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Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data. Drug Saf 2023; 46:371-389. [PMID: 36828947 PMCID: PMC10113351 DOI: 10.1007/s40264-023-01278-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/06/2023] [Indexed: 02/26/2023]
Abstract
INTRODUCTION Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS ARM of claims data may be effective in the early detection of a wide range of ADR signals.
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Coste A, Wong A, Bokern M, Bate A, Douglas IJ. Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review. Pharmacoepidemiol Drug Saf 2023; 32:28-43. [PMID: 36218170 PMCID: PMC10092128 DOI: 10.1002/pds.5548] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/21/2022] [Accepted: 10/02/2022] [Indexed: 02/06/2023]
Abstract
PURPOSE Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes. METHODS We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO. MEDLINE, EMBASE, PubMed, Web of Science, Scopus, and the Cochrane Library were searched until July 13, 2021. RESULTS The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set. CONCLUSIONS A variety of methods have been described in the literature for signal detection with routinely collected data. No method showed superior performance in all papers and across all drugs and outcomes, performance assessment and reporting were heterogeneous. However, there is limited evidence that self-controlled designs, high dimensional propensity scores, and machine learning can achieve higher performances than other methods.
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Affiliation(s)
- Astrid Coste
- Department of Non-Communicable Disease Epidemiology, LSHTM, London, UK
| | - Angel Wong
- Department of Non-Communicable Disease Epidemiology, LSHTM, London, UK
| | - Marleen Bokern
- Department of Non-Communicable Disease Epidemiology, LSHTM, London, UK
| | - Andrew Bate
- Department of Non-Communicable Disease Epidemiology, LSHTM, London, UK.,Global Safety, GSK, Brentford, UK
| | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, LSHTM, London, UK
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Saha E, Rathore P. Discovering hidden patterns among medicines prescribed to patients using Association Rule Mining Technique. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2099335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Esha Saha
- Institute of Management Technology Hyderabad, Hyderabad, India
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Pratt N, Camacho X, Vajdic C, Degenhardt L, Laba TL, Hillen J, Etherton-Beer C, Preen D, Jorm L, Donnolley N, Havard A, Pearson SA. The Medicines Intelligence Centre of Research Excellence: Co-creating real-world evidence to support the evidentiary needs of Australian medicines regulators and payers. Int J Popul Data Sci 2022; 6:1726. [PMID: 35784493 PMCID: PMC9208358 DOI: 10.23889/ijpds.v6i1.1726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidence, to improve quality use of medicines and reduce medicine-related harm.
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Affiliation(s)
- Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia,Corresponding author: Nicole Pratt
| | - Ximena Camacho
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Claire Vajdic
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Tracey-Lea Laba
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia,Centre for Health Economics Research and Evaluation, Faculty of Health, UTS Sydney, NSW 2006, Australia
| | - Jodie Hillen
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia
| | - Christopher Etherton-Beer
- WA Centre for Health and Ageing, Medical School, University of Western Australia, Perth, WA 6009, Australia
| | - David Preen
- WA Centre for Health and Ageing, Medical School, University of Western Australia, Perth, WA 6009, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Natasha Donnolley
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Alys Havard
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia,National Drug and Alcohol Research Centre, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
| | - Sallie-Anne Pearson
- Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, NSW 2052, Australia
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Yang Q, Luo T, Zhang W, Zhong X, He P, Zheng H. Data-driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering. Int J Health Plann Manage 2022; 37:2569-2584. [PMID: 35445441 DOI: 10.1002/hpm.3483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/09/2022] [Accepted: 03/30/2022] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES Due to the multidimensional, multilayered, and chronological order of the cancer data, it was challenging for us to extract treatment paths. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. METHODS We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways. RESULTS We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, three postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of p < 0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions. CONCLUSIONS We have proved that cSPADE algorithm combined system clustering is an effective technique for identifying temporal relationships between treatment modalities, enabling hierarchical and vertical mining of breast cancer treatment models. In addition, we confirmed the robustness of the results by cross-validation of these treatment pathway ordering rules. Through this method, the treatment path of early breast cancer patients can be revealed, and the real-world breast cancer treatment behaviour model can be evaluated, which can provide reference for the redesign and optimization of treatment path.
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Affiliation(s)
- Qing Yang
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Luo
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaorong Zhong
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Ping He
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zheng
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
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Schotland P, Racz R, Jackson DB, Soldatos TG, Levin R, Strauss DG, Burkhart K. Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting. Clin Pharmacol Ther 2021; 109:1232-1243. [PMID: 33090463 PMCID: PMC8246740 DOI: 10.1002/cpt.2074] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 08/31/2020] [Indexed: 12/21/2022]
Abstract
We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of approval.
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Affiliation(s)
- Peter Schotland
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
- Present address:
Office of Oncologic DiseasesOffice of New DrugsCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Rebecca Racz
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | | | | | - Robert Levin
- Office of Surveillance and EpidemiologyCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - David G. Strauss
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Keith Burkhart
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
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Gurwitz D. Repurposing current therapeutics for treating COVID-19: A vital role of prescription records data mining. Drug Dev Res 2020; 81:777-781. [PMID: 32420637 PMCID: PMC7276810 DOI: 10.1002/ddr.21689] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 12/11/2022]
Abstract
Since its outbreak in late 2019, the SARS‐Cov‐2 pandemic already infected over 3.7 million people and claimed more than 250,000 lives globally. At least 1 year may take for an approved vaccine to be in place, and meanwhile millions more could be infected, some with fatal outcome. Over thousand clinical trials with COVID‐19 patients are already listed in ClinicalTrials.com, some of them for assessing the utility of therapeutics approved for other conditions. However, clinical trials take many months, and are typically done with small cohorts. A much faster and by far more efficient method for rapidly identifying approved therapeutics that can be repurposed for treating COVID‐19 patients is data mining their past and current electronic health and prescription records for identifying drugs that may protect infected individuals from severe COVID‐19 symptoms. Examples are discussed for applying health and prescription records for assessing the potential repurposing (repositioning) of angiotensin receptor blockers, estradiol, or antiandrogens for reducing COVID‐19 morbidity and fatalities. Data mining of prescription records of COVID‐19 patients will not cancel the need for conducting controlled clinical trials, but could substantially assist in trial design, drug choice, inclusion and exclusion criteria, and prioritization. This approach requires a strong commitment of health provides for open collaboration with the biomedical research community, as health provides are typically the sole owners of retrospective drug prescription records.
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Affiliation(s)
- David Gurwitz
- Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Zhan C, Roughead E, Liu L, Pratt N, Li J. Detecting potential signals of adverse drug events from prescription data. Artif Intell Med 2020; 104:101839. [DOI: 10.1016/j.artmed.2020.101839] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 02/06/2020] [Accepted: 02/24/2020] [Indexed: 02/01/2023]
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