Browsing by Author "Kimmel, Stephen E."

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    Developing a Common Framework for Evaluating the Implementation of Genomic Medicine Interventions in Clinical Care: The IGNITE Network’s Common Measures Working Group
    (Nature Publishing group, 2018-06) Orlando, Lori A.; Sperber, Nina R.; Voils, Corrine; Nichols, Marshall; Myers, Rachel A.; Wu, R. Ryanne; Rakhra-Burris, Tejinder; Levy, Kenneth D.; Levy, Mia; Pollin, Toni I.; Guan, Yue; Horowitz, Carol R.; Ramos, Michelle; Kimmel, Stephen E.; McDonough, Caitrin W.; Madden, Ebony B.; Damschroder, Laura J.; Medicine, School of Medicine
    Purpose Implementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing GeNomics In PracTicE (IGNITE) Network’s efforts to promote: 1) a broader understanding of genomic medicine implementation research; and 2) the sharing of knowledge generated in the network. Methods To facilitate this goal the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide their approach to: identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross network analyses. Results CMG identified ten high-priority CFIR constructs as important for genomic medicine. Of those, eight didn’t have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model. Conclusion We developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.
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    Development and Validation of a Seizure Prediction Model in Neonates Following Cardiac Surgery
    (Elsevier, 2020) Naim, Maryam Y.; Putt, Mary; Abend, Nicholas S.; Mastropietro, Christopher W.; Frank, Deborah U.; Chen, Jonathan M.; Fuller, Stephanie; Gangemi, James J.; Gaynor, J. William; Heinan, Kristin; Licht, Daniel J.; Mascio, Christopher E.; Massey, Shavonne; Roeser, Mark E.; Smith, Clyde J.; Kimmel, Stephen E.; Pediatrics, School of Medicine
    BACKGROUND Electroencephalographic seizures (ES) following neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates. METHODS Development and validation of ES prediction models in a multi-center prospective cohort where all postoperative neonates with cardiopulmonary bypass (CPB) underwent CEEG. RESULTS ES occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single ventricle defect, DHCA duration, cardiac arrest, nitric oxide, ECMO, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic 0.77, Hosmer-Lemeshow p=0.56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, p=0.60); however, discrimination was poor (c-statistic 0.61) and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. Using a cut-point that emphasized negative predictive value (NPV) in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) with ES (NPV 99%, sensitivity 97%). CONCLUSIONS In this large prospective cohort, a prediction model of ES in neonates following CPB had good performance in the derivation cohort with a NB in DCA. However, performance in the validation cohort was weak with poor discrimination, calibration, and no NB in DCA. These findings support CEEG monitoring of all neonates following CPB.
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    Multisite Investigation of Outcomes With Implementation of CYP2C19 Genotype-Guided Antiplatelet Therapy After Percutaneous Coronary Intervention
    (Elsevier, 2018-01-22) Cavallari, Larisa H.; Lee, Craig R.; Beitelshees, Amber L.; Cooper-DeHoff, Rhonda M.; Duarte, Julio D.; Voora, Deepak; Kimmel, Stephen E.; McDonough, Caitrin W.; Gong, Yan; Dave, Chintan V.; Pratt, Victoria M.; Alestock, Tameka D.; Anderson, R. David; Alsip, Jorge; Ardati, Amer K.; Brott, Brigitta C.; Brown, Lawrence; Chumnumwat, Supatat; Clare-Salzler, Michael J.; Coons, James C.; Denny, Joshua C.; Dillon, Chrisly; Elsey, Amanda R.; Hamadeh, Issam; Harada, Shuko; Hillegass, William B.; Hines, Lindsay; Horenstein, Richard B.; Howell, Lucius A.; Jeng, Linda J.B.; Kelemen, Mark D.; Lee, Y.M.; Magvanjav, Oyunbileg; Montasser, May; Nelson, David R.; Nutescu, Edith A.; Nwaba, Devon C.; Pakyz, Ruth E.; Palmer, Kathleen; Peterson, Josh F.; Pollin, Toni I.; Quinn, Alison H.; Robinson, Shawn W.; Schub, Jamie; Skaar, Todd C.; Smith, Donald M.; Sriramoju, Vindhya B.; Starostik, Petr; Stys, Tomasz P.; Stevenson, James M.; Varunok, Nicholas; Vesely, Mark R.; Wake, Dyson T.; Weck, Karen E.; Weitzel, Kristin W.; Wilke, Russell A.; Willig, James; Zhao, Richard Y.; Kreutz, Rolf P.; Stouffer, George A.; Empey, Philip E.; Limdi, Nita A.; Shuldiner, Alan R.; Winterstein, Almut G.; Johnson, Julie A.; Medical and Molecular Genetics, School of Medicine
    OBJECTIVES: This multicenter pragmatic investigation assessed outcomes following clinical implementation of CYP2C19 genotype-guided antiplatelet therapy after percutaneous coronary intervention (PCI). BACKGROUND: CYP2C19 loss-of-function alleles impair clopidogrel effectiveness after PCI. METHODS: After clinical genotyping, each institution recommended alternative antiplatelet therapy (prasugrel, ticagrelor) in PCI patients with a loss-of-function allele. Major adverse cardiovascular events (defined as myocardial infarction, stroke, or death) within 12 months of PCI were compared between patients with a loss-of-function allele prescribed clopidogrel versus alternative therapy. Risk was also compared between patients without a loss-of-function allele and loss-of-function allele carriers prescribed alternative therapy. Cox regression was performed, adjusting for group differences with inverse probability of treatment weights. RESULTS: Among 1,815 patients, 572 (31.5%) had a loss-of-function allele. The risk for major adverse cardiovascular events was significantly higher in patients with a loss-of-function allele prescribed clopidogrel versus alternative therapy (23.4 vs. 8.7 per 100 patient-years; adjusted hazard ratio: 2.26; 95% confidence interval: 1.18 to 4.32; p = 0.013). Similar results were observed among 1,210 patients with acute coronary syndromes at the time of PCI (adjusted hazard ratio: 2.87; 95% confidence interval: 1.35 to 6.09; p = 0.013). There was no difference in major adverse cardiovascular events between patients without a loss-of-function allele and loss-of-function allele carriers prescribed alternative therapy (adjusted hazard ratio: 1.14; 95% confidence interval: 0.69 to 1.88; p = 0.60). CONCLUSIONS: These data from real-world observations demonstrate a higher risk for cardiovascular events in patients with a CYP2C19 loss-of-function allele if clopidogrel versus alternative therapy is prescribed. A future randomized study of genotype-guided antiplatelet therapy may be of value.
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    Qualitative study of system-level factors related to genomic implementation
    (Springer Nature, 2019-07) Zebrowski, Alexis M.; Ellis, Darcy E.; Barg, Frances K.; Sperber, Nina R.; Bernhardt, Barbara A.; Denny, Joshua C.; Dexter, Paul R.; Ginsburg, Geoffrey S.; Horowitz, Carol R.; Johnson, Julie A.; Levy, Mia A.; Orlando, Lori A.; Pollin, Toni I.; Skaar, Todd C.; Kimmel, Stephen E.; Medicine, School of Medicine
    PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.
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    The development and initial validation of the PROMIS®+HF‐27 and PROMIS+HF‐10 profiles
    (Wiley, 2022) Ahmad, Faraz S.; Jackson, Kathryn L.; Yount, Susan E.; Rothrock, Nan E.; Kallen, Michael A.; Lacson, Leilani; Bilimoria, Karl Y.; Kho, Abel N.; Mutharasan, Raja Kannan; McCullough, Peter A.; Bruckel, Jeffrey; Fedson, Savitri; Kimmel, Stephen E.; Eton, David T.; Grady, Kathleen L.; Yancy, Clyde W.; Cella, David; Surgery, School of Medicine
    Aims: Heart failure (HF) is a common and morbid condition impacting multiple health domains. We previously reported the development of the PROMIS®-Plus-HF (PROMIS+HF) profile measure, including universal and HF-specific items. To facilitate use, we developed shorter, PROMIS+HF profiles intended for research and clinical use. Methods and results: Candidate items were selected based on psychometric properties and symptom range coverage. HF clinicians (n = 43) rated item importance and clinical actionability. Based on these results, we developed the PROMIS+HF-27 and PROMIS+HF-10 profiles with summary scores (0-100) for overall, physical, mental, and social health. In a cross-sectional sample (n = 600), we measured internal consistency reliability (Cronbach's alpha and Spearman-Brown), test-retest reliability (intraclass coefficient; n = 100), known-groups validity via New York Heart Association (NYHA) class, and convergent validity with Kansas City Cardiomyopathy Questionnaire (KCCQ) scores. In a longitudinal sample (n = 75), we evaluated responsiveness of baseline/follow-up scores by calculating mean differences and Cohen's d and comparing with paired t-tests. Internal consistency was good to excellent (α 0.82-0.94) for all PROMIS+HF-27 scores and acceptable to good (α/Spearman-Brown 0.60-0.85) for PROMIS+HF-10 scores. Test-retest intraclass coefficients were acceptable to excellent (0.75-0.97). Both profiles demonstrated known-groups validity for the overall and physical health summary scores based on NYHA class, and convergent validity for nearly all scores compared with KCCQ scores. In the longitudinal sample, we demonstrated responsiveness for PROMIS+HF-27 and PROMIS+HF-10 overall and physical summary scores. For the PROMIS+HF overall summary scores, a group-based increase of 7.6-8.3 points represented a small to medium change (Cohen's d = 0.40-0.42). For the PROMIS+HF physical summary scores, a group-based increase of 5.0-5.9 points represented a small to medium change (Cohen's d = 0.29-0.35). Conclusions: The PROMIS+HF-27 and PROMIS+HF-10 profiles demonstrated good psychometric characteristics with evidence of responsiveness for overall and physical health. These new measures can facilitate patient-centred research and clinical care, such as improving care quality through symptom monitoring, facilitating shared decision-making, evaluating quality of care, assessing new interventions, and monitoring during the initiation and titration of guideline-directed medical therapy.