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Risk Assessment for Esophageal Adenocarcinoma. June 2014 Thomas Vaughan Fred Hutchinson Cancer Research Center. Two Distinct Cancers. Incidence of Esophageal Adenocarcinoma. Arithmetic. Log. WM. BM. WF. BF. NCI SEER*Stat Database: 9 Registries released April 2013.

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risk assessment for esophageal adenocarcinoma

Risk AssessmentforEsophageal Adenocarcinoma

June 2014

Thomas VaughanFred Hutchinson Cancer Research Center

incidence of esophageal adenocarcinoma
Incidence of Esophageal Adenocarcinoma

Arithmetic

Log

WM

BM

WF

BF

NCI SEER*Stat Database: 9 Registries released April 2013

natural history of ea
Natural History of EA

Normal

20%Chronic Reflux (GERD)

10% Metaplasia (Barrett’s*)

HG Dysplasia

0.2 - 0.5% annualAdenocarcinoma

*2-4 M persons in U.S. with Barrett’s

metaplastic epithelium
Metaplastic Epithelium

Wild & Hardie, Nat Can Rev, 2003

surveillance rationale
Surveillance Rationale

Survival

Cancer detected during surveillance

Local

Distant

SEER Limited Use Data, 9 registries, April 2007

male sex
Male Sex

8.0

3 - 4

2

Hardikar, PLoS ONE 2013; Edelstein, AJG 2009; Corley, Gastro 2007

genetic contribution to risk
Genetic Contribution to Risk

h2g = 25% (p~10-7)

h2g = 35% (p~10-9)

minimal ?

  • Significant genetic contribution to BE and EA
  • The many common genes contributing to these disease are largely shared between EA and BE

Ek, JNCI 2013

aspirin nsaids
Aspirin/NSAIDs

0.6 – 0.7

~ 1

~ 0.5

Liao, Gastro 2012; Vaughan, Lancet Oncology 2005

slide17

Precision Prevention ?

Reid et al, Nat Can Rev, 2010

primary care setting very low risk
Primary Care SettingVery Low Risk
  • History/Physical (lifestyle, family history, obesity)
  • Low cost clinical screening based on
    • Blood (e.g., H pylori, telomeres, inflammatory cytokines)
    • Non-endoscopic cytology markers (e.g., Cytosponge)
  • Risk prediction tool for education/referral
secondary care setting low risk
Secondary Care SettingLow Risk
  • Reproducible tissue-based markers
  • Safe interventions (e.g., lifestyle)
  • Secondary care risk model to guide surveillance and interventions
secondary care setting moderate risk
Secondary Care SettingModerate Risk
  • Cost-effective surveillance protocol
  • Cost-effective interventions (e.g., chemoprevention)
  • Stratification into highest risk category
slide21

Discrimination and calibration of risk model for BE among persons with reflux symptoms.

- Thrift, et al. Cancer Prevention Research 2012

cytosponge
“Cytosponge”
  • Non-endoscopic collection of esophageal epithelial cells
risk prediction barrett s ea
Risk PredictionBarrett’s –> EA
  • Goal: Develop risk prediction models based on sets of variables available in different contexts (increasing cost)

1. Questionnaire based

e.g., age, sex, reflux sx, obesity measures, smoking history, nsaid use

2. Adding blood measures

e.g., CRP, leptin, insulin, metabolite panel

3. Adding clinical endoscopy data

e.g., Barrett’s segment length

4. Adding somatic genetic abnormalities (biopsy-based)

e.g., specific mutations (p53) or genome-wide abnormalities

  • Steps:

Impute missing data (50 datasets)

Fit model using backward stepwise regression (also forward) with a threshold for staying in the model of p<0.2

      • Model is fit on each imputation and Rubin’s rules are used to combine the estimated coefficients and variances across imputations.
risk prediction barrett s ea1
Risk PredictionBarrett’s –> EA
  • AUC
    • Naïve AUC is calculated as average AUC of final model across imputations
    • “adjusted” AUC is based on 100-fold cross-validation with a 2/3rds vs 1/3rds training/test split using the same backward stepwise regression procedure
    • Calibration of model: still working on this but same ideas as above
  • Above steps repeated for different contexts.

Thrift, Janes, Onstad, et al., in process

challenges discussion points
Challenges & Discussion Points
  • Defining priorities
    • What context deserves top priority?
    • What data are needed/available to address each context?
      • Biomarkers & other unproven predictors
  • Confronting limited data resources
    • Small numbers for some contexts
    • Missing data
    • How to validate in consortium setting where we already have most cases (e.g., rare disease)
  • Implementing decision aids
    • Most value for physicians
    • Most value for patients/population
comparison of multiple cardiovascular risk scores

Comparison of multiple cardiovascular risk scores

Rebekah Young

rlyoung@uw.edu

June 11, 2014

cardiovascular risk scores
Cardiovascular Risk Scores
  • A risk assessment tool to predict a person's chance of having a heart-related event over some period of time (usually 10 years)
  • Risk scores are used by physicians to initiate discussion of preventive medication
slide33

1. Points-based TC

2. Points-based LDL

3. Equation-based TC

4. Equation-based LDL

framingham risk scores
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

framingham risk scores1
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

framingham risk scores2
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

framingham risk scores3
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

framingham risk scores4
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

multiple cardiovascular risk scores
Multiple Cardiovascular Risk Scores
  • A cautionary tale about using “TheFramingham risk score”
  • Different target populations (e.g.,diabetics, different age ranges)
  • Different endpoints
    • Variation within same endpoint
    • Overlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD death
  • Issue with online calculators
multiple cardiovascular risk scores1
Multiple Cardiovascular Risk Scores
  • A cautionary tale about using “TheFramingham risk score”
  • Different target populations (e.g.,diabetics, different age ranges)
  • Different endpoints
    • Variation within same endpoint
    • Overlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD death
  • Issue with online calculators
multiple cardiovascular risk scores2
Multiple Cardiovascular Risk Scores
  • A cautionary tale about using “TheFramingham risk score”
  • Different target populations (e.g.,diabetics, different age ranges)
  • Different endpoints
    • Variation within same endpoint
    • Overlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD death
  • Issue with online calculators
multiple cardiovascular risk scores3
Multiple Cardiovascular Risk Scores
  • A cautionary tale about using “TheFramingham risk score”
  • Different target populations (e.g.,diabetics, different age ranges)
  • Different endpoints
    • Variation within same endpoint
    • Overlap in the compositeFRS-98-TC-Pt:MI,CHD death, Angina, Coronary insufficiencyFRS-ATP3-Eq: MI, CHD death
  • Issue with online calculators
multiple cardiovascular risk scores4
Multiple Cardiovascular Risk Scores
  • A cautionary tale about using “TheFramingham risk score”
  • Different target populations (e.g.,diabetics, different age ranges)
  • Different endpoints
    • Variation within same endpoint
    • Overlap in the compositeFRS-98-TC-Pt: MI,CHD death, Angina, Coronary insufficiencyFRS-ATP3-Eq: MI, CHD death
  • Online calculators
slide54

http://www.mayoclinic.org/heart-disease-risk/itt-20084942

Created by Mayo Foundation for Medical Education and Research using content from Framingham Heart Study Cardiovascular Disease 10-Year BMI-Based Risk Score Calculator, Framingham Heart Study General Cardiovascular Disease 30-Year Lipid-Based and BMI-Based Calculators, and ACC/AHA Pooled Cohort Equations CV Risk Calculator.

framingham risk scores5
Framingham Risk Scores

Age: 59

Total cholesterol (TC): 148

High-density

lipoprotein (HDL): 35

Systolic blood

pressure (SBP): 120

Antihypertensive

medication use: Yes

Smoker: Former

Family history of heart

disease(FH): No

Diabetes (DM): No

development and validation of an hiv risk assessment tool for african women

Development and Validation of an HIV Risk Assessment Tool for African Women

Jen Balkus, PhD, MPH

Staff Scientist – VIDD

Associate Director – Microbicide Trials Network Statistical

and Data Management Center

Risk Prediction Seminar - Discussion

June 11, 2014

the problem
The Problem
  • Women account for more than half of all new HIV infections globally, with the greatest incidence occurring in African women
  • Several recently completed biomedical HIV prevention trials in women have reported high incidence rates
    • As great as 10% at some study sites
  • Improving our understanding of predictors of HIV acquisition in African women is urgently needed in order to:
    • Identify opportunities for intervention
    • Inform the scale-up of targeted HIV prevention activities
one potential solution
One Potential Solution…
  • An HIV risk score for African women
  • Could be used in multiple settings:
    • To improve clinical trial recruitment efficiency by enrolling women identified as “high-risk”
    • Community and counseling (VCT) settings
    • Program and policy settings to inform scale-up of novel interventions
  • Empiric HIV risk scores have recently been developed for several populations
    • African HIV serodiscordant couples
    • Men who have sex with men (MSM) in the United States

Example risk score. Kahleet al. JAIDS (2013)

the plan
The Plan
  • Derive an HIV risk score using data from women enrolled in MTN-003 (the VOICE trial)
    • 5,029 women enrolled from Uganda, South Africa and Zimbabwe

Nair et al. CROI (2014)

the plan1
The Plan
  • Predict HIV risk within 1 year of enrollment using baseline characteristics that can easily be assessed in a clinical or research setting
  • Cox proportional hazards models to assess univariate predictors
  • Fully stepwise multivariable model constructed based on lowest Akaike Information Criterion (AIC)
  • Risk score generated by dividing the coefficient for the predictor in the final model by the lowest coefficient among all predictors in the model and rounding to the nearest integer
  • Predictive ability of the total score and each predictor will be assessed by calculating the time-varying area under the curve (AUC)
  • Final score will be internally validated using 10-fold cross-validation and the AUC for the final model will be compared with the mean AUC of the 10 different models
the plan2
The Plan
  • Validate the risk score using date from 4 recently or soon to be completed HIV prevention trials in African women
challenges
Challenges…
  • Varying follow-up time due to administrative censoring
    • 3 of 5 arms were stopped early due to futility: Oral tenofovir arm, vaginal tenofovir gel arm and vaginal placebo gel arm
  • Studies proposed for external validation have similar but not identical questions at baseline
  • Measured and unmeasured differences by country
    • Too much variability?
    • Limit analyses to South Africa?