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The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data. The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance "stoplights" were compared between the LLG method and one based on 10th percentile (P10) values. Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect "green" indicators, compared with 0.9% to 6.0% for P10 value of 110 mg/dL. Guidance with lower P10 values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P10 method. Sensitivity performance of correct "red" indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P10 method up to about 80 mg/dL. To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.

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A rigorous derivation of the interconnect pattern density distribution for random logic networks is presented using the Bernoulli probability distribution. The derived analytical model provides a statistical interconnect pattern density distribution for a given wiring layer. Sampling window size, average wire length, wiring width and spacing, gate pitch, and wiring utilization are the input parameters.Monte-Carlo simulations agree with the results of the model. Comparison to product data shows that the model also successfully predicts the metal pattern density distribution of actual random logic networks.Several possible applications of the interconnect pattern density prediction are proposed. Among the applications of the model are: quantitative study of interconnect pattern density, statistical interconnect reference circuit for more realistic capacitance estimation, and assessing the impact of metal pattern density variation on system performance.