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

ABSTRACT: 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.

SUBMITTER: Lee S

PROVIDER: S-EPMC7651894 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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

Lee Suehyun S Choi Jiyeob J Kim Hun-Sung HS Kim Grace Juyun GJ Lee Kye Hwa KH Park Chan Hee CH Han Jongsoo J Yoon Dukyong D Park Man Young MY Park Rae Woong RW Kang Hye-Ryun HR Kim Ju Han JH

Journal of the American Medical Informatics Association : JAMIA 20170701 4

<h4>Objective</h4>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.<h4>Matrials and methods</h4>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 CLE ...[more]

PMID: 28087585

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Project description:BackgroundData collected for medical, filing and administrative purposes in electronic patient records (EPRs) represent a rich source of individualised clinical data, which has great potential for improved detection of patients experiencing adverse drug reactions (ADRs), across all approved drugs and across all indication areas.ObjectivesThe aim of this study was to take advantage of techniques for temporal data mining of EPRs in order to detect ADRs in a patient- and dose-specific manner.MethodsWe used a psychiatric hospital's EPR system to investigate undesired drug effects. Within one workflow the method identified patient-specific adverse events (AEs) and links these to specific drugs and dosages in a temporal manner, based on integration of text mining results and structured data. The structured data contained precise information on drug identity, dosage and strength.ResultsWhen applying the method to the 3,394 patients in the cohort, we identified AEs linked with a drug in 2,402 patients (70.8 %). Of the 43,528 patient-specific drug substances prescribed, 14,736 (33.9 %) were linked with AEs. From these links we identified multiple ADRs (p < 0.05) and found them to occur at similar frequencies, as stated by the manufacturer and in the literature. We showed that drugs displaying similar ADR profiles share targets, and we compared submitted spontaneous AE reports with our findings. For nine of the ten most prescribed antipsychotics in the patient population, larger doses were prescribed to sedated patients than non-sedated patients; five antipsychotics [corrected] exhibited a significant difference (p<0.05). Finally, we present two cases (p < 0.05) identified by the workflow. The method identified the potentially fatal AE QT prolongation caused by methadone, and a non-described likely ADR between levomepromazine and nightmares found among the hundreds of identified novel links between drugs and AEs (p < 0.05).ConclusionsThe developed method can be used to extract dose-dependent ADR information from already collected EPR data. Large-scale AE extraction from EPRs may complement or even replace current drug safety monitoring methods in the future, reducing or eliminating manual reporting and enabling much faster ADR detection.

Project description:BACKGROUND:Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE:The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning. METHODS:The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS:The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS:We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

Dataset Information

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

Publications

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

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