PS1-44: Comparing Measures of Disease Frequency Between Population Denominator Methods Using Data in the Electronic Health Record

Abstract Background Interventional studies are the fundamental method for obtaining answers to clinical question. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals. Methods We compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers’ publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases. Results In Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (713/1463) or collected from several collaborating medical institutions (351/1463). Whereas the studies conducted with large-scale integrated databases (195/1463) were mostly retrospective (68.2%), 27.2% of the single-center studies, 46.2% of the multi-center studies, and 74.4% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. Conclusions Our analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn. Using our flow diagram, researchers planning non-interventional observational studies should consider the strengths and limitations of each available database and choose the most appropriate one for their study goals. Trial registration Not applicable.

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Abstract 032: Is Bigger Data Better? Predicting Readmissions in Acute Myocardial Infarction on Admission versus Discharge With Electronic Health Record Data

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.10.suppl_3.032 ◽

2017 ◽

Vol 10 (suppl_3) ◽

Author(s):

Oanh K Nguyen ◽

Anil N Makam ◽

Christopher Clark ◽

Song Zhang ◽

Sandeep R Das ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Electronic Health Record ◽

Risk Prediction ◽

Prediction Models ◽

Predictive Ability ◽

Health Record ◽

Electronic Health Record Data ◽

Electronic Health ◽

Using Data

Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common, but the few available risk prediction models have poor predictive ability. Including more data from hospitalization may improve risk prediction. Objectives: To assess if an AMI-specific electronic health record (EHR) readmission risk prediction model derived and validated from data through the entire hospital course (‘full stay’ model) outperforms a model using data available only from the first day of hospitalization (‘first day’ model). Methods: EHR data from AMI hospitalizations from 6 diverse hospitals in north Texas from 2009-2010 were used to derive a model predicting all-cause non-elective 30-day readmissions which was then validated using five-fold cross-validation. Results: Of 826 consecutive index AMI admissions, 13% were followed by a 30-day readmission. History of diabetes (AOR 2.41, 95% CI 1.37-4.24), SBP <100 mmHg on admission (AOR 2.18, 95% CI 1.68-2.82), elevated Cr (≥2 mg/dL) on admission (AOR 2.56, 95% CI 2.52-6.08), elevated BNP on admission (AOR 6.36, 95% CI 1.65-24.47) and lack of PCI within 24 hours of admission (AOR 1.31, 95% CI 1.02-1.69) were significant predictors of readmission. Our ‘first-day’ AMI readmissions model based on these predictors had good discrimination ( Table ). Adding three other variables from the hospital course - use of IV diuretics (AOR 1.58, 95% CI 1.07-2.31), anemia (hematocrit ≤ 33%) on discharge (AOR 2.04, 95% CI 1.20-3.46), and discharge to post-acute care (AOR 1.50, 95% CI 0.90-2.50) - improved discrimination of the ‘full stay’ AMI model but only modestly improved net reclassification and calibration. Conclusions: A ‘full-stay’ AMI-specific EHR readmission model modestly outperformed a ‘first-day’ EHR model, a multi-condition EHR model, and the CMS AMI model. Surprisingly, incorporating more hospitalization data improved discrimination of the full-stay AMI model but did not meaningfully improve reclassification compared to the first-day model. Readmissions in AMI may be accurately predicted on the first day of hospitalization; waiting until later in hospitalization does not markedly improve risk prediction.

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External Evaluation of a Gentamicin Infant Population Pharmacokinetic Model Using Data from a National Electronic Health Record Database

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00669-18 ◽

2018 ◽

Vol 62 (9) ◽

Cited By ~ 2

Author(s):

Shufan Ge ◽

Ryan J. Beechinor ◽

Christoph P. Hornik ◽

Joseph F. Standing ◽

Kanecia Zimmerman ◽

...

Keyword(s):

Electronic Health Record ◽

Population Pharmacokinetic Model ◽

Pharmacokinetic Model ◽

Population Pharmacokinetic ◽

Health Record ◽

External Evaluation ◽

Data Source ◽

Electronic Health ◽

Using Data ◽

Neonates And Infants

ABSTRACTGentamicin is a common antibiotic used in neonates and infants. A recently published population pharmacokinetic (PK) model was developed using data from multiple studies, and the objective of our analyses was to evaluate the feasibility of using a national electronic health record (EHR) database for further external evaluation of this model. Our results suggest that, with proper data capture procedures, EHR data can serve as a potential data source for external evaluation of PK models.

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Enhancing the Prediction of 30-Day Readmission After Percutaneous Coronary Intervention Using Data Extracted by Querying of the Electronic Health Record

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.115.001855 ◽

2015 ◽

Vol 8 (5) ◽

pp. 477-485 ◽

Cited By ~ 16

Author(s):

Jason H. Wasfy ◽

Gaurav Singal ◽

Cashel O’Brien ◽

Daniel M. Blumenthal ◽

Kevin F. Kennedy ◽

...

Keyword(s):

Percutaneous Coronary Intervention ◽

Electronic Health Record ◽

Coronary Intervention ◽

Health Record ◽

Percutaneous Coronary ◽

Electronic Health ◽

Using Data

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Characterization and selection of Japanese electronic health record databases used as data sources for non-interventional observational studies

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01526-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yumi Wakabayashi ◽

Masamitsu Eitoku ◽

Narufumi Suganuma

Keyword(s):

Electronic Health Record ◽

Observational Studies ◽

Large Scale ◽

Data Sources ◽

Flow Diagram ◽

Health Record ◽

Medical Institutions ◽

Data Source ◽

Electronic Health ◽

Using Data

Abstract Background Interventional studies are the fundamental method for obtaining answers to clinical questions. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals. Methods We compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers’ publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases. Results In Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (663/1511) or collected from several collaborating medical institutions (315/1511). Whereas the studies conducted with large-scale integrated databases (330/1511) were mostly retrospective (73.6%), 27.5% of the single-center studies, 47.6% of the multi-center studies, and 73.7% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. We used our findings to develop an assessment flow diagram to assist researchers in evaluating and choosing the most suitable EHR database for their study goals. Conclusions Our analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn.

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