Binary cumulative sums and moving averages in nosocomial infection cluster detection.
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, alpha, Beta, p(0), p(1)) 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 < or = alpha < or = 0.25 and 0.2 < or = Beta < or = 0.25, with p(0) = 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.
Brown
Samuel M
SM
Massachusetts General Hospital, Boston, USA. sambrown@post.harvard.edu
Benneyan
James C
JC
Theobald
Daniel A
DA
Sands
Kenneth
K
Hahn
Matthew T
MT
Potter-Bynoe
Gail A
GA
Stelling
John M
JM
O'Brien
Thomas F
TF
Goldmann
Donald A
DA
D016000:Cluster Analysis; D003428:Cross Infection / Q000453:epidemiology*; D004196:Disease Outbreaks; D016521:Electrophoresis, Gel, Pulsed-Field; D006801:Humans; D016106:Methicillin Resistance / Q000235:genetics; D008826:Microbial Sensitivity Tests; D009010:Monte Carlo Method; D013211:Staphylococcus aureus / Q000187:drug effects / Q000302:isolation & purification; D014481:United States / Q000453:epidemiology