Detection of Adverse Drug Reaction Signals Using an Electronic Health Records Database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) Algorithm

MetaLAB-HOI: Template standardization of health outcomes enable massive and accurate detection of adverse drug reactions from electronic health records

PURPOSE

This study aimed to advance the MetaLAB algorithm and verify its performance with multicenter data to effectively detect major adverse drug reactions (ADRs), including drug-induced liver injury.

METHODS

Based on MetaLAB, we created an optimal scenario for detecting ADRs by considering demographic and clinical records. MetaLAB-HOI was developed to identify ADR signals using common model-based multicenter electronic health record (EHR) data from the clinical health outcomes of interest (HOI) template and design for drug-exposed and nonexposed groups. In this study, we calculated the odds ratio of 101 drugs for HOI in Konyang University Hospital, Seoul National University Hospital, Chungbuk National University Hospital, and Seoul National University Bundang Hospital.

RESULTS

The overlapping drugs in four medical centers are amlodipine, aspirin, bisoprolol, carvedilol, clopidogrel, clozapine, digoxin, diltiazem, methotrexate, and rosuvastatin. We developed MetaLAB-HOI, an algorithm that can detect ADRs more efficiently using EHR. We compared the detection results of four medical centers, with drug-induced liver injuries as representative ADRs.

CONCLUSIONS

MetaLAB-HOI's strength lies in fully utilizing the patient's clinical information, such as prescription, procedure, and laboratory results, to detect ADR signals. Considering changes in the patient's condition over time, we created an algorithm based on a scenario that accounted for each drug exposure and onset period supervised by specialists for HOI. We determined that when a template capable of detecting ADR based on clinical evidence is developed and manualized, it can be applied in medical centers for new drugs with insufficient data.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

INTRODUCTION

Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance.

METHODS

To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices.

RESULTS

We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations.

CONCLUSION

Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Detection of drug safety signal of drug-induced neutropenia and agranulocytosis in all-aged patients using electronic medical records

PURPOSE

We explored the adverse drug reaction signals of drug-induced neutropenia (DIN) and drug-induced agranulocytosis (DIA) in hospitalized patients and evaluated the novelty of these correlations.

METHOD

A two-step method was established to identify the relationship between drugs and DIN or DIA using 5-year electronic medical records (EMRs) obtained from 242 000 patients at Qilu Hospital of Shandong University. First, the drugs suspected to induce DIN or DIA were selected. The associations between suspected drugs and DIN or DIA were evaluated by a retrospective cohort study using unconditional logistic regression analysis and multiple linear regression model.

RESULTS

Twelve suspected drugs (vancomycin, meropenem, voriconazole, acyclovir, ganciclovir, fluconazole, oseltamivir, linezolid, compound borax solution, palonosetron, polyene phosphatidylcholine, and sulfamethoxazole) were associated with DIN, and six suspected drugs (vancomycin, voriconazole, acyclovir, ganciclovir, fluconazole, and oseltamivir) were associated with DIA. The multivariate linear regression model revealed that nine drugs (vancomycin, meropenem, voriconazole, ganciclovir, fluconazole, oseltamivir, compound borax solution, palonosetron, and polyene phosphatidylcholine) and four drugs (vancomycin, voriconazole, ganciclovir, and fluconazole) were found to be associated with DIN and DIA, respectively. While logistic regression analysis revealed that palonosetron and ganciclovir were associated with DIN and DIA, respectively.

CONCLUSION

Palonosetron and ganciclovir were found to be correlated with drug-induced granulocytopenia. The results of this study provide an early warning of drug safety signals for drug-induced granulocytopenia, facilitating a quick and appropriate response for clinicians.

Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database

Background: Drug-induced kidney injury (DIKI) is one of the most common complications in clinical practice. Detection signals through post-marketing approaches are of great value in preventing DIKI in pediatric patients. This study aimed to propose a quantitative algorithm to detect DIKI signals in children using an electronic health record (EHR) database.

Methods: In this study, 12 years of medical data collected from a constructed data warehouse were analyzed, which contained 575,965 records of inpatients from 1 January 2009 to 31 December 2020. Eligible participants included inpatients aged 28 days to 18 years old. A two-stage procedure was adopted to detect DIKI signals: 1) stage 1: the suspected drugs potentially associated with DIKI were screened by calculating the crude incidence of DIKI events; and 2) stage 2: the associations between suspected drugs and DIKI were identified in the propensity score-matched retrospective cohorts. Unconditional logistic regression was used to analyze the difference in the incidence of DIKI events and to estimate the odds ratio (OR) and 95% confidence interval (CI). Potentially new signals were distinguished from already known associations concerning DIKI by manually reviewing the published literature and drug instructions.

Results: Nine suspected drugs were initially screened from a total of 652 drugs. Six drugs, including diazepam (OR = 1.61, 95%CI: 1.43–1.80), omeprazole (OR = 1.35, 95%CI: 1.17–1.54), ondansetron (OR = 1.49, 95%CI: 1.36–1.63), methotrexate (OR = 1.36, 95%CI: 1.25–1.47), creatine phosphate sodium (OR = 1.13, 95%CI: 1.05–1.22), and cytarabine (OR = 1.17, 95%CI: 1.06–1.28), were demonstrated to be associated with DIKI as positive signals. The remaining three drugs, including vitamin K1 (OR = 1.06, 95%CI: 0.89–1.27), cefamandole (OR = 1.07, 95%CI: 0.94–1.21), and ibuprofen (OR = 1.01, 95%CI: 0.94–1.09), were found not to be associated with DIKI. Of these, creatine phosphate sodium was considered to be a possible new DIKI signal as it had not been reported in both adults and children previously. Moreover, three other drugs, namely, diazepam, omeprazole, and ondansetron, were shown to be new potential signals in pediatrics.

Conclusion: A two-step quantitative procedure to actively explore DIKI signals using real-world data (RWD) was developed. Our findings highlight the potential of EHRs to complement traditional spontaneous reporting systems (SRS) for drug safety signal detection in a pediatric setting.

Signal Detection of Pediatric Drug–Induced Coagulopathy Using Routine Electronic Health Records

Background: Drug-induced coagulopathy (DIC) is a severe adverse reaction and has become a significantly increased clinical problem in children. It is crucial to the detection of the DIC safety signal for drug post-marketing scientific supervision purposes. Therefore, this study aimed to detect potential signals for DIC in children using the routine electronic medical record (EMR) data.

Methods: This study extracted EMR data from Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DIC. We calculated the crude incidence by mining cases of coagulopathy to select the potential suspected drugs; then, propensity score-matched retrospective cohorts of specific screened drugs from the first stage were constructed and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The current literature evidence was used to assess the novelty of the signal.

Results:In the study, from a total of 340 drugs, 22 drugs were initially screened as potentially inducing coagulopathy. In total, we identified 19 positive DIC associations. Of these, potential DIC risk of omeprazole (OR: 2.23, 95% CI: 1.88–2.65), chlorpheniramine (OR:3.04, 95% CI:2.56–3.60), and salbutamol sulfate (OR:1.36, 95% CI:1.07–1.73) were three new DIC signals in both children and adults. Twelve associations between coagulopathy and drugs, meropenem (OR: 3.38, 95% CI: 2.72–4.20), cefoperazone sulbactam (OR: 2.80, 95% CI: 2.30–3.41), fluconazole (OR: 2.11, 95% CI: 1.71–2.59), voriconazole (OR: 2.82, 95% CI: 2.20–3.61), ambroxol hydrochloride (OR: 2.12, 95% CI: 1.74–2.58), furosemide (OR: 2.36, 95% CI: 2.08–2.67), iodixanol (OR: 2.21, 95% CI: 1.72–2.85), cefamandole (OR: 1.82, 95% CI: 1.56–2.13), ceftizoxime (OR: 1.95, 95% CI: 1.44–2.63), ceftriaxone (OR: 1.95, 95% CI: 1.44–2.63), latamoxef sodium (OR: 1.76, 95% CI: 1.49–2.07), and sulfamethoxazole (OR: 1.29, 95% CI: 1.01–1.64), were considered as new signals in children.

Conclusion: The two-stage algorithm developed in our study to detect safety signals of DIC found nineteen signals of DIC, including twelve new signals in a pediatric population. However, these safety signals of DIC need to be confirmed by further studies based on population study and mechanism research.

Development of a Data-Driven Reference Standard for Adverse Drug Reaction (RS-ADR) Signal Assessment (Preprint)

Background

Pharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not possible due to the lack of a reference standard for positive and negative associations.

Objective

This study aimed to develop a reference standard for ADR (RS-ADR) to streamline the systematic detection, assessment, and understanding of almost all drug-ADR associations suggested by RWD analyses.

Methods

We integrated well-known reference sets for drug-ADR pairs, including Side Effect Resource, Observational Medical Outcomes Partnership, and EU-ADR. We created a pharmacovigilance dictionary using controlled vocabularies and systematically annotated EHR data. Drug-ADR associations computed from MetaLAB and MetaNurse analyses of multicenter EHRs and extracted from the Food and Drug Administration Adverse Event Reporting System were integrated as “empirically determined” positive and negative reference sets by means of cross-validation between institutions.

Results

The RS-ADR consisted of 1344 drugs, 4485 ADRs, and 6,027,840 drug-ADR pairs with positive and negative consensus votes as pharmacovigilance reference sets. After the curation of the initial version of RS-ADR, novel ADR signals such as “famotidine–hepatic function abnormal” were detected and reasonably validated by RS-ADR. Although the validation of the entire reference standard is challenging, especially with this initial version, the reference standard will improve as more RWD participate in the consensus voting with advanced pharmacovigilance dictionaries and analytic algorithms. One can check if a drug-ADR pair has been reported by our web-based search interface for RS-ADRs.

Conclusions

RS-ADRs enriched with the pharmacovigilance dictionary, ADR knowledge, and real-world evidence from EHRs may streamline the systematic detection, evaluation, and causality assessment of computationally detected ADR signals.

Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm

Pharmacovigilance involves monitoring of drugs and their adverse drug reactions (ADRs) and is essential for their safety post-marketing. Because of the different types and structures of medical databases, several previous surveillance studies have analyzed only one database. In the present study, we extracted potential drug-ADR pairs from electronic health record (EHR) data using the MetaNurse algorithm and analyzed them using the Korean Adverse Event Reporting System (KAERS) database for systematic validation. The Medical Dictionary for Regulatory Activities (MedDRA) and World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) were mapped for signal detection. We used the Side Effect Resource (SIDER) database to select 2663 drug-ADR pairs to investigate unknown drug-induced ADRs. The reporting odds ratio (ROR) value was calculated for the drug-exposed and non-exposed groups of drug-ADR pairs, and 19 potential pairs showed significant signals. Appropriate terminology systems and criteria are needed to handle diverse medical databases.

Detection of unknown ototoxic adverse drug reactions: an electronic healthcare record-based longitudinal nationwide cohort analysis

Ototoxic medications can lead to significant morbidity. Thus, pre-marketing clinical trials have assessed new drugs that have ototoxic potential. Nevertheless, several ototoxic side effects of drugs may remain undetected. Hence, we sought to retrospectively investigate the potential risk of ototoxic adverse drug reactions among commonly used drugs via a longitudinal cohort study. An electronic health records-based data analysis with a propensity-matched comparator group was carried out. This study was conducted using the MetaNurse algorithm for standard nursing statements on electronic healthcare records and the National Sample Cohort obtained from the South Korea National Health Insurance Service. Five target drugs capable of causing ototoxic adverse drug reactions were identified using MetaNurse; two drugs were excluded after database-based analysis because of the absence of bilateral hearing loss events in patients. Survival analysis, log-rank test, and Cox proportional hazards regression models were used to calculate the incidence, survival rate, and hazard ratio of bilateral hearing loss among patients who were prescribed candidate ototoxic drugs. The adjusted hazard ratio of bilateral hearing loss was 1.31 (1.03–1.68), 2.20 (1.05–4.60), and 2.26 (1.18–4.33) in cimetidine, hydroxyzine, and sucralfate users, respectively. Our results indicated that hydroxyzine and sucralfate may cause ototoxic adverse drug reactions in patients. Thus, clinicians should consider avoiding co-administration of these drugs with other well-confirmed ototoxic drugs should be emphasized.

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.

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.

Signal Detection of Potentially Drug-Induced Liver Injury in Children Using Electronic Health Records

Background: This study proposes a quantitative 2-stage procedure to detect potential drug-induced liver injury (DILI) signals in pediatric inpatients using an data warehouse of electronic health records (EHRs). Methods: Eight years of medical data from a constructed database were used. A two-stage procedure was adopted: (i) stage 1: the drugs suspected of inducing DILI were selected and (ii) stage 2: the associations between the drugs and DILI were identified in a retrospective cohort study. Results: 1,196 drugs were filtered initially and 12 drugs were further potentially identified as suspect drugs inducing DILI. Eleven drugs (fluconazole, omeprazole, sulfamethoxazole, vancomycin, granulocyte colony-stimulating factor (G-CSF), acetaminophen, nifedipine, fusidine, oseltamivir, nystatin and meropenem) were showed to be associated with DILI. Of these, two drugs, nystatin [odds ratio[OR]=1.39, 95%CI:1.10-1.75] and G-CSF (OR = 1.91, 95%CI:1.55-2.35), were found to be new potential signals in adults and children. Three drugs [nifedipine [OR = 1.77, 95%CI:1.26-2.46], fusidine [OR = 1.43, 95%CI:1.08-1.86], and oseltamivi r [OR = 1.64, 95%CI:1.23-2.18]] were demonstrated to be new signals in pediatrics. The other drug-DILI associations had been confirmed in previous studies. Conclusions: A quantitative algorithm to detect potential signals of DILI has been described. Our work promotes the application of EHR data in pharmacovigilance and provides candidate drugs for further causality assessment studies.

Pediatric drug safety signal detection of non-chemotherapy drug-induced neutropenia and agranulocytosis using electronic healthcare records

Objectives: This study aimed to develop a procedure to explore the adverse drug reaction signals of drug-induced neutropenia (DIN) or drug-induced agranulocytosis (DIA) in children using an electronic health records (EHRs) database. Methods: A two-stage design was presented. First, the suspected drugs to induce DIN or DIA were selected. Second, the associations were evaluated by a retrospective cohort study. Results: Ten and five drugs were potentially identified to be associated with DIN and DIA, respectively. Finally, five (oseltamivir, chlorpheniramine, vancomycin, meropenem, and ganciclovir) and two (chlorpheniramine, and vancomycin) drugs were found to be associated with DIN and DIA, respectively. Of these, the association between oseltamivir and neutropenia (P = 9.83 × 10^-9; OR, 2.10; 95% CI, 1.62-2.69) was considered as a new signal for both adults and children. Chlorpheniramine-induced neutropenia (P = 3.01 × 10^-8; OR, 1.59; 95% CI, 1.35-1.87) and agranulocytosis (P = 3.16 × 10^-7; OR, 3.76; 95% CI, 2.25-6.26) were considered as new signals in children. Other drugs associated with DIN or DIA were confirmed by previous studies. Conclusion: A method to detect signals for DIN and DIA has been described. Several pediatric drugs were found to be associated with DIN or DIA.

Key Elements in Adverse Drug Reactions Safety Signals: Application of Legal Strategies

Adverse drug reactions, or unintended and harmful outcomes related to the administration of a pharmaceutical product, are a major public health concern, particularly for cancer patients. If counted as a separate cause of death, adverse drug reactions would represent the fourth leading cause of death in the United States. Several legal strategies are available to help mitigate their occurrences and to compensate victims for the harm that results from adverse events. Prior to FDA approval of a drug, the limited size and duration of clinical trials often fail to detect adverse drug reactions. However, after FDA approval, pharmacovigilance efforts are bolstered by recent expansions of FDA post-marketing regulatory powers codified in the 2007 Food and Drug Administration Amendments Act, as well as advances in big data analytics that improve adverse signal detection through data mining of large electronic health records. For victims of adverse drug reactions, tort lawsuits filed in the courts help compensate for the harm suffered and may also serve as warnings to manufacturers to improve drug safety to avoid future legal liability. While encouraging developments have occurred, new and existing legal structures to mitigate and compensate for adverse drug reactions must continue to be refined given increasingly complex pharmaceutical agents.

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals

Background

The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results.

Materials and methods

To construct an ADR reference dataset, we extracted known drug–laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug–laboratory event pairs, except known ones, are considered unknown. To detect a known drug–laboratory event pair, three existing algorithms—CERT, CLEAR, and PACE—were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug–laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC).

Results

All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593–0.793, specificity of 0.619–0.796, NPV of 0.645–0.727, PPV of 0.680–0.777, F1-measure of 0.629–0.709, and AUROC of 0.737–0.816. Features related to change or distribution of shape were considered important for detecting ADR signals.

Conclusions

Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.

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.

Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records

Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy.

Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)–based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing statements for all precautionary drugs (n  = 101) were comprehensively detected and validated against SIDER (Side Effect Resource) by MetaLAB and MetaNurse against 11 817 and 76 457 drug-ADR pairs, respectively.

Results. We demonstrate that MetaLAB (area under the curve, AUC = 0.61 ± 0.18) outperformed CLEAR (AUC = 0.55 ± 0.06) when we applied the same 470 drug-event pairs as the gold standard, as in our previous research. Receiver operating characteristic curves for 101 precautionary terms in the Medical Dictionary for Regulatory Activities Preferred Terms were obtained for MetaLAB and MetaNurse (0.69 ± 0.11; 0.62 ± 0.07), which complemented each other in terms of ADR signal coverage. Novel ADR signals discovered by MetaLAB and MetaNurse were successfully validated against spontaneous reports in the US Food and Drug Administration Adverse Event Reporting System database.

Discussion. The present study demonstrates the symbiosis of laboratory test results and nursing statements for ADR signal detection in terms of their system organ class coverage and performance profiles.

Conclusion. Systematic discovery and evaluation of the wide spectrum of ADR signals using standard-based observational electronic health record data across many institutions will affect drug development and use, as well as postmarketing surveillance and regulation.

Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries

PURPOSE

The Singapore regulatory agency for health products (Health Sciences Authority), in performing active surveillance of medicines and their potential harms, is open to new methods to achieve this goal. Laboratory tests are a potential source of data for this purpose. We have examined the performance of the Comparison on Extreme Laboratory Tests (CERT) algorithm, developed by Ajou University, Korea, as a potential tool for adverse drug reaction detection based on the electronic medical records of the Singapore health care system.

METHODS

We implemented the original CERT algorithm, comparing extreme laboratory results pre- and post-drug exposure, and 5 variations thereof using 4.5 years of National University Hospital (NUH) electronic medical record data (31 869 588 laboratory tests, 6 699 591 drug dispensings from 272 328 hospitalizations). We investigated 6 drugs from the original CERT paper and an additional 47 drugs. We benchmarked results against a reference standard that we created from UpToDate 2015.

RESULTS

The original CERT algorithm applied to all 53 drugs and 44 laboratory abnormalities yielded a positive predictive value (PPV) and sensitivity of 50.3% and 54.1%, respectively. By raising the minimum number of cases for each drug-laboratory abnormality pair from 2 to 400, the PPV and sensitivity increased to 53.9% and 67.2%, respectively. This post hoc variation, named CERT400, performed particularly well for drug-induced hepatic and renal toxicities.

DISCUSSION

We have demonstrated that the CERT algorithm can be applied across national boundaries. One modification (CERT400) was able to identify adverse drug reaction signals from laboratory data with reasonable PPV and sensitivity, which indicates potential utility as a supplementary pharmacovigilance tool.

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

Pilot Algorithm Designed to Help Early Detection of HMG-CoA Reductase Inhibitor-Induced Hepatotoxicity

Objectives

To enable early detection of adverse drug reactions (ADRs) in patients using HMG-CoA reductase inhibitors (statins), we developed an algorithm that automatically detects liver injury caused by statins from Electronic Medical Record (EMR) data. We verified the performance of our algorithm through manual ADR assessment and a direct chart review.

Methods

The subjects in this study were patients who had been prescribed a statin for the first time among outpatients in Seoul St. Mary's Hospital in Korea between January 2009 and December 2012. We extracted basic information about the patients, including laboratory information, underlying disease, diagnosis information, prescription information, and concomitant drugs. We developed an automatic ADR detection algorithm by using EMR data. We validated the results of the algorithm through a chart review.

Results

We developed the algorithm to assess ADR occurrences based on alanine transaminase (ALT) and alkaline phosphatase (ALP) levels. According to the proposed algorithm, any of these result options could be attained: ADR-free, little association, strong association, and weak association or indeterminable. The results of the ADR assessments obtained using the proposed algorithm showed that the data of 126 patients (1.4% of all 9,241 patients) included suspicious figures, thus indicating the possibility of an ADR. In the EMR chart review for verifying the algorithm, ADRs of 33 patients were not associated with statin use; therefore, the ADR occurrence rate was found to be 1.0% (93/9,241). Therefore, the positive predictive value was calculated to be 73.8% (93/126; 95% confidence interval, 69.2%–77.6%). No differences were observed between statin types (p = 0.472).

Conclusions

For early detection of statin-induced liver injury, we developed an automatic ADR assessment algorithm. We expect that algorithms that are more reliable can be developed if we conduct supplement clinical studies with a focus on adverse drug effects.

Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital

The objective of this study was to determine whether the Food and Drug Administration’s Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children’s Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients’ clinical narratives. We downloaded all FAERS reports submitted by medical providers and extracted the reported drug-DDSS pairs. For each patient, we aligned the drug-DDSS pairs extracted from their clinical notes with the corresponding drug-DDSS pairs from the FAERS data set to identify Drug-Reaction Pair Sentences (DRPSs). The DRPSs were processed by NLP techniques to identify ADR-related DRPSs. We used clinician annotated, real-world EHR data as reference standard to evaluate the proposed algorithm. During evaluation, the algorithm achieved promising performance and showed great potential in identifying ADRs accurately for pediatric patients.

Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record

Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.

Development of Clinical Data Mart of HMG-CoA Reductase Inhibitor for Varied Clinical Research

BACKGROUND

The increasing use of electronic medical record (EMR) systems for documenting clinical medical data has led to EMR data being increasingly accessed for clinical trials. In this study, a database of patients who were prescribed statins for the first time was developed using EMR data. A clinical data mart (CDM) was developed for cohort study researchers.

METHODS

Seoul St. Mary's Hospital implemented a clinical data warehouse (CDW) of data for ~2.8 million patients, 47 million prescription events, and laboratory results for 150 million cases. We developed a research database from a subset of the data on the basis of a study protocol. Data for patients who were prescribed a statin for the first time (between the period from January 1, 2009 to December 31, 2015), including personal data, laboratory data, diagnoses, and medications, were extracted.

RESULTS

We extracted initial clinical data of statin from a CDW that was established to support clinical studies; the data was refined through a data quality management process. Data for 21,368 patients who were prescribed statins for the first time were extracted. We extracted data every 3 months for a period of 1 year. A total of 17 different statins were extracted. It was found that statins were first prescribed by the endocrinology department in most cases (69%, 14,865/21,368).

CONCLUSION

Study researchers can use our CDM for statins. Our EMR data for statins is useful for investigating the effectiveness of treatments and exploring new information on statins. Using EMR is advantageous for compiling an adequate study cohort in a short period.

Development and validation of algorithms for the detection of statin myopathy signals from electronic medical records

The purpose of this study was to develop and validate sensitive algorithms to detect hospitalized statin-induced myopathy (SIM) cases from electronic medical records (EMRs). We developed four algorithms on a training set of 31,211 patient records from a large tertiary hospital. We determined the performance of these algorithms against manually curated records. The best algorithm used a combination of elevated creatine kinase (>4× the upper limit of normal (ULN)), discharge summary, diagnosis, and absence of statin in discharge medications. This algorithm achieved a positive predictive value of 52-71% and a sensitivity of 72-78% on two validation sets of >30,000 records each. Using this algorithm, the incidence of SIM was estimated at 0.18%. This algorithm captured three times more rhabdomyolysis cases than spontaneous reports (95% vs. 30% of manually curated gold standard cases). Our results show the potential power of utilizing data and text mining of EMRs to enhance pharmacovigilance activities.

Statins and risk for new-onset diabetes mellitus: A real-world cohort study using a clinical research database

Although concern regarding the increased risk for new-onset diabetes mellitus (NODM) after statin treatment has been raised, there has been a lack of evidence in real-world clinical practice, particularly in East Asians. We investigated whether statin use is associated with risk for NODM in Koreans. We conducted a retrospective cohort study using the clinical research database from electronic health records. The study cohort consisted of 8265 statin-exposed and 33,060 matched nonexposed patients between January 1996 and August 2013. Matching at a 1:4 ratio was performed using a propensity score based on age, gender, baseline glucose levels (mg/dL), and hypertension. The comparative risks for NODM with various statins (atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) were estimated by both statin exposure versus matched nonexposed and within-class comparisons. The incidence of NODM among the statin-exposed group (6.000 per 1000 patient-years [PY]) was higher than that of the nonexposed group (3.244 per 1000 PY). The hazard ratio (HR) of NODM after statin exposure was 1.872 (95% confidence interval [CI], 1.432-2.445). Male gender (HR, 1.944; 95% CI, 1.497-2.523), baseline glucose per mg/dL (HR, 1.014; 95% CI, 1.013-1.016), hypertension (HR, 2.232; 95% CI, 1.515-3.288), and thiazide use (HR, 1.337; 95% CI, 1.081-1.655) showed an increased risk for NODM, while angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker showed a decreased risk (HR, 0.774; 95% CI, 0.668-0.897). Atorvastatin-exposed patients showed a higher risk for NODM than their matched nonexposed counterparts (HR, 1.939; 95% CI, 1.278-2.943). However, the risk for NODM was not significantly different among statins in within-class comparisons. In conclusion, an increased risk for NODM was observed among statin users in a practical healthcare setting in Korea.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.

Comparison of the Risk of Gastrointestinal Bleeding among Different Statin Exposures with Concomitant Administration of Warfarin: Electronic Health Record-Based Retrospective Cohort Study

Background and Objective

Patients who should be treated with both warfarin and a statin are frequently seen in vascular clinics. The risk for bleeding and potential drug interactions should be considered when prescribing both medications together. This study aimed to compare the risk for gastrointestinal bleeding among different statin exposures with concomitant administration of warfarin.

Materials and Methods

This is a single-hospital retrospective cohort study. We included patients who were concomitantly exposed to one of four statins (pravastatin, simvastatin, atorvastatin, and rosuvastatin) and warfarin for up to 2 years (730 days). The observation period ended when a gastrointestinal bleeding event occurred or the observation was censored. Within-class comparisons were used, and 1:1 matching using a propensity score was performed for comparisons between each statin and all of the other statins. Kaplan-Meier analyses with log-rank tests and Cox proportional hazard regression analyses were conducted to determine associations with the risk of gastrointestinal bleeding.

Results

Data were analyzed for 1,686 patients who were concomitantly administered a statin and warfarin. Log-rank tests for the gastrointestinal bleeding-free survival rate showed that the risk for gastrointestinal bleeding was significantly lower in the pravastatin group (p = 0.0499) and higher in the rosuvastatin group (p = 0.009). In the Cox proportional hazard regression analysis, the hazard ratio of 5.394 for gastrointestinal bleeding based on statin exposure in the rosuvastatin group was significant (95% confidence interval, 1.168–24.916).

Conclusions

There was a relatively high risk of gastrointestinal bleeding with rosuvastatin when administered concomitantly with warfarin.

The relationship between the failure to eradicate Helicobacter pylori and previous antibiotics use

BACKGROUND

The previous use of antibiotics is known to correlate positively with antibiotic resistance; whether this is also the case in the eradication of Helicobacter pylori infection is unclear.

AIM

To investigate the relationship between the previous use of antibiotics and the failure of eradication therapy in H. pylori infection.

METHODS

The relationship between the clinical parameters and the failure of H. pylori eradication was analyzed in patients administered standard triple therapy and then assessed for the eradication of H. pylori based on a C13-urea breath test.

RESULTS

In a multivariate analysis, failure rates increased significantly in patients with a history of clarithromycin (odds ratio [OR], 4.445) or other macrolides (OR, 2.407) use, who were female (OR, 1.339), or who were older than 60 years of age (OR, 1.326). The eradication failure rate in patients with a history of macrolides use for >2 weeks was significantly higher than if the duration of use was <2 weeks (44.8% vs. 29.3%, p=0.047).

CONCLUSIONS

A patient's history of macrolides is a useful predictor of the likelihood of standard triple therapy failure in H. pylori eradication. The alternatives such as a bismuth-based quadruple or a levofloxacin-containing therapy should be considered in patients treated with macrolides for >2 weeks.

A normalization method for combination of laboratory test results from different electronic healthcare databases in a distributed research network

PURPOSE

Distributed research networks (DRNs) afford statistical power by integrating observational data from multiple partners for retrospective studies. However, laboratory test results across care sites are derived using different assays from varying patient populations, making it difficult to simply combine data for analysis. Additionally, existing normalization methods are not suitable for retrospective studies. We normalized laboratory results from different data sources by adjusting for heterogeneous clinico-epidemiologic characteristics of the data and called this the subgroup-adjusted normalization (SAN) method.

METHODS

Subgroup-adjusted normalization renders the means and standard deviations of distributions identical under population structure-adjusted conditions. To evaluate its performance, we compared SAN with existing methods for simulated and real datasets consisting of blood urea nitrogen, serum creatinine, hematocrit, hemoglobin, serum potassium, and total bilirubin. Various clinico-epidemiologic characteristics can be applied together in SAN. For simplicity of comparison, age and gender were used to adjust population heterogeneity in this study.

RESULTS

In simulations, SAN had the lowest standardized difference in means (SDM) and Kolmogorov-Smirnov values for all tests (p < 0.05). In a real dataset, SAN had the lowest SDM and Kolmogorov-Smirnov values for blood urea nitrogen, hematocrit, hemoglobin, and serum potassium, and the lowest SDM for serum creatinine (p < 0.05).

CONCLUSION

Subgroup-adjusted normalization performed better than normalization using other methods. The SAN method is applicable in a DRN environment and should facilitate analysis of data integrated across DRN partners for retrospective observational studies.

Role of text mining in early identification of potential drug safety issues

Drugs are an important part of today's medicine, designed to treat, control, and prevent diseases; however, besides their therapeutic effects, drugs may also cause adverse effects that range from cosmetic to severe morbidity and mortality. To identify these potential drug safety issues early, surveillance must be conducted for each drug throughout its life cycle, from drug development to different phases of clinical trials, and continued after market approval. A major aim of pharmacovigilance is to identify the potential drug-event associations that may be novel in nature, severity, and/or frequency. Currently, the state-of-the-art approach for signal detection is through automated procedures by analyzing vast quantities of data for clinical knowledge. There exists a variety of resources for the task, and many of them are textual data that require text analytics and natural language processing to derive high-quality information. This chapter focuses on the utilization of text mining techniques in identifying potential safety issues of drugs from textual sources such as biomedical literature, consumer posts in social media, and narrative electronic medical records.

The Model for End-stage Liver Disease score is potentially a useful predictor of hyperkalemia occurrence among hospitalized angiotensin receptor blocker users

WHAT IS KNOWN AND OBJECTIVE

Angiotensin receptor blockers (ARBs) are medications commonly used for treating conditions such as hypertension. However, ARBs are frequently associated with hyperkalemia, a potentially critical adverse event, in high-risk patients. Although both the liver and the kidney are major elimination routes of ARBs, the relationship between hepatorenal function and ARB-related hyperkalemia has not yet been investigated. The purpose of this study was to evaluate the risk of hyperkalemia, in terms of various hepatorenal functions, for hospitalized patients newly initiated on ARB treatment.

METHODS

We evaluated ARB-related hyperkalemia in a cohort of 5530 hospitalized patients, who had not previously used ARBs, between 12 April 2004 and 31 May 2012. Hepatorenal function was assessed by the Model for End-stage Liver Disease (MELD) score. Hyperkalemia risk was assessed by hepatorenal function, risks were categorized into the four MELD scoring groups, and the groups were compared with one another.

RESULTS AND DISCUSSION

The MELD score was significantly different between the hyperkalemic and non-hyperkalemic groups (independent t-test, P < 0.001). The MELD score 10-14, 15-19 and ≥ 20 groups showed higher risks of hyperkalemia than the lowest MELD score group {log-rank test, P < 0.001; multiple Cox proportional hazard model, hazard ratios 1.478 (P = 0.003), 2.285 (P < 0.001) and 3.024 (P < 0.001), respectively}.

WHAT IS NEW AND CONCLUSION

The MELD score showed a stronger predictive performance for hyperkalemia than either serum creatinine or estimated glomerular filtration rate alone. Furthermore, the MELD score showed good predictive performance for ARB-related hyperkalemia among hospitalized patients. The clinical implications and reasons for these findings merit future investigation.

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases

BACKGROUND

Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.

METHODS

We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs.

RESULTS

In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.

CONCLUSIONS

The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

"Big data" and the electronic health record

OBJECTIVES

Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice.

METHODS

We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria.

RESULTS

Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security.

CONCLUSION

The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

Design patterns for the development of electronic health record-driven phenotype extraction algorithms

BACKGROUND

Design patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms.

METHODS

Using phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes.

RESULTS

A total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement.

CONCLUSIONS

Repeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy.

Pediatric Pharmacovigilance: Enhancing Adverse Drug Reaction Reporting in a Tertiary Care Children's Hospital

BACKGROUND

Adverse drug reactions (ADRs) are notoriously underreported within health care facilities. In 2009-2010, ADRs were detected in only 0.5% of patients at the authors' institution, a pediatric hospital in the Midwestern United States. Additionally, historical ADRs were often inaccurately or incompletely documented in the medical record. An integrative Drug Safety Service (DSS) was implemented to improve the detection and accurate documentation of ADRs.

METHODS

The DSS incorporated standardized ADR terminology, computerized triggers to identify ADRs, and a simplified voluntary reporting system within the facility. The DSS staff provided extensive hospital staff education on ADR reporting and the role of the DSS. The primary aim of this report was to assess the impact of the DSS on the number of ADRs reported at the authors' institution. The secondary aims were to evaluate the mechanisms by which patients with ADRs were identified and to assess the accuracy of ADR documentation after implementation of the DSS.

RESULTS

A significant increase was observed (slope, 6.01; P < .001) in ADR detection after implementation of the DSS, with a greater than 4-fold increase from 10 cases per 10,000 admissions before initiation to 41 cases per 10,000 admissions after DSS implementation. Computerized triggers, International Classification of Diseases, 9th Edition (ICD-9) codes associated with ADRs, and the DSS identified 33%, 33%, and 24% of ADRs, respectively, while voluntary reporting only detected 9% of ADRs.

CONCLUSIONS

Implementation of a multifaceted, interdisciplinary DSS was more effective in detecting ADRs than voluntary reporting alone. A proactive approach to ADR detection resulted in a significant increase in the identification and evaluation of ADRs.

Onset time of hyperkalaemia after angiotensin receptor blocker initiation: when should we start serum potassium monitoring?

WHAT IS KNOWN AND OBJECTIVE

Angiotensin receptor blockers (ARBs) frequently induce hyperkalaemia in high-risk patients. Early detection of hyperkalaemia can reduce the subsequent harmful effects. This study was performed to examine the onset time of hyperkalaemia after ARB therapy.

METHODS

We carried out a retrospective analysis to determine the onset time of hyperkalaemia (serum potassium >5·5 mm) among hospitalized patients newly starting ARB therapy between 2004 and 2012, in a tertiary teaching hospital. Predefined possible risk factors and concomitant medications were evaluated.

RESULTS AND DISCUSSION

During the 97-month study period, a total of 4267 hospitalized patients started ARBs as new drugs and 225 patients showed hyperkalaemia. A significantly increased risk of hyperkalaemia was detected among patients with a high baseline potassium [odds ratio (OR) 6·0] and those who took non-potassium-sparing diuretics (OR 2·2) or potassium supplements (OR 1·6). A high glomerular filtration rate (GFR) was associated with a lower risk of hyperkalaemia (OR 0·992). Fifty-two percentage of hyperkalaemic events occurred within the first week after initiation of ARB therapy. The highest frequency of hyperkalaemia occurred on the first day after initiation of ARBs. Hyperkalaemia occurred earlier in patients with a high baseline serum potassium level, reduced GFR, diabetes and in those without heart failure.

WHAT IS NEW AND CONCLUSION

Hyperkalaemia occurs most frequently at the beginning of ARB therapy in hospitalized patients. Monitoring of serum potassium and estimated GFR after initiation of ARBs should be started within a few days or not later than 1 week, especially in patients with risk factors.

A quantitative method for assessment of prescribing patterns using electronic health records

Background

Most available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians' prescription patterns for diverse events and their level of awareness of clinical practice guidelines.

Methods and Findings

We developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia and CDAD were concordant with the standard knowledge. Prescription patterns were well correlated with individual physicians' knowledge of hyperkalemia (κ = 0.714). Prescribing behaviors according to event severity or clinical condition were plotted as a simple summary graph.

Conclusion

The algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.

Data mining methodologies for pharmacovigilance

Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine - particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been estimated to cause over 700,000 emergency department visits each year in the United States. Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance. Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs. In the pre-marketing stages of a drug, PhV primarily focuses on predicting potential ADRs using preclinical characteristics of the compounds (e.g., drug targets, chemical structure) or screening data (e.g., bioassay data). In the postmarketing stage, PhV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is currently shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records (EMRs), biomedical literature, and patient-reported data in online health forums. The emerging trend of PhV is to link preclinical data from the experimental platform with human safety information observed in the postmarketing phase. This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.

Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records

OBJECTIVE

Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs.

METHODS

Using 12 years of EMR data from Vanderbilt University Medical Center (VUMC), we designed a study to correlate abnormal laboratory results with specific drug administrations by comparing the outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance measures used in spontaneous reporting systems (SRSs): proportional reporting ratio (PRR), reporting OR (ROR), Yule's Q (YULE), the χ(2) test (CHI), Bayesian confidence propagation neural networks (BCPNN), and a gamma Poisson shrinker (GPS).

RESULTS

We systematically evaluated the methods on two independently constructed reference standard datasets of drug-event pairs. The dataset of Yoon et al contained 470 drug-event pairs (10 drugs and 47 laboratory abnormalities). Using VUMC's EMR, we created another dataset of 378 drug-event pairs (nine drugs and 42 laboratory abnormalities). Evaluation on our reference standard showed that CHI, ROR, PRR, and YULE all had the same F score (62%). When the reference standard of Yoon et al was used, ROR had the best F score of 68%, with 77% precision and 61% recall.

CONCLUSIONS

Results suggest that EMR-derived laboratory measurements and medication orders can help to validate previously reported ADRs, and detect new ADRs.

Comparison of hyperkalemic risk in hospitalized patients treated with different angiotensin receptor blockers: a retrospective cohort study using a Korean clinical research database

BACKGROUND AND AIM

Clinical use of angiotensin II type 1 receptor antagonists (angiotensin receptor blockers [ARBs]) is associated with hyperkalemia as an adverse drug reaction. Although it has significant clinical implications, the incidence and relative risks of hyperkalemia with various ARBs have not yet been fully evaluated. The purpose of this study was to determine the risk of hyperkalemic events in hospitalized patients treated with different ARBs and to compare the risk among them.

METHODS

We constructed a retrospective cohort composed of hospitalized adult patients who took ARBs in a single tertiary teaching hospital between April 2004 and March 2010. We estimated the incidence of hyperkalemia (serum potassium level >5.5 mEq/L) with various ARBs, and then compared the risk between them using a multivariate Cox proportional hazard model based on age, sex, Charlson co-morbidity score, baseline serum potassium, underlying diseases, and concomitant drugs.

RESULTS

We identified 6992 evaluable intervals from 5449 patients treated with one of the seven ARBs during hospitalization over the 71-month study period with 2521.6 patient-months. We found 381 hyperkalemic events (5.4%) during the study period and an overall event rate of 15.1/100 patient-months. Moderate to fatal hyperkalemia was relatively rare (>6.0 mEq/L, 2.1% [moderate]; >6.5 mEq/L, 0.9% [severe]; >7.0 mEq/L, 0.3% [fatal]). After adjustment for covariates, telmisartan showed a lower risk of hyperkalemia (hazard ratio 0.67; 95% confidence interval 0.51, 0.89) compared with all other ARBs.

CONCLUSION

The risk of hyperkalemic events in hospitalized patients treated with different ARBs was defined. Telmisartan showed a relatively lower hyperkalemic risk profile in hospitalized patients compared with other ARBs.

Construction of an open-access QT database for detecting the proarrhythmia potential of marketed drugs: ECG-ViEW

Information about the QT interval from surface electrocardiograms (ECGs) is essential for surveillance of the proarrhythmia potential of marketed drugs. However, ECG records obtained in daily practice cannot be easily used for this purpose without labor-intensive manual effort. This study was aimed at constructing an open-access QT database, the Electrocardiogram Vigilance with Electronic Data Warehouse (ECG-ViEW). This longitudinal observational database contains 710,369 measurements of QT and associated clinical data from 371,401 patients. The de-identified database is freely available at http://www.ecgview.org.

Recent Publications on Medications and Pharmacy

Hospital Pharmacy presents this feature to keep pharmacists abreast of new publications in the medical/pharmacy literature. Articles of interest regarding a broad scope of topics are abstracted monthly.