Dataset Information

Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.

ABSTRACT: Real-world clinical practice commonly veers from formal drug approvals in off-label use, accounting for 21% of prescriptions for common drugs. Due to its ad hoc nature, off-label use typically goes undocumented, evading the safety and efficacy scrutiny of clinical trials. A systematic and automated approach to detection of these uses in the electronic health record (EHR) would enable improved safety monitoring, provide insight into prescribing patterns, and support real-world evidence appraisal. Domain knowledge provided by medication-indication knowledge bases has been shown to improve the accuracy of EHR-based automated detection of off-label use, but remains limited due to diverse concept representations and granularities across data sources. We present a method to leverage hierarchical concept knowledge to align medication-indication knowledge with EHR data for automated detection of off-label drug use in clinical practice. We demonstrate an over two-fold increase in detected off-label diagnoses when leveraging hierarchical knowledge relative to direct concept matching alone.

SUBMITTER: Schiffer K

PROVIDER: S-EPMC10283126 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.

Schiffer Kayla K Choi Yoolim A YA Weng Chunhua C

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20230616

Real-world clinical practice commonly veers from formal drug approvals in off-label use, accounting for 21% of prescriptions for common drugs. Due to its ad hoc nature, off-label use typically goes undocumented, evading the safety and efficacy scrutiny of clinical trials. A systematic and automated approach to detection of these uses in the electronic health record (EHR) would enable improved safety monitoring, provide insight into prescribing patterns, and support real-world evidence appraisal. ...[more]

PMID: 37350872

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Project description: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.

Project description:BackgroundTo reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed. Some new models based on cytology image classification were recently developed and bring good perspectives, but there are still avenues to explore to improve their performance.ObjectiveOur team aimed to evaluate the benefit of combining the reuse of massive clinical data to build a risk factor model and a digital cytology image-based model (VisioCyt) for bladder cancer detection.MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). It provides a score corresponding to the risk of developing bladder cancer based on the patient's clinical profile. Second, we investigated 3 strategies (logistic regression, decision tree, and a custom strategy based on score interpretation) to combine the model's score with the score from an image-based model to produce a robust bladder cancer scoring system.ResultsWe collected 2 data sets. The first set, including clinical data for 5422 patients extracted from the clinical data warehouse, was used to design the risk factor-based model. The second set was used to measure the models' performances and was composed of data for 620 patients from a clinical trial for which cytology images and clinicobiological features were collected. With this second data set, the combination of both models obtained areas under the curve of 0.82 on the training set and 0.83 on the test set, demonstrating the value of combining risk factor-based and image-based models. This combination offers a higher associated risk of cancer than VisioCyt alone for all classes, especially for low-grade bladder cancer.ConclusionsThese results demonstrate the value of combining clinical and biological information, especially to improve detection of low-grade bladder cancer. Some improvements will need to be made to the automatic extraction of clinical features to make the risk factor-based model more robust. However, as of now, the results support the assumption that this type of approach will be of benefit to patients.

Project description:ObjectiveSubstance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data.Materials and methodsPediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC).ResultsThe dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively).ConclusionsIt is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies.

Dataset Information

Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.

Publications

Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.

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