Early Prediction of Prognosis in Elderly Acute Stroke Patients

Acute stroke has a high morbidity and mortality in elderly population. Baseline confounding illnesses, initial clinical examination, and basic laboratory tests may impact prognostics. In this study, we aimed to establish a model for predicting in-hospital mortality based on clinical data available within 12 hours of hospital admission in elderly (≥ 65 age) patients who experienced stroke. DESIGN: Retrospective observational cohort study. SETTING: Academic comprehensive stroke center. PATIENTS: Elderly acute stroke patients—2005–2009 (n = 462), 2010–2012 (n = 122), and 2016–2017 (n = 123). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: After institutional review board approval, we retrospectively queried elderly stroke patients’ data from 2005 to 2009 (training dataset) to build a model to predict mortality. We designed a multivariable logistic regression model as a function of baseline severity of illness and laboratory tests, developed a nomogram, and applied it to patients from 2010 to 2012. Due to updated guidelines in 2013, we revalidated our model (2016–2017). The final model included stroke type (intracerebral hemorrhage vs ischemic stroke: odds ratio [95% CI] of 0.92 [0.50–1.68] and subarachnoid hemorrhage vs ischemic stroke: 1.0 [0.40–2.49]), year (1.01 [0.66–1.53]), age (1.78 [1.20–2.65] per 10 yr), smoking (8.0 [2.4–26.7]), mean arterial pressure less than 60 mm Hg (3.08 [1.67–5.67]), Glasgow Coma Scale (0.73 [0.66–0.80] per 1 point increment), WBC less than 11 K (0.31 [0.16–0.60]), creatinine (1.76 [1.17–2.64] for 2 vs 1), congestive heart failure (2.49 [1.06–5.82]), and warfarin (2.29 [1.17–4.47]). In summary, age, smoking, congestive heart failure, warfarin use, Glasgow Coma Scale, mean arterial pressure less than 60 mm Hg, admission WBC, and creatinine levels were independently associated with mortality in our training cohort. The model had internal area under the curve of 0.83 (0.79–0.89) after adjustment for over-fitting, indicating excellent discrimination. When applied to the test data from 2010 to 2012, the nomogram accurately predicted mortality with area under the curve of 0.79 (0.71–0.87) and scaled Brier’s score of 0.17. Revalidation of the same model in the recent dataset from 2016 to 2017 confirmed accurate prediction with area under the curve of 0.83 (0.75–0.91) and scaled Brier’s score of 0.27. CONCLUSIONS: Baseline medical problems, clinical severity, and basic laboratory tests available within the first 12 hours of admission provided strong independent predictors of in-hospital mortality in elderly acute stroke patients. Our nomogram may guide interventions to improve acute care of stroke.