Development of a Database for Comparative Effectiveness Research (CER) on Prehospital and In-hospital Emergency Care
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1 Development of a Database for Comparative Effectiveness Research (CER) on Prehospital and In-hospital Emergency Care Derek DeLia, Ph.D. Associate Research Professor Center for State Health Policy Academy Health Annual Research Meeting Baltimore, MD June 23, 2013
2 Acknowledgement This research was supported by the Agency for Healthcare Research & Quality (Grant no. R01-HS ) Center for State Health Policy 2
3 Project team Rutgers Center for State Health Policy (CSHP) D. DeLia, Joel Cantor, Jose Nova, Kristen Lloyd, Dorothy Gaboda, Daisuke Goto, & Manisha Agrawal NJ Dept of Health & Senior Services (DHSS) Center for Health Statistics (CHS) Henry Wang, MD (U of AL-Birmingham) Mark Merlin, DO (NJ EMS Task Force, Newark BI MC) Jared Kutzin, DNP, MPH, RN, EMT (Winthrop U Hosp, Englewood Hosp & MC) Center for State Health Policy 3
4 Project motivation Emergency medicine Wide range of diagnoses & procedures Time sensitive, ambiguous situations Evidence base less developed than other specialties Prehospital emergency medical service (EMS) Even less evidence-based Historically separate from rest of health sector Intensely local/under-resourced IOM reports (2006/2007) ==> priority area for research Barrier: Lack of large-scale integrated databases across emergency care settings Center for State Health Policy 4
5 Specific Aims of Enhanced Data Grant 1. Build a sustainable database to support comparative effectiveness research (CER) on medical care provided across prehospital and hospital settings in NJ 2. Demonstrate the utility of the database by evaluating out-of-hospital cardiac arrest (OHCA) outcomes in therapeutic hypothermia (TH) centers versus other hospitals Center for State Health Policy 5
6 Overview of component databases 1. NJ Discharge Data Collection System Hospital inpatient/ed billing records 2. EMS Data Warehouse EHRs maintained by ambulance companies Pilot data in 2006, Statewide in 2008 Include response times, vital signs, prehospital procedures, etc. Universal participation among advanced life support (ALS) units 50% participation among basic life support (BLS) units 3. Mortality data (State vital records system) Data years: linked in process. Center for State Health Policy 6 Supplemental data collection: Hospital TH survey
7 Overview of data linkage COMPONENT 1: Prehospital EMS records COMPONENT 2: Hospital records COMPONENT 3: Mortality records Group unique individuals with common ID. Link relevant mortality records to individuals. Center for State Health Policy Remove individuals with no prehospital EMS use. FINAL LINKED DATABASE: Remove patient identifying information & retain encrypted IDs. 7
8 Mechanics of data linkage Patient identifiers across components Primary: Name, DOB, SSN Secondary: Sex, race/ethnicity, residential zip code LinkKing software Combination of deterministic & probabilistic linkage Must have at least one of the primary identifiers No problem for hospital & mortality data EMS records: Reduced from 1,253,208 to 899,314 ( 28.2%) Retain linked records w/highest probability of a valid match based on LinkKing certainty levels Center for State Health Policy 8
9 Units of analysis, Unit of Analysis Number of Observations Total EMS records 899,314 Records with EMS treatment and transport 706,584 Subset of above records linked to a hospital record 535,771* Unique EMS incidents 780,297 Unique EMS incidents linked to a hospital record 490,068 Unique individuals 512,560 Unique individuals with linked mortality record 56,354 * 75.8% of treated & transported cases linked to a hospital record (Linkage rate varies by condition treated) Center for State Health Policy 9
10 Multiple unit EMS response EMS units Number of incidents Percentage of incidents 1 664, % 2 112, % 3 2, % 4 or more % * Number of EMS units per incident varies by response type Center for State Health Policy 10
11 Cardiac arrest analysis Population: Adults treated & transported by EMS for OHCA Comparison: Transport to TH centers vs other hospitals Outcomes: Neurologically intact survival to discharge at 30 days post-arrest Center for State Health Policy 11
12 Exclusion/outcome analysis, Adult Treated OHCA N= 6,887 Resuscitation Terminated in Field, N= 1,133 Treated & Transported N= 5,754 Hospital Link Not Found, N= 737 Linkage rate: 87.2% Died in Hospital N= 4,185 Survived to Discharge N= 832 Transferred N= 166 Neurologically Intact Survival to 30 days, N= 259 Center for State Health Policy Died/Poor Neurological Outcome within 30 Days, N=407
13 Descriptive analysis of patients at TH vs other hospitals TH centers (N = 2,363) Other hospitals (N = 2,479) Neuro intact survival To discharge* 11.9% 8.2% At 30 days* 7.4% 3.4% Selected covariates EMS response time* Less than 4 minutes 16.8% 14.4% 4-8 minutes 41.6% 36.0% More than 8 minutes 41.6% 49.6% Witnessed arrest* 60.0% 55.0% Defibrillation by EMS* 52.3% 46.1% Shockable rhythm 9.2% 8.7% Number of hospital beds* Less than % 46.1% % 42.4% 400 and above 30.6% 11.5% Teaching hospital* 14.8% 7.6% Center for State Health Policy Female sex 37.2% 36.4% 13
14 Final thoughts Successful linkage of EMS/hospital/mortality data for , 2011 underway Analysis of TH for OHCA patients currently under review Linked database available for future applications CER on prehospital/hospital procedures Public health surveillance/planning Other applications Center for State Health Policy 14
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Stella Rhoda Macheso Objective Clinical Nurse Specialist Critical Care Qualification Summary More than 10 years experience in clinical settings providing nursing care to critically ill adult patients Professional
Title: Automated External Defibrillators in Long-Term Care Facilities. Date: 24 September Context and Policy Issues:
Title: Automated External Defibrillators in Long-Term Care Facilities Date: 24 September 2007 Context and Policy Issues: Out-of-hospital and in-hospital survival after a patient suffers from cardiac arrest