Biomarkers Correspond with Echocardiographic Phenotypes in Heart Failure with Preserved Ejection Fraction: A Secondary Analysis of the RELAX Trial.

Background: Little is known about the relationship between structural phenotypes in in heart failure with preserved ejection fraction (HFpEF) and cardiac biomarkers. We used cluster analysis to identify cardiac structural phenotypes and their relationships to biomarkers in HFpEF. Methods and results: Latent class analysis (LCA) was applied to echocardiographic data including left atrial enlargement (LAE), diastolic dysfunction (DD), E/e', EF≤55%, and right ventricular dysfunction from 216 patients enrolled in the RELAX trial. Three structural phenotypes were identified. Phenotype A had the most grade II DD. Phenotype B had the most grade III DD, worst LAE, elevated E/e' and right ventricular dysfunction. Phenotype C had the least DD and moderate LAE. Phenotypes B and C had prevalent atrial fibrillation (AF). Phenotype B patients had increased carboxy-terminal telopeptide of collagen type I (CITP), cystatin-c (CYSTC), endothelin-1 (ET1), NT-proBNP, and high-sensitivity troponin I (TNI). Type A had the next highest CITP and CYSTC levels while Type C had next highest NT-proBNP. Conclusions: Structural HFpEF phenotypes demonstrated different characteristics including cardiac biomarkers. These findings may help explain phenotype-specific differences in natural history and prognosis, and they may represent phenotype-specific pathophysiology that could be amenable to targeted therapy.

Implementation of clopidogrel pharmacogenetic clinical decision support for a preemptive return of results program.

PURPOSE: To describe our experiences implementing and iterating CYP2C19 genotype-guided clopidogrel pharmacogenetic clinical decision support (CDS) tools over time in the setting of a large health system-wide, preemptive pharmacogenomics program. SUMMARY: Clopidogrel-treated patients who are genetically predicted cytochrome P450 isozyme 2C19 (CYP2C19) intermediate or poor metabolizers have an increased risk of atherothrombotic events, some of which can be life-threatening. The Clinical Pharmacogenetics Implementation Consortium provides guidance for the use of clopidogrel based on CYP2C19 genotype in patients with cardiovascular and cerebrovascular diseases. Our multidisciplinary team implemented an automated, interruptive alert that fires when clopidogrel is ordered or refilled for biobank participants with structured CYP2C19 intermediate or poor metabolizer genomic indicators in the electronic health record. The implementation began with a narrow cardiovascular indication and setting and was then scaled in 4 primary dimensions: (1) clinical indication; (2) availability across health-system locations; (3) care venue (e.g., inpatient vs outpatient); and (4) provider groups (eg, cardiology and neurology). We iterated our approach over time based on evolving clinical evidence and proactive strategies to optimize CDS maintenance and sustainability. A key facilitator of expansion was socialization of the broader pharmacogenomics initiative among our academic medical center community, accompanied by clinician acceptance of pharmacogenetic alerts in practice. CONCLUSION: A multidisciplinary collaboration is recommended to facilitate the use of CYP2C19 genotype-guided antiplatelet therapy in patients with cardiovascular and cerebrovascular diseases. Evolving clopidogrel pharmacogenetic evidence necessitates thoughtful iteration of implementation efforts and strategies to optimize long-term maintenance and sustainability.

Patient-Level Exposure to Actionable Pharmacogenomic Medications in a Nationally Representative Insurance Claims Database.

BACKGROUND: The prevalence of exposure to pharmacogenomic medications is well established but little is known about how long patients are exposed to these medications. AIM: Our objective was to describe the amount of exposure to actionable pharmacogenomic medications using patient-level measures among a large nationally representative population using an insurance claims database. METHODS: Our retrospective cohort study included adults (18+ years) from the IQVIA PharMetrics® Plus for Academics claims database with incident fills of 72 Clinical Pharmacogenetics Implementation Consortium level A, A/B, or B medications from January 2012 through September 2018. Patient-level outcomes included the proportion of days covered (PDC), number of fills, and average days supplied per fill over a 12-month period. RESULTS: Over 1 million fills of pharmacogenetic medications were identified for 605,355 unique patients. The mean PDC for all medications was 0.21 (SD 0.3), suggesting patients were exposed 21% (77 days) of the year. Medications with the highest PDC (0.55-0.89) included ivacaftor, tamoxifen, clopidogrel, HIV medications, transplant medications, and statins; with the exception of statins, these medications were initiated by fewer patients. Pharmacogenomic medications were filled an average of 2.8 times (SD 3.0, range 1-81) during the year following the medication's initiation, and the average days supplied for each fill was 22.3 days (SD 22.4, range 1-180 days). CONCLUSION: Patient characteristics associated with more medication exposure were male sex, older age, and comorbid chronic conditions. Prescription fill data provide patient-level exposure metrics that can further our understanding of pharmacogenomic medication utilization and help inform opportunities for pharmacogenomic testing.

Sustained Effect of Clinical Decision Support for Heart Failure: A Natural Experiment Using Implementation Science.

OBJECTIVES: In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of ß blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. METHODS: In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). RESULTS: There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. CONCLUSION: Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.

An extensive ß1-adrenergic receptor gene signaling network regulates molecular remodeling in dilated cardiomyopathies.

We investigated the extent, biologic characterization, phenotypic specificity, and possible regulation of a ß1-adrenergic receptor-linked (ß1-AR-linked) gene signaling network (ß1-GSN) involved in left ventricular (LV) eccentric pathologic remodeling. A 430-member ß1-GSN was identified by mRNA expression in transgenic mice overexpressing human ß1-ARs or from literature curation, which exhibited opposite directional behavior in interventricular septum endomyocardial biopsies taken from patients with beta-blocker-treated, reverse remodeled dilated cardiomyopathies. With reverse remodeling, the major biologic categories and percentage of the dominant directional change were as follows: metabolic (19.3%, 81% upregulated); gene regulation (14.9%, 78% upregulated); extracellular matrix/fibrosis (9.1%, 92% downregulated); and cell homeostasis (13.3%, 60% upregulated). Regarding the comparison of ß1-GSN categories with expression from 19,243 nonnetwork genes, phenotypic selection for major ß1-GSN categories was exhibited for LV end systolic volume (contractility measure), ejection fraction (remodeling index), and pulmonary wedge pressure (wall tension surrogate), beginning at 3 months and persisting to study completion at 12 months. In addition, 121 lncRNAs were identified as possibly involved in cis-acting regulation of ß1-GSN members. We conclude that an extensive 430-member gene network downstream from the ß1-AR is involved in pathologic ventricular remodeling, with metabolic genes as the most prevalent category.

A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure.

Introduction/background: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing. Methods: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing. Results: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use. Discussion/conclusions: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.

Ventricular Arrhythmias Associated With Over-the-Counter and Recreational Opioids.

BACKGROUND: Epidemic increases in opioid deaths prompted policies limiting access to prescription opioids in North America. Consequently, the over-the-counter opioids loperamide (Imodium A-D) and mitragynine, the herbal ingredient in kratom, are increasingly used to avert withdrawal or induce euphoria. Arrhythmia events related to these nonscheduled drugs have not been systematically studied. OBJECTIVES: In this study, we sought to explore opioid-associated arrhythmia reporting in North America. METHODS: The U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), Center for Food Safety and Applied Nutrition Adverse Event Reporting System (CAERS), and Canada Vigilance Adverse Reaction (CVAR) databases were searched (2015-2021). Reports involving nonprescription drugs (loperamide, mitragynine) and diphenoxylate/atropine (Lomotil) were identified. Methadone, a prescription opioid (full agonist), served as a positive control owing to its established arrhythmia risk. Buprenorphine (partial agonist) and naltrexone (pure antagonist), served as negative controls. Reports were classified according to Medical Dictionary for Regulatory Activities terminology. Significant disproportionate reporting required a proportional reporting ratio (PRR) of ≥2, ≥3 cases, and chi-square ≥4. Primary analysis used FAERS data, whereas CAERS and CVAR data were confirmatory. RESULTS: Methadone was disproportionately associated with ventricular arrhythmia reports (PRR: 6.6; 95% CI: 6.2-7.0; n = 1,163; chi-square = 5,456), including 852 (73%) fatalities. Loperamide was also significantly associated with arrhythmia (PRR: 3.2; 95% CI: 3.0-3.4; n = 1,008; chi-square = 1,537), including 371 (37%) deaths. Mitragynine demonstrated the highest signal (PRR: 8.9; 95% CI: 6.7-11.7; n = 46; chi-square = 315), with 42 (91%) deaths. Buprenorphine, diphenoxylate, and naltrexone were not associated with arrhythmia. Signals were similar in CVAR and CAERS. CONCLUSIONS: The nonprescription drugs loperamide and mitragynine are associated with disproportionate reports of life-threatening ventricular arrhythmia in North America.

Pharmacogenetic testing among patients with depression in a US managed care population.

Actionable drug-gene pairs relevant to depression treatment include CYP2D6 and CYP2C19 with specific antidepressants. While clinical use of pharmacogenetic testing is growing, little is known about pharmacogenetic testing for depression treatment in managed care. We determined the incidence of single-gene CYP2D6 and CYP2C19 testing following a new depression episode among US managed care patients, and described characteristics and antidepressant use of patients receiving tests. We used paid medical and pharmacy claims for patients from commercial health plans in the US. For adult patients with a new depression episode from January 1, 2013 to June 30, 2018, we identified covered claims for single-gene CYP2D6 and CYP2C19 pharmacogenetic tests and antidepressant fills. Fewer than 1% (n = 1795) of the depressed cohort (n = 438,534) received a single-gene CYP2D6 or CYP2C19 test through their insurance within 365 days of their earliest depression episode. The percentage of patients who received a test nearly tripled from 0.2% in 2013 to 0.5% in 2014 before plateauing at 0.4% from 2014 to 2017. Among the patients who received a single-gene CYP2D6 or CYP2C19 test and filled an antidepressant within 365 days of their depression diagnosis, up to 30% may have had their initial antidepressant informed by the test result. Our findings describe the use of antidepressants before and after pharmacogenetic testing, which is clinically relevant as pharmacogenomic testing becomes more common in clinical practice. Our study also emphasizes the need for procedure and billing codes that capture multiple-gene panel tests to be more widely implemented in administrative databases.

Electronic Health Records and Heart Failure.

Increasing the global adoption of electronic health records (EHRs) is transforming the delivery of clinical care. EHRs offer tools that are useful in the care of heart failure ranging from individualized risk stratification and decision support to population management. EHR tools can be combined to target specific areas of need such as the standardization of care, improved quality of care, and resource management. Leveraging EHR functionality has been shown to improve select outcomes including guideline-based therapies, reduction in adverse clinical outcomes, and improved cost-efficiency. Central to success is participation by clinicians and patients in the design and feedback of EHR tools.

Heart rate as an early predictor of severe cardiomyopathy and increased mortality in peripartum cardiomyopathy.

BACKGROUND: Delays in diagnosis of peripartum cardiomyopathy (PPCM) are common and are associated with worse outcomes; however, few studies have addressed methods for improving early detection. HYPOTHESIS: We hypothesized that easily accessible data (heart rate [HR] and electrocardiograms [ECGs]) could identify women with more severe PPCM and at increased risk of adverse outcomes. METHODS: Clinical data, including HR and ECG, from patients diagnosed with PPCM between January 1998 and July 2016 at our institution were collected and analyzed. Linear and logistic regression were used to analyze the relationship between HR at diagnosis and the left ventricular ejection fraction (LVEF) at diagnosis. Outcomes included overall mortality, recovery status, and major adverse cardiac events. RESULTS: Among 82 patients meeting inclusion criteria, the overall mean LVEF at diagnosis was 26 ± 11.1%. Sinus tachycardia (HR > 100) was present in a total of 50 patients (60.9%) at the time of diagnosis. In linear regression, HR significantly predicted lower LVEF (F = 30.00, p < .0001). With age-adjusted logistic regression, elevated HR at diagnosis was associated with a fivefold higher risk of overall mortality when initial HR was >110 beats per minute (adjusted odds ratio 5.35, confidence interval 1.23-23.28), p = .025). CONCLUSION: In this study, sinus tachycardia in women with PPCM was associated with lower LVEF at the time of diagnosis. Tachycardia in the peripartum period should raise concern for cardiomyopathy and may be an early indicator of adverse prognosis.

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

OBJECTIVE: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. MATERIALS AND METHODS: We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. DISCUSSION: Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. CONCLUSION: We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

The Addition of a Defibrillator to Resynchronization Therapy Decreases Mortality in Patients With Nonischemic Cardiomyopathy.

OBJECTIVES: The aim of this study was to determine whether patients with heart failure with reduced ejection fraction (HFrEF) due to nonischemic etiology eligible for cardiac resynchronization therapy (CRT) benefit from an implantable cardioverter-defibrillator (ICD). BACKGROUND: It is uncertain whether CRT with an ICD (CRT-D) compared to without an ICD (CRT-P) is associated with a survival benefit in patients with nonischemic etiologies of HFrEF. METHODS: Analyses of the COMPANION (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure) trial were performed, using Cox proportional hazards modeling stratified by HFrEF etiology of nonischemic cardiomyopathy (NICM) or ischemic cardiomyopathy (ICM). The primary outcome was all-cause mortality (ACM), and secondary outcomes were the combination of cardiovascular mortality or heart failure hospitalization and sudden cardiac death. RESULTS: Among patients randomized to CRT (n = 1,212), 236 (19.5%) died, 131 and 105 in the CRT-P and CRT-D arms, respectively. The unadjusted and adjusted hazard ratios (HRs) for CRT-D versus CRT-P were both 0.84 (95% confidence interval [CI]: 0.65 to 1.09) for ACM, with a significant device-etiology interaction (pinteraction = 0.015 adjusted; pinteraction = 0.040 unadjusted). In patients with NICM (n = 555), CRT-D versus CRT-P was associated with reduced ACM (adjusted HR: 0.54; 95% CI: 0.34 to 0.86), while patients with ICM (n = 657) did not exhibit a between-device reduction in ACM (adjusted HR: 1.05; 95% CI: 0.77 to 1.44). The effects of CRT-D versus CRT-P on sudden cardiac death (advantage CRT-D) and cardiovascular mortality or heart failure hospitalization (no difference between CRT-P and CRT-D) were similar between the 2 HFrEF etiologies. CONCLUSIONS: COMPANION patients with NICM exhibited a decrease in ACM associated with CRT-D but not CRT-P treatment, whereas patients with ICM did not.

Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial.

BACKGROUND: Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations. OBJECTIVE: This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool. METHODS: We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM's evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach. RESULTS: Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert. CONCLUSIONS: The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool. TRIAL REGISTRATION: ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke. METHODS: We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC. RESULTS: We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS. CONCLUSIONS: Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

BACKGROUND: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality. RESEARCH QUESTIONS: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA. STUDY DESIGN AND METHODS: We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. INTERPRETATION: We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA. TAKE HOME POINTS: Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.

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.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Host methylation predicts SARS-CoV-2 infection and clinical outcome.

BACKGROUND: Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual's collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity.