Slides from a talk i gave at the EAPM 2017 in Belfast, Northern Ireland

1. Multivariate analysis enables personalized prediction
of adverse heart and kidney outcomes
Gal Dinstag, David Amar, Ron Shamir
Tel Aviv University
1st European Alliance for Personalized Medicine Congress, Belfast 28.11.2017
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2. Introduction: The SPRINT Trial
• Clinical trials showed that hypertension treatment reduces the risk
of cardiovascular related-events [Chobanianet al., JAMA 2003]
• Common practice: maintain Systolic Blood Pressure (SBP) below
~140 mmHg.
• Sprint (2010): will more intensive treatment reduce risk?
• Primary cardiovascular (CV) outcome: Myocardial infraction, Acute
coronary syndrome, Stroke, Heart failure, Death from
cardiovascular cause.
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3. Trial outline: Population and Randomization
• Trial population: ≥50 year old adults with an increased risk of
cardiovascular events.
• 9361 patients were drawn into “Intensive” and ”Standard”
treatment arms
• Participants were seen by physicians
every three months and their
medications were adjusted per
treatment arm.
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4. Trial Results (highlights)
Event No. of cases in the
Intensive treatment
group (%)
No. of cases in the
Standard treatment
group (%)
Hazard Ratio
(95% CI)
Adjusted P
Value
Primary cardiovascular
outcome
243 (5.2) 319 (6.8) 0.75 (0.64-0.89) <0.001
Novel CKD for patients
without CKD at baseline
127 (3.8) 37 (1.1) 3.49 (2.44-5.10) <0.001
Acute kidney injury or
acute renal failure
204 (4.4) 120 (2.6) 1.71 <0.001
• Patients in the Intensive treatment arm had a 25% decreased risk
for primary CV outcome.
• Also a 71% increased risk for acute kidney failure and 3.49 fold
increased risk for novel Chronic Kidney Disease (CKD).
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5. The SPRINT Challenge
• In September 2016, NEJM called researchers to analyze the
SPRINT trial data and come up with novel findings.
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6. Uni- vs. Multi-variate models
• Classical randomized trials quantify the effect of a single feature
(treatment type) on a desired outcome- a univariate model
• Supervised learning can develop models based on multiple features in
order to achieve more personalized prediction- multivariate models
• Such models are usually static, i.e., based on data taken once per patient
• Our goal: Construct a model using the static features, plus meaningful
information from longitudinal data - a dynamic multivariate model
• A more challenging task: effect of individual time points is unclear, many
missing values
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7. Our approach
• Goal: build predictive models for personalized prognosis for risk of adverse events
Two supervised models:
• Static - predicts CV and kidney outcomes based solely on patient measurements
collected upon entering the trial (“Static data”)
• Dynamic – predicts the outcomes based on static data and also blood pressure
measurements collected at periodic visits (“Dynamic data”)
Static data Dynamic data
SBP DBP
Treatment
group
Sex Ethnic
group
Framingham
risk score
Smoking
Age Prior CV Events Aspirin
use
Statin use BMI
Chronic
kidney
disease
Antihypertensive
agents usage
SBP DBP EGFR
Serum
creatinine
Cholesterol HDL Triglycerides Glucose
t1SBP DBP
SBP DBP t2SBP DBP
SBP DBP t3SBP DBP
SBP DBPSBP DBP
SBP DBPSBP DBP
SBP DBPSBP DBP
SBP DBP

SBP DBP
SBP DBP tnSBP DBP


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8. Model architecture
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Static data
Dynamic
data
Predictor
Chance of Cardio Vascular
outcome
Chance of Kidney related
outcomesCox PHSVM

9. Extracting summary statistics from dynamic data
PulsePressure
P𝑢𝑙𝑠𝑒𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝑖 = 𝑆𝐵𝑃𝑖 − 𝐷𝐵𝑃𝑖
Challenge: avoid information leakage from data at time points close to the event.
Solution: ignore time points up to t months prior to the event (t =6,12)
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10. Results: Static models
Multivariate static > Univariate static
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11. Results: Dynamic model
Dynamic multivariate > Static multivariate in predicting CV outcome
Primary outcome
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12. Primary CV outcome Prediction (probability)
AcuteRenalFailurePrediction(logrisk)
No event + IntensiveBoth Primary Kidney Standard
Risk prediction for the entire cohort
• Most patients that had CV
and/or kidney events were
predicted to be at high risk for the
outcomes.
•By considering the predicted
probabilities for both outcomes
the physician can identify those
patients that are more likely to be
harmed than helped by aggressive
treatment
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13. Summary
• We provide models relying on all available data
for personalized prognosis of cardiovascular
patients
• Static univariate < static multivariate < dynamic
multivariate
• We demonstrate the benefit of incorporating
longitudinal measurements on top of static data
in predictive models
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14. Acknowledgements
Prof. Ron ShamirDr. David AmarIdan Nurick
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Poster
#102