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

Disclosures: None 2

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

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)

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).

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.

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.

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

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

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.

Diagnostics: Standardize Mean Differences.

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.

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):2018-29.

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)

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 (0.98-1.28) 0.11 from admission Propensity-matched, Adjusted for ITB 1.03 (0.90-1.19) 0.61 Death ≤ 90 days 1.15 (1.05-1.27) 0.004 PE ≤ 1 yr. of discharge Propensity-IPW 1.05 (0.89-1.24) 0.56 DVT ≤ 1 yr. of discharge 1.53 (1.34-1.74) <.0001 2) Major Surgery  (N= 1445) (0.71-1.77) 0.63 after surgery 1.63 (0.70-3.78) 0.25 1.10 (0.76-1.60) PE ≤ 1 yr. after surgery 0.85 (0.35-2.10) 0.73 DVT ≤ 1 yr. after surgery (0.57-2.32) 0.70 3) Active Bleeding  (N=3017) 0.68 (0.52-0.88) 0.003 (0.39-0.95) 0.027 (0.59-0.90) 1.04 (0.67-1.61) 0.88 2.35 (1.56-3.52) <0.001 All hazard ratios reflect VCF use vs. No use. VCF = Vena cava filter IPW = Inverse probability weighted ITB = Immortal time bias

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

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.

Discussion