期刊
NEUROLOGY
卷 75, 期 7, 页码 626-633出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1212/WNL.0b013e3181ed9cc9
关键词
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资金
- NIH/NINDS [R01NS42167, R01 NS38916, R01 NS050372, U01 NS052510, U01 NS056975, U54 NS057405, R01 NS062675]
- American Heart Association [0625692Z]
- University of Michigan
- AGA Medical Corporation
- NIH [K23 AG032875, R01 HLO094553, R21 HL094975]
- NIH (NINDS) [K23 NS41240, U10 NS058931]
- University of California
Objective: To quantify the accuracy of commonly used intracerebral hemorrhage (ICH) predictive models in ICH patients with and without early do-not-resuscitate orders (DNR). Methods: Spontaneous ICH cases (n = 487) from the Brain Attack Surveillance in Corpus Christi study (2000-2003) and the University of California, San Francisco (June 2001-May 2004) were included. Three models (the ICH Score, the Cincinnati model, and the ICH grading scale [ICH-GS]) were compared to observed 30-day mortality with a chi(2) goodness-of-fit test first overall and then stratified by early DNR orders. Results: Median age was 71 years, 49% were female, median Glasgow Coma Scale score was 12, median ICH volume was 13 cm3, and 35% had early DNR orders. Overall observed 30-day mortality was 42.7% (95% confidence interval [CI] 38.3-47.1), with the average model-predicted 30-day mortality for the ICH Score, Cincinnati model, and ICH-GS at 39.9% (p = 0.005), 40.4% (p = 0.007), and 53.9% (p = 0.001). However, for patients with early DNR orders, the observed 30-day mortality was 83.5% (95% CI 78.0-89.1), with the models predicting mortality of 64.8% (p = 0.001), 57.2% (p = 0.001), and 77.8% (p = 0.02). For patients without early DNR orders, the observed 30-day mortality was 20.8% (95% CI 16.5-25.7), with the models predicting mortality of 26.6% (p = 0.05), 31.4% (p = 0.001), and 41.1% (p = 0.001). Conclusions: ICH prognostic model performance is substantially impacted when stratifying by early DNR status, possibly giving a false sense of model accuracy when DNR status is not considered. Clinicians should be cautious when applying these predictive models to individual patients. Neurology (R) 2010; 75: 626-633
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