External validation demonstrated the Ottawa SAH prediction models can identify pSAH using health administrative data
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abstract
OBJECTIVES: The objective of the study is to externally validate three primary subarachnoid hemorrhage (pSAH) identification models. STUDY DESIGN AND SETTING: 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. RESULTS: 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). CONCLUSIONS: All three models were highly sensitive for pSAH. Model C could be used to adjust for misclassification bias.
