The role of electronic healthcare record databases in paediatric drug safety surveillance: a retrospective cohort study

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.

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.

Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

OBJECTIVE

Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed.

MATERIALS AND METHODS

We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges.

RESULTS

Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals.

CONCLUSIONS

EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

Exploratory Study of Signals for Asthma Drugs in Children, Using the EudraVigilance Database of Spontaneous Reports

Introduction

As asthma medications are frequently prescribed for children, knowledge of the safety of these drugs in the paediatric population is important. Although spontaneous reports cannot be used to prove causality of adverse events, they are important in the detection of safety signals.

Objective

Our objective was to provide an overview of adverse drug events associated with asthma medications in children from a spontaneous reports database and to identify new signals.

Methods

Spontaneous reports concerning asthma drugs were obtained from EudraVigilance, the European Medicine Agency’s database for suspected adverse drug reactions. For each drug–event combination, we calculated the proportional reporting ratio (PRR) in the study period 2011–2017. Signals in children (aged 0–17 years) were compared with signals in the whole population. Analyses were repeated for different age categories, by sex and by therapeutic area.

Results

In total, 372,345 reports in children resulted in 385 different signals concerning asthma therapy. The largest group consisted of psychiatric events (65 signals). Only 30 signals were new, with seven, including herpes viral infections, associated with omalizumab. Stratification by age, sex and therapeutic area provided additional new signals, such as hypertrichoses with budesonide and encephalopathies with theophylline. Of all signals in children, 60 (16%) did not appear in the whole population.

Conclusions

The majority of signals regarding asthma therapy in children were already known, but we also identified new signals. We showed that signals can be masked if age stratification is not conducted. Further exploration is needed to investigate the risk and causality of the newly found signals.

Electronic supplementary material

The online version of this article (10.1007/s40264-019-00870-x) contains supplementary material, which is available to authorized users.

Traceability of Pediatric Antibiotic Purchasing Pathways in Italy: A Nationwide Real-World Drug Utilization Analysis

Purpose

The aim of the present study was to describe the purchasing patterns of a set of antibiotics used exclusively in an out-patient pediatric setting in Italy using the Farma360 wholesale drug database (IQVIA Solutions Italy), identifying the proportion of medications which are not captured by Italian National Health Service (NHS) pharmacy claims databases and examining the implications of such findings from a public health and pharmaceutical policy perspective.

Methods

Using a systematic approach, sixty-six antibiotic pediatric formulations were selected for the 5 most commonly used antibiotics in Italy in children and adolescents: amoxicillin in combination with clavulanic acid, amoxicillin, azithromycin, clarithromycin and cefixime. The Farma360 wholesale drug purchasing database was used to identify the yearly proportion of antibiotics not purchased based on NHS reimbursement in primary care from 2015–2017 at the national level. The relationship between product cost and purchase outside the NHS was assessed by a scatterplot. All analyses were stratified by geographic area: Northwest, Northeast, Central and Southern Italy.

Results

The proportion of antibiotics not reimbursed by the NHS increased nationally from 24% in 2015 to 29% in 2017. The antibiotic with the highest proportion of purchases outside the NHS was amoxicillin, with almost two-thirds of all amoxicillin purchases in Southern Italy being made in this way in 2017. The relationship between antibiotic price and antibiotic purchase outside the NHS was almost linear for many geographic areas.

Conclusions

This study showed that a large proportion of antibiotics with a pediatric formulation is purchased outside the NHS drug purchasing pathway, especially in Southern Italy, indicating that it is not possible to fully monitor drug utilization, including appropriateness, for these antibiotics. A better strategy is needed to improve drug utilization monitoring, such as better data collection or data linkage.

Identifying the "Blip on the Radar Screen": Leveraging Big Data in Defining Drug Safety and Efficacy in Pediatric Practice

The immense amount of electronic health data (pharmacy and administrative claims, electronic health records, and clinical registries) that is being generated every day in the care of patients has the potential to be leveraged for improving clinical decisions at the point of care, uncovering or validating drug efficacy and drug safety. The potential use of big data for improving safe and effective use of medications is especially important in children because of their low drug exposure relative to adults. Electronic health data is collected primarily for clinical or billing purposes and not for research purposes. The major steps involved in data acquisition, extraction, aggregation, analysis, modeling, and interpretation are discussed. It is important to understand the limitation of big data and utilize appropriate study design and statistical methods. Possible applications are presented along with specific examples of how big data has been used in drug research to find that blip on the radar screen that may give an efficacy or safety signal that can lead to further investigation.

Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective Study

Background

Data standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods.

Objective

We aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology.

Methods

We developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned.

Results

The performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests).

Conclusions

The proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

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.

Pharmacoepidemiological research for the development and evaluation of drugs in pediatrics

New regulations have come into force in Europe and the US establishing the pediatric development as an integral part of the early development of medicinal products. Parallel to the advances in pediatric clinical research, it became obvious that all available sources and research tools to gather valuable information for the safe and efficacious prescription of medicines in children should be used. Real-life, pharmacoepidemiological studies provide information that contribute to the better knowledge of drug utilization, effects and safety in the pediatric population and thereby, a better prescribing in children. In this paper, we suggest some possible applications, provide examples of impact of pharmacoepidemiological and pharmacovigilance studies and expose future perspectives in pediatric pharmacoepidemiology.

From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources

In the last decade 'big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment.

Electronic health record (EHR) based postmarketing surveillance of adverse events associated with pediatric off-label medication use: A case study of short-acting beta-2 agonists and arrhythmias

PURPOSE

Use electronic health record (EHR) data to (1) estimate the risk of arrhythmia associated with inhaled short-acting beta-2 agonists (SABA) in pediatric patients and (2) determine whether risk varied by on-label versus off-label prescribing.

METHODS

Retrospective cohort study of 335 041 children ≤18 years using EHR primary care data from 2 pediatric health systems (2011-2013). A series of monthly pseudotrials were created, using propensity score methodology to balance baseline characteristics between SABA-exposed (identified by prescription) and SABA-unexposed children. Association between SABA and subsequent arrhythmia for each health system was estimated through pooled logistic regression with separate estimates for children initiating under and over 4 years old (off-label and on-label, respectively).

RESULTS

Eleven percent of the cohort received a SABA prescription, 57% occurred under the age of 4 years (off-label). During the follow-up period, there were 283 first arrhythmia events, most commonly atrial tachyarrhythmias and premature ventricular/atrial contractions. In 1 health system, adjusted risk for arrhythmia was increased among exposed children (OR 1.89, 95% CI 1.31-2.73) without evidence of interaction between label status and risk. The absolute adjusted rate difference was 3.6/10 000 person-years of SABA exposure. The association between SABA exposure and arrhythmias was less strong in the second system (OR 1.26, 95% CI 0.30-5.33).

CONCLUSION

Using EHR data, we could estimate the risk of a rare event associated with medication use and determine difference in risk related to on-label versus off-label status. These findings support the value of EHR-based data for postmarketing drug studies in the pediatric population.

Pharmacoepidemiology in pediatrics: Needs, challenges and future directions for research

Despite international initiatives to promote clinical research in pediatrics, there are still many gaps of knowledge in the use of drugs to treat this specific population. When important information cannot be derived only from clinical trials, use of available observational research tools is required. In this paper, we provide an overview of the particular interest of pharmacoepidemiological research into the evaluation of drug effects in children and adolescents. We also sought to underline the unique challenges and specific needs regarding this research. Implementation of innovative methodologies and expansion of database networks to perform necessary studies could further improve performances of observational research.

Pharmacoepidemiological safety studies in children: a systematic review

Purpose

In order to identify challenges in pediatric pharmacoepidemiological safety studies, we assessed the characteristics of such (published) studies.

Methods

Relevant articles from inception to 2013 were retrieved from Embase and Medline. We sequentially screened titles, abstracts and full texts with independent validation. We systematically collected data regarding general information, study methods and results.

Results

Out of 4825 unique articles, 268 full texts (5.6%) were retained; 147 (54.9%) pertained to drugs rather than vaccines. Considering the 268 studies, 202 (75.4%) concerned children and adolescents (2 to 11 years) and 14 (5.3%) included preterm newborns. Most studies originated from North America (154 [57.5%]) or Europe (92 [34.3%]). Only 47 studies (17.5%) were privately funded. The majority (174 [64.9%]) were cohort studies. Out of 268 studies, 196 (73.1%) collected data retrospectively; paper medical charts were the most common data source for the exposures (85 [31.7%]) and outcomes (122 [45.5%]). Only 3 (2.0%) drug‐only studies investigated rarely used drugs. Considering all 268 studies, only 27 (10.1%) reported sample size or power calculation. Most (75 [51.0%]) drug‐only studies corrected confounding by multivariate modeling unlike stratification in 66 (55.9%) vaccine‐only studies. Considering 75 child‐only studies without any statistically significant result, 41 (54.7%) did not discuss lack of power.

Conclusions

Although the field of pediatric pharmacoepidemiology is steadily developing evaluation seldom includes neonates, is mainly focused on few drug classes and safety outcomes and concerns mainly drug use in developed countries. Small study size is a specific challenge in pediatrics. Reporting should be improved. © 2016 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.

Drug combination therapy increases successful drug repositioning

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.