Objective: To develop and validate a simple prognostic scoring system to identify patients in nontraumatic coma at high risk for poor outcomes using data available early in the hospital course.
Design: Prospective cohort study.
Setting: Five geographically diverse academic medical centers.
Patients: A total of 596 patients in nontraumatic coma enrolled in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT), including 247 in the model derivation set and 349 in the model validation set.
Main outcome measures: Death and severe disability by 2 months.
Main results: For the 596 patients studied (median age, 67 years; 52% female), the primary cause of coma was cardiac arrest in 31% and cerebral infarction or intracerebral hemorrhage in 36%. At 2 months 69% had died, 20% had survived with known severe disability, 8% were known to have survived without severe disability, and 3% survived with unknown functional status. Five clinical variables available on day 3 after enrollment were associated independently with 2-month mortality: abnormal brain stem response (adjusted odds ratio [OR] = 3.2; 95% confidence interval [CI], 1.3 to 8.1), absent verbal response (OR = 4.6; 95% CI, 1.8 to 11.7), absent withdrawal response to pain (OR = 4.3; 95% CI, 1.7 to 10.8), creatinine level greater than or equal to 132.6 mumol/L (1.5 mg/dL) (OR = 4.5; 95% CI, 1.8 to 11.0), and age of 70 years or older (OR = 5.1; 95% CI, 2.2 to 12.2). Mortality at 2 months for patients with four or five of these risk factors was 97% (58/60; 95% CI, 88% to 100%) in the validation set. Brain stem and motor responses best predicted death or severe disability by 2 months. For patients with either an abnormal brain stem response or absent motor response to pain, the rate of death or severe disability at 2 months was 96% (185/193; 95% CI, 92% to 98%) in the validation set.
Conclusions: Five readily available clinical variables identify a large subgroup of patients in nontraumatic coma at high risk for poor outcomes. This risk stratification approach offers physicians, patients, and patients' families information that may prove useful in patient care decisions and resource allocation.