MRSA ABSSSIs are associated with a significant clinical and economic burden, however rapid identification of MRSA remains a challenge. This study aimed to use a novel method of predictive modeling to determine those at highest risk of MRSA ABSSSIs. Risk factors for MRSA were derived from a combination of previously published literature and multivariable logistic regression of individual patient data (IPD) using the "adaptation method." A risk-scoring tool was derived from weight-proportional integer-adjusted coefficients of the predictive model. Likelihood ratios were used to adjust posterior probability of MRSA. Risk factors were identified from 12 previously published studies and adapted based on IPD (n = 236). Risk factors were: history of diabetes with obesity (aOR = 2.3), prior antibiotics (90 days) (aOR = 2.8), chronic kidney disease/hemodialysis (aOR = 1.3), intravenous drug use (aOR = 3.2), previous MRSA exposure/infection (12 months) (aOR = 2.6), previous hospitalization (12 months) (aOR = 1.2), and HIV/AIDS (aOR = 3.3). Baseline prevalence of MRSA was 42.7

作者：Kimberly C, Claeys；Evan J, Zasowski；Abdalhamid M, Lagnf；Donald P, Levine；Susan L, Davis；Michael J, Rybak

来源：International journal of antimicrobial agents 2017 年