词条 | Draft:ISUP (Inter Subject Unified Performance) | ||||||||||||||||
释义 |
Analytical performance in blood glucose monitoring (BGM) - New single figure characterization ‘ISUP’ to complement the currently used ‘MARD’Several approaches to BGM exists. Some 30 years ago, home-test kits were introduced, and over the last 5 years the continuous glucose monitoring (CGM) systems, e.g. from Dexcom, Abbott and Medtronic, have entered mainstream use, where statistical and graphical representations have been developed to summarize the analytical quality with regard to their primary purpose; aiding the diabetic patient in controlling his/her blood glucose levels. Since the introduction of CGM It is broadly accepted that the sole use of MARD for assessing the accuracy of glucose monitors may lead to flawed conclusions[2] and, for this reason, it has been suggested to supplement the MARD with reliability metrics, like the precision absolute relative difference (PARD)[3] and MARD reliability index[4]. As an alternative evaluation of sensor performance, a new figure of merit has been proposed, the so-called inter-subject unified performance (ISUP) parameter, which compounds statistical performance and aspects of the consensus error grid plot. Inter-subject unified performance (ISUP)The ISUP value is a composite measure that evaluates the performance of a regression model. The ISUP is, by definition, a general performance measure that can be utilized to compare the predictive capability of regression models, irrespective of the origin of the input data and the (continuous) quantity to be predicted. Yet, the ISUP has been developed to quantify the analytical performance of glucose monitoring systems that are routinely used by subjects with diabetes. The challenging aspect in quantifying the performance of glucose monitoring systems owes to the fact that subjects with diabetes show very different temporal behavior and range of glucose values. This may turn single-parameter measures, like root-mean-square-error (RMSE), MARD, and slope, to misleading figures of merit, particularly when compared between subjects. The composite ISUP parameter is evaluated on cross- or independent validated results and is designed to weight the presence of samples in zone A and B of the consensus error grid, together with a low MARD, a high slope and a high correlation coefficient. Furthermore, the ISUP takes into account the difference in the average glucose level between subjects by weighting with the median of the blood glucose concentrations. It is noted that the usability of a regression model is application specific, but past studies used a threshold on the ISUP of 0.5 to classify good and bad performing regression models.[5] FormulaThe ISUP of an subject specific regression model is calculated with the following formula: where A and B are percent samples lying in zone A and B of a consensus error grid, MARD is the mean absolute relative difference between estimated (yp) and reference (y) glucose values, a is the slope of the least-squares fitted linear polynomial, R2 is the correlation coefficient between yp and y, and ymedian is the median of the reference glucose values. MARD is calculated with the following formula: where n is the number of paired glucose measurements. ExampleIn the figures below, the following three scenarios are presented:
From a visual inspection of the consensus error grid
References1. ^Parkes JL. Slatin SL. Pardo S. Ginsberg BH. (2000) A New Consensus Error Grid to Evaluated the Clinical Significance of Inaccuracies in the Measurement of Blood Glucose. Diabetes Care 23(8), 1143-1148. 2. ^Kirchsteiger H. Heinemann L. Freckmann G. Lodwig V. Schmelzeisen-Redeker G. Schoemaker M. del Re L. (2015) Performance comparison of CGM systems: MARD values are not always a reliable indicator of their accuracy. J. Diabetes Sci. Technol. 9(5), 1030-1040. 3. ^Obermaier K. Schmelzeisen-Redeker G. Schoemaker M. Klötzer HM. Kirchsteiger H. Eikmeier H. del Re L. (2013) Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment, J. Diabetes Sci. Technol. 7(4): 824-832. 4. ^Reiterer F. Polterauer P. Schoemaker M. Schmelzeisen-Redecker G. Freckmann G. Heinemann L. del Re L. (2017) Significance and Reliability of MARD for the Accuracy of CGM Systems, J. Diabetes Sci Technol. 11(1): 59–67. 5. ^Lundsgaard-Nielsen SM, Pors A, Banke SO, Henriksen JE, Hepp DK, Weber A (2018) Critical-depth Raman spectroscopy enables home-use non-invasive glucose monitoring. PLoS ONE 13(5): e0197134. https://doi.org/10.1371/journal.pone.0197134 Inter-Subject Unified Performance (ISUP) |
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