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Data Science in Cardiac Sciences

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An exploratory proposal to a PhD study related to cardiac sciences; too novel; only preliminary

Published in: Science
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Data Science in Cardiac Sciences

  1. 1. Database Approach for Innovative Discovery Robert J. Chen, MD, MPH D93842004@ntu.edu.tw
  2. 2. Backgrounds • Data sciences: applied epidemiology • Prediction and monitoring • Exploration and confirmation • Cardiac surgery – Risk and performance – Heart failure therapeutics
  3. 3. Cardiac Surgery • Prediction of risks – EuroScore – STS Score – Our score? • Study of controversies – CABG: beating vs. arrest – Atrial fibrillation
  4. 4. Cardiac Surgery • Transplantation – Donor, awaiting, recipients • Clinical database system – Retrospective data – Prospective data – Comprehensive data • Preop, Op, Postop
  5. 5. Cardiac Surgery • Conventional vs. New methods – Robotics, trans-catheter valves, aortic stent graft • Ultimate treatment for heart failure – Cardiac stem cells – Adult somatic cells trans-differentiation – Cardiac re-genesis from adult somatic cells – Missing link?
  6. 6. Cytoscape • Http://www.cytoscape.org • Biomolecular interaction network • P-P, P-G, G-G interactions • Plug-in
  7. 7. Cytoscape
  8. 8. Specific Aims • To understand nucleostemin in the molecular level; • To master the tool of Cytoscape for its applications; • To use Cytoscape to construct the functional network of nucleostemin; • To propose a potential future direction of cardiac stem cell research.
  9. 9. Methods and Procedures • Literature review for cardiac regenerative therapy; • Literature review for cardiac stem cells; • Literature review for nucleostemin and related molecules; • Acquisition of experience and expertise for using Cytoscape;
  10. 10. Methods and Procedures • Use of Cytoscape for nucleotide-protein and protein-protein interaction analysis; • Construction of the functional network of nucleostemin; • Hypothesis generation for more research targets of cardiac stem cells starting from the network of nucleostemin.
  11. 11. Methods and Procedures • Use of Cytoscape for nucleotide-protein and protein-protein interaction analysis; • Construction of the functional network of nucleostemin; • Hypothesis generation for more research targets of cardiac stem cells starting from the network of nucleostemin.
  12. 12. Gantt Chart
  13. 13. Backgrounds • Cardiovascular Surgery – Technology-intensive – Techniques-oriented • A specialty that needs – Risk assessment – Outcome prediction – Performance feedback • Data from paper -> time and manpower demanding
  14. 14. Backgrounds • Disease/Procedures-specific – CABG, Aorta, valve, heart failure (LVAD, transplant), Af, … • Risk-adjusted outcomes • Patient-surgeon preop discussion • Decision-making for choosing therapies • Performance comparison • Quality improvement • Research, reports, and publications
  15. 15. Specific Aims • Establish the hospital-based CVS database system – Data entry (web-based, intranet) – Data storage and management (secure, confidential) – Data analysis • Features: flexible, compatible to standard syntax, and open-structured
  16. 16. Specific Aims • Data exchange – Data import from various sources – Data export to advanced statistical software • Procedure-specific: CABG, valvular, aortic, transplant, atrial fibrillation,… • Data entry once system ready – Prospective: clinical staff – Retrospective: data staff • Regular reports • Clinical research
  17. 17. Methods & Procedures • Variable selection: – Demographic, underlying, preop status – Operation-related – Postop condition – Complications, outcomes, follow-ups • Interaction with database programmers – Entry interface – Hospital IT support and HIS (hosp info system) integration
  18. 18. Methods & Procedures • Test-drive – Debugging – Feedback and revision • Data entry – Past data – Current and new data • Statistical analysis – Descriptive statistics – Inferential statistics – Stata 10.2
  19. 19. Content of Cardiovascular Surgery Database 1. Administrative 2. Demographics 3. Hospitalization 4. Risk Factors 5. Previous CV Interventions (1..n) 6. Preoperative Cardiac Status (1..n) 7. Preoperative Medications (1..n) 8. Hemodynamics, cath, and echocardiogram (1..n) ….. • There are totally 19 raw-tables, hundreds of features from patient’s special chart. • Highly de-normalized! • Transactional data tracking is required!
  20. 20. Database System Meta- structure • 1. Software • 2. Data • 3. Meta-data: mapping of variable names of identical meaning, for importing data from other existing datasets.
  21. 21. Database System Meta- structure
  22. 22. Expected Results • 1. A database system for retrospective and prospective data • 2. A trained data-entry team • 3. A preliminary analysis report • 4. A schedule for regular reporting • 5. Analysis upon request in daily basis
  23. 23. Analysis Report Outline 1) Outcome Reporting and International Comparisons 2) Overall Cardiac Surgical Activity 3) Preoperative Assessment 4) Patient Demographics 5) Risk-Stratification and Presentation of Risk- Adjusted Outcomes 6) Coronary Artery Bypass Grafting (CABG) 7) Heart Transplantation 8) Summary
  24. 24. Gantt Chart
  25. 25. Funding • Transplantation database: NT$300,000 • Nucleostemin: NT$130,000 • Cardiac surgery database: NT$2,000,000
  26. 26. Current Status • Transplantation database: – Completed in 2008 (N=3,000) – EMB and TR in HTx (N=2,000; n=200) • Presented in CAST 2007 and ASCVS 2008 • Published in Transplantation Proceedings 2008 • Cardiac surgery database: – EuroScore and our score for CAD-LMD (N=444) • Presented in 2009 ASCVS – Arrest CABG performance (N=800) • Presented in TSOC 2009 debate
  27. 27. Current Status • Cardiac Surgery Clinical Database – Dendrite, Inc. – Connection with HIS – Data entry reduced to minimum – N=600*15 (electronic *5) • Nucleostemin – Literature review – Data exploration (on-line database)
  28. 28. Perspectives • Outcome-based cardiac surgery – Evidence-based – Selection of procedure – Selection of surgeon/team • Novel and ultimate treatment for end- stage heart failure (initial stage) • Database approach both for research and clinical practice
  29. 29. Thank You! • 陳勁辰 • d93842004@ntu.edu.tw
  30. 30. Theoretical Functional Network of Nucleostemin for Cardiac Stem Cells
  31. 31. Abstract • Nucleostemin plays a pivotal role in cardiac stem cells for the regenerative function but its interactions with other key molecules are still unclear. • We would like to perform nucleotide- protein and protein-protein interaction analysis by Cytoscape (http://www.cytoscape.org) to build the functional network map for nucleostemin.
  32. 32. Abstract • New or revised bioinformatics methodology may be developed. • The proposed functional network of nucleostemin may inspire future laboratory investigation of cardiac stem cell research.
  33. 33. Backgrounds • Myocardial regeneration-> end-stage heart failure • Cardiac stem cells • Various sources: embryo, BM, iPS,… • Cellular reprogramming: avoiding the use of embryo
  34. 34. Backgrounds • Nucleostemin: a regulatory protein • Its expression is associated with proliferation and maintenance of a primitive cellular phenotype • Nucleostemin expression in cardiomyocytes is induced by fibroblast growth factor-2 and accumulates in response to Pim-1 kinase activity.
  35. 35. Backgrounds • Cardiac stem cells also express nucleostemin that is diminished in response to commitment to a differentiated phenotype. • Overexpression of nucleostemin in cultured cardiac stem cells increases proliferation while preserving telomere length, providing a mechanistic basis for potential actions of nucleostemin in promotion of cell survival and proliferation as seen in other cell types.
  36. 36. Cytoscape • Http://www.cytoscape.org • Biomolecular interaction network • P-P, P-G, G-G interactions • Plug-in
  37. 37. Cytoscape
  38. 38. Specific Aims • To understand nucleostemin in the molecular level; • To master the tool of Cytoscape for its applications; • To use Cytoscape to construct the functional network of nucleostemin; • To propose a potential future direction of cardiac stem cell research.
  39. 39. Methods and Procedures • Literature review for cardiac regenerative therapy; • Literature review for cardiac stem cells; • Literature review for nucleostemin and related molecules; • Acquisition of experience and expertise for using Cytoscape;
  40. 40. Methods and Procedures • Use of Cytoscape for nucleotide-protein and protein-protein interaction analysis; • Construction of the functional network of nucleostemin; • Hypothesis generation for more research targets of cardiac stem cells starting from the network of nucleostemin.
  41. 41. Expected Results • Molecular characteristics of nucleostemin; • Functional network of nucleostemin; • Role of nucleostemin in cardiac stem cells and cardiac regeneration therapy. • New or revised bioinformatics methodology for the network analysis
  42. 42. Gantt Chart
  43. 43. Budgets
  44. 44. Cardiovascular Surgery Database and Data Exploratory Analysis Cheng Hsin Rehabilitation Medical Center 2008/11/23
  45. 45. Outline • Objectives • Content of Cardiovascular Surgery Database • Scope and challenges • Clinical Case Management System – A possible technological innovation framework – Descriptive statistics, or beyond?
  46. 46. Objectives • Develop cardiovascular surgery database – Clinical case management system? – Including bio-information, systemic complications? • Risk assessment – Pre/post-operative probabilistic judgment? – Risk prediction model? • Outcome prediction – Co-occurrence of complications? – Major features screening? Patient screening? • Statistical analysis – Advanced data exploratory analysis?
  47. 47. Questions • Develop cardiovascular surgery database – Clinical case management system? – Including bio-information, systemic complications? • Risk assessment – Pre/post-operative probabilistic judgment? – Risk prediction model? • Outcome prediction – Co-occurrence of complications? – Major features screening? Patient screening? • Statistical analysis – Advanced data exploratory analysis?
  48. 48. Content of Cardiovascular Surgery Database 1. Administrative 2. Demographics 3. Hospitalization 4. Risk Factors 5. Previous CV Interventions (1..n) 6. Preoperative Cardiac Status (1..n) 7. Preoperative Medications (1..n) 8. Hemodynamics, cath, and echocardiogram (1..n) ….. • There are totally 19 raw-tables, hundreds of features from patient’s special chart. • Highly de-normalized! • Transactional data tracking is required!
  49. 49. Cardiovascular Surgery Database Scope • Surgery Operations – CABG – Valvular heart – Heart transplantation – Aortic – Atrial fibrillation – Ventricular restoration – Ventricular assist device – Congenital heart • Referred sites?
  50. 50. Cardiovascular Surgery Database Challenge • Multiple surgery operations – Involve different features? – Balance between physician and database designer viewpoint! (Special chart vs. relational tables) – NULL/Missing valued included! • Inter/Intra-hospital database system? • How to tracking of clinical patient records (pre/post-operative)? • Need to develop validation model?
  51. 51. Clinical Case Management System • Four-level framework • Monitoring Level – Frontend: Web-based data entry, visualization, various data format export interfaces – Backend: validation model, relational databases, co-relation among features • Surveillance Level – Preoperative: Probabilistic reasoning, Bayes decision, Bibliography – Postoperative: Time-tracking?
  52. 52. Clinical Case Management System • Model Construction Level – Prediction/classification model (DT, NN, Ensemble, etc.) – Co-relation/co-occurrence frequency graph model – Knowledge model (Apriori, Carma, GRI, etc.) – Ontology Knowledge Base? • Life Quality Level – Long-term tracking of patient status – WHOQOL-BREF Taiwan Version questionnaire
  53. 53. A Brief Model
  54. 54. Abstract • The project was motivated by the need for risk assessment, outcome prediction, and performance feedback. • Referring to other existing cardiovascular surgery database systems, we would select the variables of interest and then outsource the database design to database programmers with our • Operations such as CABG, valvular heart surgery, heart transplantation, aortic surgery, atrial fibrillation surgery would be included.
  55. 55. Abstract • The database would be established in a trustworthy system and platform. • Revision of the database system would be made after test driving. • Retrospective and prospective data entry (web- based) would be done by trained personnel. • Preliminary report would be made from the data stored in the database with the statistical analysis performed by qualified professional.
  56. 56. Backgrounds • Cardiovascular Surgery – Technology-intensive – Techniques-oriented • A specialty that needs – Risk assessment – Outcome prediction – Performance feedback • Data from paper -> time and manpower demanding
  57. 57. Backgrounds • Disease/Procedures-specific – CABG, Aorta, valve, heart failure (LVAD, transplant), Af, … • Risk-adjusted outcomes • Patient-surgeon preop discussion • Decision-making for choosing therapies • Performance comparison • Quality improvement • Research, reports, and publications
  58. 58. Specific Aims • Establish our hospital-based CVS database system – Data entry (web-based, intranet) – Data storage and management (secure, confidential) – Data analysis • Features: flexible, compatible to standard syntax, and open-structured
  59. 59. Specific Aims • Data exchange – Data import from various sources – Data export to advanced statistical software • Procedure-specific: CABG, valvular, aortic, transplant, atrial fibrillation,… • Data entry once system ready – Prospective: clinical staff – Retrospective: data staff • Regular reports • Clinical research
  60. 60. Methods & Procedures • Variable selection: – Demographic, underlying, preop status – Operation-related – Postop condition – Complications, outcomes, follow-ups • Interaction with database programmers – Entry interface – Hospital IT support and HIS (hosp info system) integration
  61. 61. Methods & Procedures • Test-drive – Debugging – Feedback and revision • Data entry – Past data – Current and new data • Statistical analysis – Descriptive statistics – Inferential statistics – Stata 10.2
  62. 62. Content of Cardiovascular Surgery Database 1. Administrative 2. Demographics 3. Hospitalization 4. Risk Factors 5. Previous CV Interventions (1..n) 6. Preoperative Cardiac Status (1..n) 7. Preoperative Medications (1..n) 8. Hemodynamics, cath, and echocardiogram (1..n) ….. • There are totally 19 raw-tables, hundreds of features from patient’s special chart. • Highly de-normalized! • Transactional data tracking is required!
  63. 63. Database System Meta- structure • 1. Software • 2. Data • 3. Meta-data: mapping of variable names of identical meaning, for importing data from other existing datasets.
  64. 64. Database System Meta- structure
  65. 65. Expected Results • 1. A database system for retrospective and prospective data • 2. A trained data-entry team • 3. A preliminary analysis report • 4. A schedule for regular reporting • 5. Analysis upon request in daily basis
  66. 66. Analysis Report Outline 1) Outcome Reporting and International Comparisons 2) Overall Cardiac Surgical Activity 3) Preoperative Assessment 4) Patient Demographics 5) Risk-Stratification and Presentation of Risk- Adjusted Outcomes 6) Coronary Artery Bypass Grafting (CABG) 7) Heart Transplantation 8) Summary
  67. 67. Gantt Chart
  68. 68. Budget
  69. 69. 台灣移植登錄資料庫分析報告 V.2.1( 西元 2008 年 1 月 ) 器官捐贈移植登錄中心 台灣移植醫學學會
  70. 70. 摘要 • 日期範圍 :2004 年 4 月至 2007 年 9 月 • 移植器官包括心臟,肝臟,腎臟,及肺 臟。 • 包含捐贈者,等候者,及受贈者之性質 ,器官利用率,等候時間,病人存活率 等等。 • 並對移植登錄資料庫升級提出建言。
  71. 71. 報告目錄
  72. 72. 報告目錄
  73. 73. 工作報告 • 回朔性分析已建立好之資料庫 • 姓名及醫院名已加密獨特編碼而無法辨識 • 資料日期更新至 2007 年 10 月 1 日 – 較可靠資料始於 2004 年 4 月 • 資料庫平台 PostgreSQL 8 – 共 36 個資料表 – 依病人獨特識別碼進行資料鏈結 – 輸出成分析所需之子資料表 (*.csv)

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