Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction.

OBJECTIVE: The study aims to investigate whether machine learning-based predictive models for cardiovascular disease (CVD) risk assessment show equivalent performance across demographic groups (such as race and gender) and if bias mitigation methods can reduce any bias present in the models. This is important as systematic bias may be introduced when collecting and preprocessing health data, which could affect the performance of the models on certain demographic sub-cohorts. The study is to investigate this using electronic health records data and various machine learning models. METHODS: The study used large de-identified Electronic Health Records data from Vanderbilt University Medical Center. Machine learning (ML) algorithms including logistic regression, random forest, gradient-boosting trees, and long short-term memory were applied to build multiple predictive models. Model bias and fairness were evaluated using equal opportunity difference (EOD, 0 indicates fairness) and disparate impact (DI, 1 indicates fairness). In our study, we also evaluated the fairness of a non-ML baseline model, the American Heart Association (AHA) Pooled Cohort Risk Equations (PCEs). Moreover, we compared the performance of three different de-biasing methods: removing protected attributes (e.g., race and gender), resampling the imbalanced training dataset by sample size, and resampling by the proportion of people with CVD outcomes. RESULTS: The study cohort included 109,490 individuals (mean [SD] age 47.4 [14.7] years; 64.5% female; 86.3% White; 13.7% Black). The experimental results suggested that most ML models had smaller EOD and DI than PCEs. For ML models, the mean EOD ranged from -0.001 to 0.018 and the mean DI ranged from 1.037 to 1.094 across race groups. There was a larger EOD and DI across gender groups, with EOD ranging from 0.131 to 0.136 and DI ranging from 1.535 to 1.587. For debiasing methods, removing protected attributes didn't significantly reduced the bias for most ML models. Resampling by sample size also didn't consistently decrease bias. Resampling by case proportion reduced the EOD and DI for gender groups but slightly reduced accuracy in many cases. CONCLUSIONS: Among the VUMC cohort, both PCEs and ML models were biased against women, suggesting the need to investigate and correct gender disparities in CVD risk prediction. Resampling by proportion reduced the bias for gender groups but not for race groups.

Evaluating the extent of reusability of CYP2C19 genotype data among patients genotyped for antiplatelet therapy selection.

PURPOSE: Genotype-guided antiplatelet therapy is increasingly being incorporated into clinical care. The purpose of this study is to determine the extent to which patients initially genotyped for CYP2C19 to guide antiplatelet therapy were prescribed additional medications affected by CYP2C19. METHODS: We assembled a cohort of patients from eight sites performingCYP2C19 genotyping to inform antiplatelet therapy. Medication orders were evaluated from time of genotyping through one year. The primary endpoint was the proportion of patients prescribed two or more CYP2C19 substrates. Secondary endpoints were the proportion of patients with a drug-genotype interaction and time to receiving a CYP2C19 substrate. RESULTS: Nine thousand one hundred ninety-one genotyped patients (17% nonwhite) with a mean age of 68 ± 3 years were evaluated; 4701 (51%) of patients received two or more CYP2C19 substrates and 3835 (42%) of patients had a drug-genotype interaction. The average time between genotyping and CYP2C19 substrate other than antiplatelet therapy was 25 ± 10 days. CONCLUSIONS: More than half of patients genotyped in the setting of CYP2C19-guided antiplatelet therapy received another medication impacted by CYP2C19 in the following year. Given that genotype is stable for a patient's lifetime, this finding has implications for cost effectiveness, patient care, and treatment outcomes beyond the indication for which it was originally performed.

Correction: Evaluating the extent of reusability of CYP2C19 genotype data among patients genotyped for antiplatelet therapy selection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Direct magnetic resonance detection of myelin and prospects for quantitative imaging of myelin density.

Magnetic resonance imaging has previously demonstrated its potential for indirectly mapping myelin density, either by relaxometric detection of myelin water or magnetization transfer. Here, we investigated whether myelin can be detected and possibly quantified directly. We identified the spectrum of myelin in the spinal cord in situ as well as in myelin lipids extracted via a sucrose gradient method, and investigated its spectral properties. High-resolution solution NMR spectroscopy showed the extract composition to be in agreement with myelin's known chemical make-up. The 400-MHz (1)H spectrum of the myelin extract, at 20 °C (room temperature) and 37 °C, consists of a narrow water resonance superimposed on a broad envelope shifted ∼3.5 ppm upfield, suggestive of long-chain methylene protons. Superimposed on this signal are narrow components resulting from functional groups matching the chemical shifts of the constituents making up myelin lipids. The spectrum could be modeled as a sum of super-Lorentzians with a T(2)* distribution covering a wide range of values (0.008-26 ms). Overall, there was a high degree of similarity between the spectral properties of extracted myelin lipids and those found in neural tissue. The normalized difference spectrum had the hallmarks of membrane proteins, not present in the myelin extract. Using 3D radially ramp-sampled proton MRI, with a combination of adiabatic inversion and echo subtraction, the feasibility of direct myelin imaging in situ is demonstrated. Last, the integrated signal from myelin suspensions is shown, both spectroscopically and by imaging, to scale with concentration, suggesting the potential for quantitative determination of myelin density.

Individualized Dose-Response to Statins Associated with Cardiovascular Disease Outcomes.

Background: Statins reduce low-density lipoprotein cholesterol (LDL-C) and are efficacious in the prevention of atherosclerotic cardiovascular disease (ASCVD). Dose-response to statins varies among patients and can be modeled using three distinct pharmacological properties: (1) E0 (baseline LDL-C), (2) ED50 (potency: median dose achieving 50% reduction in LDL-C); and (3) Emax (efficacy: maximum LDL-C reduction). However, individualized dose-response and its association with ASCVD events remains unknown. Objective: We analyze the relationship between ED50 and Emax with real-world cardiovascular disease outcomes. Method: We leveraged de-identified electronic health record data to identify individuals exposed to multiple doses of the three most commonly prescribed statins (atorvastatin, simvastatin, or rosuvastatin) within the context of their longitudinal healthcare. We derived ED50 and Emax to quantify the relationship with a composite outcome of ASCVD events and all-cause mortality. Results: We estimated ED50 and Emax for 3,033 unique individuals (atorvastatin: 1,632, simvastatin: 1,089, and rosuvastatin: 312) using a nonlinear, mixed effects dose-response model. Time-to-event analyses revealed that ED50 and Emax are independently associated with the primary endpoint. Hazard ratios were 0.85 (p < 0.01), 0.83 (p < 0.01), and 0.87 (p = 0.10) for ED50 and 1.13 (p < 0.001), 1.06 (p < 0.001), and 1.15 (p = 0.009) for Emax in the atorvastatin, simvastatin, and rosuvastatin cohorts, respectively. Conclusion: The class-wide association of ED50 and Emax with clinical outcomes indicates that these measures influence the risk for ASCVD events in patients on statins.

Sex and Gender Variables in Data Set Creation and Data Cleaning for Inclusive and Accurate Reproductive Health Research and Quality Improvement.

INTRODUCTION: Existing data is often used for reproductive research and quality improvement. Electronic health records (EHRs) with a single data field for sex and gender conflate sex assigned at birth, genotype, gender identity, and the presence of anatomic tissue and organs. This is problematic for inclusion of transgender and gender-diverse populations in research. This article discusses considerations with a single-item sex and gender variable drawn from EHR records and describes an audit to determine variable validity as a criterion for inclusion or exclusion in perinatal research. METHODS: Individuals with a live birth at a large academic medical center from 2010 to 2022 were identified via electronic query, and records with male demographic information were reviewed to validate (1) the patient's date of birth and delivery date in the EHR matched the medical record number, (2) male sex and gender demographic information, and (3) male gender terms in EHR notes. RESULTS: All health records of male birthing individuals (n = 8) had EHR evidence of giving birth within the health system during the timeframe, and the date of birth matched the medical record number of the EHR. All had male gender in the EHR demographic information. Six patients did not have any male gender terms in available EHR notes, only female gender terms. Two records had recent notes using male gender terms. DISCUSSION: Current EHRs may not have reliable data on the gender and sex of gender-diverse individuals. A single sex and gender variable drawn from EHRs should not be used as inclusion or exclusion criteria for health research or quality improvement without additional record review. EHRs can be updated to collect more data on sex, gender identity, and other relevant variables to improve research and quality improvement.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records.

OBJECTIVE: Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. MATERIALS AND METHODS: We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. RESULTS: The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001). DISCUSSION: We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. CONCLUSION: Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Performance of Phenotype Algorithms for the Identification of Opioid-Exposed Infants.

OBJECTIVE: Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record data. METHODS: We developed phenotype algorithms for the identification of opioid-exposed infants among a population of birthing person-infant dyads from an academic health care system (2010-2022). We derived phenotype algorithms from combinations of 6 unique indicators of in utero opioid exposure, including those from the infant record (NOWS or opioid-exposure diagnosis, positive toxicology) and birthing person record (opioid use disorder diagnosis, opioid drug exposure record, opioid listed on medication reconciliation, positive toxicology). We determined the positive predictive value (PPV) and 95% confidence interval for each phenotype algorithm using medical record review as the gold standard. RESULTS: Among 41 047 dyads meeting exclusion criteria, we identified 1558 infants (3.80%) with evidence of at least 1 indicator for opioid exposure and 32 (0.08%) meeting all 6 indicators of the phenotype algorithm. Among the sample of dyads randomly selected for review (n = 600), the PPV for the phenotype requiring only a single indicator was 95.4% (confidence interval: 93.3-96.8) with varying PPVs for the other phenotype algorithms derived from a combination of infant and birthing person indicators (PPV range: 95.4-100.0). CONCLUSIONS: Opioid-exposed infants can be accurately identified using electronic health record data. Our publicly available phenotype algorithms can be used to conduct research examining outcomes among opioid-exposed infants with and without NOWS.

Large language models facilitate the generation of electronic health record phenotyping algorithms.

OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms.

Objectives: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

TARGETING SOLUBLE AMYLOID-BETA OLIGOMERS WITH A NOVEL NANOBODY.

The classical amyloid cascade hypothesis postulates that the aggregation of amyloid plaques and the accumulation of intracellular hyperphosphorylated Tau tangles, together, lead to profound neuronal death. However, emerging research has demonstrated that soluble amyloid-ß oligomers (SAßOs) accumulate early, prior to amyloid plaque formation. SAßOs induce memory impairment and disrupt cognitive function independent of amyloid-ß plaques, and even in the absence of plaque formation. This work describes the development and characterization of a novel anti-SAßO (E3) nanobody generated from an alpaca immunized with SAßO. In-vitro assays and in-vivo studies using 5XFAD mice indicate that the fluorescein (FAM)-labeled E3 nanobody recognizes both SAßOs and amyloid-ß plaques. The E3 nanobody traverses across the blood-brain barrier and binds to amyloid species in the brain of 5XFAD mice. Imaging of mouse brains reveals that SAßO and amyloid-ß plaques are not only different in size, shape, and morphology, but also have a distinct spatial distribution in the brain. SAßOs are associated with neurons, while amyloid plaques reside in the extracellular matrix. The results of this study demonstrate that the SAßO nanobody can serve as a diagnostic agent with potential theragnostic applications in Alzheimer's disease.

Targeting soluble amyloid-beta oligomers with a novel nanobody.

The classical amyloid cascade hypothesis postulates that the aggregation of amyloid plaques and the accumulation of intracellular hyperphosphorylated Tau tangles, together, lead to profound neuronal death. However, emerging research has demonstrated that soluble amyloid-ß oligomers (SAßOs) accumulate early, prior to amyloid plaque formation. SAßOs induce memory impairment and disrupt cognitive function independent of amyloid-ß plaques, and even in the absence of plaque formation. This work describes the development and characterization of a novel anti-SAßO (E3) nanobody generated from an alpaca immunized with SAßO. In-vitro assays and in-vivo studies using 5XFAD mice indicate that the fluorescein (FAM)-labeled E3 nanobody recognizes both SAßOs and amyloid-ß plaques. The E3 nanobody traverses across the blood-brain barrier and binds to amyloid species in the brain of 5XFAD mice. Imaging of mouse brains reveals that SAßO and amyloid-ß plaques are not only different in size, shape, and morphology, but also have a distinct spatial distribution in the brain. SAßOs are associated with neurons, while amyloid plaques reside in the extracellular matrix. The results of this study demonstrate that the SAßO nanobody can serve as a diagnostic agent with potential theragnostic applications in Alzheimer's disease.

31P NMR relaxation of cortical bone mineral at multiple magnetic field strengths and levels of demineralization.

Recent work has shown that solid-state (1) H and (31) P MRI can provide detailed insight into bone matrix and mineral properties, thereby potentially enabling differentiation of osteoporosis from osteomalacia. However, (31) P MRI of bone mineral is hampered by unfavorable relaxation properties. Hence, accurate knowledge of these properties is critical to optimizing MRI of bone phosphorus. In this work, (31) P MRI signal-to-noise ratio (SNR) was predicted on the basis of T1 and T2 * (effective transverse relaxation time) measured in lamb bone at six field strengths (1.5-11.7 T) and subsequently verified by 3D ultra-short echo-time and zero echo-time imaging. Further, T1 was measured in deuterium-exchanged bone and partially demineralized bone. (31) P T2 * was found to decrease from 220.3 ± 4.3 µs to 98.0 ± 1.4 µs from 1.5 to 11.7 T, and T1 to increase from 12.8 ± 0.5 s to 97.3 ± 6.4 s. Deuteron substitution of exchangeable water showed that 76% of the (31) P longitudinal relaxation rate is due to (1) H-(31) P dipolar interactions. Lastly, hypomineralization was found to decrease T1, which may have implications for (31) P MRI based mineralization density quantification. Despite the steep decrease in the T2 */T1 ratio, SNR should increase with field strength as B0 (0.4) for sample-dominated noise and as B0 (1.1) for coil-dominated noise. This was confirmed by imaging experiments.

Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.

OBJECTIVE: A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. MATERIALS AND METHODS: We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement. RESULTS: Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration. CONCLUSIONS: Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.

Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records.

Objective: Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. Materials and Methods: We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. Results: The Peds-Phecodes aggregate 15,533 ICD-9-CM codes and 82,949 ICD-10-CM codes into 2,051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001). Discussion: We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. Conclusion: Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID.

Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.

Design and rationale of GUARDD-US: A pragmatic, randomized trial of genetic testing for APOL1 and pharmacogenomic predictors of antihypertensive efficacy in patients with hypertension.

RATIONALE AND OBJECTIVE: APOL1 risk alleles are associated with increased cardiovascular and chronic kidney disease (CKD) risk. It is unknown whether knowledge of APOL1 risk status motivates patients and providers to attain recommended blood pressure (BP) targets to reduce cardiovascular disease. STUDY DESIGN: Multicenter, pragmatic, randomized controlled clinical trial. SETTING AND PARTICIPANTS: 6650 individuals with African ancestry and hypertension from 13 health systems. INTERVENTION: APOL1 genotyping with clinical decision support (CDS) results are returned to participants and providers immediately (intervention) or at 6 months (control). A subset of participants are re-randomized to pharmacogenomic testing for relevant antihypertensive medications (pharmacogenomic sub-study). CDS alerts encourage appropriate CKD screening and antihypertensive agent use. OUTCOMES: Blood pressure and surveys are assessed at baseline, 3 and 6 months. The primary outcome is change in systolic BP from enrollment to 3 months in individuals with two APOL1 risk alleles. Secondary outcomes include new diagnoses of CKD, systolic blood pressure at 6 months, diastolic BP, and survey results. The pharmacogenomic sub-study will evaluate the relationship of pharmacogenomic genotype and change in systolic BP between baseline and 3 months. RESULTS: To date, the trial has enrolled 3423 participants. CONCLUSIONS: The effect of patient and provider knowledge of APOL1 genotype on systolic blood pressure has not been well-studied. GUARDD-US addresses whether blood pressure improves when patients and providers have this information. GUARDD-US provides a CDS framework for primary care and specialty clinics to incorporate APOL1 genetic risk and pharmacogenomic prescribing in the electronic health record. TRIAL REGISTRATION: ClinicalTrials.govNCT04191824.

Quantifying cortical bone water in vivo by three-dimensional ultra-short echo-time MRI.

Bone contains a significant fraction of water that is not detectable with ordinary Cartesian imaging sequences. The advent of ultra-short echo-time (UTE) methods has allowed the recovery of this submillisecond T(2)* water. In this work, we have developed a new three-dimensional hybrid-radial ultra-short echo-time (3D HRUTE) imaging technique based on slab selection by means of half-sinc pulses, variable-TE slice encoding and algorithms for quantification. The protocol consists of collecting two datasets differing in TR, from which T(1) is extracted, which is needed for quantification. Unlike T(2)*, which has been found to vary within a narrow range and does not require individual correction, T(1) is critically subject dependent (range, 100-350 ms). No soft-tissue suppression was used to preserve the signal-to-noise ratio of the short-T(2) bone water protons or to minimize the loss of relatively mobile water in large pores. Critical for quantification is correction for spatial variations in reception field and selection of the endosteal boundary for inclusion of pixels in the bone water calculation, because of the ruffled boundary stemming from trabecularization of the endosteal surface. The reproducibility, evaluated in 10 subjects covering the age range 30-80 years, yielded an average coefficient of variation of 4.2% and an intraclass correlation coefficient of 0.95, suggesting that a treatment effect on the order of 5% could be detected in as few as 10 subjects. Lastly, experiments in specimens by means of graded deuterium exchange showed that approximately 90% of the detected signal arises from water protons, whose relaxation rates (1/T(1) and 1/T(2)*) scale linearly with the isotopic volume fraction of light water after stepwise exchange with heavy water. The data thus show conclusively that the method quantifies water even though, in vivo, no distinction can be made between various fractions, such as collagen-bound vs pore-resident water.

Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project's strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study's implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

Opportunity for Genotype-Guided Prescribing Among Adult Patients in 11 US Health Systems.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene-drug pairs as "level A," for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)-affiliated health systems across the US. Inpatient and/or outpatient electronic-prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658-15,781) in 2011 to 17,335 (CI: 17,283-17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632-7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype-guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.