Appl Clin Inform 2018; 09(03): 743-751
DOI: 10.1055/s-0038-1670678
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults

Juliessa M. Pavon
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
Richard J. Sloane
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
Carl F. Pieper
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
Cathleen S. Colón-Emeric
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
Harvey J. Cohen
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
David Gallagher
1   Duke University, Durham, North Carolina, United States
,
Miriam C. Morey
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
,
Midori McCarty
1   Duke University, Durham, North Carolina, United States
,
Thomas L. Ortel
1   Duke University, Durham, North Carolina, United States
,
Susan N. Hastings
1   Duke University, Durham, North Carolina, United States
2   Geriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, Durham, North Carolina, United States
3   Duke University Claude D. Pepper Center, Duke University, Durham, North Carolina, United States
4   Health Services Research and Development Center of Innovation, Durham Veterans Affairs Health Care System, Durham, North Carolina, United States
› Author Affiliations
Funding This study received funding from the following sources: NIA GEMSSTAR Award (R03AG048007) (Pavon); Duke Older Americans Independence Center (NIA P30 AG028716–01); Duke University Internal Medicine Chair's Award; Duke University Hartford Center of Excellence; Center of Innovation for Health Services Research in Primary Care (CIN 13–410) (Hastings) at the Durham VA Health Care System; T. Franklin Williams Scholars Program (Pavon); and K24 NIA P30 AG028716–01 (Colon-Emeric). The funding sources had no role in the design and conduct of the study, analysis or interpretation of the data, preparation or final approval of the manuscript before publication, or decision to submit the manuscript for publication.
Further Information

Publication History

01 June 2018

09 August 2018

Publication Date:
26 September 2018 (online)

Abstract

Objective Venous thromboembolism (VTE) prophylaxis is an important consideration for hospitalized older adults, and the Padua Prediction Score (PPS) is a risk prediction tool used to prioritize patient selection. We developed an automated PPS (APPS) algorithm using electronic health record (EHR) data. This study examines the accuracy of APPS and its individual components versus manual data extraction.

Methods This is a retrospective cohort study of hospitalized general internal medicine patients, aged 70 and over. Fourteen clinical variables were collected to determine their PPS; APPS used EHR data exports from health system databases, and a trained abstractor performed manual chart abstractions. We calculated sensitivity and specificity of the APPS, using manual PPS as the gold standard for classifying risk category (low vs. high). We also examined performance characteristics of the APPS for individual variables.

Results PPS was calculated by both methods on 311 individuals. The mean PPS was 3.6 (standard deviation, 1.8) for manual abstraction and 2.8 (1.4) for APPS. In detecting patients at high risk for VTE, the sensitivity and specificity of the APPS algorithm were 46 and 94%, respectively. The sensitivity for APPS was poor (range: 6–34%) for detecting acute conditions (i.e., acute myocardial infarction), moderate (range: 52–74%) for chronic conditions (i.e., heart failure), and excellent (range: 94–98%) for conditions of obesity and restricted mobility. Specificity of the automated extraction method for each PPS variable was > 87%.

Conclusion APPS as a stand-alone tool was suboptimal for classifying risk of VTE occurrence. The APPS accurately identified high risk patients (true positives), but lower scores were considered indeterminate.

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was approved by the Duke University Institutional Review Board.


 
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