Data Fusion: Integrating patientreported survey data and EHR data for health outcomes research

Transcription

1 Data Fusion: Integrating patientreported survey data and EHR data for health outcomes research Lulu K. Lee, PhD Director, Health Outcomes Research Our Development Journey Research Goals Data Sources and Variables Methodology Framework Results Ongoing/Future Work Conclusions 1

2 Research Goals Primary goal: Using a propensity score matching methodology to identify a cohort of type 2 diabetes (T2D) patients in the electronic health records dataset (EHR) who closely resembled T2D patients in Kantar Health s PaCeR survey database. Secondary (ambitious) goal: Post-match, impute patient-reported variables from the PaCeR survey into the EHR data set which could then used to address research questions involving both clinical and patient-reported variables. 3 Data Sources EHR PaCeR (patient-reported survey data) Large US ambulatory EHR database Over 50 million unique patients, with over 28 million of those currently active Over 100,000 licensed providers, from a variety of specialties Patient-level historical data from 2012-current Data from the 2016 (N=97,500) US National Health and Wellness Survey (NHWS) Self-administered, Internet-based questionnaire from a sample of adults (aged 18 or older) Recruited from an Internet panel using a random stratified sampling framework to be representative of the US adult population (US Census) 2

3 EHR Variables Age Geography Common Ethnicity Diagnoses/ comorbidities Gender Treatment Journey Clinical Characteristics e.g.: Past Tx Switching Discontinuation Current Tx Dose e.g.: (past & present) BP, HbA1C, lipids Health Status (SF-36, EQ5D) Healthcare Resource Use Work Productivity & Activity Impairment Dr. visits ER visits Hospitalizations PaCeR Comorbidities Healthcare Resource Use (Dr. visits) Tx Satisfaction, Adherence Other Lifestyle PROs High-Level Overview of Methodology 1. Select Disease & Research 2. Create Cohorts & Common Variables Phase I Study and Data Preparation 3. Run Propensity Matching on Cohorts 4. Select Nearest Weighted EHR 5. Impute PaCeR Variables into Phase II Match and Imputation 6. Validate Results 7. Run Research Phase III Validation and Delivery 3

4 High-Level Overview of Methodology 1. Select Disease & Research 2. Create Cohorts & Common Variables Phase I Study and Data Preparation 3. Run Propensity Matching on Cohorts 4. Select Nearest Weighted EHR 5. Impute PaCeR Variables into Phase II Match and Imputation 6. Validate Results 7. Run Research Phase III Validation and Delivery Phase I: Select Disease & Research What therapeutic area to focus on? Type 2 diabetes (T2D) Why this therapeutic area? Large numbers of patients in both datasets Individually each dataset lacks important information about T2D T2D is an important condition that impacts patients and society What are the types of research questions we hope to address? What is the relationship between HbA1c control and quality of life? What is the utility (as measured by SF6D health utilities index) of patients that take an oral medication compared to patients that take both oral and injectable? 8 4

5 Phase I: Create Cohorts PaCeR Inclusion Criteria: Self-reported a physician diagnosis of T2D and completed detailed diabetes module in the survey (randomly assigned to panelists with T2D). EHR Inclusion Criteria: Active patients in the database AND They had at least one (1) encounter with a T2D diagnosis (identified using ICD-9, ICD-10, or SNOMED diagnosis codes or text strings in the diagnosis field indicating T2D) OR They had two (2) or more prescriptions of oral antidiabetic medications or GLP-1 injections 9 Phase I: Select Common Variables Considerations: The intention of the model was to predict the probability of EHR patients participating in the PaCeR survey Demographics, general health status variables Close attention was paid to missing values Variables with too many missing values were not used as common variables Final list of variables for match: Age, gender, ethnicity, geographic region, Charlson Comorbidity Index (CCI), individual comorbidities (e.g., heart attack, kidney disease), and common diabetes comorbidities (dyslipidemia, hypertension) 10 5

6 Phase II: Propensity Score Matching Weighted regression model to derive propensity scores representing probability of participating in the PaCeR survey Rationale: To ensure propensity scores were based on a nationally representative sample; PaCeR sampling weight used for PaCeR respondents and a value of 1 for EHR patients The propensity scores formed the basis for a variable ratio match of PaCeR respondents to EHR patients where the number of matches (K) was taken as the scaled PaCeR respondent s sampling weight A greedy matching algorithm macro (SAS) was used to produce the match 11 Phase II: Pre-Match Results PaCeR EHR A total of 4,113 patients with T2D* The mean age of patients with T2D was 58 years old 59.3% were male A total of 1,733,003 active patients with T2D The mean age of patients with T2D was 63 years old 46.8% were male *Note that there was a total of 8,424 diagnosed patients with T2D, however we only included those who completed the diabetes module. 6

7 Phase II: Post-Match Results EHR PaCeR N = Age N 221,117 4,113 Mean ± Std Dev ± ± Median (min - max) Gender ( ) ( ) Male (%) 128,269 (58.0%) 2,438 (59.3%) Female (%) 92,848 (42.0%) 1,675 (40.7%) Region of the US Northeast (%) 39,030 (17.7%) 753 (18.3%) Midwest (%) 49,918 (22.6%) 936 (22.8%) South (%) 84,425 (38.2%) 1,529 (37.2%) West (%) 47,744 (21.6%) 895 (21.8%) CCI categories 0 (%) 147,002 (66.5%) 2,579 (62.7%) 1 (%) 40,300 (18.2%) 746 (18.1%) 2 (%) 20,609 (9.3%) 445 (10.8%) 3+ (%) 13,206 (6.0%) 343 (8.3%) Phase II: Post-Match Diagnostics Figure 1. Density of linear predictors (lp) plot pre-match Figure 2. Density plot of linear predictors (lp) post-match Density Density 14 7

8 Phase 2a: Post-Match Diagnostics Figure 3. Absolute standardized effect size for common variables pre- vs. post-match. 15 High-Level Overview of Methodology 1. Select Disease & Research 2. Create Cohorts & Common Variables Phase I Study and Data Preparation 3. Run Propensity Matching on Cohorts 4. Select Nearest Weighted EHR 5. Impute PaCeR Variables into Phase II Match and Imputation 6. Validate Results 7. Run Research Phase III Validation and Delivery 8

9 Ongoing & Future Work Identifying the optimal imputation method Need to ensure that the imputed data can address research questions Quality of imputation checks use of metrics for classification error Iterative process may need to test out various different methods Imputations methods Adapted carry-forward Adapted mean value imputation Random imputation Parametric and non-parametric random regression and multiple imputation utilizing a specified regression function 17 Conclusions A propensity score matching method was successful in creating an EHR dataset of patients with T2D that resembled the PaCeR dataset of patients with T2D The challenge remains to find the optimal imputation method in order to create a more comprehensive EHR dataset This novel methodology may be applied to other disease conditions and to other types of disparate datasets (e.g., claims and patient surveys) 18 9

10 Acknowledgements Team members: Ryan Liebert, Kantar Health Tom Haskell, Kantar Health Consultant: Sherri Rose, Harvard Medical School Advisors: Pankaj Patel, Kantar Health Shaloo Gupta, Kantar Health Andy Stankus, Kantar Health Thank you! Appendix 20 10

11 Post-Match Results (Full) EHR PaCeR N = 221,117 4,113 CCI categories 0 (%) 147,002 (66.5%) 2,579 (62.7%) 1 (%) 40,300 (18.2%) 746 (18.1%) 2 (%) 20,609 (9.3%) 445 (10.8%) 3+ (%) 13,206 (6.0%) 343 (8.3%) Individual comorbidities Heart attack No (%) 207,088 (93.7%) 3,791 (92.2%) Yes (%) 14,029 (6.3%) 322 (7.8%) Congestive heart failure No (%) 212,006 (95.9%) 3,915 (95.2%) Yes (%) 9,111 (4.1%) 198 (4.8%) Cerebrovascular disease No (%) 210,141 (95.0%) 3,859 (93.8%) Yes (%) 10,976 (5.0%) 254 (6.2%) Rheumatic disease No (%) 209,105 (94.6%) 3,837 (93.3%) Yes (%) 12,012 (5.4%) 276 (6.7%) Peptic ulcer No (%) 208,852 (94.5%) 3,863 (93.9%) Yes (%) 12,265 (5.5%) 250 (6.1%) Kidney disease (Renal or chronic kidney disease No (%) 211,522 (95.7%) 3,873 (94.2%) Yes (%) 9,595 (4.3%) 240 (5.8%) Any cancer (Leukemia, lymphoma, non/metastatic tumor) No (%) 209,171 (94.6%) 3,836 (93.3%) Yes (%) 11,946 (5.4%) 277 (6.7%) Diagnosed depression No (%) 162,712 (73.6%) 2,928 (71.2%) Yes (%) 58,405 (26.4%) 1,185 (28.8%) Diabetes related comorbidities Dyslipidemia No (%) 82,043 (37.1%) 1,629 (39.6%) Yes (%) 139,074 (62.9%) 2,484 (60.4%) Hypertension No (%) 73,182 (33.1%) 1,355 (32.9%) Yes (%) 147,935 (66.9%) 2,758 (67.1%) 11