Evaluating novel methods of outcome assessment following cardiac arrest

Abstract

Introduction: We compared novel methods of long-term follow-up after resuscitation from cardiac arrest to a query of the National Death Index (NDI). We hypothesized use of the electronic health record (EHR), and internet-based sources would have high sensitivity for identifying decedents identified by the NDI.

Methods: We performed a retrospective study including patients treated after cardiac arrest at a single academic center from 2010 to 2018. We evaluated two novel methods to ascertain long-term survival and modified Rankin Scale (mRS): 1) a structured chart review of our health system's EHR; and 2) an internet-based search of: a) local newspapers, b) Ancestry.com, c) Facebook, d) Twitter, e) Instagram, and f) Google. If a patient was not reported deceased by any source, we considered them to be alive. We compared results of these novel methods to the NDI to calculate sensitivity. We queried the NDI for 200 in-hospital decedents to evaluate sensitivity against a true criterion standard.

Results: We included 1,097 patients, 897 (82%) alive at discharge and 200 known decedents (18%). NDI identified 197/200 (99%) of known decedents. The EHR and local newspapers had highest sensitivity compared to the NDI (87% and 86% sensitivity, respectively). Online sources identified 10 likely decedents not identified by the NDI. Functional status estimated from EHR, and internet sources at follow up agreed in 38% of alive patients.

Conclusions: Novel methods of outcome assessment are an alternative to NDI for determining patients' vital status. These methods are less reliable for estimating functional status.

Keywords: Cardiac arrest; Outcomes; Recovery.

Figure 1:. Regional Cardiac Arrest Referral Patterns.

This figure illustrates the referral region of cardiac arrest patients for this tertiary care hospital (red triangle). Patients were grouped based on their home address’ zip code and the shaded regions correspond to the patient population density of each zip code.

Figure 2:. Patient Social Media Posting.

This figure illustrates an example of a cardiac arrest survivor’s social media activity. These postings allowed us to determine patient vital statuses and to estimate functional recovery indirectly.