Data mining methodologies for pharmacovigilance

Detection of statin-induced rhabdomyolysis and muscular related adverse events through data mining technique

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.

Marked Increase of Gamma-Glutamyltransferase as an Indicator of Drug-Induced Liver Injury in Patients without Conventional Diagnostic Criteria of Acute Liver Injury

INTRODUCTION

Clinically significant drug-induced liver injury (DILI) is defined by elevations of alanine aminotransferase (ALT) ≥5 times the upper limit of normal (ULN), alkaline phosphatase (ALP) ≥2 × ULN, or ALT ≥3 × ULN and total bilirubin TBIL >2 × ULN. However, DILI might also occur in patients who do not reach those thresholds and still may benefit from discontinuation of medication.

METHODS

Fifteen patients recruited for our prospective study on potentially hepatotoxic drugs were included. DILI diagnosis was based on RUCAM (Roussel Uclaf Causality Assessment Method) score and expert opinion and was supported by an in vitro test using monocyte-derived hepatocyte-like (MH) cells.

RESULTS

Median RUCAM score was 6 (range 4-8), indicating that DILI was possible or probable in all cases. The predominant types of liver injury were mixed (60%) and cholestatic (40%). While no elevation above 2 × ULN of ALP and TBIL was observed, gamma-glutamyltransferase (GGT) above 2 × ULN was identified in 8 of the patients. Six of the 15 patients did not achieve full remission and showed persistent elevation of GGT, which was significantly associated with peak GGT elevation above 2 × ULN (p = 0.005).

CONCLUSION

Here we present a case series of patients with liver enzyme elevation below the conventional thresholds who developed DILI with a predominant GGT elevation leading to drug withdrawal and/or chronic elevation of liver parameters, in particular of GGT. Thus, we propose that DILI should be considered in particular in cases with marked increase of GGT even if conventional DILI threshold levels are not reached, resulting in discontinuation of the causative drug and/or close monitoring of the patients.

Evolution of HLA-B Pharmacogenomics and the Importance of PGx Data Integration in Health Care System: A 10 Years Retrospective Study in Thailand

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.

Comparison of laboratory threshold criteria in drug-induced liver injury detection algorithms for use in pharmacovigilance

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.

Systematic review and meta-analysis of algorithms used to identify drug-induced liver injury (DILI) in health record databases

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.

Estimation of the prevalence of adverse drug reactions from social media

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.

Systems approaches in risk assessment

Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors.