A New Bacterial Growth Graph Pattern Analysis to Improve Positive Predictive Value of Continuous Monitoring Blood Culture System.
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 f...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 10; pp. 1 - 2 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132085520&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132085520 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2018 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132085520 132085520 132085520 10.1007/s10916-018-1046-y 132085520 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A New Bacterial Growth Graph Pattern Analysis to Improve Positive Predictive Value of Continuous Monitoring Blood Culture System. aug: au: Ahn, Kwangjin Ahn, Jae-Hyeong Kim, Juwon Lee, Jong-Han Hwang, Gyu Yel Yoo, Gilsung Yoon, Kap Jun Uh, Young affil: Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju, South Korea sug: subj: Blood Culture Bacteria Metabolism Data Analysis, Computer Assisted Human Technology, Medical Signal Processing, Computer Assisted Clinical Laboratories False Positive Results Predictive Value of Tests Staining and Labeling Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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