison of two treatments, we explain how to estimate the observational analogs of intention-to-treat and per-protocol effects, using hazard ratios and survival curves. For per-protocol effects, we describe two methods for adherence adjustment via inverse-probability weighting. STUDY DESIGN AND SETTING:Prospective observational study using electronic medical records of individuals aged 55-84 with coronary heart disease from >500 practices in the United Kingdom between 2000 and 2010. RESULTS:The intention-to-treat mortality hazard ratio (95% confidence interval) was 0.90 (0.84, 0.97) for statins vs. no treatment, 0.88 (0.73, 1.06) for statins plus antihypertensives vs. no treatment, and 0.91 (0.77, 1.06) for atorvastatin vs. simvastatin. When censoring nonadherent person-times, the per-protocol mortality hazard ratio was 0.74 (0.64, 0.85) for statins vs. no treatment, 0.55 (0.35, 0.87) for statins plus antihypertensives vs. no treatment, and 1.13 (0.88, 1.45) for atorvastatin vs. simvastatin. We estimated per-protocol hazard ratios for a 5-year treatment using different dose-response marginal structural models and standardized survival curves for each target trial using intention-to-treat and per-protocol analyses. CONCLUSION:When randomized trials are not available or feasible, observational analyses can emulate a variety of target trials.

SUBMITTER: Danaei G

PROVIDER: S-EPMC5847447 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Electronic medical records can be used to emulate target trials of sustained treatment strategies.

Danaei Goodarz G García Rodríguez Luis Alberto LA Cantero Oscar Fernández OF Logan Roger W RW Hernán Miguel A MA

Journal of clinical epidemiology 20180401

<h4>Objective</h4>To emulate three target trials: single treatment vs. no treatment, joint treatment vs. no treatment, and head-to-head comparison of two treatments, we explain how to estimate the observational analogs of intention-to-treat and per-protocol effects, using hazard ratios and survival curves. For per-protocol effects, we describe two methods for adherence adjustment via inverse-probability weighting.<h4>Study design and setting</h4>Prospective observational study using electronic m ...[more]

PMID: 29203418

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Project description:BackgroundThe use of the electronic medical record (EMR) system in recruitment in clinical trials has the potential for providing a very reliable and cost-effective recruiting methodology which may improve participant recruitment in clinical trials. We examined a recruitment approach centered on the use of the EMR, as well as other traditional methods, in the Lifestyle Intervention for Treatment of Diabetes (LIFT Diabetes) trial.MethodsLIFT Diabetes is a randomized controlled trial designed to investigate the effects of two contrasting interventions on cardiovascular disease risk: a community-based intensive lifestyle program aimed at achieving weight loss and a clinic-based enhanced diabetes self-management program. Eligible participants were overweight/obese (body mass index, BMI ≥25 kg/m2) patients with type 2 diabetes who were aged 21 years or older. Recruitment strategies included the use of the EMR system (primary), direct referrals, media advertisements, and community screenings.ResultsA total of 1102 telephone screens were conducted, resulting in randomization of 260 participants (61.5 % from EMR, mean age 56.3 years, 66.2 % women, 48.1 % non-Hispanic blacks) over a 21-month period, with a yield of 23.6 %. Recruitment yields differed by recruitment method, with referrals having the highest yield (27.5 %). A history of cardiovascular disease was the main health reason for exclusion from the study (16.5 %). An additional 8.9 % were excluded for BMI <25 kg/m2 (<27 kg/m2 for insulin users), 5.4 % could not exercise, 5.2 % had an HbA1c >11 %, and 34.9 % were excluded for other non-medical reasons. Exclusion criteria did not appear to differentially affect enrollment in terms of race or ethnicity.ConclusionsFuture clinical studies should tailor their recruitment strategies based on the participant demographics of interest. Efficient methods such as using the EMR system and referrals should be prioritized over labor-intensive, low-yielding methods such as community screenings and mass mailings.Trial registrationClinicalTrials.gov: NCT01806727 . Registered on 5 March 2013.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Project description:Despite the benefits associated with longer buprenorphine treatment duration (i.e., >180 days) (BTD) for opioid use disorder (OUD), retention remains poor. Research on the impact of co-occurring psychiatric issues on BTD has yielded mixed results. It is also unknown whether the genetic risk in the form of polygenic scores (PGS) for OUD and other comorbid conditions, including problematic alcohol use (PAU) are associated with BTD. We tested the association between somatic and psychiatric comorbidities and long BTD and determined whether PGS for OUD-related conditions was associated with BTD. The study included 6686 individuals with a buprenorphine prescription that lasted for less than 6 months (short-BTD) and 1282 individuals with a buprenorphine prescription that lasted for at least 6 months (long-BTD). Recorded diagnosis of substance addiction and disorders (Odds Ratio (95% CI) = 22.14 (21.88-22.41), P = 2.8 × 10-116), tobacco use disorder (OR (95% CI) = 23.4 (23.13-23.68), P = 4.5 × 10-111), and bipolar disorder (OR(95% CI) = 9.70 (9.48-9.92), P = 1.3 × 10-91), among others, were associated with longer BTD. The PGS of OUD and several OUD co-morbid conditions were associated with any buprenorphine prescription. A higher PGS for OUD (OR per SD increase in PGS (95%CI) = 1.43(1.16-1.77), P = 0.0009), loneliness (OR(95% CI) = 1.39(1.13-1.72), P = 0.002), problematic alcohol use (OR(95%CI) = 1.47(1.19-1.83), P = 0.0004), and externalizing disorders (OR(95%CI) = 1.52(1.23 to 1.89), P = 0.0001) was significantly associated with long-BTD. Associations between BTD and the PGS of depression, chronic pain, nicotine dependence, cannabis use disorder, and bipolar disorder did not survive correction for multiple testing. Longer BTD is associated with diagnoses of psychiatric and somatic conditions in the EHR, as is the genetic score for OUD, loneliness, problematic alcohol use, and externalizing disorders.

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Electronic medical records can be used to emulate target trials of sustained treatment strategies.

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Electronic medical records can be used to emulate target trials of sustained treatment strategies.

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