guidelines & personalized medicine friends or enemies · guidelines & personalized medicine...
TRANSCRIPT
Guidelines & Personalized MedicineFriends or Enemies
Prof. Yehonatan Sharabi, MD, FAHA
Director, Hypertension Unit
Immediate past president, Israeli Society of Hypertension,
Sheba Medical Center, Tel Hashomer
Tel Aviv University
Figure 4
The Lancet 2018 392, 2052-2090DOI: (10.1016/S0140-6736(18)31694-5)
Leading 20 causes of YLLs globally in 2016 and 2040 by rank order
Figure 4
The Lancet 2018 392, 2052-2090DOI: (10.1016/S0140-6736(18)31694-5) Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC
BY 4.0 license Terms and Conditions
Figure 6
The Lancet 2018 392, 2052-2090DOI: (10.1016/S0140-6736(18)31694-5)
Figure 6
The Lancet 2018 392, 2052-2090DOI: (10.1016/S0140-6736(18)31694-5) Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC
BY 4.0 license Terms and Conditions
WHO: “high blood pressure – No 1 contributor for preventable death worldwide”
Mills KT, Circulation, 2016:
We are doing better but can do much better…
1995 2015
Awareness 58% 67%
Treatment 44% 55%
Control 18% 28%
Why?
Eyal Zimlichman MD, MSc8
Solution: structured protocols?
• Protocol based treatment
– ACS
– Decompensated HF
– Pneumonia
• Chronic conditions?
Clinical Practice Guidelines
12
“Clinical practice guidelines are systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances.”Purposes:
• standardization of medical care
• Reduce discrepancies
• Improve outcome
• Reduce costs
* Institute of Medicine. Guidelines for clinical practice. 1990
2018 ESC/ESH Guidelines for the managementof arterial hypertension
• 89 pages
• 33 tables
• 629 references
The quest to clinically significant treatment:
Penicillin to pneumonia…
• Tilghman CR, Finland M,Arch Intern Med, 1937
• Observation
• N= dozens – hundreds
• Effect on mortality: -85%
The quest for statistically significant treatments…
𝑥
𝑁≫ 𝑃𝑣𝑎𝑙𝑢𝑒
The quest to P<0.05…
N P
The quest for statistically significant treatments…
The quest for statistically significant treatments…
• Large clinical trials THOUSANDS
• Examples:• PROGRESS N=6105
• ADVANCE N=11000
• HYVET N=3845
• LIFE N=9193
• SCOPE N=4964
• ASCOT N=19257
• ACCOMPLISH N=10995
The quest for statistically significant treatments…
• Metanalyses of HUNDREDS OF THOUSANDS
• Examples from ESH guidelines on how to treat your patient:
• Thomopoulos et al N=247,006
• Emdin et al N=100,354
• Ettehad et al N=613,815
Published in highly ranked journals
Small step to mankind, Giant leap to CV…
The quest for statistically significant treatments…
• Databases – MILLIONS !!
Old school statistics applied on huge populations
Where is my patient? An average of all?
Patients differ from each other
• 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 ...
• If it were not for the great variability among individuals, medicine might as well be a science and not an art
Sir William Osler
34 y/o black female 52 y/o Caucasian male
Patients differ from each other
Meta-regression analyses assessing the relationship between the hypertension cure rate
after PTRA and mean age, publication year.
Trinquart L et al. Hypertension 2010;56:525-532
Copyright © American Heart Association
PERSONALIZED MEDICINE
Personalized medicine is a model that proposes the customization of healthcare, with decisions and practices being tailored to the individual patient.
Basic data analytics: focus on one factor
• LIFE study – LVH
• PROGRESS – post CVA
• RENAAL - CKD
Advanced data analytics: multiple factors modeling
Modeling in medicine: current generation – scoring systems
• Atrial fibrillation and anticoagulants
– From yes/no to ChadsVasc score
• Statins in primary prevention
– From LDL levels to ESC/AHA risk score
Future modeling: BIG DATA
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.
Big data enables machine learning
Machine Learning and Prediction in Medicine
Chen et al, NEJM, June 2017:
• Machine-learning methods are particularly suited to predictions based on existing data
• Machine-learning algorithms can improve the accuracy of prediction over the use of conventional regression models by capturing complex, nonlinear relationships in the data
• Genomics
• Epigenomics
• Pharmacogenomics
• Proteomics
• Metabolomics
• Microbiotomics
• Omics, Omics, Omics…
35Wearable into Health Predictor
MACHINE LEARNINGSMARTPHONESENSORS & DEVICES STORAGE
24/7 Continuous - not sample diagnostic
MicrosoftJawboneGoogleSamsung
BandManage
RCT Vs Big Data analytics
38ר איל צימליכמן"ד
Children’s Hospital, BostonSCAMPS – Standardized Clinical Assessment and Management Plan
Guidelines – next generation
Guidelines & Personalized Medicinecan be friends
• Guidelines, with its limitations, provides framework
• Guidelines should be followed NOT religiously
• Physicians should be comfortable to personalize treatment
• Clinical experience
• Experts guidance
• Common sense
• Modern technology would assist us in the near future
Overview of the
meeting
Patient Centered Meeting is about translating the wealth of data to clinical practice
Case based learning
Different pateint prototypes from real world
A journey from the very beginning, through the management of a chronic disease to complex challenging cases
Motivational interview – how to make it happen
THANK YOU AND WELCOME TO PCM !