1. Using Observational Data in Outcomes Research: Advantages and Drawbacks
Insights Using Data from California
Richard H. White MD
Division of General Medicine, UC Davis
Director, UCDMC Anticoagulation Service

2. Disclosures:
None 2

3. You Can Not Always Conduct a Clinical Trial.
Fiscal constraints
Operational constraints
Ethical constraints
Potential bias by excluding special populations
Potential bias by not including a representative population

4. Observational Data Defined as routinely collected health data:
Health Administrative Data (government)
Health Administrative Data (insurance provider)
Health Administrative Data (other, e.g. Kaiser)
Cancer Registry
Disease Registry (e.g. RIETE registry for VTE)
Electronic Health Medical Records
Government Statistical Databases
Primary Care Databases (e.g. GPRD, CPRD, IPCI)

5. Key Elements for Sound Research
Need a well defined population followed over time
Quality of the data source is critical; varies with the database
Exposure parameter; is it valid? (e.g. anti-Phospholipid Ab Synd)
Outcome data; have the outcomes been validated? (e.g. DVT)
Data analysis: must use sound methodology and appropriate statistical analysis, which must account for potential confounding (as the data allows).

6. Example California Patient Discharge Database
Hospital discharge database (lists diagnoses and procedures)
All patients hospitalized in a public hospital (not VAH, military)
Minimal # of admissions to hospitals in adjoining states (Ore, Nev, Ari)
Cases can be linked over time (by RLN, encrypted SSN) and since 2005 with Emergency Department (ED), Ambulatory Surgery (AS) and Master Death files.
Great for exposures that require hospitalization and outcomes that require re-hospitalization: e.g. PE & Recurrent PE; Total Knee Arthroplasty & re-hospitalization for prosthetic knee infection.

7. Constraints Using Hospital Discharge Data
The exposure (e.g. Acute DVT) must require ED, AS or Hospital Admission for Dx. Outpatient diagnoses missing.
Exposure has to be listed in coded discharge data.
(You have to know all the ICD-10 codes, and coding rules!!!).
Exposure should be validated (less critical for procedures)
Race/ethnicity may not be accurately coded.
Some codes may be non-specific (e.g. VTE, NOS).
Patients emigrate over time, or have outcomes when out of state.
Outcomes may occur in outpatient setting.

8. Research Questions that can be Asked.
Simple descriptive epidemiology analysis
Analysis of specific risk factors (e.g. race/ethnicity)
Comparisons of outcomes (e.g. VTE after hip vs. knee arthroplasty)
Time course of outcomes after specific exposure
Treatment effect

9. Example: IVC filter in Patients with Acute VTE
We compared outcomes in patients with acute VTE who received an IVC filter vs. those not receiving an IVC filter. Such studies are designed exactly like randomized clinical trials
Direct comparison of those with and without IVC filter is biased. Only sicker patients may get a filter, or only healthier patients may get a filter; only insured patients may get a filter, only large hospitals may place IVC filters.
We assumed that coding of the exposure (insertion of an IVC filter) was valid (Hospitals do not want to be caught coding for a procedure that was not done; Medicare audits hospitals

10. Methodology: Mimic Randomized Trial
Stratified the analysis by indication for IVC filter.
Acute bleeding present on admission or that developed during the hospitalization (indication for an IVC filter)
Immediate before or after major surgery
No contraindication to anticoagulation
Developed a propensity model (to receive an IVC filter).
Then we used both propensity matching and inverse probability weighting (IPW) to create acceptable balance in baseline covariates in the IVC+ and IVC- groups.
Multivariate proportional hazard modeling, adjusting for potential confounders.

11. Diagnostics: Standardize Mean Differences.

12. Have to Account for Immortal Time Bias.
You can not simply compare patients who received an IVC filter to those who did not (as has been done in numerous published articles).
Patients who do not get a filter may die before the filter can be placed!
Conversely, patients who get an IVC filter are “immortal” (always alive) through the day they receive the filter.
We used IVC filter placement as a time-dependent covariate in the proportional hazard model; can use other methods.

13. Outcomes after Vena Cava Filter Use in Non-cancer Patients with Acute Venous Thromboembolism:
A Population-Based Study. RH White, MD, Ann Brunson, Patrick S. Romano, Zhongmin Li, PhD , Ted Wun,
Circulation 2016 May 24;133(21):

14. Outcomes Death < 30 days, Death < 90 days, Death < 180 days
Recurrent PE (a purported benefit)
Acute DVT (a reported adverse outcome associated with IVC use)

15. Study Sub-Group Outcome Analytic Method No Contraindication
Hazard Ratio
(VCF Use
vs. No VCF)
95% CIs
P- value
No Contraindication
To Anticoagulation
  (N = 80,679)
Death ≤ 30 days
Propensity-IPW, Adjusted for ITB
1.12
( )
0.11
from admission
Propensity-matched, Adjusted for ITB
1.03
( )
0.61
Death ≤ 90 days
1.15
( )
0.004
PE ≤ 1 yr. of discharge
Propensity-IPW
1.05
( )
0.56
DVT ≤ 1 yr. of discharge
1.53
( )
<.0001
2) Major Surgery
 (N= 1445)
( )
0.63
after surgery
1.63
( )
0.25
1.10
( )
PE ≤ 1 yr. after surgery
0.85
( )
0.73
DVT ≤ 1 yr. after surgery
( )
0.70
3) Active Bleeding
 (N=3017)
0.68
( )
0.003
( )
0.027
( )
1.04
( )
0.88
2.35
( )
<0.001
All hazard ratios reflect VCF use vs. No use.
VCF = Vena cava filter
IPW = Inverse probability weighted
ITB = Immortal time bias

16. Benefits of Observational Administrative Data
Big numbers!! Potentially lots of observations.
You do not have to wait for a clinical trial, which might never be performed.
You can include all of the patients who had the exposure.
Ability to stratify by clinical indication/severity etc.
Can analyze effects of sex, race/ethnicity, insurance, weight, renal failure, severity of illness on admission, risk of mortality etc
Government (State, Medicare) data allow identification of the outcomes irrespective of wherever it occurs. e.g. Patient may have a total knee arthroplasty (TKA) at one hospital and have the revision for infection at another hospital

17. Drawbacks of Observational Administrative Data
Obtaining the data may take time and $$.
It may not include all the data you want (e.g. clinical/laboratory data).
Must learn about coding of data (e.g. ICD-9-CM and ICD-10 coding)- which is not a small undertaking.
Must ask an answerable question.
Methodology must be sound, and it requires statistical/programming expertise. (Get an MPH!)
Changes in practice over time may make it difficult to address some questions. e.g., outpatient diagnosis and treatment of VTE.

18. Discussion