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Name of Media:
Recognizing, Naming, and Measuring a Family Intensive Care Unit Syndrome
Giora Netzer and Donald R. Sullivan
Publisher or Source:
Annals of the American Thoracic Society
Type of Media:
Media Originally for:
Critical Care Physicians, General Medical Professionals, Nurses and/or Other Critical Care Medical Professionals
Country of Origin:
Primary Focus of Media:
Post Intensive Care Syndrome (PICS)
Most major decisions in the intensive care unit (ICU) regarding goals of care are shared by clinicians and someone other than the patient. Multicenter clinical trials focusing on improved communication between clinicians and these surrogate decision makers have not reported consistently improved outcomes. We suggest that acquired maladaptive reasoning may contribute importantly to failure of the intervention strategies tested to date. Surrogate decision makers often suffer significant psychological morbidity in the form of stress, anxiety, depression, and post-traumatic stress disorder. Family members in the ICU also suffer cognitive blunting and sleep deprivation. Their decision-making abilities are eroded by anticipatory grief and cognitive biases, while personal and family conflicts further impact their decision making. We propose recognizing a family ICU syndrome to describe the morbidity and associated decision-making impairment experienced by many family members of patients with acute critical illness (in the ICU) and chronic critical illness (in the long-term, acute care hospital). Research rigorously using models of compromised decision making may help elucidate both mechanisms of impairment and targets for intervention. Better quantifying compromised decision making and its relationship to poor outcomes will allow us to formulate and advance useful techniques. The use of decision aids and improving ICU design may provide benefit now and in the near future. In measuring interventions targeting cognitive barriers, clinically significant outcomes, such as time to decision, should be considered. Statistical approaches, such as survival models and rank statistic testing, will increase our power to detect differences in our interventions.
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