False positive signals (FPSs) of continuous monitoring blood culture system (CMBCS) cause delayed reporting time and increased laboratory cost. This study aimed to analyze growth graphs digitally in order to identify specific patterns of FPSs and true positive signals (TPSs) and to find the method for improving positive predictive value (PPV) of FPS and TPS. 606 positive signal samples from the BACTEC FX (BD, USA) CMBCS with more than one hour of monitoring data after positive signal were selected, and were classified into FPS and TPS groups using the subculture results. The pattern of bacterial growth graph was analyzed in two steps: the signal stage recorded using the monitoring data until positive signal and the post-signal stage recorded using one additional hour of monitoring data gained after the positive signal. The growth graph before the positive signal consists of three periods; initial decline period, stable period, and steeping period. Signal stage analyzed initial decline period and stable period, and classified the graphs as standard, increasing, decreasing, irregular, or defective pattern, respectively. Then, all patterns were re-assigned as confirmed or suspicious pattern in the post-signal stage. Standard, increasing, and decreasing patterns with both initial decline period and stable period are typical patterns; irregular patterns lacking a smooth stable period and defective patterns without an initial decline period are false positive patterns. The false positive patterns have 77.2% of PPV for FPS. The confirmed patterns, showing a gradually increasing fluorescence level even after positive signal, have 97.0% of PPV for TPS.
Keywords: Algorithm; Continuous monitoring blood culture system; Growth graph; Monitoring; Positive predictive value; Signal.