Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis

Background

Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA).

Objective

We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year.

Methods

We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype.

Results

We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference.

Conclusions

We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

Factors associated with the arrival of smartphone-activated first responders before the emergency medical services in Out-of-Hospital cardiac arrest dispatch

BACKGROUND

First responder programs were developed to speed up access to cardiopulmonary resuscitation and defibrillation for out-of-hospital cardiac arrest (OHCA) victims. Little is known about the factors influencing the efficiency of the first responders arriving before the EMS and, therefore, effectively contributing to the chain of survival.

OBJECTIVES

The primary objective of this retrospective observational study was to identify the factors associated with first responders' arrival before EMS in the context of a regional first responder program arranged to deliver automated external defibrillators on suspected OHCA scenes.

METHODS

Eight hundred ninety-six dispatches where FRs intervened were collected from 2018 to 2022. A robust Poisson regression was performed to estimate the role of the time of day, the immediate availability of a defibrillator, the type of first responder, distances between the responder, the event and the dispatched vehicle, and the nearest available defibrillator on the probability of responder arriving before EMS. Moreover, a geospatial logistic regression model was built.

RESULTS

Responders arrived before EMS in 13.4% of dispatches and delivered a shock in 0.9%. The immediate availability of a defibrillator for the responder (OR = 3.24) and special categories such as taxi drivers and police (OR = 1.74) were factors significantly associated with the responder arriving before EMS. Moreover, a geospatial effect suggested that first responder programs may have a greater impact in rural areas.

CONCLUSIONS

When dispatched to OHCA scenes, responders already carrying defibrillators could more probably reach the scene before EMS. Special first responder categories are more competitive and should be further investigated.