Pharmacogenomic-Based Decision Support to Predict Adherence to Medications.
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ABSTRACT:
SUBMITTER: Christian C
PROVIDER: S-EPMC7363565 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Variable responses to medications complicates perioperative care. As a potential solution, we evaluated and synthesized pharmacogenomic evidence that may inform anesthesia and pain prescribing to identify clinically actionable drug/gene pairs. Clinical decision-support (CDS) summaries were developed and were evaluated using Appraisal of Guidelines for Research and Evaluation (AGREE) II. We found that 93/180 (51%) of commonly-used perioperative medications had some published pharmacogenomic information, with 18 having actionable evidence: celecoxib/diclofenac/flurbiprofen/ibuprofen/piroxicam/CYP2C9, codeine/oxycodone/tramadol CYP2D6, desflurane/enflurane/halothane/isoflurane/sevoflurane/succinylcholine/RYR1/CACNA1S, diazepam/CYP2C19, phenytoin/CYP2C9, succinylcholine/mivacurium/BCHE, and morphine/OPRM1. Novel CDS summaries were developed for these 18 medications. AGREE II mean ± standard deviation scores were high for Scope and Purpose (95.0 ± 2.8), Rigor of Development (93.2 ± 2.8), Clarity of Presentation (87.3 ± 3.0), and Applicability (86.5 ± 3.7) (maximum score = 100). Overall mean guideline quality score was 6.7 ± 0.2 (maximum score = 7). All summaries were recommended for clinical implementation. A critical mass of pharmacogenomic evidence exists for select medications commonly used in the perioperative setting, warranting prospective examination for clinical utility.
| S-EPMC9267777 | biostudies-literature
Project description:BackgroundEvery year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.MethodsWe developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles.ResultsOur methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners.ConclusionsThe ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.
| S-EPMC4340468 | biostudies-literature
Project description:BackgroundThe development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects.ResultsWe developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities.ConclusionsThe MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine.
| S-EPMC4008421 | biostudies-literature
Project description:PurposeThis evidence-based guideline intends to support patients, clinicians, and others regarding interventions and processes to support patient adherence to oral anticancer medications (OAMs).Methodologic approachA panel of healthcare professionals and patient representatives developed a clinical practice guideline to support patients taking OAMs. GRADE (Grading of Recommendations Assessment, Development, and Evaluation) methodology and criteria for trustworthy guidelines were followed. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. A quantitative or narrative synthesis of the evidence was completed. Certainty of the evidence was assessed using GRADE.FindingsThe panel agreed on recommendations and suggested an adherence risk assessment, education addressing adherence, ongoing assessment, proactive follow-up, coaching, and motivational interviewing in addition to usual care. The panel suggested the implementation of a structured OAM program.Implications for nursingAs cancer treatment shifts from clinic to home settings, interventions and programs to support patients on OAMs are needed.
| S-EPMC9303042 | biostudies-literature
Project description:OBJECTIVES:To determine whether utilization of clinical decision support (CDS) is correlated with improved patient clinical and financial outcomes. STUDY DESIGN:Observational study of 26,424 patient encounters. In the treatment group, the provider adhered to all CDS recommendations. In the control group, the provider did not adhere to CDS recommendations. METHODS:An observational study of provider adherence to a CDS system was conducted using inpatient encounters spanning 3 years. Data comprised alert status (adherence), provider type (resident, attending), patient demographics, clinical outcomes, Medicare status, and diagnosis information. We assessed the associations between alert adherence and 4 outcome measures: encounter length of stay, odds of 30-day readmission, odds of complications of care, and total direct costs. The associations between alert adherence and the outcome measures were estimated using 4 generalized linear models that adjusted for potential confounders, such as illness severity and case complexity. RESULTS:The total encounter cost increased 7.3% (95% CI, 3.5%-11%) for nonadherent encounters versus adherent encounters. We found a 6.2% (95% CI, 3.0%-9.4%) increase in length of stay for nonadherent versus adherent encounters. The odds ratio for readmission within 30 days increased by 1.14 (95% CI, 0.998-1.31) for nonadherent versus adherent encounters. The odds ratio for complications increased by 1.29 (95% CI, 1.04-1.61) for nonadherent versus adherent encounters. CONCLUSIONS:Consistent improvements in measured outcomes were seen in the treatment group versus the control group. We recommend that provider organizations consider the introduction of real-time CDS to support adherence to evidence-based guidelines, but because we cannot determine the cause of the associations between CDS interventions and improved clinical and financial outcomes, further study is required.
| S-EPMC6813785 | biostudies-other
Project description:ObjectiveTo evaluate the individual-level impact of an electronic clinical decision support (ECDS) tool, PedsGuide, on febrile infant clinical decision making and cognitive load.MethodsA counterbalanced, prospective, crossover simulation study was performed among attending and trainee physicians. Participants performed simulated febrile infant cases with use of PedsGuide and with standard reference text. Cognitive load was assessed using the NASA-Task Load Index (NASA-TLX), which determines mental, physical, temporal demand, effort, frustration, and performance. Usability was assessed with the System Usability Scale (SUS). Scores on cases and NASA-TLX scores were compared between condition states.ResultsA total of 32 participants completed the study. Scores on febrile infant cases using PedsGuide were greater compared with standard reference text (89% vs 72%, P = .001). NASA-TLX scores were lower (ie, more optimal) with use of PedsGuide versus control (mental 6.34 vs 11.8, P < .001; physical 2.6 vs 6.1, P = .001; temporal demand 4.6 vs 8.0, P = .003; performance 4.5 vs 8.3, P < .001; effort 5.8 vs 10.7, P < .001; frustration 3.9 vs 10, P < .001). The SUS had an overall score of 88 of 100 with rating of acceptable on the acceptability scale.ConclusionsUse of PedsGuide led to increased adherence to guidelines and decreased cognitive load in febrile infant management when compared with the use of a standard reference tool. This study employs a rarely used method of assessing ECDS tools using a multifaceted approach (medical decision-making, assessing usability, and cognitive workload,) that may be used to assess other ECDS tools in the future.
| S-EPMC6732014 | biostudies-literature
Project description:Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed.
| S-EPMC6291481 | biostudies-other
Project description:To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses.
| S-EPMC6339514 | biostudies-literature
Project description:Several high-profile examples of adverse outcomes from medications used in the perioperative setting are well known (e.g., malignant hyperthermia, prolonged apnea, respiratory depression, inadequate analgesia), leading to an increased understanding of genetic susceptibilities underlying these risks. Pharmacogenomic information is increasingly being utilized in certain areas of medicine. Despite this, routine preoperative genetic screening to inform medication risk is not yet standard practice. In this review, we assess the current readiness of pharmacogenomic information for clinical consideration for several common perioperative medications, including description of key pharmacogenes, pharmacokinetic implications and potential clinical outcomes. The goal is to highlight medications for which emerging or considerable pharmacogenomic information exists and identify areas for future potential research.
| S-EPMC6949515 | biostudies-literature
Project description:Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children's Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians' adherence to 4783 pharmacogenomically guided CDS alerts that triggered for 12 genes and 60 drugs. Clinicians adhered to the therapeutic recommendations provided in 4392 alerts (92%). In our population of pediatric patients with catastrophic illnesses, the most frequently presented gene/drug CDS alerts were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications (n = 51), and CYP2C19 and proton pump inhibitors (omeprazole and pantoprazole; n = 50). The high adherence rate was facilitated by our team approach to prescribing and our collaborative CDS design and delivery.
| S-EPMC9748527 | biostudies-literature