Statistics for critical clinical decision making based on readings of pairs of implanted sensors.

Low error rates are essential if lives of patients are to depend on readings of implanted sensors, such as glucose sensors in insulin-dependent diabetic patients. To verify the operation and to calibrate on demand an implanted sensor, it is necessary that calibration through a single, independent measurement involving withdrawal of only one sample of blood and its independent analysis be feasible. Such a one-point calibration must be accurate. Borrowing from nuclear reactor safety assurance, where a likelihood ratio test is applied to readings of pairs of pressure sensors for shutdown/no shutdown decisions, we apply a similar test to sensor pairs implanted in rats. We show, for five sets of glucose sensor pairs, calibrated in vivo by withdrawal of a single sample of blood, that application of the likelihood ratio test increases the fraction of the clinically correct readings from 92.4% for their averaged readings to 98.8%.