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Bariatric Surgery: Outcomes and SafetyMISS 2010
Bruce M. Wolfe, MDProfessor of Surgery
Oregon Health & Science University
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Iezzoni’s “Algebra of Effectiveness”
Patient Factors+
Effectiveness of Care+
Random Events=
Outcome
Lezzoni. Ann Thorac Surg 1994;58:1822 2
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Goals of Databases
• Define volume of care
• Determine outcomes
• Basis for determination of expected outcomes or ratio of actual/expected outcomes
• Risk adjustment
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Uses of Databases
• Self Assessment• Quality Assurance• Credentialing/Certification• Patient Information• Promotion• Research
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Types of Databases
• Administrative– Based on claims data– Coding by administrative
personnel
• Clinical– Data collected by clinical
personnel
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Administrative Database
• Discharge abstract data
• Population based
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Administrative Database
• Advantages– Completeness of data– Available– Low cost to acquire
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• Disadvantages– Coding imperfect– Done by
administrative personnel
– Inpatient only– Not procedure or
disease specific– Needed data not
present
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Examples of Administrative Databases
• Medicare national claims history• Nationwide Inpatient Sample (NIS)• University Healthsystem Consortium (UHC)• Patient discharge database (states)
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Charlson Comorbidity IndexJAMA October 2005
0 1 2 3
Flum 94% 6% 0.5% 0.1%
Santry 64 29 6 1.4
Zingmond 56 31 9 4
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Society of Thoracic Surgeons Database (STS)
• Variations of outcomes in cardiac surgery
• Hospital/surgeon volume an important factor
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Society of Thoracic Surgeons Database (STS)
• Prospective clinical data
• Multiple parameters– Possible risk factors– Outcomes
• Voluntary, self-reported
• Agree to audit
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Society of Thoracic Surgeons Database (STS)
• 1989 – Data collection begun
• Present > 2 million cases
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Compare STS with Administrative Database CABG
Source of Data
Risk-Adjusting Algorithm
Reported Volume
In-Hospital Mortality Rate
Predicted Mortality
Risk-Adjusted Rate
STS
Database
STS 505 4.2 5.4 3.1
Medicare None 423 4.7 N/A N/A
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Mack. J Thoracic Cardiovascular Surgery 2005;129:1309
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Reasons for Data Variation
• Medicare not primary payer
• Coding problems
• Variations of definitions
• No risk adjustment
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STS Controversies
• Low numbers at a site limit identification of variance from expected outcomes
• Volume-outcomes relationship is inconsistent• Factors involved include:
– High volume team at low volume center– Past experience– Process of care
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Bariatric Surgery Databases
• Single institution reports• Multiple institution reports• Meta-analysis• LABS: NIH multicenter consortium• BOLD: ASMBS/SRC• Bariatric NSQIP: ACS
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Obesity Surgery Mortality Risk Score
• BMI > 50kg/m²• Male• Hypertension• DVT/PE risk• Age ≥ 45y
DeMaria: SOARD 2007;3:34-30 17
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Obesity Surgery Mortality Risk Score
1 point for each risk factor:
0-1 A : Lowest risk2-3 B : Intermediate risk4-5 C : Highest Risk
DeMaria: SOARD 2007;3:34-40 18
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Obesity Surgery Mortality Risk Score: Validation
• 4 centers, 4431 patients
DeMaria: Ann Surg 2007;246:578 19
Class Mortality
A 0.2%
B 1.1%
C 2.4%
All 0.7%
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Development of Bariatric Surgery-Specific Risk Assessment Tool
Databases:
• National Hospital Discharge Summary
• AHRQ/NIS
• 25,000+ bariatric surgery cases
20Livingston: SOARD 2007;3:14-20
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Correlated with Adverse Event
• Chronic Pulmonary Disease• Hypertension• Diabetes Complications• Deficiency Anemia• Depression• Age• Male
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