Monitoring antimicrobial resistance (AMR) using CUSUM control charts

Abstract

We evaluated the use of the Cumulative Summation (CUSUM) control chart methodology for detection of an excessive increase in antimicrobial-resistant (AMR) bacteria acquisition. We used administrative, clinical and bacteriological data from all 157,570 patients hospitalized for at least 48 h from January 1, 2010 to December 31, 2015 in a 654-bed university teaching hospital in Paris, France. Monthly computed CUSUM were evaluated for the detection of out-of-control situations, defined as incidence rates of acquired AMR bacterial colonization exceeding acceptable thresholds at the hospital and ward levels (based on six selected wards) for AMR bacteria overall and Extended-spectrum beta-lactamases Enterobacteriaceae (ESBL-E) and Methicillin-resistant Staphylococcus aureus (MRSA), specifically. During the study period, 1,403 samples of acquired AMR bacteria were identified including 1,129 ESBL-E and 151 MRSA. The incidence rate of acquired AMR bacteria was stable at the hospital and the wards level. When based on AMR bacteria overall, CUSUM alarms were triggered at the hospital level and at the ward level in four units. For ESBL-E, CUSUM tests generated alarms at the hospital level and for the same four wards, and for MRSA, CUSUM tests detected out-of-control situations in all the wards. The CUSUM approach appears complementary with hospital infection control strategies currently in practice and appears of interest in common practice as a simple tool for AMR surveillance.

Significance of Simultaneous Use of Different Methods for Interpretation of Internal Quality Control Data An Experience of a Tertiary Care Hospital

“…[12] Righi L et al used CUSUM controls charts for monitoring of antimicrobial resistance and found its use to be complimentary for the hospital infection control strategies. [13] Rasmussen TB et al applied risk adjusted CUSUM charts for the monitoring of the 30 day hospital mortality and found that a alarm from CUSUM chart can used identify performance problem. [14] Sampson ML used CUSUM with logistic regression (CSLR) method to predict the testing errors and found that it to a rapid and sensitive detection method for laboratory errors.…”