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Saito K, Gabbeta A, Mulvihill E, Al-Jaberi L, Beukelman T, Lewis JD, Rose CD, Strom BL, Horton DB. Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis. Pharmacoepidemiol Drug Saf 2024; 33:e5803. [PMID: 38685851 DOI: 10.1002/pds.5803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE To facilitate claims-based research on populations with juvenile idiopathic arthritis (JIA), we sought to validate an algorithm of new medication use as a proxy for worsening JIA disease activity. METHODS Using electronic health record data from three pediatric centers, we defined new JIA medication use as (re)initiation of disease-modifying antirheumatic drugs or glucocorticoids (oral or intra-articular). Data were collected from 201 randomly selected subjects with (101) or without (100) new medication use. We assessed the positive predictive value (PPV) and negative predictive value (NPV) based on a reference standard of documented worsening of JIA disease activity. The algorithm was refined to optimize test characteristics. RESULTS Overall, the medication-based algorithm had suboptimal performance in representing worsening JIA disease activity (PPV 69.3%, NPV 77.1%). However, algorithm performance improved for definitions specifying longer times after JIA diagnosis (≥1-year post-diagnosis: PPV 82.9%, NPV 80.0%) or after initiation of prior JIA treatment (≥1-year post-treatment: PPV 89.7%, NPV 80.0%). CONCLUSION An algorithm for new JIA medication use appears to be a reasonable proxy for worsening JIA disease activity, particularly when specifying new use ≥1 year since initiating a prior JIA medication. This algorithm will be valuable for conducting research on JIA populations within administrative claims databases.
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Affiliation(s)
- Kyoko Saito
- Brown University, Providence, Rhode Island, USA
| | - Avinash Gabbeta
- St. Christopher's Hospital for Children, Philadelphia, Pennsylvania, USA
| | | | - Lina Al-Jaberi
- Arkansas Children's Hospital, Little Rock, Arkansas, USA
| | - Timothy Beukelman
- Childhood Arthritis & Rheumatology Research Alliance, Washington, DC, USA
| | - James D Lewis
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Carlos D Rose
- Nemours Children's Hospital, Wilmington, Delaware, USA
| | - Brian L Strom
- Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA
- Rutgers Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, New Brunswick, New Jersey, USA
| | - Daniel B Horton
- Rutgers Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, New Brunswick, New Jersey, USA
- Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, New Jersey, USA
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Tanigawa M, Kohama M, Hirata K, Izukura R, Kandabashi T, Kataoka Y, Nakashima N, Kimura M, Uyama Y, Yokoi H. Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET ®) in Japan. Ther Innov Regul Sci 2024:10.1007/s43441-024-00619-4. [PMID: 38644459 DOI: 10.1007/s43441-024-00619-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/08/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND The Medical Information Database Network (MID-NET®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET®. METHODS This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated. RESULTS A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%). CONCLUSION This study developed valuable GIP identification algorithms for MID-NET®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms.
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Affiliation(s)
- Masatoshi Tanigawa
- Clinical Research Support Center, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan.
| | - Mei Kohama
- Office of Medical Informatics and Epidemiology, Pharmaceutical and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, 100-0013, Japan
| | - Kaori Hirata
- Office of Medical Informatics and Epidemiology, Pharmaceutical and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, 100-0013, Japan
| | - Rieko Izukura
- Social Medicine, Department of Basic Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tadashi Kandabashi
- Medical Information Center, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yoko Kataoka
- Clinical Research Support Center, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan
| | - Naoki Nakashima
- Medical Information Center, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Michio Kimura
- Department of Medical Informatics, Hamamatsu University Hospital, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka, 431-3192, Japan
| | - Yoshiaki Uyama
- Office of Medical Informatics and Epidemiology, Pharmaceutical and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, 100-0013, Japan
| | - Hideto Yokoi
- Department of Medical Informatics, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan
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Anthony MS, Aroda VR, Parlett LE, Djebarri L, Berreghis S, Calingaert B, Beachler DC, Crowe CL, Johannes CB, Juhaeri J, Lanes S, Pan C, Rothman KJ, Saltus CW, Walsh KE. Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study. Diabetes Care 2024; 47:712-719. [PMID: 38363873 DOI: 10.2337/dc23-1911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/18/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE To assess risk of anaphylaxis among patients with type 2 diabetes mellitus who are initiating therapy with a glucagon-like peptide 1 receptor agonist (GLP-1 RA), with a focus on those starting lixisenatide therapy. RESEARCH DESIGN AND METHODS A cohort study was conducted in three large, U.S. claims databases (2017-2021). Adult (aged ≥18 years) new users of a GLP-1 RA who had type 2 diabetes mellitus and ≥6 months enrollment in the database before GLP-1 RA initiation (start of follow-up) were included. GLP-1 RAs evaluated were lixisenatide, an insulin glargine/lixisenatide fixed-ratio combination (FRC), exenatide, liraglutide or insulin degludec/liraglutide FRC, dulaglutide, and semaglutide (injectable and oral). The first anaphylaxis event during follow-up was identified using a validated algorithm. Incidence rates (IRs) and 95% CIs were calculated within each medication cohort. The unadjusted IR ratio (IRR) comparing anaphylaxis rates in the lixisenatide cohort with all other GLP-1 RAs combined was analyzed post hoc. RESULTS There were 696,089 new users with 456,612 person-years of exposure to GLP-1 RAs. Baseline demographics, comorbidities, and use of other prescription medications in the 6 months before the index date were similar across medication cohorts. IRs (95% CIs) per 10,000 person-years were 1.0 (0.0-5.6) for lixisenatide, 6.0 (3.6-9.4) for exenatide, 5.1 (3.7-7.0) for liraglutide, 3.9 (3.1-4.8) for dulaglutide, and 3.6 (2.6-4.9) for semaglutide. The IRR (95% CI) for the anaphylaxis rate for the lixisenatide cohort compared with the pooled other GLP-1 RA cohort was 0.24 (0.01-1.35). CONCLUSIONS Anaphylaxis is rare with GLP-1 RAs. Lixisenatide is unlikely to confer higher risk of anaphylaxis than other GLP-1 RAs.
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Wang W, Liu M, He Q, Wang M, Xu J, Li L, Li G, He L, Zou K, Sun X. Validation and impact of algorithms for identifying variables in observational studies of routinely collected data. J Clin Epidemiol 2024; 166:111232. [PMID: 38043830 DOI: 10.1016/j.jclinepi.2023.111232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Among observational studies of routinely collected health data (RCD) for exploring treatment effects, algorithms are used to identify study variables. However, the extent to which algorithms are reliable and impact the credibility of effect estimates is far from clear. This study aimed to investigate the validation of algorithms for identifying study variables from RCD, and examine the impact of alternative algorithms on treatment effects. METHODS We searched PubMed for observational studies published in 2018 that used RCD to explore drug treatment effects. Information regarding the reporting, validation, and interpretation of algorithms was extracted. We summarized the reporting and methodological characteristics of algorithms and validation. We also assessed the divergence in effect estimates given alternative algorithms by calculating the ratio of estimates of the primary vs. alternative analyses. RESULTS A total of 222 studies were included, of which 93 (41.9%) provided a complete list of algorithms for identifying participants, 36 (16.2%) for exposure, and 132 (59.5%) for outcomes, and 15 (6.8%) for all study variables including population, exposure, and outcomes. Fifty-nine (26.6%) studies stated that the algorithms were validated, and 54 (24.3%) studies reported methodological characteristics of 66 validations, among which 61 validations in 49 studies were from the cross-referenced validation studies. Of those 66 validations, 22 (33.3%) reported sensitivity and 16 (24.2%) reported specificity. A total of 63.6% of studies reporting sensitivity and 56.3% reporting specificity used test-result-based sampling, an approach that potentially biases effect estimates. Twenty-eight (12.6%) studies used alternative algorithms to identify study variables, and 24 reported the effects estimated by primary analyses and sensitivity analyses. Of these, 20% had differential effect estimates when using alternative algorithms for identifying population, 18.2% for identifying exposure, and 45.5% for classifying outcomes. Only 32 (14.4%) studies discussed how the algorithms may affect treatment estimates. CONCLUSION In observational studies of RCD, the algorithms for variable identification were not regularly validated, and-even if validated-the methodological approach and performance of the validation were often poor. More seriously, different algorithms may yield differential treatment effects, but their impact is often ignored by researchers. Strong efforts, including recommendations, are warranted to improve good practice.
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Affiliation(s)
- Wen Wang
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China.
| | - Mei Liu
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Qiao He
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Mingqi Wang
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Jiayue Xu
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Ling Li
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Guowei Li
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada; Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, China; Biostatistics Unit, Research Institute at St. Joseph's Healthcare Hamilton, Hamilton, Ontario L8N 4A6, Canada
| | - Lin He
- Intelligence Library Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Kang Zou
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
| | - Xin Sun
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China.
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