1. AMERICAN SOCIETY OF EXTRACORPOREAL TECHNOLOGY (AmSECT)
53rd International Conference
April 14-18, 2015 • Marriott Tampa Waterside • Tampa, FL
Ethics in ECLS Care
Jeffrey P. Jacobs, M.D., FACS, FACC, FCCP Professor of Surgery, Johns Hopkins University Director, Andrews/Daicoff Cardiovascular Program, Surgical Director of Heart Transplantation and Extracorporeal Life Support Programs, Johns Hopkins All Children’s Heart Institute

2. Ethical Framework Framework for Ethical Decision Making
Ethics of Transparency
ECMO – Enough is enough – When should we stop?

3. Ethical Framework Framework for Ethical Decision Making
Ethics of Transparency
ECMO – Enough is enough – When should we stop?

4. Ethical Framework
Autonomy (Informed Consent),

5. Ethical Framework Autonomy (Informed Consent),
Beneficence (do good, paternalism),

6. Ethical Framework Autonomy (Informed Consent),
Beneficence (do good, paternalism),
Non-malfeasance (do no harm), and

7. Ethical Framework Autonomy (Informed Consent),
Beneficence (do good, paternalism),
Non-malfeasance (do no harm), and
Justice (equal treatment for all).

8. Ethical Framework
In any bioethical discussion, a balance of these four principles is difficult and often impossible to strike.
Complicating these important issues are outcomes data, socioeconomic realities, religious beliefs, and philosophical tenets

9. Ethical Framework Framework for Ethical Decision Making
Ethics of Transparency
ECMO – Enough is enough – When should we stop?

10. New Directions for Quality Improvement in Congenital Heart Disease
Jeffrey P. Jacobs, M.D., FACS, FACC, FCCP Professor of Surgery, Johns Hopkins University Director, Andrews/Daicoff Cardiovascular Program, Surgical Director of Heart Transplantation and Extracorporeal Life Support Programs, Johns Hopkins All Children’s Heart Institute

11. Building Blocks Towards Transparency
Jeffrey P. Jacobs, M.D., FACS, FACC, FCCP Professor of Surgery, Johns Hopkins University Director, Andrews/Daicoff Cardiovascular Program, Surgical Director of Heart Transplantation and Extracorporeal Life Support Programs, Johns Hopkins All Children’s Heart Institute

12. Rationale for Public Reporting
It is our professional responsibility
Patients and their families have the right to know
STS and its members are committed to transparency and accurate reporting of CT surgery outcomes
It is the next responsible step in continuing our dedication to professional accountability
STS continues to lead efforts in the reporting of clinical outcomes data to the public
If we do not publish our own results, the public will judge our performance based on unadjusted or inadequately adjusted administrative data
Jacobs
Source:

13. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Requirements
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

14. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

15. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement .

16. .

17. International Pediatric and Congenital Cardiac Code (IPCCC)
Joint collaborative international effort between pediatric cardiologists and pediatric cardiac surgeons to further standardize nomenclature
International Pediatric and Congenital Cardiac Code (IPCCC)
based on the EACTS-STS nomenclature and the AEPC nomenclature
Proposed at The World Congress in Toronto in 2001
Presented at World Congress in Argentina in 2005
Matured and Evolved at World Congress in Australia in 2009
Matured and Evolved at World Congress in South Africa in 2013

18. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

19. 8 centers in Canada perform pediatric and congenital heart surgery
The Report of the 2010 STS Congenital Heart Surgery Practice and Manpower Survey
undertaken by the Society of Thoracic Surgeons Workforce on Congenital Heart Surgery
125 centers in the United States of America perform pediatric and congenital heart surgery
8 centers in Canada perform pediatric and congenital heart surgery
Jacobs ML, Daniel M, Mavroudis C, Morales DLS, Jacobs JP, Fraser CD, Turek JW, Mayer JE, Tchervenkov C, Conte JV. Report of the 2010 Society of Thoracic Surgeons Congenital Heart Surgery Practice and Manpower Survey. The Annals of Thoracic Surgery, 2011;92:762–9, August 2011.

20. Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK
Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK. Executive Summary: The Society of Thoracic Surgeons Congenital Heart Surgery Database – Twenty-first Harvest – (July 1, 2010 – June 30, 2014). The Society of Thoracic Surgeons (STS) and Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, North Carolina, United States, Fall 2014 Harvest.

21. Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK
Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK. Executive Summary: The Society of Thoracic Surgeons Congenital Heart Surgery Database – Twenty-first Harvest – (July 1, 2010 – June 30, 2014). The Society of Thoracic Surgeons (STS) and Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, North Carolina, United States, Fall 2014 Harvest.

22. Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK
Jacobs JP, Jacobs ML, Mavroudis C, Tchervenkov CI, Pasquali SK. Executive Summary: The Society of Thoracic Surgeons Congenital Heart Surgery Database – Twenty-first Harvest – (July 1, 2010 – June 30, 2014). The Society of Thoracic Surgeons (STS) and Duke Clinical Research Institute (DCRI), Duke University Medical Center, Durham, North Carolina, United States, Fall 2014 Harvest.

23. STS Database Penetrance in USA
The STS Congenital Heart Surgery Database (STS-CHSD) is the largest clinical database in the world for congenital and pediatric cardiac surgery.
The Report of the 2010 STS Congenital Heart Surgery Practice and Manpower Survey, undertaken by the STS Workforce on Congenital Heart Surgery, documented that 125 hospitals in the United States of America and 8 hospitals in Canada perform pediatric and congenital heart surgery.
The STS-CHSD contains data from 120 of the 125 hospitals (96% penetrance by hospital) in the United States of America and 3 of the 8 centers in Canada.

25. The Growth in JCCVSD

26. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

27. Complexity stratification
Complexity stratification is a method of analysis in which the data are divided into relatively homogeneous groups (called strata).

28. Two traditional methodologies for Complexity Adjustment
Risk Adjustment in Congenital Heart Surgery-1 (RACHS-1 )
Aristotle Complexity Score
Aristotle Basic Complexity Score (ABC Score)
Aristotle Comprehensive Complexity Score
Jacobs JP, Jacobs ML, Lacour-Gayet FG, Jenkins KJ, Gauvreau K, Bacha EA, Maruszewski B, Clarke DR, Tchervenkov CI, Gaynor JW, Spray, TL, Stellin G, O'Brien SM, Elliott MJ, Mavroudis C. Stratification of Complexity Improves Utility and Accuracy of Outcomes Analysis in a Multi-institutional Congenital Heart Surgery Database – Application of the RACHS-1 and Aristotle Systems in the STS Congenital Heart Surgery Database. Pediatric Cardiology, 2009, DOI /s

29. STS 2006 Congenital Database 45,635 cases
RACHS-1 Level
Jacobs JP, Jacobs ML, Lacour-Gayet FG, Jenkins KJ, Gauvreau K, Bacha EA, Maruszewski B, Clarke DR, Tchervenkov CI, Gaynor JW, Spray, TL, Stellin G, O'Brien SM, Elliott MJ, Mavroudis C. Stratification of Complexity Improves Utility and Accuracy of Outcomes Analysis in a Multi-institutional Congenital Heart Surgery Database – Application of the RACHS-1 and Aristotle Systems in the STS Congenital Heart Surgery Database. Pediatric Cardiology, 2009, DOI /s

30. STS 2006 Congenital Database 45,635 cases
Aristotle Basic Level
Jacobs JP, Jacobs ML, Lacour-Gayet FG, Jenkins KJ, Gauvreau K, Bacha EA, Maruszewski B, Clarke DR, Tchervenkov CI, Gaynor JW, Spray, TL, Stellin G, O'Brien SM, Elliott MJ, Mavroudis C. Stratification of Complexity Improves Utility and Accuracy of Outcomes Analysis in a Multi-institutional Congenital Heart Surgery Database – Application of the RACHS-1 and Aristotle Systems in the STS Congenital Heart Surgery Database. Pediatric Cardiology, 2009, DOI /s

31. From Subjective Probability to Objective Data
STAT Mortality Score
The Society of Thoracic Surgeons - European Association for Cardio-Thoracic Surgery Congenital Heart Surgery Mortality Score
and
STAT Mortality Categories
The Society of Thoracic Surgeons - European Association for Cardio-Thoracic Surgery Congenital Heart Surgery Mortality Categories
O'Brien SM, Clarke DR, Jacobs JP, Jacobs ML, Lacour-Gayet FG, Pizarro CP, Welke KF, Maruszewski B, Tobota Z, Miller WJ, Hamilton L , Peterson ED, Mavroudis C, Edwards FH. An empirically based tool for analyzing mortality associated with congenital heart surgery. The Journal of Thoracic and Cardiovascular Surgery, 2009 Nov;138(5), November 2009.

32. STAT Mortality Categories
STAT Mortality Score and Categories
were previously developed based on analysis of
77,294 operations entered in the STS Congenital Heart Surgery Databases and the EACTS Congenital Heart Surgery Database
EACTS = 33,360 operations
STS = 43,934 operations

33. STAT Mortality Categories
Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators.

34. STAT Mortality Categories
Operations were sorted by increasing risk and grouped into 5 categories that were designed to
minimize within-category variation
and
maximize between-category variation

35. Combined EACTS and STS Congenital Heart Surgery Databases: 111,494 index cardiac operations
STAT Category
Jacobs JP, Jacobs ML, Maruszewski B, Lacour-Gayet FG, Tchervenkov CI, Tobota Z, Stellin G, Kurosawa H, Murakami A, Gaynor JW, Pasquali SK, Clarke DR, Austin EH 3rd, Mavroudis C. Initial application in the EACTS and STS Congenital Heart Surgery Databases of an empirically derived methodology of complexity adjustment to evaluate surgical case mix and results. Eur J Cardiothorac Surg Nov;42(5): doi: /ejcts/ezs026. Epub 2012 Jun 14. PMID:

36. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

37. Database Audit
In 2013, the audit of the STS Congenital Heart Surgery Database documented the following rates of completeness and accuracy for the specified fields of data:
Primary Diagnosis
Completeness = 100%, Accuracy = 96.2%
Primary Procedure
Completeness = 100%, Accuracy = 98.7%
Mortality Status at Hospital Discharge
Completeness = 100%, Accuracy = 98.8%

38. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

39. The MultiSocietal Database for Pediatric and Congenital Heart Disease
The Society of Thoracic Surgeons (STS) Congenital Database Taskforce, chaired by Jeffrey P. Jacobs, MD
The European Association for Cardio-Thoracic Surgery (EACTS) Congenital Heart Committee, headed by Bohdan Maruszewski, PhD, MD
The Society of Thoracic Surgeons (STS) Congenital Database Taskforce Core Users Group, headed by Hal Walters
The Society of Thoracic Surgeons (STS) Congenital Database Data Verification Subcommittee, headed by David Robinson Clarke, MD
The Aristotle Institute, developers of the Aristotle Complexity Score, headed by Francois Lacour-Gayet, MD
The Multi-Center Panel of Experts for Cardiac Surgical Outcomes, developers of the Risk Adjustment in Congenital Heart Surgery-1 system, headed by Kathy Jenkins, MD
The Pediatric Cardiac Intensive Care Society (PCICS) VPS Database
The Congenital Cardiac Anesthesia Society (CCAS) Database, headed by David Vener, MD
The Joint Council on Congenital Heart Disease, chaired by Gerard Martin, MD
ACC, AHA, ABP, AAP
The Association for European Paediatric Cardiology (AEPC) Nomenclature Committee headed by Rodney Franklin, MD
The Pediatric Committee of the International Consortium of Evidence Based Perfusion
The International Working Group for Mapping and Coding of Nomenclatures for Paediatric and Congenital Heart Disease, otherwise known as the Nomenclature Working Group, headed by Christo Tchervenkov, MD
The World Society for Pediatric and Congenital Heart Surgery, headed by Christo Tchervenkov, MD
The Center for Quality Improvement and Patient Safety of Agency for Healthcare Research and Quality (AHRQ) of the United States Department of Health and Human Services, represented by Darryl T. Gray, MD, ScD
The Birth Defect Branch of the Centers for Disease Control and Prevention (CDC)

40. Jacobs JP. (Editor) Cardiology in the Young Supplement: Databases and The Assessment of Complications associated with The Treatment of Patients with Congenital Cardiac Disease, Prepared by: The Multi-Societal Database Committee for Pediatric and Congenital Heart Disease, Cardiology in the Young, Volume 18, Supplement S2, pages 1 –530, December 9, 2008.

41. Tenth Annual Meeting of The Multi-Societal Database Committee for Pediatric and Congenital Heart Disease
Meeting Theme: Dashboards and Quality Atlanta, Georgia Hosted by Emory The Emory Conference Center, Thursday and Friday, September 4 and 5, 2014

42. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

43. 2006 – 2010
Nine benchmark
operations

44. Benchmark Operations Complication Rates, PLOS, and Cost

45. Average excess cost per case in patients with any postoperative complication compared with those without a complication was $56,584 and ranged from $16,097 (ASD repair) to $146,571 (truncus arteriosus repair).
Average excess cost per case associated with major complications was higher ($132,483), ranging from $52,127 (VSD repair) to $261,188 (truncus arteriosus repair).

46. Congenital Heart Disease Meaningful Multi-institutional Outcomes Analysis
Accomplishments
Common Language = Nomenclature
Mechanism of Data Collection (Database - Registry)
Mechanism of Evaluating Case Complexity
Mechanism to Verify Data Validity and Accuracy
Collaboration Between Subspecialties
Longitudinal Follow-Up and Linked Databases
Quality Improvement

47. 21 Quality Measures for Pediatric and Congenital Cardiac Surgery
Jacobs JP, Jacobs ML, Austin EH, Mavroudis M, Pasquali SK, Lacour–Gayet FG, Tchervenkov CI, Walters III HW, Bacha EA, del Nido PJ, Fraser CD, Gaynor JW, Hirsch JC, Morales DLS, Pourmoghadam KK, Tweddell JT, Prager RL, Mayer JE. Quality Measures for Congenital and Pediatric Cardiac Surgery. The World Journal for Pediatric and Congenital Heart Surgery 2012 Volume 3, Issue 1, Pages 32-47, DOI / , January 2012.

48. Building Blocks Towards Transparency
Jeffrey P. Jacobs, M.D., FACS, FACC, FCCP Professor of Surgery, Johns Hopkins University Director, Andrews/Daicoff Cardiovascular Program, Surgical Director of Heart Transplantation and Extracorporeal Life Support Programs, Johns Hopkins All Children’s Heart Institute

49. 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
The empirically derived 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model incorporates adjustment for both procedure type and patient specific factors.

50. Adjustment for Case Mix
“Differences in medical outcomes may result from disease severity, treatment effectiveness, or chance.
Because most outcome studies are observational….risk adjustment is necessary
to account for case mix”
Shahian DM, Blackstone EH, Edwards FH, Grover FL, Grunkemeier GL, Naftel DC, Nashef SA, Nugent WC, Peterson ED.
STS workforce on evidence-based surgery. Cardiac surgery risk models: a position article.
Ann Thorac Surg. 2004;78(5):1868–77.

51. Developing Risk Models:
When Risk Models were developed by STS for Isolated CAB:
the STS-ACSD included > 50,000 Isolated CAB procedures per year
In a recent one year period in STS-CHSD:
VSD closure: n = % of all
Arterial Switch: n = % of all
Norwood: n = % of all
More than 150 unique procedure codes

52. Developing Risk Models:
A Step-wise Process
The Aristotle Basic Complexity Levels
(ABC Levels)
The Risk Adjustment for Congenital Heart Surgery (RACHS-1) Categories
The STS-EACTS Mortality Categories
(STAT Mortality Categories)

53. Developing Risk Models:
Prior to 2014
Model 1 - STAT Mortality Category
Model 1 was applied to neonates and infants only. It adjusted for the following risk factors:
1. Age
2. Weight
3. Type of procedure
4. STAT Mortality Category.
The variables age, weight, and procedure were chosen because they are important risk factors that can be ascertained with a high degree of accuracy and have been collected with a high degree of data completeness throughout the history of the STS Congenital Heart Surgery Database. Adjustment for procedure was facilitated by the use of STAT Mortality Category

54. 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
Variable
Age group
Primary procedure*
Weight (neonates and infants)
Prior cardiothoracic operation
Any non-cardiac congenital anatomic abnormality
Any chromosomal abnormality or syndrome
Prematurity (neonates and infants)
Preoperative Factors
·         Preoperative/Preprocedural mechanical circulatory support (IABP, VAD, ECMO, or CPS)
·         Shock, Persistent at time of surgery
·         Mechanical ventilation to treat cardiorespiratory failure
·         Renal failure requiring dialysis and/or Renal dysfunction
·         Preoperative neurological deficit
·         Any other preoperative factor
Procedure x age group indicator (random effects)
* Coefficients obtained via shrinkage estimation with STAT Mortality Category as an auxiliary variable.

55. 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
25,476 index operations were included in analysis
Overall discharge mortality 3.7%, n = 943

57. 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
All index cardiac operations in the STS-CHSD (January 1, 2010–December 31, 2013) were eligible for inclusion.
Isolated PDA closures in patients <2.5kg were excluded, as were centers with >10% missing data and patients with missing data for key variables.

58. 52,267 operations from 87 centers were included.
2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
52,267 operations from 87 centers were included.

59. Development Sample C-Stat Validation Sample C-Stat
Model
Covariates
Development Sample C-Stat
Validation Sample C-Stat 1
STAT Levels
C = 0.771
C = 0.787 2
STAT Levels + age/weight
C = 0.818
C = 0.819 3
STAT Levels + age/weight + patient factors
C = 0.859
C = 0.854 4
Primary procedure + age/weight
C = 0.846
C = 0.833
(Final)
Primary procedure + age/weight + patient factors
C = 0.874
C = 0.860
11/20/2018

60. Development Sample C-Stat Validation Sample C-Stat
2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
Model
Covariates
Development Sample C-Stat
Validation Sample C-Stat 1
STAT Levels
C = 0.771
C = 0.787 2
STAT Levels + age/weight
C = 0.818
C = 0.819 3
STAT Levels + age/weight + patient factors
C = 0.859
C = 0.854 4
Primary procedure + age/weight
C = 0.846
C = 0.833
(Final)
Primary procedure + age/weight + patient factors
C = 0.874
C = 0.860
11/20/2018

61. Distribution of hospital-specific O/E ratios for Operative Mortality with 95% confidence intervals
11/20/2018

62. Very Low Volume Programs
Distribution of hospital-specific O/E ratios for Operative Mortality with 95% confidence intervals:
Very Low Volume Programs
11/20/2018

63. Lower Than Expected Mortality
Distribution of hospital-specific O/E ratios for Operative Mortality with 95% confidence intervals:
Lower Than Expected Mortality
11/20/2018

64. Same as Expected Mortality
Distribution of hospital-specific O/E ratios for Operative Mortality with 95% confidence intervals:
Same as Expected Mortality
11/20/2018

65. Higher Than Expected Mortality
Distribution of hospital-specific O/E ratios for Operative Mortality with 95% confidence intervals:
Higher Than Expected Mortality
11/20/2018

66. What about Confidence Intervals
Total Programs
Programs with higher-than expected mortality
Programs with same-as expected mortality
Programs with lower-than expected mortality
Number (%)
80% Confidence Intervals
87 (100%)
19 (22%)
53 (61%)
15 (17%)
90% Confidence Intervals
13 (15%)
64 (74%)
10 (11%)
95% Confidence Intervals
12 (14%)
68 (78%)
7 (8%)
99% Confidence Intervals
78 (90%)
2 (2%)
11/20/2018

67. What about Confidence Intervals
Total Programs
Programs with higher-than expected mortality
Programs with same-as expected mortality
Programs with lower-than expected mortality
Number (%)
80% Confidence Intervals
87 (100%)
19 (22%)
53 (61%)
15 (17%)
90% Confidence Intervals
13 (15%)
64 (74%)
10 (11%)
95% Confidence Intervals
12 (14%)
68 (78%)
7 (8%)
99% Confidence Intervals
78 (90%)
2 (2%)
11/20/2018

68. What about Confidence Intervals
Total Programs
Programs with higher-than expected mortality
Programs with same-as expected mortality
Programs with lower-than expected mortality
Number (%)
80% Confidence Intervals
87 (100%)
19 (22%)
53 (61%)
15 (17%)
90% Confidence Intervals
13 (15%)
64 (74%)
10 (11%)
95% Confidence Intervals
12 (14%)
68 (78%)
7 (8%)
99% Confidence Intervals
78 (90%)
2 (2%)
11/20/2018

69. What about Confidence Intervals
Total Programs
Programs with higher-than expected mortality
Programs with same-as expected mortality
Programs with lower-than expected mortality
Number (%)
80% Confidence Intervals
87 (100%)
19 (22%)
53 (61%)
15 (17%)
90% Confidence Intervals
13 (15%)
64 (74%)
10 (11%)
95% Confidence Intervals
12 (14%)
68 (78%)
7 (8%)
99% Confidence Intervals
78 (90%)
2 (2%)
11/20/2018

70. What about Confidence Intervals
Total Programs
Programs with higher-than expected mortality
Programs with same-as expected mortality
Programs with lower-than expected mortality
Number (%)
80% Confidence Intervals
87 (100%)
19 (22%)
53 (61%)
15 (17%)
90% Confidence Intervals
13 (15%)
64 (74%)
10 (11%)
95% Confidence Intervals
12 (14%)
68 (78%)
7 (8%)
99% Confidence Intervals
78 (90%)
2 (2%)
11/20/2018

71. 2014 STS Congenital Heart Surgery Database (STS-CHSD) Mortality Risk Model
The 2014 STS-CHSD Mortality Risk Model facilitates description of outcomes (mortality) adjusted for procedural and for patient-level factors.
Identification of low performing and high performing programs may be useful in facilitating quality improvement efforts.

72. Jacobs JP, O'Brien SM, Pasquali SK, Jacobs ML, Lacour-Gayet FG, Tchervenkov CI, Austin EH 3rd, Pizarro C, Pourmoghadam KK, Scholl FG, Welke KF, Gaynor JW, Clarke DR, Mayer JE Jr, Mavroudis C. Variation in Outcomes for Risk-Stratified Pediatric Cardiac Surgical Operations: An Analysis of the STS Congenital Heart Surgery Database. Ann Thorac Surg Aug; 94(2): Epub 2012 Jun 15. PMID:

73. Jacobs JP, O'Brien SM, Pasquali SK, Jacobs ML, Lacour-Gayet FG, Tchervenkov CI, Austin EH 3rd, Pizarro C, Pourmoghadam KK, Scholl FG, Welke KF, Gaynor JW, Clarke DR, Mayer JE Jr, Mavroudis C. Variation in Outcomes for Risk-Stratified Pediatric Cardiac Surgical Operations: An Analysis of the STS Congenital Heart Surgery Database. Ann Thorac Surg Aug; 94(2): Epub 2012 Jun 15. PMID:

74. Funnel Plots: Centers characterized as outliers
STAT Category Outliers High Performing
Jacobs JP, O'Brien SM, Pasquali SK, Jacobs ML, Lacour-Gayet FG, Tchervenkov CI, Austin EH 3rd, Pizarro C, Pourmoghadam KK, Scholl FG, Welke KF, Gaynor JW, Clarke DR, Mayer JE Jr, Mavroudis C. Variation in Outcomes for Risk-Stratified Pediatric Cardiac Surgical Operations: An Analysis of the STS Congenital Heart Surgery Database. Ann Thorac Surg Aug; 94(2): Epub 2012 Jun 15. PMID:

75. IMPLICATION
Variation in outcomes across centers demonstrates opportunities for multi–institutional collaboration to improve quality.

76. “Science tells us what we can do; Guidelines what we should do; &
Registries what we are actually doing.”

77. Ethical Framework Framework for Ethical Decision Making
Ethics of Transparency
ECMO – Enough is enough – When should we stop?

78. ECMO – Enough is enough – When should we stop?
AMERICAN SOCIETY OF EXTRACORPOREAL TECHNOLOGY (AmSECT)
53rd International Conference
April 14-18, 2015 • Marriott Tampa Waterside • Tampa, FL
ECMO – Enough is enough – When should we stop?
Jeffrey P. Jacobs, M.D., FACS, FACC, FCCP Professor of Surgery, Johns Hopkins University Director, Andrews/Daicoff Cardiovascular Program, Surgical Director of Heart Transplantation and Extracorporeal Life Support Programs, Johns Hopkins All Children’s Heart Institute

79. - Marc de Leval

86. “Permanent” Painful mediastinal tracheostomy
Blunt Tracheal Trauma
“Permanent” Painful mediastinal tracheostomy

91. Conclusions
Medicine is blessed with incredible successes

92. Conclusions Medicine is blessed with incredible successes
Medicine is tortured with incredible failures

93. Conclusions Medicine is blessed with incredible successes
Medicine is tortured with incredible failures
All Cases are different

94. Conclusions Medicine is blessed with incredible successes
Medicine is tortured with incredible failures
All Cases are different
The decision of “When should we stop” is not easy

95. Conclusions Medicine is blessed with incredible successes
Medicine is tortured with incredible failures
All Cases are different
The decision of “When should we stop” is not easy
The decision of “When should we stop” must be compassionately, scientifically, and ethically individualized

96. Ethical Framework Autonomy (Informed Consent),
Beneficence (do good, paternalism),
Non-malfeasance (do no harm), and
Justice (equal treatment for all).

97. “Science tells us what we can do; Guidelines what we should do; &
Registries what we are actually doing.”