Evaluation plan project: literature review

Contents lists available at ScienceDirect

International Journal of Medical Informatics

journal homepage: www.elsevier.com/locate/ijmedinf

Development and evaluation of an osteoarthritis risk model for integration
into primary care health information technology
Jason E. Blacka,*, Amanda L. Terryb, Daniel J. Lizottec
a Graduate Program in Epidemiology & Biostatistics, Western University, 1151 Richmond Street, London, Ontario, N6A 5C1, Canada
b Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Western University, 1151 Richmond Street,
London, Ontario, N6A 3K7, Canada
c Department of Computer Science, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Department of Statistical and Actuarial
Sciences, 1151 Richmond Street, Western University, London, Ontario, N6A 3K7, Canada

A R T I C L E I N F O

Keywords:
Prognostic prediction model
Risk
Electronic medical record
Osteoarthritis
CPCSSN

A B S T R A C T

Background: We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use
by patients and practitioners that is designed to be appropriate for integration into primary care health in-
formation technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes sig-
nificant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses
data that are readily available in primary care settings, it supports targeting of interventions delivered as part of
clinical practice that are aimed at risk reduction.
Methods: We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains
aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a
prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data
availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients
who were included in the cohort if they had an encounter with their primary care practitioner between 1
January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to
their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to
predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the
model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a
model that is to be integrated into the same context from which the data were derived.
Results: The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art
discrimination (estimated AUROC 0.84) and good calibration (asse

Research and Applications

Feasibility of capturing real-world data from health infor-

mation technology systems at multiple centers to assess

cardiac ablation device outcomes: A fit-for-purpose infor-

matics analysis report

Guoqian Jiang,
1

Sanket S. Dhruva ,
2

Jiajing Chen,
3

Wade L. Schulz,
4,5

Amit A. Doshi,
6

Peter A. Noseworthy,
7

Shumin Zhang,
8

Yue Yu,
9

H. Patrick Young ,
10

Eric Brandt,3 Keondae R. Ervin,11 Nilay D. Shah,12 Joseph S. Ross,5,10 Paul Coplan,13,14

and Joseph P. Drozda Jr 3

1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA, 2School of Medicine, University

of California, San Francisco, and San Francisco Veterans Affairs Medical Center, San Francisco, California, USA, 3Mercy Re-

search, Mercy, Chesterfield, Missouri, USA, 4Department of Laboratory Medicine, Yale University School of Medicine, New Ha-

ven, Connecticut, USA, 5Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut,

USA, 6Mercy Clinic, Mercy, St. Louis, Missouri, USA, 7Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minne-

sota, USA, 8Medical Device Epidemiology and Real-World Data Science, Office of the Chief Medical Officer, Johnson & Johnson,

New Brunswick, New Jersey, USA, 9Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA, 10De-

partment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA, 11National Evaluation System for Health

Technology Coordinating Center, Medical Device Innovation Consortium, Arlington, Virginia, USA, 12Robert D. and Patricia E. Kern

Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA, 13Medical Device Epidemiology and

RWD Science, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey, USA, and 14Perelman

School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA

Corresponding Author: Guoqian Jiang, Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First Street,

SW, Rochester, MN 55905, USA ([email protected])

Received 25 January 2021; Revised 22 April 2021; Editorial Decision 25 May 2021; Accepted 28 May 2021

ABSTRACT

Objective: The study sought to conduct an informatics analysis on the National Evaluation System for Health

Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informat-

ics approaches in the feasibility assessment of capturing real-world data using unique device identifier

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