A Web-Based Clinical System for Cohort Surveillance of Specific Clinical Effectiveness and Safety Outcomes: A Cohort Study of Non–Vitamin K Antagonist Oral Anticoagulants and Warfarin

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Abstract

Background

Conventional systems of drug surveillance lack a seamless workflow, which makes it crucial to have an active drug surveillance system that proactively assesses adverse drug events.

Objective

The aim of this study was to develop a seamless, Web-based workflow for comparing the safety and effectiveness of drugs in a database of electronic medical records.

Methods

We proposed a comprehensive integration process for cohort surveillance using the National Taiwan University Hospital Clinical Surveillance System (NCSS). We studied a practical application of the NCSS that evaluates the drug safety and effectiveness of novel oral anticoagulants (NOACs) and warfarin by cohort tree analysis in an efficient and interoperable platform.

Results

We demonstrated a practical example of investigating the differences in effectiveness and safety between NOACs and warfarin in patients with nonvalvular atrial fibrillation (AF) using the NCSS. We efficiently identified 2357 patients with nonvalvular AF with newly prescribed oral anticoagulants between 2010 and 2015 and further developed 1 main cohort and 2 subcohorts for separately measuring ischemic stroke as the clinical effectiveness outcome and intracranial hemorrhage (ICH) as the safety outcome. In the subcohort of ischemic stroke, NOAC users exhibited a significantly lower risk of ischemic stroke than warfarin users after adjusting for age, sex, comorbidity, and comedication in an intention-to-treat (ITT) analysis ( P=.01) but did not exhibit a significantly distinct risk in an as-treated (AT) analysis ( P=.12) after the 2-year follow-up. In the subcohort of ICH, NOAC users did not exhibit a different risk of ICH both in ITT ( P=.68) and AT analyses ( P=.15).

Conclusions

With a seamless and Web-based workflow, the NCSS can serve the critical role of forming associations between evidence and the real world at a medical center in Taiwan.

Related collections

Most cited references 24

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A comparison of 12 algorithms for matching on the propensity score

Peter C Austin (2013)

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

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Major hemorrhage and tolerability of warfarin in the first year of therapy among elderly patients with atrial fibrillation.

Elaine Hylek, Carmella Evans-Molina, Carol Shea … (2007)

Warfarin is effective in the prevention of stroke in atrial fibrillation but is under used in clinical care. Concerns exist that published rates of hemorrhage may not reflect real-world practice. Few patients > or = 80 years of age were enrolled in trials, and studies of prevalent use largely reflect a warfarin-tolerant subset. We sought to define the tolerability of warfarin among an elderly inception cohort with atrial fibrillation. Consecutive patients who started warfarin were identified from January 2001 to June 2003 and followed for 1 year. Patients had to be > or = 65 years of age, have established care at the study institution, and have their warfarin managed on-site. Outcomes included major hemorrhage, time to termination of warfarin, and reason for discontinuation. Of 472 patients, 32% were > or = 80 years of age, and 91% had > or = 1 stroke risk factor. The cumulative incidence of major hemorrhage for patients > or = 80 years of age was 13.1 per 100 person-years and 4.7 for those or = 80 years, and international normalized ratio (INR) > or = 4.0 were associated with increased risk despite trial-level anticoagulation control. Within the first year, 26% of patients > or = 80 years of age stopped taking warfarin. Perceived safety issues accounted for 81% of them. Rates of major hemorrhage and warfarin termination were highest among patients with CHADS2 scores (an acronym for congestive heart failure, hypertension, age > or = 75, diabetes mellitus, and prior stroke or transient ischemic attack) of > or = 3. Rates of hemorrhage derived from younger noninception cohorts underestimate the bleeding that occurs in practice. This finding coupled with the short-term tolerability of warfarin likely contributes to its underutilization. Stroke prevention among elderly patients with atrial fibrillation remains a challenging and pressing health concern.

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The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.

Peter C Austin (2015)

The propensity score is a balancing score: conditional on the propensity score, treated and untreated subjects have the same distribution of observed baseline characteristics. Four methods of using the propensity score have been described in the literature: stratification on the propensity score, propensity score matching, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. However, the relative ability of these methods to reduce systematic differences between treated and untreated subjects has not been examined. The authors used an empirical case study and Monte Carlo simulations to examine the relative ability of the 4 methods to balance baseline covariates between treated and untreated subjects. They used standardized differences in the propensity score matched sample and in the weighted sample. For stratification on the propensity score, within-quintile standardized differences were computed comparing the distribution of baseline covariates between treated and untreated subjects within the same quintile of the propensity score. These quintile-specific standardized differences were then averaged across the quintiles. For covariate adjustment, the authors used the weighted conditional standardized absolute difference to compare balance between treated and untreated subjects. In both the empirical case study and in the Monte Carlo simulations, they found that matching on the propensity score and weighting using the inverse probability of treatment eliminated a greater degree of the systematic differences between treated and untreated subjects compared with the other 2 methods. In the Monte Carlo simulations, propensity score matching tended to have either comparable or marginally superior performance compared with propensity-score weighting.

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Author and article information

Contributors

Chih-Fen Huang:

Graduate Institute of Clinical Pharmacy, College of MedicineNational Taiwan University33 Linsen S RoadTaipei, 10050Taiwan886 223562162cfhuang1023@ntu.edu.tw

ORCID: http://orcid.org/0000-0003-1311-8828

Journal

Journal ID (nlm-ta): JMIR Med Inform

Journal ID (iso-abbrev): JMIR Med Inform

Journal ID (publisher-id): JMI

Title: JMIR Medical Informatics

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-9694

Publication date Collection: Jul-Sep 2019

Publication date (Electronic): 03 July 2019

Volume: 7

Issue: 3

Electronic Location Identifier: e13329

Affiliations

[1 ] Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan

[2 ] Department of Pharmacy National Taiwan University Hospital Taipei Taiwan

[3 ] Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University Taipei Taiwan

[4 ] School of Pharmacy, College of Medicine National Taiwan University Taipei Taiwan

[5 ] Information Technology Office National Taiwan University Hospital Taipei Taiwan

[6 ] Department of Development and Planning National Taiwan University Hospital Taipei Taiwan

[7 ] Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan

[8 ] Department of Internal Medicine National Taiwan University Hospital Hsinchu Taiwan

[9 ] Institute of Epidemiology and Preventive Medicine, College of Public Health National Taiwan University Taipei Taiwan

[10 ] Department of Computer Science & Information Engineering National Taiwan University Taipei Taiwan

[11 ] Department of Electrical Engineering National Taiwan University Taipei Taiwan

Author notes

Corresponding Author: Chih-Fen Huang cfhuang1023@ 123456ntu.edu.tw

Author information

Fong-Ci Lin http://orcid.org/0000-0001-9031-7113

Shih-Tsung Huang http://orcid.org/0000-0002-1738-5571

Rung Ji Shang http://orcid.org/0000-0003-1088-4180

Chi-Chuan Wang http://orcid.org/0000-0002-4597-4859

Fei-Yuan Hsiao http://orcid.org/0000-0003-1660-9120

Fang-Ju Lin http://orcid.org/0000-0002-8249-7481

Mei-Shu Lin http://orcid.org/0000-0002-1607-9337

Kuan-Yu Hung http://orcid.org/0000-0003-3472-5512

Jui Wang http://orcid.org/0000-0003-4800-5276

Li-Jiuan Shen http://orcid.org/0000-0002-2854-3205

Feipei Lai http://orcid.org/0000-0001-7147-8122

Chih-Fen Huang http://orcid.org/0000-0003-1311-8828

Article

Publisher ID: v7i3e13329

DOI: 10.2196/13329

PMC ID: 6636345

PubMed ID: 31271151

SO-VID: e1fe7dc3-bc9a-4bee-88e9-884f46b160c7

Copyright © ©Fong-Ci Lin, Shih-Tsung Huang, Rung Ji Shang, Chi-Chuan Wang, Fei-Yuan Hsiao, Fang-Ju Lin, Mei-Shu Lin, Kuan-Yu Hung, Jui Wang, Li-Jiuan Shen, Feipei Lai, Chih-Fen Huang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.07.2019.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/.as well as this copyright and license information must be included.

History

Date received : 11 January 2019

Date revision requested : 23 March 2019

Date revision received : 16 May 2019

Date accepted : 17 May 2019

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