PCORnet Antibiotics and Childhood Growth Study: Process for Cohort Creation and Cohort Description

Block, Jason P.; Bailey, L. Charles; Gillman, Matthew W.; Lunsford, Douglas; Boone‐Heinonen, Janne; Cleveland, Lauren P.; Finkelstein, Jonathan A.; Horgan, Casie; Jay, Melanie; Reynolds, Juliane S.; Sturtevant, Jessica L.; Forrest, Christopher B.; Adams, William; Appelhans, Brad; Brickman, Andrew L.; Bian, Jiang; Daley, Matthew F.; Davidson, Arthur J.; Dugas, Lara R.; Eneli, Ihuoma; Fitzpatrick, Stephanie L.; Heerman, William J.; Horberg, Michael A.; Hsia, Daniel S.; Ingber, Jenny; Isasi, Carmen R.; Janicke, David M.; Kane, Doug; Kharbanda, Elyse O.; Meltzer, David O.; Messito, Mary Jo; Nadkarni, Prakash M.; O’Bryan, Kevin; Peay, Holly; Puro, Jon; Ranade, Daksha; Rao, Goutham; Tirado-Ramos, Alfredo; Rayas, Maria; Razzaghi, Hanieh; Ricket, Iben; Rosenman, Marc B.; Siegel, Robert; Solomonides, Tony; Taveras, Elsie M.; Taylor, Bradley; Tolia, Veeral N.; Willis, Zachary; VanWormer, Jeffrey J.; Wysocki, Tim; Zhou, Xiaobo

doi:10.1016/j.acap.2018.02.008

Cited by 18 publications

(21 citation statements)

References 16 publications

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“…An example of the breadth of rich data and study potential in an environment of independent entities using a common data model may be seen in the study Short- and Long-Term Effects of Antibiotics on Childhood Growth. Using the strict criteria of same day height and weight in each of three distinct age periods (0 to ≪12, 12 to ≪30, and 30 to ≪72 months), working across 35 institutions, a diverse cohort of 362,550 children were found to be eligible for the study 35 . Of these, just over 58% had received at least one antibiotic prescription, with over 33% receiving a broad-spectrum antibiotic.…”

Section: Resultsmentioning

confidence: 99%

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Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

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Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

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Section: Resultsmentioning

confidence: 99%

“… Conflict of Interest Anthony Solomonides is a co-author of reference 8 , offered as an exemplar of process mining work, and a collaborating co-author of references 35 and 36 , part of an extended series of papers on the study Short- and Long-Term Effects of Antibiotics on Childhood Growth. …”

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confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

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“…The ABX study is described in detail elsewhere [67]. Briefly, to be included in the cohort, children were required to have at least one same-day height and weight measured in each of three age periods: 1) before 12 months, 2) 12 to 30 months, and 3) after 24 months, ensuring an ongoing connection to the health care institution to more accurately capture antibiotic prescriptions; the overlap between the second and third age periods accommodated children who received their 2-year well-child visit after their second birthday.…”

Section: Methodsmentioning

confidence: 99%

“…This study utilized CDM version 3.0 which included 15 tables and over 100 variables (http://www.pcornet.org/pcornet-common-data-model/). The ABX study collected data from the prescribing and dispensing tables for oral antibiotics and intramuscular ceftriaxone and penicillin [6], as well as variables from other tables including demographics, procedures, encounters, diagnoses, and vital measures.…”

Section: Methodsmentioning

confidence: 99%

“…The PCORnet Antibiotics and Childhood Growth Study (ABX Study) [67] is one of the first observational demonstration projects funded to do research within PCORnet, and the first PCORnet study to examine medication utilization in depth. PCORnet includes a number of large health systems with substantial data on children throughout childhood, making it in an important setting to investigate medication use.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study

Lin

Daley

Boone‐Heinonen

et al. 2019

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Researchers often use prescribing data from electronic health records (EHR) or dispensing data from medication or medical claims to determine medication utilization. However, neither source has complete information on medication use. We compared antibiotic prescribing and dispensing records for 200,395 patients in the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics and Childhood Growth Study. We stratified analyses by delivery system type [closed integrated (cIDS) and non-cIDS]; 90.5 percent and 39.4 percent of prescribing records had matching dispensing records, and 92.7 percent and 64.0 percent of dispensing records had matching prescribing records at cIDS and non-cIDS, respectively. Most of the dispensings without a matching prescription did not have same-day encounters in the EHR, suggesting they were medications given outside the institution providing data, such as those from urgent care or retail clinics. The sensitivity of prescriptions in the EHR, using dispensings as a gold standard, was 99.1 percent and 89.9 percent for cIDS and non-cIDS, respectively. Only 0.7 percent and 6.1 percent of patients at cIDS and non-cIDS, respectively, were classified as false-negative, i.e. entirely unexposed to antibiotics when they in fact had dispensings. These patients were more likely to have a complex chronic condition or asthma. Overall, prescription records worked well to identify exposure to antibiotics. EHR data, such as the data available in PCORnet, is a unique and vital resource for clinical research. Closing data gaps by understanding why prescriptions may not be captured can improve this type of data, making it more robust for observational research.

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Association of Early Antibiotic Exposure With Childhood Body Mass Index Trajectory Milestones

et al. 2021

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IMPORTANCE Past studies have showed associations between antibiotic exposure and child weight outcomes. Few, however, have documented alterations to body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) trajectory milestone patterns during childhood after early-life antibiotic exposure. OBJECTIVE To examine the association of antibiotic use during the first 48 months of life with BMI trajectory milestones during childhood in a large cohort of children.

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PCORnet Antibiotics and Childhood Growth Study: Process for Cohort Creation and Cohort Description

Abstract: The PCORnet Antibiotics and Childhood Growth Study is a large national longitudinal observational study in a diverse population that will examine the relationship between early antibiotic use and subsequent growth patterns in children.

Cited by 18 publications

References 16 publications

Review of Clinical Research Informatics

Review of Clinical Research Informatics

Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study

Association of Early Antibiotic Exposure With Childhood Body Mass Index Trajectory Milestones

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