A novel weighted cumulative delta-check method for highly sensitive detection of specimen mix-up in the clinical laboratory.

BACKGROUND: We sought to detect specimen mix-up by developing a new cumulative delta-check method applicable to a mixture of test items with heterogeneous units and distribution patterns. METHODS: The distributions of all test results were successfully made Gaussian using power transformation. Values were then standardized into z-score (zx) based on reference interval (RI) so that limits of RI take zx=±1.96. To find a weight for summing absolute value of delta between current and previous zx (Dz), we evaluated the distribution of Dz. Its central portion was always regarded as Gaussian despite the presence of symmetrical long tails. Thus, an adjusted SD (aSD) representing the center was estimated with an iterative method. By setting 1/aSD2 as a weight factor, we computed a weighted mean of Dz as an index for specimen mix-up (wCDI). RESULTS: The performance of wCDI was evaluated, using a model laboratory database consisting of 32 basic test items, by a simulation study generating artificial cases of mix-up. When wCDI was computed from three commonly ordered test sets consisting of 6-9 items each, its diagnostic efficiency in detecting the artificial cases was 0.937-0.967 expressed as area under ROC curves (AUC). When the performance of wCDI was evaluated simply by the number of test items (p) included in the computation, AUC gradually increased from 0.944 (p=5) to 0.976 (p=8). However, when p≥10, AUC stayed at approximately 0.98. CONCLUSIONS: wCDI was proven to be highly effective in uncovering cases of specimen mix-up. The diagnostic efficiency of wCDI depends only on the number of test items included in the computation.

The moment segmentation analysis of heart sound pattern.

UNLABELLED: This paper presents two new ideas. The first one is to apply the Viola integral waveform method to analyze the heart sounds recorded by an electric stethoscope, and the multi-scale moment analysis is proposed to locate each cycle of heart sounds. A fast algorithm for calculating characteristic waveform (CW) and characteristic moment waveform (CMW) of heart sound can be expressed by the Viola integral method, and their calculation time has nothing to do with their scales. The second idea is easier to segment the heart sound based on its approximate cyclical characteristic than the ordinary methods. Each heart sound cycle can be quickly found by CMW's Local Extreme Points (LEPs). Based on the information of LEPs and CW, a high accurate search algorithm to segment S1 and S2 sounds is submitted. By numerical experiments, the important parameters of time scale delta=0.05s for CW and l=0.45s for CMW are obtained and validated for segmentation of heart sound. CONCLUSION: More exact segmentation boundaries of the heart sound signal could be located fast in an automated way, and a further performance analysis is presented. Owing to the use of the rhythm of CMW curves, the proposed method not only gives a higher success segmentation rate, but also it is actually simpler and faster than the wavelet method.