Many observational and epidemiological studies have been published which have identified various parameters which are predictive of outcome after acute neuro-emergencies. Most of these are comprised of clinical, radiological, and laboratory variables, many of which are available at the time of initial patient evaluation. Various outcomes have been used to develop these models, including short-term mortality and long-term functional outcome. Numerous formal prediction models or algorithms have been developed from these studies for several different conditions, including non-traumatic intracerebral hemorrhage (ICH), severe traumatic brain injury (TBI), and hypoxic-ischemic encephalopathy (HIE) after resuscitation from cardiac arrest.
Non-traumatic intracerebral hemorrhage remains without a treatment of proven benefit. Predictors of short-term mortality, and to a lesser degree long-term functional outcome, are relatively well described. Most ICH prediction models have found that clinical status, such as measured by the Glasgow Coma Scale (GCS) score or National Institutes of Health Stroke Scale (NIHSS), and hematoma volume are strong predictors of 30-day mortality risk and longer-term functional outcome. Other clinical predictors present in various models include age, presence and volume of intraventricular hemorrhage (IVH), infratentorial hemorrhage location, admission blood pressure, and coagulopathy [12
]. The most commonly used ICH prediction model, the ICH Score, involves a sum score of points assigned for GCS [3–4 = 2, 5–12 = 1, 13–15 = 0], hematoma volume [≥30 ml = 1, < 30 ml = 0], presence of IVH [yes = 1, no = 0], infratentorial origin [yes = 1, no = 0], and patient age ≥ 80 [yes = 1, no =0] [14
]. ICH Scores may range from 0 to 6, and each increase in the ICH Score is associated with increased risk of 30-day mortality. While the ICH Score was developed to help standardize communication and risk stratification for ICH clinical care and clinical research, we have found clinicians increasingly tempted to use this as an early triage tool. Specifically, the first author of this manuscript has had other physicians suggest that patients with an ICH Score of 4 (predicted 30-day mortality of 97% in the original cohort) should not receive critical care or inter-facility transport because of perceived futility.
There are at least two problems with this approach. First, it assumes that a 3% chance of survival constitutes medical futility. To date, the only widely accepted definitions of futility are those that include only circumstances in which treatment will not
accomplish the intended goals [17
]. Second, there is considerable uncertainty around point estimates from such mortality prediction models. The fact that the 95% confidence interval of the mortality estimate in the above example extends from 81% to 100% (unpublished data), emphasizes this point.
In patients with extensive traumatic injury to the brain, predictors of death or disability include low GCS score after initial resuscitation, findings of intracranial hemorrhage or swelling on CT scan, older age, abnormal pupillary function, and hypotension early after injury [18
]. In general, the motor aspect is the most reliable and informative part of the GCS score. However, current TBI guidelines emphasize that a low GCS score early after injury lacks precision for precise prediction of a poor outcome. Thus, the recognition of uncertainty remains. Interestingly, there have been attempts to develop prediction models that would drive early decisions to limit care in TBI patients with a perceived poor prognosis. A mathematical model derived on 672 patients treated at a single center from 1978–1993 suggested that long-term prognosis could be sufficiently predicted at 24 hours after TBI accurately enough to terminate life sustaining treatments in patients unlikely to survive a severe head injury (GCS ≤ 8) [2
]. Notably however, the overall mortality in this cohort at 6 months was 58.8% which is nearly double that of most other series of patients with severe TBI [19
]. Whether the extremely high mortality rate in this modeling study was due to physician bias in the care of severely ill TBI patients or other factors is unclear. However, it does clearly demonstrate the importance of understanding the context in which a particular prediction model is developed and deciding whether they are likely to apply to a specific patient (or population) in which care decisions are being made.
There have been many attempts to predict outcome in comatose survivors of cardiac arrest with HIE. Numerous studies have focused on clinical, neuroimaging, laboratory, and electrophysiological predictors. A commonly cited study published in 1985 described the outcome of patients with various clinical examination findings at different time points after resuscitation from cardiac arrest [24
]. Generally findings at 3 days post-arrest have been considered the most informative. Other studies have examined the likelihood of an unfavorable outcome based on a range of predictors [25
]. Importantly, recent practice parameters from the American Academy of Neurology suggested that, in the absence of brain death, clinical examination findings at day 3 of absent pupil or corneal reflexes or a motor response which was absent or no better than extensor had a sufficiently low false positive rate to reliably predict extremely poor long-term functional outcome [26
]. This emphasizes that even in the setting of deep coma, some period of waiting is usually desirable to clarify the persistence and validity of clinical examination findings. Whether a trial of aggressive therapy (such as moderate hypothermia [27
]) alters these predictive parameters in hypoxic-ischemic coma is not clearly known.
A common finding in these attempts to predict outcome early across various types of neuro-emergencies is intuitive. Patients in coma tend to do worse, especially if they are older. The finding of extensive injury on head imaging studies is also suggestive. The challenge is how to use this information in planning patient treatment. Many of these models and prediction tools described above have been validated and are used in various forms in the context of current clinical management. However, most of the time, clinicians prognosticate based not on a specific formal outcome prediction model, but rather on their own impressions based on experience, knowledge of the medical literature, and clinical intuition. This informal prognostic method is probably really an individual physician’s internalized outcome prediction model. However, a central question is whether this informal method is accurate and consistent. Furthermore, recent work has raised the concern that inaccuracy or variability in prognostication could lead to self-fulfilling prophecies of poor outcome.