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

1. Database Approach for
Innovative Discovery
Robert J. Chen, MD, MPH
D93842004@ntu.edu.tw

2. Backgrounds
• Data sciences: applied epidemiology
• Prediction and monitoring
• Exploration and confirmation
• Cardiac surgery
– Risk and performance
– Heart failure therapeutics

3. Cardiac Surgery
• Prediction of risks
– EuroScore
– STS Score
– Our score?
• Study of controversies
– CABG: beating vs. arrest
– Atrial fibrillation

4. Cardiac Surgery
• Transplantation
– Donor, awaiting, recipients
• Clinical database system
– Retrospective data
– Prospective data
– Comprehensive data
• Preop, Op, Postop

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?

12. Cytoscape
• Http://www.cytoscape.org
• Biomolecular interaction network
• P-P, P-G, G-G interactions
• Plug-in

13. Cytoscape

14. 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.

15. 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;

16. 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.

17. 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.

18. Gantt Chart

19. Backgrounds
• Cardiovascular Surgery
– Technology-intensive
– Techniques-oriented
• A specialty that needs
– Risk assessment
– Outcome prediction
– Performance feedback
• Data from paper -> time and manpower
demanding

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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!

26. 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.

27. Database System Meta-
structure

28. 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

29. 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

30. Gantt Chart

31. Funding
• Transplantation database: NT$300,000
• Nucleostemin: NT$130,000
• Cardiac surgery database:
NT$2,000,000

32. 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

34. 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)

35. 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

36. Thank You!
• 陳勁辰
• d93842004@ntu.edu.tw

37. Theoretical Functional Network of
Nucleostemin for Cardiac Stem
Cells

38. 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.

39. 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.

40. Backgrounds
• Myocardial regeneration-> end-stage
heart failure
• Cardiac stem cells
• Various sources: embryo, BM, iPS,…
• Cellular reprogramming: avoiding the
use of embryo

41. 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.

42. 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.

45. Cytoscape
• Http://www.cytoscape.org
• Biomolecular interaction network
• P-P, P-G, G-G interactions
• Plug-in

46. Cytoscape

52. 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.

53. 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;

54. 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.

55. 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

56. Gantt Chart

57. Budgets

58. Cardiovascular Surgery
Database and Data Exploratory
Analysis
Cheng Hsin Rehabilitation Medical Center
2008/11/23

59. Outline
• Objectives
• Content of Cardiovascular Surgery
Database
• Scope and challenges
• Clinical Case Management System
– A possible technological innovation
framework
– Descriptive statistics, or beyond?

60. 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?

61. 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?

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. Cardiovascular Surgery
Database
Scope
• Surgery Operations
– CABG
– Valvular heart
– Heart transplantation
– Aortic
– Atrial fibrillation
– Ventricular restoration
– Ventricular assist device
– Congenital heart
• Referred sites?

64. 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?

65. 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?

66. 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

67. A Brief Model

69. 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.

70. 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.

71. Backgrounds
• Cardiovascular Surgery
– Technology-intensive
– Techniques-oriented
• A specialty that needs
– Risk assessment
– Outcome prediction
– Performance feedback
• Data from paper -> time and manpower
demanding

72. 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

73. 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

74. 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

75. 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

76. 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

77. 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!

78. 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.

79. Database System Meta-
structure

80. 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

81. 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

82. Gantt Chart

83. Budget

84. 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

85. 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。

86. 報告目錄

87. 報告目錄

88. 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)