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Objective: Through manual review of clinical notes for patients with type 2 diabetes mellitus attending a Danish diabetes center, the aim of the study was to identify adverse drug reactions (ADRs) associated with three classes of glucose-lowering medicines: "Combinations of oral blood-glucose lowering medicines" (A10BD), "dipeptidyl peptidase-4 (DDP-4) inhibitors" (A10BH), and "other blood glucose lowering medicines" (A10BX). Specifically, we aimed to describe the potential of clinical notes to identify new ADRs and to evaluate if sufficient information can be obtained for causality assessment. Methods: For observed adverse events (AEs) we extracted time to onset, outcome, and suspected medicine(s). AEs were assessed according to World Health Organization-Uppsala Monitoring Centre causality criteria and analyzed with respect to suspected medicines, type of ADR (system organ class), seriousness and labeling status. Findings: A total of 207 patients were included in the study leading to the identification of 163 AEs. 14% were categorized as certain, 60% as probable/likely, and 26% as possible. 15 (9%) ADRs were unlabeled of which two were serious: peripheral edema associated with sitagliptin and stomach ulcer associated with liraglutide. Of the unlabeled ADRs, 13 (87%) were associated with "other blood glucose lowering medications," the remaining 2 (13%) with "DDP-4 inhibitors." Conclusion: Clinical notes could potentially reveal unlabeled ADRs associated with prescribed medicines and sufficient information is generally available for causality assessment. However, manual review of clinical notes is too time-consuming for routine use and hence there is a need for developing information technology (IT) tools for automatic screening of patient records with the purpose to detect information about potentially serious and unlabeled ADRs

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs

Switch in Therapy from Methylphenidate to Atomoxetine in Children and Adolescents with Attention-Deficit/Hyperactivity Disorder:An Analysis of Patient Records

Objective: The purpose of this study was to investigate therapy switching from methylphenidate (MPH) to atomoxetine (ATX) in a clinical sample of Danish children and adolescents with attention-deficit/hyperactivity disorder (ADHD); specifically, to determine the duration of MPH treatment before switching to ATX, and the reasons leading to a switch in therapy. Methods: We included 55 patients with ADHD who switched from first-line MPH to second-line ATX during January 01, 2012 and May 15, 2014. Patient and treatment characteristics along with clinical reasons for switching therapy were extracted from individual patients' records. Results: Mean duration of MPH treatment until switch to ATX was 11.2 months (range = 0.3–28.5 months); 36% of the patients switched within the first 6 months, 56% within the first year, and 76% within 1.5 years of initiating MPH; 24% continued MPH treatment for up to 2.5 years prior to switching. Most common reasons for switching were “adverse events” (AEs) (78%), “wish for more optimal day coverage” (24%), and “lack of efficacy” (16%). Other reasons for switching included “patient/parental request” (13%) and “noncompliance” (2%). Most common AEs leading to switch were psychiatric disorders (insomnia, aggression, tic, depression, anxiety) and decreased appetite. Conclusions: Our findings highlight the importance of continuous evaluation of the need for prescription switch to ATX in children and adolescents treated with MPH, taking into consideration various factors including potential AEs, non-optimal day coverage, lack of efficacy, patient/parental preferences, and noncompliance. These factors should be considered, not only at the initial stage of MPH treatment but throughout the whole treatment course