Challenges of Using Probabilistic Linkage Methodology to Characterize Post-Cardiac Arrest Care in Michigan. (4th March 2018)

• Record Type: Journal Article
• Authors: Swor, Robert; Qu, Lihua; Putman, Kevin; Sawyer, Kelly N.; Domeier, Robert; Fowler, Jennifer; Fales, William
• Is Part Of: Prehospital emergency care. Volume 22:Number 2(2018)
• Page Start: 208
• Page End: 213
• Publication Date: 2018-03-04
• Subjects: cardiac arrest -- outcome -- linkage -- emergency medical services; 362.18
• DOI: 10.1080/10903127.2017.1362086
• Languages: English
• ISSNs: 1090-3127

Abstract

Abstract: Background: To improve survival of patients resuscitated from out of hospital cardiac arrest (OCHA), data is needed to assess and improve inpatient post-resuscitation care. Our objective was to apply probabilistic linkage methodology to link EMS and inpatient databases and evaluate whether it may be used to describe post-arrest care in Michigan. Methods: We performed a retrospective study to describe post-cardiac arrest care in adult OHCA patients who were transported to Michigan hospitals from July 1, 2010, to June 30, 2013. Using probabilistic linkage methodology we linked two databases, the Michigan EMS Information System (MI_EMSIS) and the Michigan Inpatient Database (MIDB), which describes inpatient care and outcome of all admissions. Rates of case incidence and survival were compared to published literature. We compared the linked dataset to existing cardiac arrest databases from three counties to evaluate the quality of this linkage. Results: Multiple iterations of match strategies were used to create a linked EMS-inpatient dataset. There were 12, 838 MI_EMSIS cardiac arrest records of which 1, 977 were matched with MIDB records, identifying them as surviving to hospital admission. Of these 590 (30.0%) survived to hospital discharge. The annual survival incidence/100, 000 population to admission was 6.93/100, 000 and survival incidence to discharge was 2.1/100, 000. The matched dataset was compared to county databases identified a limited sensitivity [48.2%, 95% CI 42.1%–55.3%)] and positive predictive value [64.4%, 95% CI 56.8%–71.3%)]. Conclusion: Use of the MI_EMSISEMS database and the Michigan Inpatient database was feasible and produced rates of cardiac arrest admission and survival rates similar to published literature. This process yielded a limited match compared to existing county cardiac arrest databases. We conclude that such a linked dataset is useful for descriptive purposes but not as a population based dataset to evaluate statewide post-cardiac arrest care.