1. Rui Zhang, Ph.D.2,5, Genevieve B. Melton-Meaux, M.D., Ph.D.2,5
S27: A Study on Phenotyping and Visualizing Black Box Warnings of Antineoplastic Breast Cancer Medications
Deyu Sun, Ph.D.1, Gopal Sarda, M.S.3, Steven Skube, M.D.2, Anne Blaes, M.D.4,
Rui Zhang, Ph.D.2,5, Genevieve B. Melton-Meaux, M.D., Ph.D.2,5
1School of Statistics, 2Department of Surgery, 3Department of Computer Science,
4Department of Medicine, 5Institute for Health Informatics, University of Minnesota
March 29, 2017
automated detection

2. The authors have nothing to disclose.
Disclosure
The authors have nothing to disclose.

3. Learning Objectives
After participating in this activity, the learner should be better able to
Gain a better understanding of phenotyping algorithms
Explore the benefits of visualizing integrated knowledge 1

4. INTRODUCTION
Phenotyping
In medical contexts, phenotyping refers to classify patients with specific diseases and traits.
E.g., adverse events caused by medication
Phenotyping shows who the patient is, based on which physicians can decide what can be done to cure the patient
Phenotyping provides more insight than ICD codes
In this study, we focus on detecting most serious adverse events of two first-line breast cancer drugs: tamoxifen and docetaxel
Q: lower case for all
Phenotyping: not equal to Deviation: more about the characteristics, current state … … 2

5. INTRODUCTION
Visualization
Visualization uses graphic displays of health information that make sense and effectively communicate insight behind data
Visualization in phenotyping helps render causality between exposure and outcome
Visualization assists physicians integrate information
Most current visualization systems focus on showing structured data alone, without integrating additional knowledge 3

6. INTRODUCTION
Objectives
We focus on detecting “potential” adverse events caused by docetaxel & tamoxifen
We proposed a visualization prototype rendering potential adverse events associated with medication usage
In this study, we focus on detecting most serious adverse events of two first-line breast cancer drugs: tamoxifen and docetaxel
Q: lower case for all
Phenotyping: not equal to Deviation: more about the characteristics, current state … … 4

7. METHODS
Data Collection
4,084 patients diagnosed with breast cancer in University of Minnesota Medical Center ( )
Diagnosis codes (ICD-9, 10)
Vital signs (e.g., blood pressure, oxygen saturation)
Lab tests (e.g., white blood count, blood lactate concentration)
Black box warnings are strictest drug associated adverse events put in the labeling of prescription drugs by the FDA
WBC: ful name
More e.g. 5

8. Extraction of Black Box Warnings
METHODS
Extraction of Black Box Warnings
Medication
Black Box Warnings
docetaxel
neutropenia
bronchospasm
febrile neutropenia
anaphylactic shock
infection
fluid retention
thrombocytopenia
oedema
stomatitis
pleural effusion
hypersensitivity
dyspnoea at rest
rash generalized
cardiac tamponade
erythema
abdominal distension
hypotension
tamoxifen
cerebrovascular accident
endometrial adenocarcinoma
pulmonary embolism
sarcoma uterus
Black box warnings are strictest drug associated adverse events put in the labeling of prescription drugs by the FDA
What’s the relationship among adverse events? 6

9. Phenotyping Algorithms
METHODS
Phenotyping Algorithms
Title: Hypo /ou/ Example :
Flow chart: Hypo- vs. No hypo-
Details: how to search, when & how to develop algorithms
Physician: 4th year resident 7

10. Validation of Algorithms
METHODS
Validation of Algorithms
Hypothesis
Prevalence of adverse events detected in the group who take the medication is higher than the group without taking the medication 8

11. RESULTS Medication Black box warning Incidence in exposed patients
Incidence in non-exposed patients
p-value
95% CI of RR
docetaxel
neutropenia
0.5466*†
0.3312
1x10-15
(1.3251,2.0554)
febrile neutropenia
0.4405*†
0.3386
1x10-4
(1.0439,1.6213)
infection
0.5627*†
0.4314
2x10-6
(1.0461,1.6264)
thrombocytopenia
0.3312*
0.2834
0.03
(0.9265,1.4742)
stomatitis
0.1286*
0.0988
0.04
(0.9398,1.8027)
hypersensitivity
0.0386
0.0266
0.10
(0.8252,2.5519)
rash generalized
0.2476
0.2100
0.055
(0.9154,1.5187)
erythema
0.0032
0.0014
0.21
(0.3413, )
hypotension
0.8328
0.8582
0.90
(0.7242,0.8965)
bronchospasm
0.0064
0.0179
0.94
(0.0894,1.4297)
anaphylactic shock
0.3344*†
0.1707
4x10-14
(1.5564,2.4657)
fluid retention
0.0289
0.0658
1.00
(0.2273,0.8487)
oedema
0.2122
0.2616
0.97
(0.6203,1.0607)
pleural effusion
0.0932
0.0929
0.49
(0.6885,1.4619)
dyspnoea at rest
0.2830
0.2980
0.70
(0.7473,1.2146)
cardiac tamponade
0.0037
0.86 NA
abdominal distension
0.1640*†
0.1157
4x10-3
(1.0563,1.9021)
The proposed phenotyping algorithms detected significantly higher incidence of 5 (by RR) or 7 (by z-test) out of 17 black box warnings in the patients taking Docetaxel, while the incidence in the patients taking Tamoxifen is not significantly different from the population.
* denotes a significant difference for one-tailed two-proportion z-test at the level of 0.05.
† denotes if RR is significantly larger than 1.
Why not significant?
Why p-value differ from RR? Conservative.
Color to highlight the difference. 9

12. RESULTS Medication Black box warning Incidence in exposed patients
Incidence in non-exposed patients
p-value
95% CI of RR
tamoxifen
cerebrovascular accident
0.0143
0.0217
0.83
(0.2754,1.5766)
pulmonary embolism
0.3657
0.3557
0.35
(0.8302,1.3505)
endometrial adenocarcinoma
0.0314
0.0678
1.00
(0.2553,0.9964)
sarcoma uterus
0.0200
0.0344
0.93
(0.2772,1.2194)
Why not significant? Why p-value differ from RR?
Bcs comparing with docetaxel, the AE is longer time scale, e.g. heart diseases, cancer, etc. 10

13. RESULTS Visualization of Patient 1 Visualization of Patient 2 11
? Explain why sarcoma uterus, endo- happens right after ? 11
Visualization of Patient 2

14. Discussion
We proposed use cases to detect black box warnings of two first-line chemotherapy drugs: docetaxel and tamoxifen
A visualization prototype is devised to integrate medication and outcome information
Study limitations
Need to validate these techniques by gold standard
Need to incorporate clinical notes
Summary of what we achieved:
Use case drug
Viz ->
Limitations:
rephrase: improve standard, deeper analysis, confounding factors, advanced modeling 12

15. Acknowledgement
 This research was supported and publication costs were covered by the Agency for Healthcare Research & Quality grant (#1R01HS022085) (GBM), and the University of Minnesota Clinical and Translational Science Award (#8UL1TR000114) (Blazer)
Summary of what we achieved:
Use case drug
Viz ->
Limitations:
rephrase: improve standard, deeper analysis, confounding factors, advanced modeling 13

16. Questions?
Thank you!