Role of patient journey in science, predictive modeling and high-risk patients in early stages of disease progression, in real world context. Presented by Michael Rebhan, Novartis at the DayOne Basel event in June 2020

1. Towards a systems approach in chronic diseases
Role of patient journey science, predictive modeling, and high-
risk patients in early stages of disease progression, in a real
world context – Fast progressors in CKD
Michael Rebhan (Novartis: NIBR), for the team & network
June 2020
NIBR
DAx

2. The health-disease continuum
Systems approach for fast progressors (Michael Rebhan)2
very healthy very sick
Healthcare systems
Medical tech
Pharma industry
Strong scientific
foundations
Awareness about risk factors, in
populations, education, ...
Consumer-centric tech
“Healthy stuff” you can buy
Weak scientific foundations

3. The grey zone, in between
Systems approach for fast progressors (Michael Rebhan)3
Sporadic contact with healthcare
Health checks?
Scientific foundation
for good decisions?
Quality
of Life
very healthy very sick

4. The patient journey (PJ)
Systems approach for fast progressors (Michael Rebhan)4
A useful pattern?
Anchor pattern
very healthy very sick
t
We have more reliable
real world data here
First signs of fast progression?

5. AI history, for context
Systems approach for fast progressors (Michael Rebhan)5
MYCIN
(1973-78)
AI winter
(1987-93)
The age of
Expert Systems
Neuro-symbolic
AI
Machine
Learning
Deep Learning
("black box«)
Semantic
Web
Internet grows...
Boolean
reasoning
Probabilistic
reasoning
Knowledge
Graphs
Early symbolic AI
Early neural
networks
Dartmouth 1956
NLP
Computer Vision
Learned
Representations
NLU
Question-
Answer
Systems
AI winter
(1974-80)
Divide between
symbolic AI +
connectionist AI

6. VQA systems Algorithm of FVQA
system
• Input: an image,
and a natural
language question
• Concepts
extracted from
image, merge with
question to get
known facts from
Knowledge Base
• Consider known
facts when
answering the
question
• Explains the
answer, using
supporting facts
(the reasoning
behind its
prediction)
An example for a neuro-symbolic AI system, that combines strengths of
deep learning (find concepts in that image) and tapping into digitalized
knowledge (facts). It can explain its prediction, using reasoning.
Wang et al. (2017) Fact-based
Visual Question Answering

7. CKD Burden: Population Level (DALY, GBD)
Systems approach for fast progressors (Michael Rebhan)7
• DALY is a widely
used measure for
disease-related
burden
• IHME use it to
compare trends
across countries, in
GBD
• Differences across
countries can be
informative
About 10% in many populations have some form of CKD. Some can stay long in early
stages with good outcomes, others progress to kidney failure. Would an early
intervention in CKD stages 1-2 make sense?

8. Introducing: Fast Progressors in CKD
Systems approach for fast progressors (Michael Rebhan)8
eGFR data can help to
estimate kidney status
stage 5
CKD stage 2
Examples for PJ
events, above. EHR
data typically contain:
- diagnoses
- treatments
- measurements
- lab values
- observations
- risk factors
Can we learn how to intervene before a fast health decline?
Does this PJc capture much burden at population level?
Hypothesis: earlier interventions can improve outcomes at
population and individual level (by preventing the fast decline)
PJ level statistics:
allow quick inspection
PJc characteristics:
patients in early stages of
CKD (chronic kidney
disease), at risk of a fast
progression towards
kidney failure in the next
1-2 years.

9. Towards a systems approach
CKD KSQ Innovation Axis – Michael Rebhan - Business Use Only9
model
Biomarkers
Kidney health decline
“slow”
fast
Digitalization of healthcare (large populations)
Who will soon have a fast decline of kidney
health, and who will be more stable?
→ Predictive models for CKD stage 1-2 (eGFR
above 60), in non-diabetic CKD
(Digital innovation for high-risk patients)
Kidney failure
Patient history
Dialysis
Transplantation
Cardiovascular
Morbidity / Mortality
Too late
to intervene
eGFR: 60