Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs

Lin, Kueiyu Joshua; Schneeweiß, Sebastian

doi:10.1002/cpt.359

Cited by 64 publications

(50 citation statements)

References 51 publications

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“…We found that in a real‐world setting, treatment with liraglutide 3.0 mg, in addition to recommended diet and exercise, was associated with a significant loss of 7.0 to 8.0 kg and with a 6.5% to 7.1% decrease in body weight 4 and 6 months post initiation. This study thus confirms that the effectiveness of liraglutide 3.0 mg, as an adjunct to diet and exercise, demonstrated in randomized clinical trials is also evident in a real‐world clinical setting, which may not always be evident given variability in the general population and in adherence to treatment .…”

Section: Discussionsupporting

confidence: 78%

“…The clinical efficacy of liraglutide 3.0 mg has been established in randomized controlled clinical trials. Although these clinical trials offer a wealth of valuable information about safety and efficacy, the data derived from the highly controlled and specific population in the trials might not translate to a real‐world setting . Patients in the real world are subject to an uncontrolled environment and often experience a wider range of comorbidities and variable adherence to treatment.…”

Section: Introductionmentioning

confidence: 99%

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Real‐World Clinical Effectiveness of Liraglutide 3.0 mg for Weight Management in Canada

Wharton¹,

Liu²,

Pakseresht³

et al. 2019

Obesity

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Objective Real‐world clinical effectiveness of liraglutide 3.0 mg, in combination with diet and exercise, was investigated 4 and 6 months post initiation. Changes in absolute and percent body weight were examined from baseline. Methods A cohort of liraglutide 3.0 mg initiators in 2015 and 2016 was identified from six Canadian weight‐management clinics. Post initiation values at 4 and 6 months were compared with baseline values using a paired t test. Results The full cohort consisted of 311 participants, with 210 in the ≥ 4‐month persistence group and 167 in the ≥ 6‐month persistence group. Average baseline BMI was 40.7 kg/m2, and weight was 114.8 kg. There was a significant change in body weight 6 and 4 months after initiation of treatment in persistent subjects (≥ 6‐month: −8.0 kg, P < 0.001; ≥ 4‐month: −7.0 kg, P < 0.001) and All Subjects, regardless of persistence (−7.3 kg; P < 0.001). Percentage change in body weight from baseline was −7.1% in the ≥ 6‐month group and −6.3% in the ≥ 4‐month group, and All Subjects lost 6.5% body weight. Of participants in the ≥ 6‐month group, 64.10% and 34.5% lost ≥ 5% and > 10% body weight, respectively. Conclusions In a real‐world setting, liraglutide 3.0 mg, when combined with diet and exercise, was associated with clinically meaningful weight loss.

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Real‐World Clinical Effectiveness of Liraglutide 3.0 mg for Weight Management in Canada

Wharton¹,

Liu²,

Pakseresht³

et al. 2019

Obesity

View full text Add to dashboard Cite

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“…We used linked claims EHR data for higher data quality: The claims data provided comprehensive data across care settings, and EHRs provided necessary clinical information to ascertain TTR and important predictors. However, requiring overlap of the 2 databases reduces our sample size substantially and limits our ability to investigate each clinical outcome individually . Besides, for biophysiologic variables (eg, body mass index, albumin levels), we had 29% to 34% people with missing data in the relevant period.…”

Section: Discussionmentioning

confidence: 99%

“…However, requiring overlap of the 2 databases reduces our sample size substantially and limits our ability to investigate each clinical outcome individually. 55 Besides, for biophysiologic variables (eg, body mass index, albumin levels), we had 29% to 34% people with missing data in the relevant period. We handled it by the missing indicator method because not having certain tests done could, by itself, be informative of the general health state, and this approach allows use of these scores even if some variables are not available.…”

Section: Discussionmentioning

confidence: 99%

Prediction Score for Anticoagulation Control Quality Among Older Adults

Lin

Singer

Glynn

et al. 2017

JAHA

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BackgroundTime in the therapeutic range (TTR) is associated with the effectiveness and safety of vitamin K antagonist (VKA) therapy. To optimize prescribing of VKA, we aimed to develop and validate a prediction model for TTR in older adults taking VKA for nonvalvular atrial fibrillation and venous thromboembolism.Methods and ResultsThe study cohort comprised patients aged ≥65 years who were taking VKA for atrial fibrillation or venous thromboembolism and who were identified in the 2 US electronic health record databases linked with Medicare claims data from 2007 through 2014. With the predictors identified from a systematic review and clinical knowledge, we built a prediction model for TTR, using one electronic health record system as the training set and the other as the validation set. We compared the performance of the new models to that of a published prediction score for TTR, SAMe‐TT 2R2. Based on 1663 patients in the training set and 1181 in the validation set, our optimized score included 42 variables and the simplified model included 7 variables, abbreviated as PROSPER (Pneumonia, Renal dysfunction, Oozing blood [prior bleeding], Staying in hospital ≥7 days, Pain medication use, no Enhanced [structured] anticoagulation services, Rx for antibiotics). The PROSPER score outperformed SAMe‐TT 2R2 when predicting both TTR ≥70% (area under the receiver operating characteristic curve 0.67 versus 0.55) and the thromboembolic and bleeding outcomes (area under the receiver operating characteristic curve 0.62 versus 0.52).ConclusionsOur geriatric TTR score can be used as a clinical decision aid to select appropriate candidates to receive VKA therapy and as a research tool to address confounding and treatment effect heterogeneity by anticoagulation quality.

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“…Ultimately, treatments should be linked to clinical outcomes so that the best treatment can be selected. Increasingly, traditional sources of health-related data, such as claims databases, can be linked with clinical data from EHRs to better understand the safety of medications we prescribe to patients [8]. In fact, the wealth of information in all sources of data can be used to improve efficiency in clinical care [9] as well as compare the effectiveness of therapeutic options [10].…”

Section: Actors Of Reuse Research and Patient Carementioning

confidence: 99%

Update on Data Reuse in Health Care

Safran¹

2017

Yearb Med Inform

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SummaryObjective: Reuse of clinical data has broad use in clinical, research, governmental, and business settings. This summary provides an update on the benefits, barriers to use with large clinical databases, policy frameworks that have been formulated, and challenges. Methods: This report highlights some recent publications on the diverse uses of clinical data and some policy initiatives to promote reuse. It also contains the opinions of the author. Results: Although many examples of the benefits of data reuse have been documented, this summary also reviews why the quality of clinical data needs to be the focus of future informatics work. Conclusion: The promise of reusing data outweighs potential risks, but concerns about privacy and the need to modernize our legal framework will be necessary to realize the full benefits of real-world evidence.

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Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs

Cited by 64 publications

References 51 publications

Real‐World Clinical Effectiveness of Liraglutide 3.0 mg for Weight Management in Canada

Real‐World Clinical Effectiveness of Liraglutide 3.0 mg for Weight Management in Canada

Prediction Score for Anticoagulation Control Quality Among Older Adults

Update on Data Reuse in Health Care

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