Estimating the measurement uncertainties of the international sensitivity index of 12 thromboplastins through Monte Carlo simulation.

BACKGROUND: Measurement uncertainty (MU) estimation has become an important process in clinical laboratories; however, calculating the MUs of the international sensitivity index (ISI) of thromboplastins is difficult because of the complex mathematical calculations required in calibration. Therefore, this study quantifies the MUs of ISIs through the Monte Carlo simulation (MCS), which involves random sampling of numerical values to solve a complex mathematical calculation. METHODS: Eighty blood plasmas and commercially available certified plasmas (ISI Calibrate) were used to assign the ISIs of each thromboplastin. Prothrombin times were measured using reference thromboplastin and 12 commercially available thromboplastins (Coagpia PT-N, PT Rec, ReadiPlasTin, RecombiPlasTin 2G, PT-Fibrinogen, PT-Fibrinogen HS PLUS, Prothrombin Time Assay, Thromboplastin D, Thromborel S, STA-Neoplastine CI Plus, STA-Neoplastine R 15, and STA-NeoPTimal) with two automated coagulation instruments: ACL TOP 750 CTS (ACL TOP; Instrumentation Laboratory, Bedford, MA, USA) and STA Compact (Diagnostica Stago, Asnières-sur-Seine, France). Then, the MUs of each ISI were simulated through MCS. RESULTS: The MUs of ISIs ranged from 9.7 % to 12.1 % and 11.6 % to 12.0 % when blood plasma and ISI Calibrate were used, respectively. For some thromboplastins, the ISI claimed by manufacturers significantly differed from the estimated results. CONCLUSIONS: MCS is adequate to estimate the MUs of ISI. These results would be clinically useful for estimating the MUs of the international normalized ratio in clinical laboratories. However, the claimed ISI significantly differed from the estimated ISI of some thromboplastins. Therefore, manufacturers should provide more accurate information about the ISI value of thromboplastins.

Estimation of Measurement Uncertainty of Factor Assays Using the Monte Carlo Simulation.

OBJECTIVES: We aimed to quantify the measurement uncertainty (MU) for factor activities using the Monte Carlo simulation (MCS), which is a computational algorithm that simulates statistical sampling to obtain numerical results through complex mathematical calculations. METHODS: The uncertainties of factor V (FV), factor VIII (FVIII), and factor X (FX) were simulated with two coagulation testing systems: ACL TOP 750 CTS (Instrumentation Laboratory) and STA Compact (Diagnostica Stago). RESULTS: When the factor activities were 74% (FV), 68% (FVIII), and 89% (FX), the MUs were 3.5% (FV), 9.3% (VIII), and 2.8% (FX) for ACL TOP and 8.5% (FV), 18.2% (FVII), and 6.5% (FX) for STA Compact. MCS MUs were compared with MUs obtained from running actual samples (not simulated) using a method called the top-down approach. The MCS MU results were interchangeable with MUs from the top-down approach, except for FVIII from STA Compact. CONCLUSIONS: The MCS procedure is well suited for the quantification of MUs for factor assays over the entire measurement range.

Identification of Uncommon Candida Species Using Commercial Identification Systems.

Recently, several studies have revealed that commercial microbial identification systems do not accurately identify the uncommon causative species of candidiasis, including Candida famata, Meyerozyma guilliermondii, and C. auris. We investigated the accuracy of species-level identification in a collection of clinical isolates previously identified as C. famata (N = 38), C. lusitaniae (N = 1 2), and M. guilliermondii (N = 5) by the Vitek 2 system. All 55 isolates were re-analyzed by the Phoenix system (Becton Dickinson Diagnostics), two matrix-assisted laser desorption ionization-time of flight mass spectrometry analyzers (a Vitek MS and a Bruker Biotyper), and by sequencing of internal transcribed spacer (ITS) regions or 26S rRNA gene D1/D2 domains. Among 38 isolates previously identified as C. famata by the Vitek 2 system, the majority (27/38 isolates, 71.1%) were identified as C. tropicalis (20 isolates) or C. albicans (7 isolates) by ITS sequencing, and none was identified as C. famata. Among 20 isolates that were identified as C. tropicalis, 17 (85%) were isolated from urine. The two isolates that were identified as C. auris by ITS sequencing originated from ear discharge. The Phoenix system did not accurately identify C. lusitaniae, C. krusei, or C. auris. The correct identification rate for 55 isolates was 92.7% (51/55 isolates) for the Vitek MS and 94.6% (52/55 isolates) for the Bruker Biotyper, as compared with results from ITS sequencing. These results suggest that C. famata is very rare in Korea, and that the possibility of misidentification should be noted when an uncommon Candida species is identified.