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Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data.

ABSTRACT: Objective:Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods. Materials and Methods:We used The Health Improvement Network database to study medications associated with Clostridium difficile infection (CDI). We performed case-control studies for each medication in The Health Improvement Network to obtain odds ratios (ORs) for association with CDI. We then calculated the association of each pharmacologic class with CDI using logistic regression and MCEA. We also performed simulation studies in which we assessed the sensitivity and specificity of logistic regression compared to MCEA for ORs 0.1-2.0. Results:When analyzing pharmacologic classes using logistic regression, 47 of 110 pharmacologic classes were identified as associated with CDI. When analyzing pharmacologic classes using MCEA, only fluoroquinolones, a class of antibiotics with biologically confirmed causation, and heparin products were associated with CDI. In simulation, MCEA had superior specificity compared to logistic regression across all tested effect sizes and equal or better sensitivity for all effect sizes besides those close to null. Discussion:Although these results demonstrate the promise of MCEA, additional studies that include inpatient administered medications are necessary for validation of the algorithm. Conclusions:In clinical and simulation studies, MCEA demonstrated superior sensitivity and specificity for identifying pharmacologic classes associated with CDI compared to logistic regression.

SUBMITTER: Vajravelu RK

PROVIDER: S-EPMC6016702 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data.

Vajravelu Ravy K RK Scott Frank I FI Mamtani Ronac R Li Hongzhe H Moore Jason H JH Lewis James D JD

Journal of the American Medical Informatics Association : JAMIA 20180701 7

<h4>Objective</h4>Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods.<h4>Materials and methods</h4>We used The Hea ...[more]

PMID: 29378062

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Project description:ImportanceDespite billions spent in public investment, electronic health records (EHRs) have not delivered on the promise of large quality and safety improvement. Simultaneously, there is debate on whether public quality reporting is a useful tool to incentivize quality improvement.ObjectiveTo evaluate whether publicly reported feedback was associated with hospital improvement in an evaluation of medication-related clinical decision support (CDS) safety performance.Design, settings, and participantsThis nonrandomized controlled trial included US hospitals that participated in the Computerized Provider Order Entry (CPOE) Evaluation Tool in the Leapfrog Hospital Survey, a national quality reporting program that evaluates safety performance of hospital CDS using simulated orders and patients, in 2017 to 2018. A sharp regression discontinuity design was used to identify the association of receiving negative feedback with hospital performance improvement in the subsequent year. Data were analyzed from January through September 2020.ExposuresPublicly reported quality feedback.Main outcomes and measuresThe main outcome was improvement from 2017 to 2018 on the Leapfrog CPOE Evaluation Tool, using regression discontinuity model estimates of the association of receiving negative publicly reported feedback with quality improvement.ResultsA total of 1183 hospitals were included, with a mean (SD) CPOE score of 59.3% (16.3%) at baseline. Hospitals receiving negative feedback improved 8.44 (95% CI, 0.09 to 16.80) percentage points more in the subsequent year compared with hospitals that received positive feedback on the same evaluation. This change was driven by differences in improvement in basic CDS capabilities (β = 8.71 [95%CI, 1.67 to 18.73]) rather than advanced CDS (β = 6.15 [95% CI, -9.11 to 26.83]).Conclusions and relevanceIn this nonrandomized controlled trial, publicly reported feedback was associated with quality improvement, suggesting targeted measurement and reporting of process quality may be an effective policy lever to encourage improvement in specific areas. Clinical decision support represents an important tool in ensuring patient safety and decreasing adverse drug events, especially for complex patients and those with multiple chronic conditions who often receive several different drugs during an episode of care.

Project description:BackgroundMedication-wide association studies (MWAS) have been applied to assess the risk of individual prescription use and a wide range of health outcomes, including cancer, acute myocardial infarction, acute liver failure, acute renal failure, and upper gastrointestinal ulcers. Current literature on the use of preconception and periconception medication and its association with the risk of multiple gestation pregnancies (eg, monozygotic and dizygotic) is largely based on assisted reproductive technology (ART) cohorts. However, among non-ART pregnancies, it is unknown whether other medications increase the risk of multifetal pregnancies.ObjectiveThis study aimed to investigate the risk of multiple gestational births (eg, twins and triplets) following preconception and periconception exposure to prescription medications in patients who delivered at Penn Medicine.MethodsWe used electronic health record data between 2010 and 2017 on patients who delivered babies at Penn Medicine, a health care system in the Greater Philadelphia area. We explored 3 logistic regression models: model 1 (no adjustment); model 2 (adjustment for maternal age); and model 3-our final logistic regression model (adjustment for maternal age, ART use, and infertility diagnosis). In all models, multiple births (MBs) were our outcome of interest (binary outcome), and each medication was assessed separately as a binary variable. To assess our MWAS model performance, we defined ART medications as our gold standard, given that these medications are known to increase the risk of MB.ResultsOf the 63,334 distinct deliveries in our cohort, only 1877 pregnancies (2.96%) were prescribed any medication during the preconception and first trimester period. Of the 123 medications prescribed, we found 26 (21.1%) medications associated with MB (using nominal P values) and 10 (8.1%) medications associated with MB (using Bonferroni adjustment) in fully adjusted model 3. We found that our model 3 algorithm had an accuracy of 85% (using nominal P values) and 89% (using Bonferroni-adjusted P values).ConclusionsOur work demonstrates the opportunities in applying the MWAS approach with electronic health record data to explore associations between preconception and periconception medication exposure and the risk of MB while identifying novel candidate medications for further study. Overall, we found 3 novel medications linked with MB that could be explored in further work; this demonstrates the potential of our method to be used for hypothesis generation.

Project description:Importance:Complex medication regimens pose self-management challenges, particularly among populations with low levels of health literacy. Objective:To test medication management tools delivered through a commercial electronic health record (EHR) with and without a nurse-led education intervention. Design, Setting, and Participants:This 3-group cluster randomized clinical trial was performed in community health centers in Chicago, Illinois. Participants included 794 patients with hypertension who self-reported using 3 or more medications concurrently (for any purpose). Data were collected from April 30, 2012, through February 29, 2016, and analyzed by intention to treat. Interventions:Clinics were randomly assigned to to groups: electronic health record-based medication management tools (medication review sheets at visit check-in, lay medication information sheets printed after visits; EHR-alone group), EHR-based tools plus nurse-led medication management support (EHR plus education group), or usual care. Main Outcomes and Measures:Outcomes at 12 months included systolic blood pressure (primary outcome), medication reconciliation, knowledge of drug indications, understanding of medication instructions and dosing, and self-reported medication adherence. Medication outcomes were assessed for all hypertension prescriptions, all prescriptions to treat chronic disease, and all medications. Results:Among the 794 participants (68.6% women; mean [SD] age, 52.7 [9.6] years), systolic blood pressure at 12 months was greater in the EHR-alone group compared with the usual care group by 3.6 mm Hg (95% CI, 0.3 to 6.9 mm Hg). Systolic blood pressure in the EHR plus education group was not significantly lower compared with the usual care group (difference, -2.0 mm Hg; 95% CI, -5.2 to 1.3 mm Hg) but was lower compared with the EHR-alone group (-5.6 mm Hg; 95% CI, -8.8 to -2.4 mm Hg). At 12 months, hypertension medication reconciliation was improved in the EHR-alone group (adjusted odds ratio [OR], 1.8; 95% CI, 1.1 to 2.9) and the EHR plus education group (adjusted odds ratio [OR], 2.0; 95% CI, 1.3 to 3.3) compared with usual care. Understanding of medication instructions and dosing was greater in the EHR plus education group than the usual care group for hypertension medications (OR, 2.3; 95% CI, 1.1 to 4.8) and all medications combined (OR, 1.7; 95% CI, 1.0 to 2.8). Compared with usual care, the EHR tools alone and EHR plus education interventions did not improve hypertension medication adherence (OR, 0.9; 95% CI, 0.6-1.4 for both) or knowledge of chronic drug indications (OR for EHR tools alone, 1.0 [95% CI, 0.6 to 1.5] and OR for EHR plus education, 1.1 [95% CI, 0.7-1.7]). Conclusions and Relevance:The study found that EHR tools in isolation improved medication reconciliation but worsened blood pressure. Combining these tools with nurse-led support suggested improved understanding of medication instructions and dosing but did not lower blood pressure compared with usual care. Trial Registration:ClinicalTrials.gov identifier: NCT01578577.

Dataset Information

Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data.

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Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data.

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