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Knowledge concerning stability is important in the development and assessment of microbial molecular typing systems and is critical for the interpretation of their results. Typing system stability is usually measured as the fraction of isolates that change type after several in vivo passages, but this does not necessarily reflect in vivo stability. The aim of this study was to utilize survival analysis to provide an informative quantitative measure of in vivo stability and to compare the stabilities of various techniques employed in typing methicillin-resistant Staphylococcus aureus (MRSA). We identified 100 MRSA pairs (isolated from the same patient ≥ 1 month apart) and typed them using multilocus sequence typing (MLST), phage-derived open reading frame (PDORF) typing, toxin gene profiling (TGP), staphylococcal cassette chromosome mec (SCCmec) subtyping, pulsed-field gel electrophoresis (PFGE), and spa sequence typing. Discordant isolate pairs, belonging to different MLST clonal complexes, were excluded, leaving 81 pairs for analysis. The stabilities of these methods were examined using Kaplan-Meier survival analysis, and discriminatory power was measured by Simpson's index of diversity. The probability percentages that the type remained unchanged at 6 months for spa sequence typing, TGP, multilocus variable number of tandem repeats analysis (MLVA), SCCmec subtyping, PDORF typing, and PFGE were 95, 95, 88, 82, 71, and 58, respectively, while the Simpson's indices of diversity were 0.48, 0.47, 0.70, 0.72, 0.89, and 0.88, respectively. Survival analysis using sequential clinical isolates adds an important quantitative dimension to the measurement of stability of a microbial typing system. Of the methods compared here, PDORF typing provides high discriminatory power, comparable with that of PFGE, and a level of stability suitable for MRSA surveillance and outbreak investigations.

Quantitative Estimation of the Stability of Methicillin-Resistant Staphylococcus aureus Strain-Typing Systems by Use of Kaplan-Meier Survival Analysis