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

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

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

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

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

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

METHODS Data Collection 4,084 patients diagnosed with breast cancer in University of Minnesota Medical Center (2010-16) 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

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

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

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

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 (0.3413,15.3062) 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

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

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

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

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

Questions? Thank you! sunxx697@umn.edu