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Developing Information Systems for Cancer Research

Developing Information Systems for Cancer Research. Christopher Flowers, MD, MSc Assistant Professor Medical Director, Oncology Data Center Bone Marrow and Stem Cell Transplant Center Winship Cancer Institute Emory University. Health Care Data Integration Medical Intelligence Applications.

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Developing Information Systems for Cancer Research

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  1. Developing Information Systems for Cancer Research Christopher Flowers, MD, MSc Assistant Professor Medical Director, Oncology Data Center Bone Marrow and Stem Cell Transplant Center Winship Cancer Institute Emory University

  2. Health Care Data IntegrationMedical Intelligence Applications

  3. What Data are available? • Patient Genomics • Microarrays and Gene Chip • Analysis Results • Quality Values • Hospital Patient Management • Patient Demographics • Inpatient, Outpatient, Patient Types • Location, Physician, Visits • Hospital Patient Accounting • Financial Data • Patient charges • Payments and Collections • Summarized Financial Visit Data • Charge Description

  4. What Data are available? • Pharmacy • Orders, Drugs, Medication • Formulary • Drug Interactions • Costs • Medical Records • Procedures & Diagnosis (CPT4 & ICD9) • Visit, Abstract • Physician • Admit Diagnosis, Admit Source and Type • RDRG/DRG

  5. What Data are available? • Clinic Patient Accounting • Patient Registration; Demographics, Insurance (FSC), Employer, Case • Provider • General Ledger • Financial Data & Invoices • Laboratory Results • Lab Orders, General Results and Micro • Clinic and Hospital Patients

  6. What Data are available? • Radiation Oncology • Treatment Plans • Clinical Trials • Studies • Patient Demographics • Pathology • Cancer Registry • Patient Demographics and abstract • Pathology, Treatment Plans and Discharge Summary • Progress Notes, Radiology results, Charges

  7. What Data are available? • Patient Chart Information • Physician Notes • Radiology Reports • HLA • Cancer Anatomic Path • Lab Test Results • Other (Forms entry) • IBMTR/ABMTR Form • Acute Myelogenous Form • Patient Profile Form • Informed Consent

  8. Analysis of Search Algorithms for Oncologic Disease Identification Using GeneSys SI Michael Graiser, PhD1,Ashley Hilliard1, Rochelle Victor1, Ragini Kudchadkar, MD1, Leroy Hill1, Michael S. Keehan, PhD2, Jonathan Simons, MD1, Christopher Flowers, MD1 1 Winship Cancer Institute, Department of Hematology and Oncology, Emory University School of Medicine, Atlanta, GA (http://www.winshipcancerinstitute.org) 2 NuTec Health Systems, Atlanta, GA* (email: info@nutechealthsystems.com) * Emory University has a financial interest in NuTec Health Systems, which designed and built GeneSys SI. Emory may financially benefit from this interest if NuTec is successful in marketing GeneSys SI. This project may produce income for Emory’s charitable purposes and for NuTec’s commercial purposes.

  9. Development of GeneSys SI • Collaborative effort between Emory’s Winship Cancer Institute and NuTec Health Systems • Web-based query tool and genomic analysis tools designed with a team of Emory oncologists and research investigators • August, 2002 – 175,000 Emory patients identified by cancer diagnosis loaded into GeneSys SI • New patients added by individual patient consent • Ongoing efforts to add new sources of data • Tissue Banking • Genomic tools

  10. GeneSys SI ModulesHealth Care Applications

  11. Clinical Information External Databases Gene Expression Information Sequence Information GeneSys SI

  12. Physician Notes Radiology Reports 2 Cytogenetics Lab 3 3 Anatomic Path 3 Radiation Oncology 3 3 4 4 1 Lab Results 4 1 1 Pharmacy 5 1 1 Medical Records Scheduling Billing GeneSys SI: Architecture • Linked patient-level data • Pathology • Cancer Registry • Laboratory Results • Radiology Results • Medication utilization • Clinical outcomes • Genomics Microarrays Enterprise Application Interface Query Engine Cancer Registry Data Warehouse Legacy Databases Clinical Trials Pyxis 5 Occupational Exposure 5 Family History 5 Cancer Epidemiology Tissue Banking (under construction)

  13. Physician Notes Radiology Reports 2 Cytogenetics Lab 3 3 Anatomic Path 3 Radiation Oncology 3 3 4 4 1 Lab Results 4 1 1 Pharmacy 5 1 1 Medical Records Scheduling Billing GeneSys SI: Architecture GeneSys SI Microarrays Enterprise Application Interface Query Engine Cancer Registry Data Warehouse Legacy Databases Clinical Trials Pyxis 5 Occupational Exposure 5 Family History 5 Cancer Epidemiology Tissue Banking (under construction)

  14. Physician Notes Radiology Reports 2 Cytogenetics Lab 3 3 Anatomic Path 3 Radiation Oncology 3 3 4 4 1 Lab Results 4 1 1 Pharmacy 5 1 1 Medical Records Scheduling Billing Investigator Defined Forms Data GeneSys SI Microarrays Enterprise Application Interface Public Databases Genetic Protein Query Engine Cancer Registry Data Warehouse Legacy Databases Clinical Trials Pyxis 5 Occupational Exposure 5 Family History 5 Cancer Epidemiology Tissue Banking (under construction)

  15. Database Population GeneSys SI contains information on patients who have visited Emory University Hospital, Crawford Long Hospital, or The Emory Clinic and have received an oncology diagnosis. Benign neoplasms are also included.

  16. Numbers • Total patients 175,748 • Newly consented 551 • By ICD9 & ICD10

  17. Data currently available in GeneSys SIDATA SOURCE ENTRY DATE HISTORY (YEARS)

  18. Linked Oncology Database Useful for: • Retrospective clinical outcomes research • Clinical trials planning • Cost effectiveness analyses • Storage of unique clinical data • Linking to public genomic and proteomic databases • Pharmacogenomics

  19. Limitations of linked heterogeneous databases • Reliance on patient identifiers such as SSN to link • data entry errors, missing data, business practices • Patchwork of different databases not intended for research purposes • Reliance upon coded outcomes (e.g. ICD-9 codes) • frequently assigned by personnel unfamiliar with patient, disease, or procedure • Multiple sources for the same data • diagnosis, treatment, DOB, DOE, other demographics Breitfeld et.al. J Clin Epi, 2001. Earle et al. Med Care, 2002. Verstraeten et.al. Expert Rev. Vaccines, 2003.

  20. Research Objectives • Develop query algorithms to identify pts with a histological diagnosis • Follicular lymphoma • Examine sensitivity and specificity of query algorithms • Develop query strategies for identifying pts with other diseases of interest

  21. 32% Breast 12% Lung & bronchus 11% Colon & rectum 6% Uterine corpus 4% Non-Hodgkin’s lymphoma 4% Melanoma of skin 3% Ovary 3% Thyroid 2% Urinary bladder 2% Pancreas 20% All other sites 10 Leading Cancer Sites by Gender, US, 2005 Men710,040 Women662,870 Prostate 33% Lung & bronchus 13% Colon & rectum 11% Urinary bladder 7% Melanoma of skin 5% Non-Hodgkin’s lymphoma 4% Leukemia 3% Kidney 3% Oral cavity 3% Pancreas 2% All other sites 17% *Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. American Cancer Society, 2005.

  22. Lymph Node Secondary Follicle Afferent Lymphatic Vessel Mantle Zone Marginal Zone Germinal Center Primary Follicle Postcapillary Venule Subcapsular Sinus Artery Cortex Medullary Cord Medula Medullary Sinus Efferent Lymphatic Vessel Courtesy of Thomas Grogan, MD.

  23. B-cell Precursor B-cell neoplasms B-acute lymphoblastic leukemia (B-ALL) Lymphoblastic lymphoma (LBL) Peripheral B-cell neoplasms B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma B-cell prolymphocytic leukemia Lymphoplasmacytic lymphoma/immunocytoma Mantle cell lymphoma Follicular lymphoma Extranodal marginal zone B-cell lymphoma of MALT type Nodal marginal zone B-cell lymphoma Splenic marginal zone lymphoma Hairy cell leukemia Plasmacytoma/plasma cell myeloma Diffuse large B-cell lymphoma Burkitt’s lymphoma T-cell/NK-cell Precursor T-cell neoplasm Precursor T-acute lymphoblastic leukemia (T-ALL) Lymphoblastic lymphoma (LBL) Peripheral T-cell/NK-cell neoplasms T-cell chronic lymphocytic leukemia/prolymphocytic leukemia T-cell granular lymphocytic leukemia Mycosis fungoides/Sézary syndrome Peripheral T-cell lymphoma not otherwise characterized Hepatosplenic gamma/delta T-cell lymphoma Angioimmunoblastic T-cell lymphoma Extranodal T-/NK-cell lymphoma, nasal type Enteropathy-type intestinal T-cell lymphoma Adult T-cell lymphoma/leukemia (HTLV1+) Anaplastic large cell lymphoma, primary systemic type Anaplastic large cell lymphoma, primary cutaneous type Aggressive NK-cell leukemia WHO NHL Classification Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967.Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.

  24. B-cell Precursor B-cell neoplasms B-acute lymphoblastic leukemia (B-ALL) Lymphoblastic lymphoma (LBL) Peripheral B-cell neoplasms B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma B-cell prolymphocytic leukemia Lymphoplasmacytic lymphoma/immunocytoma Mantle cell lymphoma Follicular lymphoma Extranodal marginal zone B-cell lymphoma of MALT type Nodal marginal zone B-cell lymphoma Splenic marginal zone lymphoma Hairy cell leukemia Plasmacytoma/plasma cell myeloma Diffuse large B-cell lymphoma Burkitt’s lymphoma T-cell/NK-cell Precursor T-cell neoplasm Precursor T-acute lymphoblastic leukemia (T-ALL) Lymphoblastic lymphoma (LBL) Peripheral T-cell/NK-cell neoplasms T-cell chronic lymphocytic leukemia/prolymphocytic leukemia T-cell granular lymphocytic leukemia Mycosis fungoides/Sézary syndrome Peripheral T-cell lymphoma not otherwise characterized Hepatosplenic gamma/delta T-cell lymphoma Angioimmunoblastic T-cell lymphoma Extranodal T-/NK-cell lymphoma, nasal type Enteropathy-type intestinal T-cell lymphoma Adult T-cell lymphoma/leukemia (HTLV1+) Anaplastic large cell lymphoma, primary systemic type Anaplastic large cell lymphoma, primary cutaneous type Aggressive NK-cell leukemia WHO NHL Classification Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967.Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.

  25. Methods • Selected disease for initial query algorithm study (follicular lymphoma - FL) • Developed and ran queries for FL using all available sources for diagnosis • Clinic & Hospital ICD9 codes, Cancer Registry histology codes, Medical record text reports: chart, pathology • Verified diagnosis for each patient • pathology reports • other chart reports • For each query calculated specificity and sensitivity

  26. GeneSys SI queries to find follicular lymphoma patients

  27. Patients found with follicular lymphoma queries

  28. Q3 Q2 Q1 Q5 Q4 Q7 Q6 QC Schematic Diagram of Query Outcomes

  29. Schematic Diagram of Query Outcomes Follicular Lymphoma Other Diagnosis n =1520

  30. Schematic Diagram of Query Outcomes Follicular Lymphoma Other Diagnosis n =1520 Q1

  31. RESULTS – Analysis of follicular lymphoma cases Purple=Path verified Red =Chart verifiedWhite=Total verifiedQuery# #Pat #True Pos #False Pos #True Neg #False Neg

  32. Results: Algorithms Sensitivity & Specificity * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.

  33. Results: Algorithms Sensitivity & Specificity * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.

  34. Results: Algorithms Sensitivity & Specificity * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.

  35. Results: Algorithms Sensitivity & Specificity * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.

  36. 100% Q10 Q9 Q8 Q4 Q2 90% 80% 70% 60% Sensitivity Q1 50% Q6 Q5 40% Q7 Q3 30% 20% 10% 0% 0% 20% 40% 60% 80% 100% 1 - Specificity ROC Plot for Search Algorithms

  37. Conclusions • Highest Sensitivity • Free Text search w/ near algorithm • Combination queries • Highest Specificity • Cancer Registry code, Free Text query “follicular lymphoma” • Limiting search to pathology reports improves specificity • Best Overall Performance • Free Text query “follicular lymphoma” +/- Cancer Registry code

  38. Future Directions • Use query results for outcomes research on FL (n=405) • Test query algorithms for: • other Non-Hodgkin’s lymphoma • Breast ca., prostate ca., colorectal ca. • Develop and test query algorithms for treatments and outcomes • Modify the query engine and interface to automate algorithms

  39. Winship Cancer InstituteOncology Informatics • Leroy Hill • Michael Graiser, PhD • Rochelle Victor • Ragini Kudchadkar, MD • Susan Moore MD, MPH • Bonita Feinstein RN • Ashley Hilliard • James Yang • John Tumeh • Simone Parker

  40. Potential Projects • Cancer Outcomes Research • Genomic Discovery / Pharmacogenomics • Clinical Trials Support • Medical Informatics

  41. Cancer Outcomes Research • Examining Treatment Strategies & Outcomes for Fludarabine Refractory CLL • The influence of Comorbidity on Outcome in patients undergoing Allogeneic Transplantation • Other Cancer Treatments • Examining Treatment Strategies & Outcomes for Relapsed Follicular Lymphoma • Management of Squamous Cell Cancer of the Anus (Reducing Surgical Morbidity) • Examining Regimen-Related Toxicity

  42. Pharmacogenomics • Provide utilization data for cost-effectiveness studies • Provide resources to support observational studies and clinical trials in pharmacogenomics • Resource for developing algorithms for pattern recognition

  43. Clinical Trials Support • Screening algorithms for identifying patients eligible for clinical trials • Identify populations that would permit clinical trial investigation • Data resource for monitoring trial outcomes • Regimen-related toxicity • Treatment Response • Survival

  44. Medical Informatics • Advanced database search algorithms • Pattern Recognition • Neural Networks • Bayesian Networks • Hierarchical Statistical Models

  45. Scientific Applications caCORE Biomedical Objects Common Data Elements Enterprise Vocabulary

  46. Common Data Elements (CDEs) • Data descriptors or “metadata” for cancer research • Precisely defining the questions and answers • What question are you asking, exactly? • What are the possible answers, and what do they mean? • Ongoing projects covering various domains • Clinical Trials • Imaging • Biomarkers • Genomics

  47. caBIO Overview • Software industry design paradigms • Unified Modeling Language (UML) representations of biomedical “objects” • Java 2 Enterprise Edition “n-tier” system architecture • Broad coverage of biomedicine (but not comprehensive yet): • Genomics • Gene expression • Model systems for cancer • Human clinical trials • Data “on-tap” via application programming interfaces

  48. Cancer Clinical Database Application SystemWeb Form Generation

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