External validation demonstrated the Ottawa SAH prediction models can identify pSAH using health administrative data

The objective of the study is to externally validate three primary subarachnoid hemorrhage (pSAH) identification models.

We evaluated three models that identify pSAH using recursive partitioning (A), logistic regression (B), and a prevalence-adjusted logistic regression(C), respectively. Blinded chart review and/or linkage to existing registries determined pSAH status. We included all patients aged ≥18 in four participating center registries or whose discharge abstracts contained ≥1 administrative codes of interest between January 1, 2012 and December 31, 2013.

A total of 3,262 of 193,190 admissions underwent chart review (n = 2,493) or registry linkage (n = 769). A total of 657 had pSAH confirmed (20·1% sample, 0·34% admissions). The sensitivity, specificity, and positive predictive value (PPV) were as follows: i) model A: 98·3% (97·0-99·2), 53·5% (51·5-55·4), and 34·8% (32·6-37·0); ii) model B (score ≥6): 98·0% (96·6-98·9), 47·4% (45·5-49·4), and 32·0% (30·0-34·1); and iii) model C (score ≥2): 95·7% (93·9-97·2), 85·5% (84·0-86·8), and 62·3 (59·3-65·3), respectively. Model C scores of 0, 1, 2, 3, or 4 had probabilities of 0·5% (0·2-1·5), 1·5% (1·0-2·2), 24·8% (21·0-29·0), 90·0% (86·8-92·0), and 97·8% (88·7-99·6), without significant difference between centers (P = 0·86). The PPV of the International Classification of Diseases code (I60) was 63·0% (95% confidence interval: 60·0-66·0).

All three models were highly sensitive for pSAH. Model C could be used to adjust for misclassification bias.