Abstract
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.
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Melamed, A., and Sorvillo, F. J., The burden of sepsis-associated mortality in the United States from 1999 to 2005: an analysis of multiple-cause-of-death data. Crit. Care 13:R28, 2009.
Kumar, A., Ellis, P., Arabi, Y., Roberts, D., Light, B., Parrillo, J. E., Dodek, P., Wood, G., Kumar, A., and Simon, D., Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 136:1237–1248, 2009.
Dilnessa, T., Demeke, G., Mengistu, G., and Bitew, A., Emerging Blood Culture Technologies for Isolation of Blood Pathogens at Clinical Microbiology Laboratories. J. Med. Microbiol. Diagn. 5:227, 2016.
Marchandin, H., Compan, B., De Buochberg, M. S., Despaux, E., and Perez, C., Detection kinetics for positive blood culture bottles by using the VITAL automated system. J. Clin. Microbiol. 33:2098–2101, 1995.
Reimer, L. G., Wilson, M. L., and Weinstein, M. P., Update on detection of bacteremia and fungemia. Clin. Microbiol. Rev. 10:444–465, 1997.
Fiori, B., D'Inzeo, T., Di Florio, V., De Maio, F., De Angelis, G., Giaquinto, A., Campana, L., Tanzarella, E., Tumbarello, M., and Antonelli, M., Performance of two resin-containing blood culture media in the detection of bloodstream infections and in direct MALDI-TOF broth assays for isolate identification: clinical comparison of the BacT/ALERT Plus and BACTEC Plus Systems. J. Clin. Microbiol. 52:3558–3567, 2014.
Schieffer, K., Tan, K., Stamper, P., Somogyi, A., Andrea, S., Wakefield, T., Romagnoli, M., Chapin, K., Wolk, D., and Carroll, K., Multicenter evaluation of the Sepsityper™ extraction kit and MALDI-TOF MS for direct identification of positive blood culture isolates using the BD BACTEC™ FX and VersaTREK® diagnostic blood culture systems. J. Appl. Microbiol. 116:934–941, 2014.
Uh, Y., Jang, I. H., Park, S. D., Kim, K. S., Seo, D. M., Yoon, K. J., Choi, H. K., Kim, Y. K., and Kim, H. Y., Factors influencing the false positive signals of continuous monitoring blood culture system. Ann. Clin. Microbiol. 17:58–64, 2014.
Lee, C., Lin, W., Shih, H., Wu, C., Chen, P., Lee, H., Lee, N., Chang, C., Wang, L., and Ko, W., Clinical significance of potential contaminants in blood cultures among patients in a medical center. J. Microbiol. Immunol. Infect. 40:438, 2007.
Park, S. H., Shim, H., Yoon, N. S., and Kim, M.-N., Clinical relevance of time-to-positivity in BACTEC9240 blood culture system. Korean J. Lab. Med. 30:276–283, 2010.
Li, J., Plorde, J. J., and Carlson, L. G., Effects of volume and periodicity on blood cultures. J. Clin. Microbiol. 32:2829–2831, 1994.
Mermel, L. A., and Maki, D. G., Detection of bacteremia in adults: consequences of culturing an inadequate volume of blood. Ann. Intern. Med. 119:270–272, 1993.
Plorde, J. J., Tenover, F. C., and Carlson, L., Specimen volume versus yield in the BACTEC blood culture system. J. Clin. Microbiol. 22:292–295, 1985.
Garthright, W., Refinements in the prediction of microbial growth curves. Food Microbiol. 8:239–248, 1991.
Perni, S., Andrew, P. W., and Shama, G., Estimating the maximum growth rate from microbial growth curves: definition is everything. Food Microbiol. 22:491–495, 2005.
Yates, G. T., and Smotzer, T., On the lag phase and initial decline of microbial growth curves. J. Theor. Biol. 244:511–517, 2007.
Peleg, M., Corradini, M. G., and Normand, M. D., The logistic (Verhulst) model for sigmoid microbial growth curves revisited. Food Res. Int. 40:808–818, 2007.
Peleg, M., and Corradini, M. G., Microbial growth curves: what the models tell us and what they cannot. Crit. Rev. Food Sci. Nutr. 51:917–945, 2011.
Chang-Li, X., Hou-Kuhan, T., Zhau-Hua, S., Song-Sheng, Q., Yao-Ting, L., and Hai-Shui, L., Microcalorimetric study of bacterial growth. Thermochim. Acta 123:33–41, 1988.
Braissant, O., Bonkat, G., Wirz, D., and Bachmann, A., Microbial growth and isothermal microcalorimetry: growth models and their application to microcalorimetric data. Thermochim. Acta 555:64–71, 2013.
Braissant, O., Bachmann, A., and Bonkat, G., Microcalorimetric assays for measuring cell growth and metabolic activity: Methodology and applications. Methods 76:27–34, 2015.
Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D. A., and Najarian, K., Big data analytics in healthcare. Biomed. Res. Int. 2015:370194, 2015.
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This article is part of the Topical Collection on Image & Signal Processing
K. A. and J.-H. A. contributed equally to this article.
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Ahn, K., Ahn, JH., Kim, J. et al. A New Bacterial Growth Graph Pattern Analysis to Improve Positive Predictive Value of Continuous Monitoring Blood Culture System. J Med Syst 42, 189 (2018). https://doi.org/10.1007/s10916-018-1046-y
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DOI: https://doi.org/10.1007/s10916-018-1046-y