Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest

Abstract

OBJECTIVES: Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. DESIGN: Retrospective observational cohort study. SETTING: Two academic medical centers (“UPMC” and “University of Alabama Birmingham” [UAB]). PATIENTS: Comatose adults resuscitated from cardiac arrest. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients’ Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome. CONCLUSIONS: Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.

Outcome prediction in comatose cardiac arrest patients with initial shockable and non‐shockable rhythms

“…WLST will always interfere with outcome prediction in CA patients. 3,6,24 Interestingly, 20% of patients with good outcome woke up late in a recent study where WLST was not performed. 6 Therefore, early WLST must be based on a reliable prediction of poor prognosis.…”

Disambiguating Consciousness in Clinical Settings

“…For some families, these conversations may center on the importance of proper communication of the level of prognostic certainty in likelihood of recovery of behavioral consciousness so that families can accurately assess the value of maintaining a potential state of nonbehavioral phenomenal consciousness. [32][33][34] Given the high degree of prognostic uncertainty in the field of disorders of consciousness, some may feel that even a small chance at recovering behavioral consciousness may justify continuing life-sustaining treatment for a period. 1,14 By eliciting and acknowledging the individualized beliefs, values, and goals that might importantly bear on such treatment preferences, clinicians have the opportunity to cultivate trustworthy relationships by inviting caregivers into the diagnostic and prognostic process, highlighting how potential endpoints might align with or conflict with individual patient goals, and providing multidisciplinary, longitudinal support that affords families the time to navigate a complex decision-making process and revisit goals of care discussions as needed.…”

Postresuscitation management

“…A particular importance is given to pupillary and corneal reflexes and to the presence of myoclonus; however, the presence of abnormalities in brainstem reflexes or abnormal movements should always be considered in relationship with other prognostic tools [70]. Second, clinicians must be cautious about the “self-fulfilling prophecy” bias, where the results of prognostic tests predicting poor outcomes influence treatment decisions, particularly regarding life-sustaining therapies [71]. Third, a multimodal prognostication should include, together with clinical examination, electrophysiological tests, assessment of biomarkers of cerebral injury and brain imaging.…”