Limits...
Etiologic diagnosis of lower respiratory tract bacterial infections using sputum samples and quantitative loop-mediated isothermal amplification.

Kang Y, Deng R, Wang C, Deng T, Peng P, Cheng X, Wang G, Qian M, Gao H, Han B, Chen Y, Hu Y, Geng R, Hu C, Zhang W, Yang J, Wan H, Yu Q, Wei L, Li J, Tian G, Wang Q, Hu K, Wang S, Wang R, Du J, He B, Ma J, Zhong X, Mu L, Cai S, Zhu X, Xing W, Yu J, Deng M, Gao Z - PLoS ONE (2012)

Bottom Line: With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients.In conclusion, qLAMP is a reliable method in quantifying bacterial titer.Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.

View Article: PubMed Central - PubMed

Affiliation: Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China.

ABSTRACT

Unlabelled: Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.

Trial registration: ClinicalTrials.gov NCT00567827.

Show MeSH

Related in: MedlinePlus

Examples of S. pneumonia showing the relationship between qLAMP and culture results (logistic regression) and cutoff determination based on competitive relationship (piece-wise linear regression).The horizontal axis displays the bacterial natural logarithmic titer in sputum sample. (A) Logistic regression curve (green line). Solid circles indicate patients; they are placed at the top of the chart when being test as positive and at the bottom of the chart when being tested as negative in the culture assays. The height and width of the bars display the frequency and the number of patients being tested positive in cultures, respectively. (B) Piecewise linear regression (black lines) of S. pneumonia in COPD patients. Open circles indicate patients; they are placed at the top of the chart when being PC (Pathogen Candidate) and at the bottom of the chart when NOT being PC.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3375278&req=5

pone-0038743-g003: Examples of S. pneumonia showing the relationship between qLAMP and culture results (logistic regression) and cutoff determination based on competitive relationship (piece-wise linear regression).The horizontal axis displays the bacterial natural logarithmic titer in sputum sample. (A) Logistic regression curve (green line). Solid circles indicate patients; they are placed at the top of the chart when being test as positive and at the bottom of the chart when being tested as negative in the culture assays. The height and width of the bars display the frequency and the number of patients being tested positive in cultures, respectively. (B) Piecewise linear regression (black lines) of S. pneumonia in COPD patients. Open circles indicate patients; they are placed at the top of the chart when being PC (Pathogen Candidate) and at the bottom of the chart when NOT being PC.

Mentions: We assess the relatedness between the two assays based on contingency table and logistical regression curve. For this part of the analysis, we only used the culture results from local hospitals, since there are variable bacterial mortalities (ranged 0–1 and averaged 0.604) detected in the central laboratories, largely due to refrigeration during the sample storage and transport periods. We made two observations. First, we noticed that the p values in contingency table, which evaluates independence between qLAMP and culture data, are extremely low overall. This suggests that the titers quantified via qLAMP are not stochastic and have a strong correlation to culture results (Table 2). Second, when we look at logistical regression for each bacterium, the probability of being positive in culture and the titer quantified via qLAMP fit logistical regression curves (Figure S3). As an example, we show a result of logistic regression for S. pneumoniae in Figure 3 A. The p values of logistic regression are all extremely low (Table 2) except for H. influenzae (p value 0.0115>0.01), mainly due to its fragility and low positive rate in culture. The strong correlations between the two methods based on the results of contingency table and logistic regression demonstrate the robustness of qLAMP assay.


Etiologic diagnosis of lower respiratory tract bacterial infections using sputum samples and quantitative loop-mediated isothermal amplification.

Kang Y, Deng R, Wang C, Deng T, Peng P, Cheng X, Wang G, Qian M, Gao H, Han B, Chen Y, Hu Y, Geng R, Hu C, Zhang W, Yang J, Wan H, Yu Q, Wei L, Li J, Tian G, Wang Q, Hu K, Wang S, Wang R, Du J, He B, Ma J, Zhong X, Mu L, Cai S, Zhu X, Xing W, Yu J, Deng M, Gao Z - PLoS ONE (2012)

Examples of S. pneumonia showing the relationship between qLAMP and culture results (logistic regression) and cutoff determination based on competitive relationship (piece-wise linear regression).The horizontal axis displays the bacterial natural logarithmic titer in sputum sample. (A) Logistic regression curve (green line). Solid circles indicate patients; they are placed at the top of the chart when being test as positive and at the bottom of the chart when being tested as negative in the culture assays. The height and width of the bars display the frequency and the number of patients being tested positive in cultures, respectively. (B) Piecewise linear regression (black lines) of S. pneumonia in COPD patients. Open circles indicate patients; they are placed at the top of the chart when being PC (Pathogen Candidate) and at the bottom of the chart when NOT being PC.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375278&req=5

pone-0038743-g003: Examples of S. pneumonia showing the relationship between qLAMP and culture results (logistic regression) and cutoff determination based on competitive relationship (piece-wise linear regression).The horizontal axis displays the bacterial natural logarithmic titer in sputum sample. (A) Logistic regression curve (green line). Solid circles indicate patients; they are placed at the top of the chart when being test as positive and at the bottom of the chart when being tested as negative in the culture assays. The height and width of the bars display the frequency and the number of patients being tested positive in cultures, respectively. (B) Piecewise linear regression (black lines) of S. pneumonia in COPD patients. Open circles indicate patients; they are placed at the top of the chart when being PC (Pathogen Candidate) and at the bottom of the chart when NOT being PC.
Mentions: We assess the relatedness between the two assays based on contingency table and logistical regression curve. For this part of the analysis, we only used the culture results from local hospitals, since there are variable bacterial mortalities (ranged 0–1 and averaged 0.604) detected in the central laboratories, largely due to refrigeration during the sample storage and transport periods. We made two observations. First, we noticed that the p values in contingency table, which evaluates independence between qLAMP and culture data, are extremely low overall. This suggests that the titers quantified via qLAMP are not stochastic and have a strong correlation to culture results (Table 2). Second, when we look at logistical regression for each bacterium, the probability of being positive in culture and the titer quantified via qLAMP fit logistical regression curves (Figure S3). As an example, we show a result of logistic regression for S. pneumoniae in Figure 3 A. The p values of logistic regression are all extremely low (Table 2) except for H. influenzae (p value 0.0115>0.01), mainly due to its fragility and low positive rate in culture. The strong correlations between the two methods based on the results of contingency table and logistic regression demonstrate the robustness of qLAMP assay.

Bottom Line: With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients.In conclusion, qLAMP is a reliable method in quantifying bacterial titer.Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.

View Article: PubMed Central - PubMed

Affiliation: Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China.

ABSTRACT

Unlabelled: Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.

Trial registration: ClinicalTrials.gov NCT00567827.

Show MeSH
Related in: MedlinePlus