Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest

Prognostication in Neurocritical Care

OBJECTIVE

This article synthesizes the current literature on prognostication in neurocritical care, identifies existing challenges, and proposes future research directions to reduce variability and enhance scientific and patient-centered approaches to neuroprognostication.

LATEST DEVELOPMENTS

Patients with severe acute brain injury often lack the capacity to make their own medical decisions, leaving surrogate decision makers responsible for life-or-death choices. These decisions heavily rely on clinicians' prognostication, which is still considered an art because of the previous lack of specific guidelines. Consequently, there is significant variability in neuroprognostication practices. This article examines various aspects of neuroprognostication. It explores the cognitive approach to prognostication, highlights the use of statistical modeling such as Bayesian models and machine learning, emphasizes the importance of clinician-family communication during prognostic disclosures, and proposes shared decision making for more patient-centered care.

ESSENTIAL POINTS

This article identifies ongoing challenges in the field and emphasizes the need for future research to ameliorate variability in neuroprognostication. By focusing on scientific methodologies and patient-centered approaches, this research aims to provide guidance and tools that may enhance neuroprognostication in neurocritical care.

Comparing strategies for prehospital transport to specialty care after cardiac arrest

Outcomes are better when patients resuscitated from out-of-hospital cardiac arrest (OHCA) are treated at specialty centers. The best strategy to transport patients from the scene of resuscitation to specialty care is unknown.

METHODS

We performed a retrospective cohort study. We identified patients treated at a single specialty center after OHCA from 2010 to 2021 and used OHCA geolocations to develop a catchment area using a convex hull. Within this area, we identified short term acute care hospitals, OHCA receiving centers, adult population by census block group, and helicopter landing zones. We determined population-level times to specialty care via: (1) direct ground transport; (2) transport to the nearest hospital followed by air interfacility transfer; and (3) ground transport to air ambulance. We used an instrumental variable (IV) adjusted probit regression to estimate the causal effect of transport strategy on functionally favorable survival to hospital discharge.

RESULTS

Direct transport to specialty care by ground to air ambulance had the shortest population-level times from OHCA to specialty care (median 56 [IQR 47-66] minutes). There were 1,861 patients included in IV regression of whom 395 (21%) had functionally favorable survival. Most (n = 1,221, 66%) were transported to the nearest hospital by ground EMS then to specialty care by air. Patient outcomes did not differ across transport strategies in our IV analysis.

DISCUSSION

We did not find strong evidence in favor of a particular strategy for transport to specialty care after OHCA. Population level time to specialty care was shortest with ground ambulance transport to the nearest helicopter landing zone.