Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data

Transcription

1 Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data Kaelber, et al, JAMIA, 3 July 2012 David C Kaelber, MD, PhD, MPH, FAAP, FACP Associate Professor of Internal Medicine, Pediatrics, Epidemiology, and Biostatistics Director of the Center for Clinical Informatics Research and Education Chief Medical Informatics Officer The MetroHealth System Case Clinical and Translational Science Center (CTSC) Case Western Reserve University

2 Learning Objectives To discuss and demonstrate the potential of electronic health records and clinical research informatics tools for retrospective clinical research. To provide a prototypical case example using electronic health records for retrospective clinical research. To examine the principles behind how electronic health records and clinical research informatics tools can transform clinical research.

3 Disclosures I receive no compensation from Epic, although tens of millions of dollars of institutional funds and my academic career are committed to Epic J. I have no financial relationship with Explorys. The MetroHealth System was one of the first Explorys partners and contributes all of its electronic health record data in exchange for use of the Explorys Explore tool. And Explorys seems to be helping my academic career J. Foster, Gilder, and Jain are Explorys employees

4 Outline Introduction/Background VTE Case Example Discussion/Conclusions/Questions

5

6 Explorys Pooled, normalized, standardized EHR data 13,971,540 million patients (10/2/2012; +~50,000/week) Web interface Google like speed Data Types Demographic (gender, age, race/ethnicity, insurance, zip-3) Diagnoses (ICD-9, SNOMED-CT) Procedures (CPT) Labs (LOINC) Medications (RxNorm) Vital signs

7 Explorys Patients 80 hospitals, hundreds of ambulatory practices and thousands of providers caring for 14 million patients.

8 Explorys Partners

9

10 Outline Introduction/Background VTE Case Example Discussion/Conclusions/Questions

11 26,714 patients 461 incident VTE events 14 year prospective study 18 years from enrollment to publication Data elements (demographics, diagnoses, medications, vital signs)

12 Explorys Study Design Design: Cohort study (retrospec4ve) Se8ng: All pa4ents in Explorys as of May of 2011 Pa4ents: 959,030 pa4ents ages 26 years and order seen between June 1999 and May 2011 (Total popula4on ~4.5 million but <26 or missing data) Main Outcome Measures: VTEs (venous thrombolembolic events) associated with pa4ents factors (BMI, height, gender, race/ethnicity)

13 Standard Informatics Ontologies for Study Demographics age, gender, race/ethnicity Vital signs height, weight Diagnoses ICD-9 (mapped to Systematized Unified Medical Language System Nomenclature for Medicine Clinical Terms (UMLS) (SNOMED-CT)) Labs - Logical Observation Identifiers Names and Codes (LOINC ) Medications - RxNorm

14 Study Design (continued) VTE defined as: SNOMED- CT related Deep Venous Thrombosis (DVT) or Pulmonary Embolism (PE) codes AND RxNorm prescrip4on for Thromboly4c or An4coagulant First BMI and most recent height used Duplicate pa4ents iden4fied Social Security Death Index used For sta4s4cal de- inden4fica4on, numbers rounded to the nearest 10

15 Study Design (continued) Weight categoriza4ons: Normal Weight (BMI < 25 kg/m2) Overweight (BMI between 25 kg/m2 and 30 kg/m2) Obese (BMI > kg/m2) Height categoriza4ons (different for males/females): Short Medium Tall Time in study: 1-4,377 days (1,280 average) Fisher s exact test and Cochran- Armitage test IRB deemed not human subjects research

16 Results Study Population (female) Women # of pa4ents # of VTE events (%) Mean Age (y) Mean BMI (kg/m^2) Smoking % (#) Diabetes % (#) Diabetes+A1C+ADA % (#) Heart Disease % (#) Hormone Therapy % (#) Death Rate % (#) Normal (BMI<25) 216,090 2,950 (1) (39,630) 4 (9,340) 0.4 (900) 17 (37,470) 35 (75,330) 4 (8,350) BMI (kg/m2) Overweight (BMI ) 150,210 2,800 (2) (26,740) 10 (14,530) 3 (4,110) 20 (30,160) 29 (43,910) 3 (5,000) Obese (BMI>=30) 201,160 6,100 (3) (36,830) 22 (43,550) 7 (14,540) 23 (45,410) 18 (35,340) 3 (6,580)

17 Results Study Population (male) Men # of pa4ents # of VTE events (%) Mean Age (y) Mean BMI (kg/m^2) Smoking % (#) Diabetes % (#) Diabetes+A1C+ADA % (#) Heart Disease % (#) Hormone Therapy % (#) Death Rate % (#) Normal (BMI<25) 92,330 2,060 (2) (23,380) 8 (7,770) 3 (2,280) 26 (24,210) n/a 8 (7,350) BMI (kg/m2) Overweight (BMI ) 159,730 3,300 (2) (31,000) 12 (18,830) 3 (4,110) 28 (44,450) n/a 4 (7,160) Obese (BMI>=30) 139,510 4,000 (3) (27,550) 23 (32,300) 10 (14,540) 30 (42,030) n/a 4 (5,800)

18 Results VTE by Weight then Height (female) Women Weight (kg/m2) Height (cm) Persons at risk Pa4ents with VTE VTE Rate Within Stratum Odds Ra4o 95% CI Joint Effects Odds Ra4o 95% CI BMI < ,690 1, % ,720 1, % 0.80 (0.74, 0.87) 0.80 (0.74, 0.87) BMI p- value 46, % 0.68 <0.001 (0.61, 0.75) 0.68 (0.61, 0.75) ,610 1, % (1.15, 1.38) ,640 1, % 0.84 (0.77, 0.91) 1.05 (0.97, 1.15) p- value 24, % (0.79, 0.99) 1.12 (1.00, 1.25) BMI ,370 1, % (1.76, 2.06) ,660 2, % 0.95 (0.89, 1.00) 1.80 (1.68, 1.94) p- value 39,130 1, % (0.89, 1.03) 1.83 <0.001 (1.68, 2.00)

19 Results VTE by Weight then Height (male) Men Weight (kg/m2) BMI <25 BMI BMI 30 Height (cm) p- value Persons at risk 30,220 35,830 26,280 Pa4ents with VTE VTE Rate 2.5% 2.0% 2.2% Within Stratum Odds Ra4o 95% CI (0.69, 0.85) 0.88 (0.79, 0.98) Joint Effects Odds Ra4o 95% CI (0.69, 0.85) 0.88 (0.79, 0.98) p- value p- value 49,100 64,450 46,180 41,180 55,130 43,200 1,090 1,200 1,010 1,200 1,470 1, % 1.9% 2.2% 2.9% 2.7% 3.1% (0.77, 0.91) (0.90, 1.08) (0.85, 0.99) (0.98, 1.14) (0.79, 0.96) (0.67, 0.80) (0.78, 0.95) (1.04, 1.26) (0.96, 1.14) (1.10, 1.32)

20 Results VTE by Height then Weight (female) Women Height (cm) BMI (kg/m2) < p- value < p- value < p- value Persons at risk 60,690 48,610 62, ,720 76,640 99,660 46,680 24,960 39,130 Pa4ents with VTE 1,000 1, ,430 1,330 2, ,180 VTE Rate 1.6% 2.1% 3.1% 1.3% 1.7% 3.0% 1.1% 1.8% 3.0% Within Stratum Odds Ra4o 95% CI (1.15, 1.38) 1.91 (1.76, 2.06) < (1.22, 1.42) 2.26 (2.12, 2.41) < (1.45, 1.88) 2.71 (2.44, 3.01) <0.001 Joint Effects Odds Ra4o 95% CI 1.26 (1.15, 1.38) 1.91 (1.76, 2.06) 0.80 (0.74, 0.87) 1.05 (0.97, 1.15) 1.80 (1.68, 1.94) 0.68 (0.61, 0.75) 1.12 (1.00, 1.25) 1.83 (1.68, 1.96) <0.001

21 Results VTE by Height then Weight (male) Men Within Stratum Height (cm) BMI (kg/m2) Persons at risk Pa4ents with VTE VTE Rate Odds Ra4o < p- value 30,220 49,100 41, ,090 1, % 2.2% 2.9% < p- value 35,830 64,450 55, ,200 1, < p- value 26,280 46,180 43, ,010 1,330 Joint Effects 95% CI Odds Ra4o 95% CI <0.001 (0.79, 0.96) (1.04, 1.26) (0.79, 0.96) (1.04, 1.26) 2.0% 1.9% 2.7% <0.001 (0.87, 1.05) (1.25, 1.50) (0.69, 0.85) (0.67, 0.80) (0.96, 1.14) 2.2% 2.2% 3.1% <0.001 (0.88, 1.08) (1.24, 1.52) (0.79, 0.98) (0.78, 0.95) (1.10, 1.32)

22 Results Race/Ethnicity Females Caucasian African American Hispanic/La4no Other Males Caucasian African American Hispanic/La4no Other Persons at Pa4ents with Risk VTE VTE Rate Odds Ra4o 399,560 7, % 96,860 17,950 15,170 3, % 0.95% 0.79% Persons at Pa4ents with Risk VTE VTE Rate Odds Ra4o 271,430 6, % 54,940 21,760 15,050 2, % 0.60% 0.80% % CI (1.19, 1.76) (0.41, 0.55) (0.33, 0.48) 95% CI (1.50, 1.66) (0.20, 0.28) (0.26, 0.38)

23 Study Conclusions Posi4ve correla4ons between BMI and height and VTE Correla4on stronger with BMI then height Correla4on stronger in women then men Racial/Ethnic disparity in VTEs (regardless of gender) African- American > Caucasian > Hispanic/Other Stronger correla4ons then with reference study (bemer p- values, confidence intervals) Took 5 people ~125 hours total to complete (right data, right tool, right people, right ques4on)

24 959,030 patients (vs 26,714 -> ~40 times more) 21,210 VTE patients (vs 451 -> ~50 times more) 12 year retrospective study (vs 14 years) ~2 months from idea to submission (vs 18 years) Similar results with much higher power! Not human studies research (No PHI; No IRB)!

25 Methodological Points Rolling enrollment period Do not know when someone died only if someone died Odds ratios (Fisher exact and Cochran-Armitage tests) versus Cox regression analysis

26 De-identified Population Data Advantages Not human studies research (no IRB) No HIPAA issues (no security issues) Disadvantages Limited data analytic (statistical) tools Limited research questions

27 Outline Introduction/Background VTE Case Example Discussion/Conclusions/Questions

28 Keys to Using EHR Data Understanding Data Sources Corroborating Data/Findings Internal versus external corroboration Clinical Data versus Research Data Understand your data sources, corroborate your data/findings, and realize that the data represents clinical practice.

29 EHR Data Quality Type of data Relative Quality Very High Demographic (age, gender, race/ethnicity) Very High Lab Results Very High1 Prescriptions Vital Lots of Signs information desired for research ishigh not stored Medium (variable) in Diagnoses the electronic health record as digital data during (ICD-9 codes) routine clinical care. Low Family/PMH/Social History??? Other 1- for prescriptions written; up to ~40% of prescriptions are never filled

30 Clinical Research Paradigm Characteristic Data Infrastructure Resources Queries/Analysis Self-Service Old Paradigm New Paradigm siloed aggregated significant none/minimal days/weeks/ months real-time/near-real time minimal high Researchers want quick, easy, access to all data themselves!

31 Clinical Research Implications Characteristic Old Paradigm New Paradigm Data Separate Research Database Shared Research and Clinic Database (EHR) Time hours 100+ hours Money 100,000-1,000, ,000+ People Many Few Order of magnitude less time and money with electronic health records.

32 EHR Based Research Can be used to discover ground-breaking clinical research findings Expands who can do clinical research Decreases time for clinical research Reduces cost for clinical research Epic has ~125 million patients (~30% of the US population) (1 in 4 US physicians) (~3% of the world population?)

33 EHR data and clinical research informatics tools are creating a paradigm shift in clinical research. THE FUTURE IS NOW!

34 Questions?? Comments?? Discussion??