Management of low-grade gliomas continues to be a challenging task, because CT and MRI do not always differentiate from nontumoral lesions. Furthermore, tumor extent and aggressiveness often remain unclear because of a lack of contrast enhancement. Previous studies indicated that large neutral amino acid tracers accumulate in most brain tumors, including low-grade gliomas, probably because of changes of endothelial and blood-brain barrier function. We describe 11C-methionine uptake measured with PET in a series of 196 consecutive patients, most of whom were studied because of suspected low-grade gliomas. Uptake in the most active lesion area, relative to contralateral side, was significantly different among high-grade gliomas, low-grade gliomas, and chronic or subacute nontumoral lesions, and this difference was independent from contrast enhancement in CT or MRI. Corticosteroids had no significant effect on methionine uptake in low-grade gliomas but reduced uptake moderately in high-grade gliomas. Differentiation between gliomas and nontumoral lesions by a simple threshold was correct in 79%. Recurrent or residual tumors had a higher uptake than primary gliomas. In conclusion, the high sensitivity of 11C-methionine uptake for functional endothelial or blood-brain barrier changes suggests that this tracer is particularly useful for evaluation and follow-up of low-grade gliomas.

Practices that support early intervention for asthma flare-ups by parents at home, particularly written management plans, are strongly associated with reduced risk of adverse outcomes among children with asthma.

Effective management of populations with asthma requires methods for identifying patients at high risk for adverse outcomes. The aim of this study was to develop and validate prediction models that used computerized utilization data from a large health-maintenance organization (HMO) to predict asthma-related hospitalization and emergency department (ED) visits. In this retrospective cohort design with split-sample validation, variables from the baseline year were used to predict asthma-related adverse outcomes during the follow-up year for 16,520 children with asthma-related utilization. In proportional-hazard models, having filled an oral steroid prescription (relative risk [RR]: 1.9; 95% confidence interval [CI]: 1.3 to 2.8) or having been hospitalized (RR: 1.7; 95% CI: 1.1 to 2.7) during the prior 6 mo, and not having a personal physician listed on the computer (RR: 1.6; 95% CI: 1.1 to 2.3) were associated with increased risk of future hospitalization. Classification trees identified previous hospitalization and ED visits, six or more beta-agonist inhalers (units) during the prior 6 mo, and three or more physicians prescribing asthma medications during the prior 6 mo as predictors. The classification trees performed similarly to proportional-hazards models, and identified patients who had a threefold greater risk of hospitalization and a twofold greater risk of ED visits than the average patient. We conclude that computer-based prediction models can identify children at high risk for adverse asthma outcomes, and may be useful in population-based efforts to improve asthma management.

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