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Exemplary Care Cutting-edge Research World-class Education Raghavan Murugan MD, MS, FRCP Associate Professor Dept. of Critical Care Medicine Clinical and Translational Science Center for Critical Care Nephrology, CRISMA University of Pittsburgh School of Medicine Intensivist, Abdominal Organ Transplant ICU University of Pittsburgh Medical Center Pittsburgh, PA, USA Exploring Heterogeneity of Treatment Effect in Intensity of RRT Trial
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Exemplary Care Cutting-edge Research World-class Education Background Severe acute kidney injury (AKI) requiring renal replacement therapy (RRT) is associated with 40%- 60% mortality Despite decades of research a number of questions remain should “one size fits all” approach in be used for all patients? Uchino S et al., JAMA 2004 Symons JM et al., CJASN 2007
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Exemplary Care Cutting-edge Research World-class Education Tailoring of medical treatment to the individual patient characteristics, needs, and preferences of each patient The concept is not new…. Clinicians use personalized medicine everyday in caring for patients to individualize therapy What is Personalized Medicine? “It’s far more important to know what person the disease has than what disease the person has” - Hippocrates
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Exemplary Care Cutting-edge Research World-class Education Why do we need Personalized Medicine in AKI? Many treatment options are available e.g., initiate or not initiate renal replacement therapy (RRT) e.g., what is the right dose of RRT? intermittent vs. continuous renal replacement therapy Heterogeneity in treatment responses Across patients: what works for one may not work for other (e.g., early, late, or no RRT) Within a patient: what works now may not work later (e.g., intermittent dialysis)
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Exemplary Care Cutting-edge Research World-class Education Personalized Medicine in AKI Goal Lower morbidity and mortality for patients by delivering the right treatment, right dose, and at the right time How do we apply personalized medicine in AKI research? Learn individualized treatment rules: tailor treatments based on patient characteristics
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Exemplary Care Cutting-edge Research World-class Education Personalized Medicine: The Basic Steps Current approaches to developing personalized medicine typically includes 5 key elements obtain key patient characteristics identify one or more biomarkers develop new or select available therapies measure the relationship between biomarkers and clinical outcomes, including prognosis and response to treatment; and verify the relationship in a clinical trial
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Exemplary Care Cutting-edge Research World-class Education Heterogeneity of Treatment Effect Analysis- Not a new concept “Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease” Sir William Osler
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Exemplary Care Cutting-edge Research World-class Education Heterogeneity of Treatment Effect (HTE) Considerable variation in risk of outcome in clinical trial population Differences in risk of outcome causes clinically important HTE across trial population Balance between “benefit” and “risk” may vary widely between patient subgroups in the trial population Conventional “single” variable subgroup analyses are often insensitive to multiple patient characteristics that affect treatment effect simultaneously HTE analysis offers a potential to understand clinical trial results
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Exemplary Care Cutting-edge Research World-class Education The problem of averages…… ‘Personalized medicine’ is all about moving away from naïve expectation that data on average treatment effect are sufficient to inform decision making for individual patients
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Exemplary Care Cutting-edge Research World-class Education Individual-specific Treatment Effects Kravitz et al. Millbank Q 2004
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Exemplary Care Cutting-edge Research World-class Education Examples of risk-based HTE
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Exemplary Care Cutting-edge Research World-class Education Artificial Pancreas fda.gov
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Exemplary Care Cutting-edge Research World-class Education Inflammatory and Apoptosis Markers and Mortality Adjusted HR (95%CI) IL-6: 1.19 (1.12-1.27) IL-8: 1.36 (1.27-1.46) IL-18: 1.27 (1.15 -1.40) TNFR-I: 1.47 (1.16-1.86) MIF: 1.26 (1.17-1.35) Murugan R, et al., Nephrol Dial Transplant (2014)
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Exemplary Care Cutting-edge Research World-class Education Biomarker concentration and intensity of RRT Murugan R, et al., CJASN (peer review)
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Exemplary Care Cutting-edge Research World-class Education Interaction between IL-6 and RRT intensity Intensive RRT Less-intensive RRT Murugan R, et al., CJASN (peer review)
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Exemplary Care Cutting-edge Research World-class Education Interaction between RRT intensity and biomarker concentration Murugan R, et al., CJASN (peer review)
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Exemplary Care Cutting-edge Research World-class Education A proposal for HTE analysis using BioMaRK Examine the distribution of risk of death and non recovery of renal function across the overall population and by treatment arm using risk prediction model incorporating baseline biomarkers. Risk-stratified subgroup analyses reporting how the RRR and ARR varies for intensive and less-intensive RRT groups. Pre-specified apriori subgroup analysis Statistical significance testing for HTE of RRT intensity across subgroups using interaction terms adjusting for no. of subgroups
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Exemplary Care Cutting-edge Research World-class Education Conclusions Personalized medicine and HTE analysis has the potential to improve outcomes from AKI More research needs to be done on how to apply personalized medicine in AKI research which patients (phenotype) when to apply (timing) how much treatment (dose) how should such treatments be tailored (e.g. biomarker monitoring)
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