Abstract
Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections.
Highlights
Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients
We evaluated the performance of these techniques in simulated real-time detection of two genotypically characterized outbreaks of nosocomial infection caused by antimicrobial-resistant bacteria
We illustrated the performance of a system designed for real-time monitoring of clinical microbiology data from the hospital laboratory information system
Summary
Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. Nosocomial infections afflict 2 to 5 million patients in the United States annually and contribute to approximately 88,000 deaths [1,2] These infections are the second most frequent adverse effect of hospitalization [3,4]. Increments are added or decrements are subtracted from a running total over time, according to measurements of quality of serial items The behavior of this cumulative sum is tracked until one of two conditions is met, with CUSUM values beyond these thresholds signaling either 1) a statistically significant change in quality to some prespecified level or 2) acceptance of the hypothesis of no change. Various MA techniques have been applied to disease rates in public health surveillance [29], they have not previously been applied to monitor changes in strain characteristics, such as antimicrobial resistance
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