Introduction: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. We aim to conduct a systematic study of published CPMs predicting mortality, functional outcome or stroke recurrence for patients with stroke.
Methods: The Tufts PACE CPM Registry is based on a systematic review of cerebrovascular and cardiovascular CPMs published in English-language articles from 1/1990-3/2015, and includes 1084 unique CPMs extracted from 747 articles. CPMs predicting outcomes for patients with stroke were characterized based on index condition (hemorrhagic, ischemic or all stroke) and outcome (mortality, functional outcome or stroke recurrence). We identified the most commonly occurring covariates in models grouped by index condition-outcome pair (I-O pair).
Results: Among 1084 total models in the registry, 116 (11%) predicted mortality, functional outcomes or stroke recurrence among patients with stroke. The top three most frequent models predicted functional outcomes among ischemic stroke patients (n=23), mortality among all stroke patients (n=19), and mortality among patients with hemorrhagic stroke (n=18). The median reported C statistic was 0.84 (among n=78 models reporting this measure). About half (45%) of models reported internal validations, with only 25% reporting external validations. The most commonly occurring covariates in the models were age (77%), stroke severity (51%), and functional status (26%) (see Figure). Neuroimaging findings were included relatively infrequently (21%), but were included in all 9 models predicting functional outcome among hemorrhagic stroke patients.
Conclusions: There is an abundance of CPMs to predict clinically important outcomes in stroke populations. More work is needed to understand how this prognostic information might be used to improve decision making and outcomes for stroke patients.