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Kunakorntham P, Pattanaprateep O, Dejthevaporn C, Thammasudjarit R, Thakkinstian A. Detection of statin-induced rhabdomyolysis and muscular related adverse events through data mining technique. BMC Med Inform Decis Mak 2022; 22:233. [PMID: 36064346 PMCID: PMC9446837 DOI: 10.1186/s12911-022-01978-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/31/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Rhabdomyolysis (RM) is a life-threatening adverse drug reaction in which statins are the one commonly related to RM. The study aimed to explore the association between statin used and RM or other muscular related adverse events. In addition, drug interaction with statins were also assessed. METHODS All extracted prescriptions were grouped as lipophilic and hydrophilic statins. RM outcome was identified by electronically screening and later ascertaining by chart review. The study proposed 4 models, i.e., logistic regression (LR), Bayesian network (BN), random forests (RF), and extreme gradient boosting (XGBoost). Features were selected using multiple processes, i.e., bootstrapping, expert opinions, and univariate analysis. RESULTS A total of 939 patients who used statins were identified consisting 15, 9, and 19 per 10,000 persons for overall outcome prevalence, using statin alone, and co-administrations, respectively. Common statins were simvastatin, atorvastatin, and rosuvastatin. The proposed models had high sensitivity, i.e., 0.85, 0.90, 0.95 and 0.95 for LR, BN, RF, and XGBoost, respectively. The area under the receiver operating characteristic was significantly higher in LR than BN, i.e., 0.80 (0.79, 0.81) and 0.73 (0.72, 0.74), but a little lower than the RF [0.817 (95% CI 0.811, 0.824)] and XGBoost [0.819 (95% CI 0.812, 0.825)]. The LR model indicated that a combination of high-dose lipophilic statin, clarithromycin, and antifungals was 16.22 (1.78, 148.23) times higher odds of RM than taking high-dose lipophilic statin alone. CONCLUSIONS The study suggested that statin uses may have drug interactions with others including clarithromycin and antifungal drugs in inducing RM. A prospective evaluation of the model should be further assessed with well planned data monitoring. Applying LR in hospital system might be useful in warning drug interaction during prescribing.
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Affiliation(s)
- Patratorn Kunakorntham
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok, 10400, Thailand
| | - Oraluck Pattanaprateep
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok, 10400, Thailand.
| | - Charungthai Dejthevaporn
- Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok, 10400, Thailand.
| | - Ratchainant Thammasudjarit
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok, 10400, Thailand
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok, 10400, Thailand
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Koomdee N, Kloypan C, Jinda P, Rachanakul J, Jantararoungtong T, Sukprasong R, Prommas S, Nuntharadthanaphong N, Puangpetch A, Ershadian M, John S, Biswas M, Sukasem C. Evolution of HLA-B Pharmacogenomics and the Importance of PGx Data Integration in Health Care System: A 10 Years Retrospective Study in Thailand. Front Pharmacol 2022; 13:866903. [PMID: 35450046 PMCID: PMC9016335 DOI: 10.3389/fphar.2022.866903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The HLA-B is the most polymorphic gene, play a crucial role in drug-induced hypersensitivity reactions. There is a lot of evidence associating several risk alleles to life-threatening adverse drug reactions, and a few of them have been approved as valid biomarkers for predicting life-threatening hypersensitivity reactions. Objectives: The objective of this present study is to present the progression of HLA-B pharmacogenomics (PGx) testing in the Thai population during a 10‐year period, from 2011 to 2020. Methods: This was a retrospective observational cohort study conducted at the Faculty of Medicine Ramathibodi Hospital. Overall, 13,985 eligible patients who were tested for HLA-B risk alleles between periods of 2011–2020 at the study site were included in this study. Results: The HLA PGx testing has been increasing year by year tremendously, 94 HLA-B testing was done in 2011; this has been raised to 2,880 in 2020. Carbamazepine (n = 4,069, 33%), allopurinol (n = 4,675, 38%), and abacavir (n = 3,246, 26%) were the most common drugs for which the HLA-B genotyping was performed. HLA-B*13:01, HLA-B*15:02 and HLA-B*58:01 are highly frequent, HLA-B*51:01 and HLA-B*57:01 are moderately frequent alleles that are being associated with drug induced hypersensitivity. HLA-B*59:01 and HLA-B*38:01 theses alleles are rare but has been reported with drug induced toxicity. Most of the samples were from state hospital (50%), 36% from private clinical laboratories and 14% from private hospitals. Conclusion: According to this study, HLA-B PGx testing is increasing substantially in Thailand year after year. The advancement of research in this field, increased physician awareness of PGx, and government and insurance scheme reimbursement assistance could all be factors. Incorporating PGx data, along with other clinical and non-clinical data, into clinical decision support systems (CDS) and national formularies, on the other hand, would assist prescribers in prioritizing therapy for their patients. This will also aid in the prediction and prevention of serious adverse drug reactions.
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Affiliation(s)
- Napatrupron Koomdee
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Chiraphat Kloypan
- Unit of Excellence in Integrative Molecular Biomedicine, School of Allied Health Sciences, University of Phayao, Phayao, Thailand.,Division of Clinical Immunology and Transfusion Science, Department of Medical Technology, School of Allied Health Sciences, University of Phayao, Phayao, Thailand
| | - Pimonpan Jinda
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Jiratha Rachanakul
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Thawinee Jantararoungtong
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Rattanaporn Sukprasong
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Santirhat Prommas
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Nutthan Nuntharadthanaphong
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Apichaya Puangpetch
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Maliheh Ershadian
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Shobana John
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand
| | - Mohitosh Biswas
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand.,Department of Pharmacy, University of Rajshahi, Rajshahi, Bangladesh
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand.,Pharmacogenomics and Precision Medicine, The Preventive Genomics and Family Check-up Services Center, Bumrungrad International Hospital, Bangkok, Thailand.,MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Liverpool, United Kingdom
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Tan EH, Ling ZJ, Ang PS, Sung C, Dan YY, Tai BC. Comparison of laboratory threshold criteria in drug-induced liver injury detection algorithms for use in pharmacovigilance. Pharmacoepidemiol Drug Saf 2020; 29:1480-1488. [PMID: 32844466 DOI: 10.1002/pds.5099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 06/28/2020] [Accepted: 07/21/2020] [Indexed: 01/13/2023]
Abstract
PURPOSE For the purpose of pharmacovigilance, we sought to determine the best performing laboratory threshold criteria to detect drug-induced liver injury (DILI) in the electronic medical records (EMR). METHODS We compared three commonly used liver chemistry criteria from the DILI expert working group (DEWG), DILI network (DILIN), and Council for International Organizations of Medical Sciences (CIOMS), based on hospital EMR for years 2010 and 2011 (42 176 admissions), using independent medical record review. The performance characteristics were compared in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, F-measure, and area under the receiver operating characteristic curve (AUROC). RESULTS DEWG had the highest PPV (5.5%, 95% CI: 4.1%-7.2%), specificity (97.0%, 95% CI: 96.8%-97.2%), accuracy (96.8%, 95% CI: 96.6%-97.0%) and F-measure (0.099). CIOMS had the highest sensitivity (74.0%, 95% CI: 64.3%-82.3%) and AUROC (85.2%, 95% CI: 80.8%-89.7%). Besides the laboratory criteria, including additional keywords in the classification algorithm improved the PPV and F-measure to a maximum of 29.0% (95% CI: 22.3%-36.5%) and 0.379, respectively. CONCLUSIONS More stringent criteria (DEWG and DILIN) performed better in terms of PPV, specificity, accuracy and F-measure. CIOMS performed better in terms of sensitivity. An algorithm with high sensitivity is useful in pharmacovigilance for detecting rare events and to avoid missing cases. Requiring at least two abnormal liver chemistries during hospitalization and text-word searching in the discharge summaries decreased false positives without loss in sensitivity.
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Affiliation(s)
- Eng Hooi Tan
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Zheng Jye Ling
- Regional Health System Office, National University Health System, Singapore
| | - Pei San Ang
- Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore
| | - Cynthia Sung
- Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.,Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Yock Young Dan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Division of Gastroenterology & Hepatology, National University Hospital, National University Health System, Singapore
| | - Bee Choo Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Tan EH, Low EXS, Dan YY, Tai BC. Systematic review and meta-analysis of algorithms used to identify drug-induced liver injury (DILI) in health record databases. Liver Int 2018; 38:742-753. [PMID: 29193566 DOI: 10.1111/liv.13646] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/19/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND & AIMS Drug induced liver injury (DILI) is largely underreported, leading to underestimation of its burden. Electronic detection of DILI in healthcare databases shows promise to overcome the issues of spontaneous reporting. The performance of detection algorithms may vary because of inconsistent DILI definition and detection criteria. We performed a systematic review and meta-analysis to identify the DILI detection criteria used in health record databases and determine the performance characteristics of the detection algorithms. METHODS We searched PubMed, EMBASE and Scopus for studies that utilized laboratory threshold criteria to identify DILI cases. Validation studies were included in the meta-analysis. Data were abstracted using standardized forms and quality was assessed using modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. We evaluate the performance characteristics of the detection algorithm by obtaining the pooled estimate of the positive predictive value (PPV) assuming a random effects model. RESULTS A total of 29 studies met the inclusion criteria for the systematic review; 25 of these studies (n = 35 948) had PPV estimates for performing the meta-analysis. The PPV of DILI detection algorithms was low, ranging from 1.0% to 40.2%, with a pooled estimate of 14.6% (95% CI 10.7-18.9). Algorithms that performed better had prespecified exclusion diagnoses as well as drugs of interest to minimize false positives. CONCLUSION Algorithm performance varied with different case definitions of DILI attributed to different laboratory threshold criteria, diagnosis codes, and study drugs.
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Affiliation(s)
- Eng Hooi Tan
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
| | - En Xian Sarah Low
- Division of Gastroenterology and Hepatology, National University Health System, Singapore City, Singapore
| | - Yock Young Dan
- Division of Gastroenterology and Hepatology, National University Health System, Singapore City, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
| | - Bee Choo Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore.,Investigational Medicine Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
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Nguyen T, Larsen ME, O'Dea B, Phung D, Venkatesh S, Christensen H. Estimation of the prevalence of adverse drug reactions from social media. Int J Med Inform 2017; 102:130-137. [PMID: 28495341 DOI: 10.1016/j.ijmedinf.2017.03.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 02/23/2017] [Accepted: 03/21/2017] [Indexed: 01/21/2023]
Abstract
This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale.
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Affiliation(s)
- Thin Nguyen
- Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
| | - Mark E Larsen
- Black Dog Institute, University of New South Wales, Australia.
| | - Bridianne O'Dea
- Black Dog Institute, University of New South Wales, Australia.
| | - Dinh Phung
- Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
| | - Svetha Venkatesh
- Centre for Pattern Recognition and Data Analytics, Deakin University, Australia.
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