crossmark
Detection of Airway Colonization by Aspergillus fumigatus by Use of Electronic Nose Technology in Patients with Cystic Fibrosis K. de Heer,a M. G. M. Kok,a N. Fens,b E. J. M. Weersink,b A. H. Zwinderman,c M. P. C. van der Schee,b C. E. Visser,d M. H. J. van Oers,a P. J. Sterkb Department of Hematology,a Department of Respiratory Medicine,b Department of Clinical Epidemiology, Biostatistics, and Bioinformatics,c and Department of Medical Microbiology,d Academic Medical Center, Amsterdam, the Netherlands
Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whether Aspergillus fumigatus colonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined the A. fumigatus colonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. The P value was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized by A. fumigatus. In total, 3 subjects were misclassified, resulting in a crossvalidated accuracy of the Cyranose detecting IA of 89% (P ⴝ 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate that A. fumigatus colonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.
T
he diagnosis of invasive pulmonary aspergillosis (IA) in patients with prolonged chemotherapy-induced neutropenia (PCIN) remains a frequently occurring and difficult challenge (1– 3). The signs and symptoms are nonspecific (4, 5). Mortality is high, especially in case of a diagnostic delay (3, 6, 7). The Platelia galactomannan assay (Bio-Rad) performed on bronchoalveolar lavage fluid samples is the only test with sufficient sensitivity and specificity to demonstrate or rule out IA (8). Its major drawback is the invasive and highly uncomfortable nature of bronchoalveolar lavage (9–13). The Platelia galactomannan assay was originally developed to be used on serum. Although diagnostic accuracy is significantly lower in this setting, it creates the opportunity to serially monitor patients who are at high risk, such as acute myeloid leukemia (AML) patients treated with intensive induction chemotherapy (14). This might enable earlier diagnosis and thus a better prognosis. This is the topic of a number of ongoing studies. Unfortunately, two already-published studies did not show that serial monitoring of galactomannan leads to an earlier diagnosis (15, 16). Previously, we showed that patients with PCIN and IA have a distinct exhaled breath profile that can be discriminated by an electronic nose (eNose) (17). Very recently, this was confirmed by using gas chromatography-mass spectrometry (GC-MS), demonstrating exhaled metabolite signatures of Aspergillus fumigatus in vitro and IA in vivo (18). An eNose is a cheap and noninvasive instrument with realtime measurement capacity. It can discriminate complex mixtures of volatile organic compounds (VOCs) by measuring these VOCs by pattern recognition using a limited number of cross-reactive sensors. This is achievable, because VOCs react differently with each sensor (19, 20). Exhaled breath can contain about 2,000 to 3,000 VOCs, and numerous disease states may be diagnosed by
March 2016 Volume 54 Number 3
their exhaled breath profile (or breathprint, the composite pattern in exhaled VOCs) (21–30). For asthma and chronic obstructive pulmonary disease (COPD), this has been corroborated by external validation (23). In cystic fibrosis (CF), proper chloride and sodium channel functioning is hampered, leading to an altered rheology of airway secretions, among other manifestations (31). The secretions become thick and difficult to clear, leading to chronic colonization with microorganisms. The reported incidence of A. fumigatus colonization varies between 6 and 58% (32–38). It is unknown whether colonization by A. fumigatus in CF results in a change in exhaled breath profile. If so, it would extend the proof-of-principle study that showed that an eNose may be used to diagnose invasive pulmonary aspergillosis (17). However, if an eNose can detect A. fumigatus colonization, this might also form the basis of false-positive test results in detecting invasive pulmonary aspergillosis. We hypothesized that A. fumigatus colonization in CF patients leads to a distinct exhaled breath profile. Therefore, we aimed to
Received 14 August 2015 Returned for modification 8 September 2015 Accepted 10 December 2015 Accepted manuscript posted online 16 December 2015 Citation de Heer K, Kok MGM, Fens N, Weersink EJM, Zwinderman AH, van der Schee MPC, Visser CE, van Oers MHJ, Sterk PJ. 2016. Detection of airway colonization by Aspergillus fumigatus by use of electronic nose technology in patients with cystic fibrosis. J Clin Microbiol 54:569 –575. doi:10.1128/JCM.02214-15. Editor: D. W. Warnock Address correspondence to K. de Heer, [email protected]. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
Journal of Clinical Microbiology
jcm.asm.org
569
de Heer et al.
TABLE 1 Study subject characteristics, stratified according to A. fumigatus colonization status Characteristic No. of subjects Age (mean [SD]) (yr) Male gender (%) % of predicted FEV1 (mean [SD])
Colonized with A. fumigatus
Not colonized with A. fumigatus
P value
9 28.0 (23.0) 67 46.0 (28.3)
18 30.3 (9.6) 67 63.5 (23.6)
⬎0.2 ⬎0.2 ⬎0.2 ⬎0.2
determine whether an eNose can discriminate CF patients with and without A. fumigatus colonization. MATERIALS AND METHODS Design. The study had a cross-sectional design in which exhaled breath analysis was performed once at a regular outpatient visit for each subject for pulmonary function assessment and sputum culture. Within 5 min of the exhaled breath analysis, subjects were asked to expectorate sputum for culture. Subjects with a culture positive for A. fumigatus were considered to be cases, and subjects with a negative culture were controls. We compared the breathprints between cases and controls. Subjects. Twenty-seven patients (age range, 17 to 50 years) from the CF clinic of the Academic Medical Center, Amsterdam, the Netherlands, with a confirmed diagnosis of CF (defined by 2 known CF mutations and/or a sweat chloride level of ⬎60 mEq/liter) (39, 40), participated in this study (Table 1). Due to the noninvasive nature of merely collecting exhaled breath samples during routine lung function testing, the ethics review board of the Academic Medical Center considered that the protocol did not require ethical review under Dutch legislation. We calculated the P values in Table 1 using a two-sample t test (for age and the median forced expiratory volume in 1 s [FEV1]) and a Pearson’s chi-square test with Yates’ continuity correction (for gender). Exhaled breath analysis. Exhaled breath analysis was performed as described previously (17, 22, 24). Subjects had to breathe through a mouthpiece with their nostrils closed by a clip for 5 min. The mouthpiece was connected by a three-way nonrebreathing valve to a VOC filter (A2; North Safety, Middelburg, the Netherlands) at the inspiratory port and a silica reservoir at the expiratory port. Without interruption, the subject was then asked to inspire deeply and exhale a vital capacity volume. The exhaled breath was stored in a 10-liter Tedlar bag attached to the silica reservoir. Analysis was performed within 30 min with a Cyranose 320 (Smith Detections, Pasadena, CA), a hand-held eNose with 32 nanocomposite sensors (19). The electrical resistance differential in all sensors was recorded. The procedure of sampling the Tedlar bag was done twice, using the first measurement solely to prime the sensors for the exhaled VOC mixture, as previously described (22), and as recommended by the manufacturer, in order to avoid a “first smell effect.” The first measurement was not used in the statistical analysis. Index culture. Subjects were asked to expectorate sputum once within 5 min of the exhaled breath maneuver. The sputum samples were processed and cultured according to the standard operating procedure of our hospital. The sputum samples were inoculated for bacterial culture on the following media: sheep blood agar (COS; bioMérieux), chromogenic agar (CPS; bioMérieux), chocolate agar containing thymidine, Columbia red agar containing thymidine, and N-acetyl-D-glucosamine agar. For the fungal cultures, samples were inoculated on two Sabouraud agars containing gentamicin and chloramphenicol (SGC2; bioMérieux). These were incubated for 14 days at 10°C and 36°C, respectively, and assessed twice a week. If growth was observed, fungi were identified based on morphology (both macroscopically and microscopically) and growth characteristics (41). We reviewed all sputum cultures from a study subject that were performed during the 2 years before the study culture. If one of those cultures showed a nonconsistent result with regard to Aspergillus
570
jcm.asm.org
spp., the subject was excluded from analysis. Sputum samples from our CF patients are routinely cultured twice per year, and often, more frequently. Analysis. As a primary analysis, we compared the breathprints of subjects with and without colonization by A. fumigatus, using the method of analysis from our previous study (17). We used the R statistical package (version 2.11.1). Principal component analysis (PCA) was executed to reduce the raw data of the 32 sensors to a smaller number of principal components. The number of components was chosen to include at least 99.9% of the variance within the data set. With a t test (equal variance assumed depending on an F-test), all PCA factors were categorized as either discriminatory between the two groups or not. A two-sided P value of 0.10 was chosen as a cutoff. Next, a diagnostic algorithm was constructed by using the discriminatory PCA factors as input for a linear canonical discriminant analysis, assuming an equal chance of being a member of one of the two groups. The discriminant function was optimized to best distinguish between categories. The percentage of correctly classified subjects (combining cases and controls) was cross-validated using the leave-one-out method. This resulted in cross-validated accuracy. The P value of our cross-validated accuracy was derived by generating 100,000 random classifications of our subjects (whether or not colonized by A. fumigatus) and calculating the chance that a random classification would have resulted in the same cross-validated accuracy or better, constructing a new pattern recognition algorithm for each of the random classifications. Receiver operating characteristic (ROC) curve analysis was performed.
RESULTS
All measurements were performed according to the predefined protocol. No patients had an exacerbation at the time of the measurement. None of the subjects were treated with antifungal therapy in the 2 years preceding the eNose measurement, either systemically or as an inhalant. All subjects were able to produce sputum. None of the subjects had a sputum culture in the previous 2 years that was nonconsistent with respect to Aspergillus spp., i.e., all cultures were either positive or negative. The entire eNose analysis took about 45 min in our experimental setup, including data handling. Principal component analysis resulted in 7 PCA factors that described ⬎99.9% of the variance. In 9 of the 27 subjects, A. fumigatus was cultured from sputum. Of the 7 PCA factors, 3 discriminated between subjects with and without A. fumigatus colonization. Canonical discriminant analysis based on these 3 principal components yielded a cross-validated accuracy of 88.9%. Figure 1 shows the individual discriminant scores of subjects with and without colonization. Three subjects were misclassified in the cross-validation, two false positively and one false negatively, corresponding to a sensitivity of 78% and a specificity of 94%. The area under the curve of the ROC curve was 0.89. ROC analysis of the cross-validated posterior probabilities of group membership showed an area under the curve of 80.2% (Fig. 2). In our simulation, 0.4% of the random classifications resulted in a cross-validated accuracy of ⱖ88.9% (Fig. 3). The raw eNose data are presented in Fig. 4. We explored the effect of cocolonization by other microorganisms, for example, to exclude the possibility that the eNose had merely “smelled” another microorganism with a coinciding colonization pattern. Pseudomonas spp., Staphylococcus aureus, and Haemophilus influenzae were found in the index culture of more than one subject (see Table 2). The extent to which the colonization pattern of these microorganisms coincided with colonization by A. fumigatus is also shown in Table 2, as we calculated the percentage of cases in which a subject was colonized by both A.
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
FIG 1 Breathprints of CF patients with and without A. fumigatus colonization, in a one-dimensional “jittered” canonical discriminant analysis plot. Squares with a cross represent subjects that were misclassified in the crossvalidation. Box-and-whisker plots (with the median shown as a line through the box, the first and third quartiles shown as the lower and upper edges of the box, respectively, and the 95% confidence interval of the median shown as whiskers) are superimposed.
fumigatus and the other microorganism, or by neither of these two. We determined the cross-validated accuracy with which an eNose could detect those microorganisms by repeating the statistical method of the primary analysis afresh. Only P. aeruginosa
FIG 2 Receiver operating characteristic (ROC) curves showing the true-positive and false-positive rates in detecting A. fumigatus colonization with an eNose as a function of the cutoff value. Dashed line, ROC curve of the noncross-validated measurements; solid line, the ROC curve of the cross-validated measurements.
March 2016 Volume 54 Number 3
FIG 3 Histogram of the cross-validated accuracies produced by the random simulation.
colonization was associated with a PCA factor that differed statistically significantly between groups. The cross-validated accuracy of an eNose for detecting P. aeruginosa colonization was 63%, only slightly ⬎50%. DISCUSSION
To the best of our knowledge, this study is the first to examine whether an eNose can detect A. fumigatus colonization with moderate to good accuracy. It shows that A. fumigatus colonization in CF patients leads to a distinct breathprint. The low P value obtained by comparison with 100,000 random classifications indicates that there is only a minor chance that this represents a falsepositive outcome merely due to coincidence. Our study has a number of strengths. It is novel, and we were able to examine a unique cohort of CF patients according to a strict protocol, using both electronic nose technology and microbiological techniques. The study also has limitations. First and foremost, the sample size was too small to allow split-half analysis. However, the 100,000 random classifications and internal validation using the leave-one-out method strengthened the validity of our results. Furthermore, our data indicate that it is highly unlikely that another microorganism was mistaken for A. fumigatus by our algorithm. We explored this possibility by repeating the statistical method of our analysis for every bacterial species that was encountered in the sputum culture results of more than one subject. Only P. aeruginosa colonization was associated with one or more PCA factors that differed statistically significantly between groups. The cross-validated accuracy was only 63.0%. Eventually, only external validation, i.e., the confirmation of our diagnostic algorithm in a separate group of subjects, would definitively establish the ability of an eNose to diagnose A. fumigatus colonization (42). Such validation has already been performed successfully in a similar eNose study on differentiating between COPD and asthma (23).
Journal of Clinical Microbiology
jcm.asm.org
571
de Heer et al.
FIG 4 Spider graph of the raw eNose data showing a circular representation of the 32 sensor signals from all 27 samples, zoomed in to demonstrate betweensample differences. As the sensors are exposed to the breath sample, the electrical resistance over the sensors changes. On the axes of the spider graph, the quotient (calculated as [Rmax ⫺ Rmin]/[Rmin]) is displayed (Rmax, greatest amount of resistance; Rmin, smallest amount of resistance). The data have not been normalized.
The second shortcoming of our study is that there is no gold standard reference test available for A. fumigatus colonization. As the sensitivity and specificity of a single culture are insufficient to conclude airway colonization by Aspergillus spp., we reviewed the sputum cultures of all subjects during the 2 years prior to the study. Sputum samples from CF patients are routinely cultured twice per year at our outpatient clinic, and much more often in practice. A subject was excluded from the study if one of the sputum cultures during the previous 2 years showed a result that was
TABLE 2 Incidence and pattern of cocolonization
Factor Pseudomonas spp. colonization S. aureus colonization H. influenzae colonization Nebulizing pulmonary medication a
No. of subjects
Coincidence with A. fumigatus colonization (%)
Cross-validated accuracy (%)
17
67
63.0
14 6 15
52 67 56
NSa NS NS
NS, no statistically significant principal component was found.
572
jcm.asm.org
different from the index culture with respect to Aspergillus species. No patient had to be excluded from our study. The present data add to the findings of a number of earlier studies. First of all, our in vivo findings extend the in vitro data of a number of research groups (43–45). These studies showed that A. fumigatus can be discriminated in vitro from multiple other microorganisms by an analysis of the metabolites they produce. Second, it was already suggested that the analysis of an exhaled metabolite profile may diagnose aspergillosis by use of gas chromatography-mass spectrometry identification of the exhaled biomarker 2-pentylfuran, which is specific for colonization and invasive disease by A. fumigatus. This biomarker was demonstrated to be exhaled by patients and to be produced by A. fumigatus in vitro (46, 47). Similarly, recent GC-MS and gas chromatography–solidphase microextraction studies of A. fumigatus in vitro and in vivo demonstrated other candidate VOCs for IA, such as monoterpenes and sesquiterpenes (18, 48). Last, the current study strengthens the suggestion that eNose technology can detect such a distinct profile of metabolites produced by A. fumigatus in vivo by means of exhaled breath analysis. Our previous study already showed that invasive aspergillosis is associated with a distinctive
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
breathprint, and as most subjects in the control group of that study had a pulmonary infection other than invasive aspergillosis, the distinct breathprint was unlikely to be caused by inflammation in general (17). eNose technology has been designed to capture and compare complex molecular mixtures (20). Like human olfaction, this is extremely effective but does not allow the identification of the responsible VOCs, as it does not measure individual molecules or their concentrations (49). Therefore, eNoses are highly suitable for probabilistic diagnostic assessment, whereas gas chromatography-mass spectrometry (GC-MS) is required for pathophysiological research into the roles of individual molecular compounds (50). The combined output of all cross-reactive eNose sensors provides a pattern that can be subjected to pattern recognition algorithms. The VOCs that yielded the distinct breathprint in our study might of course be produced by the mold itself, as suggested above. 2-Pentylfuran and sesquiterpenes might be examples of this. However, differences in exhaled breath profiles may also be due to other factors than A. fumigatus metabolites. First, there might be an unknown confounding factor predisposing to (a positive culture suggesting) A. fumigatus colonization, such as a higher fungal load, more-severe CF, or more exposure to antibiotics or steroids, etc. A more likely possibility is the inflammatory host response, as Aspergillus spp. can trigger an immune response in the airways (37, 51, 52). Interestingly, asthma, CF and COPD, all inflammatory airway diseases, can be discriminated fairly accurately using eNose technology (22, 24, 25, 53, 54). More pathophysiological research on this topic is warranted, as the question is of more than theoretical interest: eNoses can be fine-tuned to react to specific VOCs. A comparison of the VOCs produced by molds in vitro and VOCs exhaled by patients with colonization or invasive disease by A. fumigatus will shed more light on this issue. This was recently done in patients with ventilator-associated pneumonia (VAP), demonstrating that the VOCs of tracheal aspirates were not influenced by colonization (55). Taken together, the success of the eNose can most likely be explained by a combination of VOCs originating in the specific inflammatory host response and/or VOCs originating from the mold itself. The present results raise the hypothesis that in the future, an eNose could be used to diagnose invasive pulmonary aspergillosis, which frequently occurs during or after intensive high-risk treatment of acute leukemia or stem cell transplantation (1–3). Mortality due to invasive pulmonary aspergillosis is high, and while diagnosis is challenging, it is dependent on the amount of diagnostic delay (3–7). An eNose could be used to test patients regularly (e.g., twice a week) for the development of invasive pulmonary aspergillosis, instead of the current practice in some institutions of performing a serum galactomannan assay twice a week in high-risk populations (56, 57). The resulting reduction in diagnostic delay will hopefully improve prognosis. If accurate enough, exhaled breath analysis might also obviate bronchoscopy, an uncomfortable procedure associated with complications (9– 13). As this study shows that colonization by A. fumigatus leads to a distinct breathprint, future research on exhaled breath analysis as a tool to diagnose invasive pulmonary mycoses should examine the possibility of colonization as a cause of false-positive test results. In summary, our study shows that electronic nose technology can detect A. fumigatus colonization with moderate to good accuracy. These proof-of-concept data merit external validation and
March 2016 Volume 54 Number 3
justify research to clarify the responsible compounds. Longitudinal monitoring studies are warranted to examine whether an electronic nose can detect developing invasive pulmonary aspergillosis at an earlier stage than the current state-of-the-art diagnostic strategies. ACKNOWLEDGMENTS We thank the 27 patients who made this study possible. This study was supported by the Egbers Foundation. The funder had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
FUNDING INFORMATION Egbers Foundation provided funding to K. de Heer.
REFERENCES 1. Dettenkofer M, Wenzler-Röttele S, Babikir R, Bertz H, Ebner W, Meyer E, Rüden H, Gastmeier P, Daschner FD, Hospital Infection Surveillance System for Patients with Hematologic/Oncologic Malignancies Study Group. 2005. Surveillance of nosocomial sepsis and pneumonia in patients with a bone marrow or peripheral blood stem cell transplant: a multicenter project. Clin Infect Dis 40:926 –931. http://dx.doi.org/10 .1086/428046. 2. Cornillet A, Camus C, Nimubona S, Gandemer V, Tattevin P, Belleguic C, Chevrier S, Meunier C, Lebert C, Aupe M, Caulet-Maugendre S, Faucheux M, Lelong B, Leray E, Guiguen C, Gangneux JP. 2006. Comparison of epidemiological, clinical, and biological features of invasive aspergillosis in neutropenic and nonneutropenic patients: a 6-year survey. Clin Infect Dis 43:577–584. http://dx.doi.org/10.1086/505870. 3. Robenshtok E, Gafter-Gvili A, Goldberg E, Weinberger M, Yeshurun M, Leibovici L, Paul M. 2007. Antifungal prophylaxis in cancer patients after chemotherapy or hematopoietic stem-cell transplantation: systematic review and meta-analysis. J Clin Oncol 25:5471–5489. http://dx.doi .org/10.1200/JCO.2007.12.3851. 4. Barth PJ, Rossberg C, Koch S, Ramaswamy A. 2000. Pulmonary aspergillosis in an unselected autopsy series. Pathol Res Pract 196:73– 80. http: //dx.doi.org/10.1016/S0344-0338(00)80036-9. 5. Groll AH, Shah PM, Mentzel C, Schneider M, Just-Nuebling G, Huebner K. 1996. Trends in the postmortem epidemiology of invasive fungal infections at a university hospital. J Infect 33:23–32. http://dx.doi.org/10 .1016/S0163-4453(96)92700-0. 6. Aisner J, Wiernik PH, Schimpff SC. 1977. Treatment of invasive aspergillosis: relation of early diagnosis and treatment to response. Ann Intern Med 86:539 –543. http://dx.doi.org/10.7326/0003-4819-86-5-539. 7. von Eiff M, Roos N, Schulten R, Hesse M, Zuhlsdorf M, van de Loo J. 1995. Pulmonary aspergillosis: early diagnosis improves survival. Respiration 62:341–347. http://dx.doi.org/10.1159/000196477. 8. Guo YL, Chen YQ, Wang K, Qin SM, Wu C, Kong JL. 2010. Accuracy of BAL galactomannan in diagnosing invasive aspergillosis: a bivariate metaanalysis and systematic review. Chest 138:817– 824. http://dx.doi.org /10.1378/chest.10-0488. 9. Tukey MH, Wiener RS. 2012. Population-based estimates of transbronchial lung biopsy utilization and complications. Respir Med 106:1559 – 1565. http://dx.doi.org/10.1016/j.rmed.2012.08.008. 10. Carr IM, Koegelenberg CFN, von Groote-Bidlingmaier F, Mowlana A, Silos K, Haverman T, Diacon AH, Bolliger CT. 2012. Blood loss during flexible bronchoscopy: a prospective observational study. Respiration 84: 312–318. http://dx.doi.org/10.1159/000339507. 11. Facciolongo N, Patelli M, Gasparini S, Lazzari Agli L, Salio M, Simonassi C, Del Prato B, Zanoni P. 2009. Incidence of complications in bronchoscopy. Multicentre prospective study of 20,986 bronchoscopies. Monaldi Arch Chest Dis 71:8 –14. 12. Jin F, Mu D, Chu D, Fu E, Xie Y, Liu T. 2008. Severe complications of bronchoscopy. Respiration 76:429 – 433. http://dx.doi.org/10.1159 /000151656. 13. Pue CA, Pacht ER. 1995. Complications of fiberoptic bronchoscopy at a university hospital. Chest 107:430 – 432. http://dx.doi.org/10.1378/chest .107.2.430. 14. Leeflang MM, Debets-Ossenkopp YJ, Visser CE, Scholten RJ, Hooft L, Bijlmer HA, Reitsma JB, Bossuyt PM, Vandenbroucke-Grauls CM.
Journal of Clinical Microbiology
jcm.asm.org
573
de Heer et al.
15.
16.
17.
18.
19. 20. 21.
22.
23.
24.
25.
26. 27.
28.
29.
30.
574
2008. Galactomannan detection for invasive aspergillosis in immunocompromized [sic] patients. Cochrane Database Syst Rev (4):CD007394. Hebart H, Klingspor L, Klingebiel T, Loeffler J, Tollemar J, Ljungman P, Wandt H, Schaefer-Eckart K, Dornbusch HJ, Meisner C, Engel C, Stenger N, Mayer T, Ringden O, Einsele H. 2009. A prospective randomized controlled trial comparing PCR-based and empirical treatment with liposomal amphotericin B in patients after allo-SCT. Bone Marrow Transplant 43:553–561. http://dx.doi.org/10.1038/bmt.2008.355. Cordonnier C, Pautas C, Maury S, Vekhoff A, Farhat H, Suarez F, Dhdin N, Isnard F, Ades L, Kuhnowski F, Foulet F, Kuentz M, Maison P, Bretagne S, Schwarzinger M. 2009. Empirical versus preemptive antifungal therapy for high-risk, febrile, neutropenic patients: a randomized, controlled trial. Clin Infect Dis 48:1042–1051. http://dx.doi.org/10.1086 /597395. de Heer K, van der Schee MP, Zwinderman K, van den Berk IA, Visser CE, van Oers R, Sterk PJ. 2013. Electronic nose technology for detection of invasive pulmonary aspergillosis in prolonged chemotherapy-induced neutropenia: a proof-of-principle study. J Clin Microbiol 51:1490 –1495. http://dx.doi.org/10.1128/JCM.02838-12. Koo S, Thomas HR, Daniels SD, Lynch RC, Fortier SM, Shea MM, Rearden P, Comolli JC, Baden LR, Marty FM. 2014. A breath fungal secondary metabolite signature to diagnose invasive aspergillosis. Clin Infect Dis 59:1733–1740. http://dx.doi.org/10.1093/cid/ciu725. Röck F, Barsan N, Weimar U. 2008. Electronic nose: current status and future trends. Chem Rev 108:705–725. http://dx.doi.org/10.1021/cr068121q. Wilson AD. 2015. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites 5:140 –163. http://dx.doi.org/10.3390/metabo5010140. Pauling L, Robinson AB, Teranishi R, Cary P. 1971. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A 68:2374 –2376. http://dx.doi.org/10.1073/pnas .68.10.2374. Fens N, Zwinderman AH, van der Schee MP, de Nijs SB, Dijkers E, Roldaan AC, Cheung D, Bel EH, Sterk PJ. 2009. Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am J Respir Crit Care Med 180:1076 –1082. http://dx.doi.org/10 .1164/rccm.200906-0939OC. Fens N, Roldaan AC, van der Schee MP, Boksem RJ, Zwinderman AH, Bel EH, Sterk PJ. 2011. External validation of exhaled breath profiling using an electronic nose in the discrimination of asthma with fixed airways obstruction and chronic obstructive pulmonary disease. Clin Exp Allergy 41:1371–1378. http://dx.doi.org/10.1111/j.1365-2222.2011.03800.x. Dragonieri S, Schot R, Mertens BJ, Le Cessie S, Gauw SA, Spanevello A, Resta O, Willard NP, Vink TJ, Rabe KF, Bel EH, Sterk PJ. 2007. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 120:856 – 862. http://dx.doi.org/10.1016/j.jaci .2007.05.043. Dragonieri S, Annema JT, Schot R, van der Schee MP, Spanevello A, Carratu P, Resta O, Rabe KF, Sterk PJ. 2009. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer 64:166 –170. http://dx.doi.org/10.1016/j.lungcan.2008.08 .008. van der Schee MP, Paff T, Brinkman P, van Aalderen WMC, Haarman EG, Sterk PJ. 2015. Breathomics in lung disease. Chest 147:224 –231. http://dx.doi.org/10.1378/chest.14-0781. Machado RF, Laskowski D, Deffenderfer O, Burch T, Zheng S, Mazzone PJ, Mekhail T, Jennings C, Stoller JK, Pyle J, Duncan J, Dweik RA, Erzurum SC. 2005. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 171:1286 –1291. http://dx.doi .org/10.1164/rccm.200409-1184OC. Di Natale C, Macagnano A, Martinelli E, Paolesse R, D’Arcangelo G, Roscioni C, Finazzi-Agro A, D’Amico A. 2003. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 18:1209 –1218. http://dx.doi.org/10.1016 /S0956-5663(03)00086-1. Peng G, Hakim M, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A, Tisch U, Haick H. 2010. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br J Cancer 103:542–551. http://dx.doi.org/10.1038/sj.bjc.6605810. Friedrich MJ. 2009. Scientists seek to sniff out diseases: electronic “noses” may someday be diagnostic tools. JAMA 301:585–586. http://dx.doi.org /10.1001/jama.2009.90.
jcm.asm.org
31. Collins FS. 1992. Cystic fibrosis: molecular biology and therapeutic implications. Science 256:774 –779. http://dx.doi.org/10.1126/science.1375392. 32. Milla CE, Wielinski CL, Regelmann WE. 1996. Clinical significance of the recovery of Aspergillus species from the respiratory secretions of cystic fibrosis patients. Pediatr Pulmonol 21:6 –10. http://dx.doi.org/10.1002 /(SICI)1099-0496(199601)21:1⬍6::AID-PPUL1⬎3.0.CO;2-R. 33. Skov M, McKay K, Koch C, Cooper PJ. 2005. Prevalence of allergic bronchopulmonary aspergillosis in cystic fibrosis in an area with a high frequency of atopy. Respir Med 99:887– 893. http://dx.doi.org/10.1016/j .rmed.2004.11.018. 34. Valenza G, Tappe D, Turnwald D, Frosch M, Konig C, Hebestreit H, Abele-Horn M. 2008. Prevalence and antimicrobial susceptibility of microorganisms isolated from sputa of patients with cystic fibrosis. J Cyst Fibros 7:123–127. http://dx.doi.org/10.1016/j.jcf.2007.06.006. 35. Nelson LA, Callerame ML, Schwartz RH. 1979. Aspergillosis and atopy in cystic fibrosis. Am Rev Respir Dis 120:863– 873. 36. Bakare N, Rickerts V, Bargon J, Just-Nubling G. 2003. Prevalence of Aspergillus fumigatus and other fungal species in the sputum of adult patients with cystic fibrosis. Mycoses 46:19 –23. http://dx.doi.org/10.1046/j .1439-0507.2003.00830.x. 37. Mroueh S, Spock A. 1994. Allergic bronchopulmonary aspergillosis in patients with cystic fibrosis. Chest 105:32–36. http://dx.doi.org/10.1378 /chest.105.1.32. 38. Skov M, Koch C, Reimert CM, Poulsen LK. 2000. Diagnosis of allergic bronchopulmonary aspergillosis (ABPA) in cystic fibrosis. Allergy 55:50 – 58. http://dx.doi.org/10.1034/j.1398-9995.2000.00342.x. 39. Boyle MP. 2003. Nonclassic cystic fibrosis and CFTR-related diseases. Curr Opin Pulm Med 9:498 –503. http://dx.doi.org/10.1097/00063198 -200311000-00009. 40. De Boeck K, Wilschanski M, Castellani C, Taylor C, Cuppens H, Dodge J, Sinaasappel M, Diagnostic Working Group. 2006. Cystic fibrosis: terminology and diagnostic algorithms. Thorax 61:627– 635. http://dx.doi .org/10.1136/thx.2005.043539. 41. de Hoog GS, Guarro J, Gené J, Figueras MJ. 2001. Atlas of clinical fungi, 2nd edition. ASM Press, Washington, DC. 42. Broadhurst D, Kell DB. 2006. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2:171–196. 43. Moens M, Smet A, Naudts B, Verhoeven J, Ieven M, Jorens P, Geise HJ, Blockhuys F. 2006. Fast identification of ten clinically important microorganisms using an electronic nose. Lett Appl Microbiol 42:121–126. http: //dx.doi.org/10.1111/j.1472-765X.2005.01822.x. 44. Fend R, Kolk AH, Bessant C, Buijtels P, Klatser PR, Woodman AC. 2006. Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum. J Clin Microbiol 44:2039 –2045. http://dx.doi.org/10.1128/JCM.01591-05. 45. Dutta R, Hines EL, Gardner JW, Boilot P. 2002. Bacteria classification using Cyranose 320 electronic nose. Biomed Eng Online 1:4. http://dx.doi .org/10.1186/1475-925X-1-4. 46. Chambers ST, Syhre M, Murdoch DR, McCartin F, Epton MJ. 2009. Detection of 2-pentylfuran in the breath of patients with Aspergillus fumigatus. Med Mycol 47:468 – 476. http://dx.doi.org/10.1080/13693780802475 212. 47. Syhre M, Scotter JM, Chambers ST. 2008. Investigation into the production of 2-pentylfuran by aspergillus fumigatus and other respiratory pathogens in vitro and human breath samples. Med Mycol 46:209 –215. http://dx.doi.org/10.1080/13693780701753800. 48. Heddergott C, Calvo AM, Latge JP. 2014. The volatome of Aspergillus fumigatus. Eukaryot Cell 13:1014 –1025. http://dx.doi.org/10.1128/EC .00074-14. 49. Bushdid C, Magnasco MO, Vosshall LB, Keller A. 2014. Humans can discriminate more than 1 trillion olfactory stimuli. Science 343:1370 – 1372. http://dx.doi.org/10.1126/science.1249168. 50. Boots AW, Bos LD, van der Schee MP, van Schooten FJ, Sterk PJ. 2015. Exhaled molecular fingerprinting in diagnosis and monitoring: validating volatile promises. Trends Mol Med 21:633– 644. http://dx.doi.org/10 .1016/j.molmed.2015.08.001. 51. Peetermans M, Goeminne P, De Boeck C, Dupont LJ. 2014. IgE sensitization to Aspergillus fumigatus is not a bystander phenomenon in cystic fibrosis lung disease. Chest 146:e99 – e100. http://dx.doi.org/10.1378 /chest.14-0635. 52. Baxter CG, Moore CB, Jones AM, Webb AK, Denning DW. 2013. IgE-mediated immune responses and airway detection of Aspergillus and
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
Candida in adult cystic fibrosis. Chest 143:1351–1357. http://dx.doi.org /10.1378/chest.12-1363. 53. Joensen O, Paff T, Haarman EG, Skovgaard IM, Jensen P, Bjarnsholt T, Nielsen KG. 2014. Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections. PLoS One 9:e115584. http://dx.doi.org/10.1371/journal.pone .0115584. 54. Paff T, van der Schee MP, Daniels JMA, Pals G, Postmus PE, Sterk PJ, Haarman EG. 2013. Exhaled molecular profiles in the assessment of cystic fibrosis and primary ciliary dyskinesia. J Cyst Fibros 12:454 – 460. http: //dx.doi.org/10.1016/j.jcf.2012.12.010. 55. Bos LDJ, Martin-Loeches I, Kastelijn JB, Gili G, Espasa M, Povoa P, Kolk AHJ, Janssen HG, Sterk PJ, Artigas A, Schultz MJ. 2014. The
March 2016 Volume 54 Number 3
volatile metabolic fingerprint of ventilator-associated pneumonia. Intensive Care Med 40:761–762. http://dx.doi.org/10.1007/s00134-014 -3260-5. 56. Maertens J, Theunissen K, Verhoef G, Verschakelen J, Lagrou K, Verbeken E, Wilmer A, Verhaegen J, Boogaerts M, Van Eldere J. 2005. Galactomannan and computed tomography-based preemptive antifungal therapy in neutropenic patients at high risk for invasive fungal infection: a prospective feasibility study. Clin Infect Dis 41:1242–1250. http://dx.doi .org/10.1086/496927. 57. Maertens J, Van Eldere J, Verhaegen J, Verbeken E, Verschakelen J, Boogaerts M. 2002. Use of circulating galactomannan screening for early diagnosis of invasive aspergillosis in allogeneic stem cell transplant recipients. J Infect Dis 186:1297–1306. http://dx.doi.org/10.1086/343804.
Journal of Clinical Microbiology
jcm.asm.org
575
Detection of Airway Colonization by Aspergillus fumigatus by Use of Electronic Nose Technology in Patients with Cystic Fibrosis K. de Heer,a M. G. M. Kok,a N. Fens,b E. J. M. Weersink,b A. H. Zwinderman,c M. P. C. van der Schee,b C. E. Visser,d M. H. J. van Oers,a P. J. Sterkb Department of Hematology,a Department of Respiratory Medicine,b Department of Clinical Epidemiology, Biostatistics, and Bioinformatics,c and Department of Medical Microbiology,d Academic Medical Center, Amsterdam, the Netherlands
Currently, there is no noninvasive test that can reliably diagnose early invasive pulmonary aspergillosis (IA). An electronic nose (eNose) can discriminate various lung diseases through an analysis of exhaled volatile organic compounds. We recently published a proof-of-principle study showing that patients with prolonged chemotherapy-induced neutropenia and IA have a distinct exhaled breath profile (or breathprint) that can be discriminated with an eNose. An eNose is cheap and noninvasive, and it yields results within minutes. We determined whether Aspergillus fumigatus colonization may also be detected with an eNose in cystic fibrosis (CF) patients. Exhaled breath samples of 27 CF patients were analyzed with a Cyranose 320. Culture of sputum samples defined the A. fumigatus colonization status. eNose data were classified using canonical discriminant analysis after principal component reduction. Our primary outcome was cross-validated accuracy, defined as the percentage of correctly classified subjects using the leave-one-out method. The P value was calculated by the generation of 100,000 random alternative classifications. Nine of the 27 subjects were colonized by A. fumigatus. In total, 3 subjects were misclassified, resulting in a crossvalidated accuracy of the Cyranose detecting IA of 89% (P ⴝ 0.004; sensitivity, 78%; specificity, 94%). Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89. The results indicate that A. fumigatus colonization leads to a distinctive breathprint in CF patients. The present proof-of-concept data merit external validation and monitoring studies.
T
he diagnosis of invasive pulmonary aspergillosis (IA) in patients with prolonged chemotherapy-induced neutropenia (PCIN) remains a frequently occurring and difficult challenge (1– 3). The signs and symptoms are nonspecific (4, 5). Mortality is high, especially in case of a diagnostic delay (3, 6, 7). The Platelia galactomannan assay (Bio-Rad) performed on bronchoalveolar lavage fluid samples is the only test with sufficient sensitivity and specificity to demonstrate or rule out IA (8). Its major drawback is the invasive and highly uncomfortable nature of bronchoalveolar lavage (9–13). The Platelia galactomannan assay was originally developed to be used on serum. Although diagnostic accuracy is significantly lower in this setting, it creates the opportunity to serially monitor patients who are at high risk, such as acute myeloid leukemia (AML) patients treated with intensive induction chemotherapy (14). This might enable earlier diagnosis and thus a better prognosis. This is the topic of a number of ongoing studies. Unfortunately, two already-published studies did not show that serial monitoring of galactomannan leads to an earlier diagnosis (15, 16). Previously, we showed that patients with PCIN and IA have a distinct exhaled breath profile that can be discriminated by an electronic nose (eNose) (17). Very recently, this was confirmed by using gas chromatography-mass spectrometry (GC-MS), demonstrating exhaled metabolite signatures of Aspergillus fumigatus in vitro and IA in vivo (18). An eNose is a cheap and noninvasive instrument with realtime measurement capacity. It can discriminate complex mixtures of volatile organic compounds (VOCs) by measuring these VOCs by pattern recognition using a limited number of cross-reactive sensors. This is achievable, because VOCs react differently with each sensor (19, 20). Exhaled breath can contain about 2,000 to 3,000 VOCs, and numerous disease states may be diagnosed by
March 2016 Volume 54 Number 3
their exhaled breath profile (or breathprint, the composite pattern in exhaled VOCs) (21–30). For asthma and chronic obstructive pulmonary disease (COPD), this has been corroborated by external validation (23). In cystic fibrosis (CF), proper chloride and sodium channel functioning is hampered, leading to an altered rheology of airway secretions, among other manifestations (31). The secretions become thick and difficult to clear, leading to chronic colonization with microorganisms. The reported incidence of A. fumigatus colonization varies between 6 and 58% (32–38). It is unknown whether colonization by A. fumigatus in CF results in a change in exhaled breath profile. If so, it would extend the proof-of-principle study that showed that an eNose may be used to diagnose invasive pulmonary aspergillosis (17). However, if an eNose can detect A. fumigatus colonization, this might also form the basis of false-positive test results in detecting invasive pulmonary aspergillosis. We hypothesized that A. fumigatus colonization in CF patients leads to a distinct exhaled breath profile. Therefore, we aimed to
Received 14 August 2015 Returned for modification 8 September 2015 Accepted 10 December 2015 Accepted manuscript posted online 16 December 2015 Citation de Heer K, Kok MGM, Fens N, Weersink EJM, Zwinderman AH, van der Schee MPC, Visser CE, van Oers MHJ, Sterk PJ. 2016. Detection of airway colonization by Aspergillus fumigatus by use of electronic nose technology in patients with cystic fibrosis. J Clin Microbiol 54:569 –575. doi:10.1128/JCM.02214-15. Editor: D. W. Warnock Address correspondence to K. de Heer, [email protected]. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
Journal of Clinical Microbiology
jcm.asm.org
569
de Heer et al.
TABLE 1 Study subject characteristics, stratified according to A. fumigatus colonization status Characteristic No. of subjects Age (mean [SD]) (yr) Male gender (%) % of predicted FEV1 (mean [SD])
Colonized with A. fumigatus
Not colonized with A. fumigatus
P value
9 28.0 (23.0) 67 46.0 (28.3)
18 30.3 (9.6) 67 63.5 (23.6)
⬎0.2 ⬎0.2 ⬎0.2 ⬎0.2
determine whether an eNose can discriminate CF patients with and without A. fumigatus colonization. MATERIALS AND METHODS Design. The study had a cross-sectional design in which exhaled breath analysis was performed once at a regular outpatient visit for each subject for pulmonary function assessment and sputum culture. Within 5 min of the exhaled breath analysis, subjects were asked to expectorate sputum for culture. Subjects with a culture positive for A. fumigatus were considered to be cases, and subjects with a negative culture were controls. We compared the breathprints between cases and controls. Subjects. Twenty-seven patients (age range, 17 to 50 years) from the CF clinic of the Academic Medical Center, Amsterdam, the Netherlands, with a confirmed diagnosis of CF (defined by 2 known CF mutations and/or a sweat chloride level of ⬎60 mEq/liter) (39, 40), participated in this study (Table 1). Due to the noninvasive nature of merely collecting exhaled breath samples during routine lung function testing, the ethics review board of the Academic Medical Center considered that the protocol did not require ethical review under Dutch legislation. We calculated the P values in Table 1 using a two-sample t test (for age and the median forced expiratory volume in 1 s [FEV1]) and a Pearson’s chi-square test with Yates’ continuity correction (for gender). Exhaled breath analysis. Exhaled breath analysis was performed as described previously (17, 22, 24). Subjects had to breathe through a mouthpiece with their nostrils closed by a clip for 5 min. The mouthpiece was connected by a three-way nonrebreathing valve to a VOC filter (A2; North Safety, Middelburg, the Netherlands) at the inspiratory port and a silica reservoir at the expiratory port. Without interruption, the subject was then asked to inspire deeply and exhale a vital capacity volume. The exhaled breath was stored in a 10-liter Tedlar bag attached to the silica reservoir. Analysis was performed within 30 min with a Cyranose 320 (Smith Detections, Pasadena, CA), a hand-held eNose with 32 nanocomposite sensors (19). The electrical resistance differential in all sensors was recorded. The procedure of sampling the Tedlar bag was done twice, using the first measurement solely to prime the sensors for the exhaled VOC mixture, as previously described (22), and as recommended by the manufacturer, in order to avoid a “first smell effect.” The first measurement was not used in the statistical analysis. Index culture. Subjects were asked to expectorate sputum once within 5 min of the exhaled breath maneuver. The sputum samples were processed and cultured according to the standard operating procedure of our hospital. The sputum samples were inoculated for bacterial culture on the following media: sheep blood agar (COS; bioMérieux), chromogenic agar (CPS; bioMérieux), chocolate agar containing thymidine, Columbia red agar containing thymidine, and N-acetyl-D-glucosamine agar. For the fungal cultures, samples were inoculated on two Sabouraud agars containing gentamicin and chloramphenicol (SGC2; bioMérieux). These were incubated for 14 days at 10°C and 36°C, respectively, and assessed twice a week. If growth was observed, fungi were identified based on morphology (both macroscopically and microscopically) and growth characteristics (41). We reviewed all sputum cultures from a study subject that were performed during the 2 years before the study culture. If one of those cultures showed a nonconsistent result with regard to Aspergillus
570
jcm.asm.org
spp., the subject was excluded from analysis. Sputum samples from our CF patients are routinely cultured twice per year, and often, more frequently. Analysis. As a primary analysis, we compared the breathprints of subjects with and without colonization by A. fumigatus, using the method of analysis from our previous study (17). We used the R statistical package (version 2.11.1). Principal component analysis (PCA) was executed to reduce the raw data of the 32 sensors to a smaller number of principal components. The number of components was chosen to include at least 99.9% of the variance within the data set. With a t test (equal variance assumed depending on an F-test), all PCA factors were categorized as either discriminatory between the two groups or not. A two-sided P value of 0.10 was chosen as a cutoff. Next, a diagnostic algorithm was constructed by using the discriminatory PCA factors as input for a linear canonical discriminant analysis, assuming an equal chance of being a member of one of the two groups. The discriminant function was optimized to best distinguish between categories. The percentage of correctly classified subjects (combining cases and controls) was cross-validated using the leave-one-out method. This resulted in cross-validated accuracy. The P value of our cross-validated accuracy was derived by generating 100,000 random classifications of our subjects (whether or not colonized by A. fumigatus) and calculating the chance that a random classification would have resulted in the same cross-validated accuracy or better, constructing a new pattern recognition algorithm for each of the random classifications. Receiver operating characteristic (ROC) curve analysis was performed.
RESULTS
All measurements were performed according to the predefined protocol. No patients had an exacerbation at the time of the measurement. None of the subjects were treated with antifungal therapy in the 2 years preceding the eNose measurement, either systemically or as an inhalant. All subjects were able to produce sputum. None of the subjects had a sputum culture in the previous 2 years that was nonconsistent with respect to Aspergillus spp., i.e., all cultures were either positive or negative. The entire eNose analysis took about 45 min in our experimental setup, including data handling. Principal component analysis resulted in 7 PCA factors that described ⬎99.9% of the variance. In 9 of the 27 subjects, A. fumigatus was cultured from sputum. Of the 7 PCA factors, 3 discriminated between subjects with and without A. fumigatus colonization. Canonical discriminant analysis based on these 3 principal components yielded a cross-validated accuracy of 88.9%. Figure 1 shows the individual discriminant scores of subjects with and without colonization. Three subjects were misclassified in the cross-validation, two false positively and one false negatively, corresponding to a sensitivity of 78% and a specificity of 94%. The area under the curve of the ROC curve was 0.89. ROC analysis of the cross-validated posterior probabilities of group membership showed an area under the curve of 80.2% (Fig. 2). In our simulation, 0.4% of the random classifications resulted in a cross-validated accuracy of ⱖ88.9% (Fig. 3). The raw eNose data are presented in Fig. 4. We explored the effect of cocolonization by other microorganisms, for example, to exclude the possibility that the eNose had merely “smelled” another microorganism with a coinciding colonization pattern. Pseudomonas spp., Staphylococcus aureus, and Haemophilus influenzae were found in the index culture of more than one subject (see Table 2). The extent to which the colonization pattern of these microorganisms coincided with colonization by A. fumigatus is also shown in Table 2, as we calculated the percentage of cases in which a subject was colonized by both A.
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
FIG 1 Breathprints of CF patients with and without A. fumigatus colonization, in a one-dimensional “jittered” canonical discriminant analysis plot. Squares with a cross represent subjects that were misclassified in the crossvalidation. Box-and-whisker plots (with the median shown as a line through the box, the first and third quartiles shown as the lower and upper edges of the box, respectively, and the 95% confidence interval of the median shown as whiskers) are superimposed.
fumigatus and the other microorganism, or by neither of these two. We determined the cross-validated accuracy with which an eNose could detect those microorganisms by repeating the statistical method of the primary analysis afresh. Only P. aeruginosa
FIG 2 Receiver operating characteristic (ROC) curves showing the true-positive and false-positive rates in detecting A. fumigatus colonization with an eNose as a function of the cutoff value. Dashed line, ROC curve of the noncross-validated measurements; solid line, the ROC curve of the cross-validated measurements.
March 2016 Volume 54 Number 3
FIG 3 Histogram of the cross-validated accuracies produced by the random simulation.
colonization was associated with a PCA factor that differed statistically significantly between groups. The cross-validated accuracy of an eNose for detecting P. aeruginosa colonization was 63%, only slightly ⬎50%. DISCUSSION
To the best of our knowledge, this study is the first to examine whether an eNose can detect A. fumigatus colonization with moderate to good accuracy. It shows that A. fumigatus colonization in CF patients leads to a distinct breathprint. The low P value obtained by comparison with 100,000 random classifications indicates that there is only a minor chance that this represents a falsepositive outcome merely due to coincidence. Our study has a number of strengths. It is novel, and we were able to examine a unique cohort of CF patients according to a strict protocol, using both electronic nose technology and microbiological techniques. The study also has limitations. First and foremost, the sample size was too small to allow split-half analysis. However, the 100,000 random classifications and internal validation using the leave-one-out method strengthened the validity of our results. Furthermore, our data indicate that it is highly unlikely that another microorganism was mistaken for A. fumigatus by our algorithm. We explored this possibility by repeating the statistical method of our analysis for every bacterial species that was encountered in the sputum culture results of more than one subject. Only P. aeruginosa colonization was associated with one or more PCA factors that differed statistically significantly between groups. The cross-validated accuracy was only 63.0%. Eventually, only external validation, i.e., the confirmation of our diagnostic algorithm in a separate group of subjects, would definitively establish the ability of an eNose to diagnose A. fumigatus colonization (42). Such validation has already been performed successfully in a similar eNose study on differentiating between COPD and asthma (23).
Journal of Clinical Microbiology
jcm.asm.org
571
de Heer et al.
FIG 4 Spider graph of the raw eNose data showing a circular representation of the 32 sensor signals from all 27 samples, zoomed in to demonstrate betweensample differences. As the sensors are exposed to the breath sample, the electrical resistance over the sensors changes. On the axes of the spider graph, the quotient (calculated as [Rmax ⫺ Rmin]/[Rmin]) is displayed (Rmax, greatest amount of resistance; Rmin, smallest amount of resistance). The data have not been normalized.
The second shortcoming of our study is that there is no gold standard reference test available for A. fumigatus colonization. As the sensitivity and specificity of a single culture are insufficient to conclude airway colonization by Aspergillus spp., we reviewed the sputum cultures of all subjects during the 2 years prior to the study. Sputum samples from CF patients are routinely cultured twice per year at our outpatient clinic, and much more often in practice. A subject was excluded from the study if one of the sputum cultures during the previous 2 years showed a result that was
TABLE 2 Incidence and pattern of cocolonization
Factor Pseudomonas spp. colonization S. aureus colonization H. influenzae colonization Nebulizing pulmonary medication a
No. of subjects
Coincidence with A. fumigatus colonization (%)
Cross-validated accuracy (%)
17
67
63.0
14 6 15
52 67 56
NSa NS NS
NS, no statistically significant principal component was found.
572
jcm.asm.org
different from the index culture with respect to Aspergillus species. No patient had to be excluded from our study. The present data add to the findings of a number of earlier studies. First of all, our in vivo findings extend the in vitro data of a number of research groups (43–45). These studies showed that A. fumigatus can be discriminated in vitro from multiple other microorganisms by an analysis of the metabolites they produce. Second, it was already suggested that the analysis of an exhaled metabolite profile may diagnose aspergillosis by use of gas chromatography-mass spectrometry identification of the exhaled biomarker 2-pentylfuran, which is specific for colonization and invasive disease by A. fumigatus. This biomarker was demonstrated to be exhaled by patients and to be produced by A. fumigatus in vitro (46, 47). Similarly, recent GC-MS and gas chromatography–solidphase microextraction studies of A. fumigatus in vitro and in vivo demonstrated other candidate VOCs for IA, such as monoterpenes and sesquiterpenes (18, 48). Last, the current study strengthens the suggestion that eNose technology can detect such a distinct profile of metabolites produced by A. fumigatus in vivo by means of exhaled breath analysis. Our previous study already showed that invasive aspergillosis is associated with a distinctive
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
breathprint, and as most subjects in the control group of that study had a pulmonary infection other than invasive aspergillosis, the distinct breathprint was unlikely to be caused by inflammation in general (17). eNose technology has been designed to capture and compare complex molecular mixtures (20). Like human olfaction, this is extremely effective but does not allow the identification of the responsible VOCs, as it does not measure individual molecules or their concentrations (49). Therefore, eNoses are highly suitable for probabilistic diagnostic assessment, whereas gas chromatography-mass spectrometry (GC-MS) is required for pathophysiological research into the roles of individual molecular compounds (50). The combined output of all cross-reactive eNose sensors provides a pattern that can be subjected to pattern recognition algorithms. The VOCs that yielded the distinct breathprint in our study might of course be produced by the mold itself, as suggested above. 2-Pentylfuran and sesquiterpenes might be examples of this. However, differences in exhaled breath profiles may also be due to other factors than A. fumigatus metabolites. First, there might be an unknown confounding factor predisposing to (a positive culture suggesting) A. fumigatus colonization, such as a higher fungal load, more-severe CF, or more exposure to antibiotics or steroids, etc. A more likely possibility is the inflammatory host response, as Aspergillus spp. can trigger an immune response in the airways (37, 51, 52). Interestingly, asthma, CF and COPD, all inflammatory airway diseases, can be discriminated fairly accurately using eNose technology (22, 24, 25, 53, 54). More pathophysiological research on this topic is warranted, as the question is of more than theoretical interest: eNoses can be fine-tuned to react to specific VOCs. A comparison of the VOCs produced by molds in vitro and VOCs exhaled by patients with colonization or invasive disease by A. fumigatus will shed more light on this issue. This was recently done in patients with ventilator-associated pneumonia (VAP), demonstrating that the VOCs of tracheal aspirates were not influenced by colonization (55). Taken together, the success of the eNose can most likely be explained by a combination of VOCs originating in the specific inflammatory host response and/or VOCs originating from the mold itself. The present results raise the hypothesis that in the future, an eNose could be used to diagnose invasive pulmonary aspergillosis, which frequently occurs during or after intensive high-risk treatment of acute leukemia or stem cell transplantation (1–3). Mortality due to invasive pulmonary aspergillosis is high, and while diagnosis is challenging, it is dependent on the amount of diagnostic delay (3–7). An eNose could be used to test patients regularly (e.g., twice a week) for the development of invasive pulmonary aspergillosis, instead of the current practice in some institutions of performing a serum galactomannan assay twice a week in high-risk populations (56, 57). The resulting reduction in diagnostic delay will hopefully improve prognosis. If accurate enough, exhaled breath analysis might also obviate bronchoscopy, an uncomfortable procedure associated with complications (9– 13). As this study shows that colonization by A. fumigatus leads to a distinct breathprint, future research on exhaled breath analysis as a tool to diagnose invasive pulmonary mycoses should examine the possibility of colonization as a cause of false-positive test results. In summary, our study shows that electronic nose technology can detect A. fumigatus colonization with moderate to good accuracy. These proof-of-concept data merit external validation and
March 2016 Volume 54 Number 3
justify research to clarify the responsible compounds. Longitudinal monitoring studies are warranted to examine whether an electronic nose can detect developing invasive pulmonary aspergillosis at an earlier stage than the current state-of-the-art diagnostic strategies. ACKNOWLEDGMENTS We thank the 27 patients who made this study possible. This study was supported by the Egbers Foundation. The funder had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
FUNDING INFORMATION Egbers Foundation provided funding to K. de Heer.
REFERENCES 1. Dettenkofer M, Wenzler-Röttele S, Babikir R, Bertz H, Ebner W, Meyer E, Rüden H, Gastmeier P, Daschner FD, Hospital Infection Surveillance System for Patients with Hematologic/Oncologic Malignancies Study Group. 2005. Surveillance of nosocomial sepsis and pneumonia in patients with a bone marrow or peripheral blood stem cell transplant: a multicenter project. Clin Infect Dis 40:926 –931. http://dx.doi.org/10 .1086/428046. 2. Cornillet A, Camus C, Nimubona S, Gandemer V, Tattevin P, Belleguic C, Chevrier S, Meunier C, Lebert C, Aupe M, Caulet-Maugendre S, Faucheux M, Lelong B, Leray E, Guiguen C, Gangneux JP. 2006. Comparison of epidemiological, clinical, and biological features of invasive aspergillosis in neutropenic and nonneutropenic patients: a 6-year survey. Clin Infect Dis 43:577–584. http://dx.doi.org/10.1086/505870. 3. Robenshtok E, Gafter-Gvili A, Goldberg E, Weinberger M, Yeshurun M, Leibovici L, Paul M. 2007. Antifungal prophylaxis in cancer patients after chemotherapy or hematopoietic stem-cell transplantation: systematic review and meta-analysis. J Clin Oncol 25:5471–5489. http://dx.doi .org/10.1200/JCO.2007.12.3851. 4. Barth PJ, Rossberg C, Koch S, Ramaswamy A. 2000. Pulmonary aspergillosis in an unselected autopsy series. Pathol Res Pract 196:73– 80. http: //dx.doi.org/10.1016/S0344-0338(00)80036-9. 5. Groll AH, Shah PM, Mentzel C, Schneider M, Just-Nuebling G, Huebner K. 1996. Trends in the postmortem epidemiology of invasive fungal infections at a university hospital. J Infect 33:23–32. http://dx.doi.org/10 .1016/S0163-4453(96)92700-0. 6. Aisner J, Wiernik PH, Schimpff SC. 1977. Treatment of invasive aspergillosis: relation of early diagnosis and treatment to response. Ann Intern Med 86:539 –543. http://dx.doi.org/10.7326/0003-4819-86-5-539. 7. von Eiff M, Roos N, Schulten R, Hesse M, Zuhlsdorf M, van de Loo J. 1995. Pulmonary aspergillosis: early diagnosis improves survival. Respiration 62:341–347. http://dx.doi.org/10.1159/000196477. 8. Guo YL, Chen YQ, Wang K, Qin SM, Wu C, Kong JL. 2010. Accuracy of BAL galactomannan in diagnosing invasive aspergillosis: a bivariate metaanalysis and systematic review. Chest 138:817– 824. http://dx.doi.org /10.1378/chest.10-0488. 9. Tukey MH, Wiener RS. 2012. Population-based estimates of transbronchial lung biopsy utilization and complications. Respir Med 106:1559 – 1565. http://dx.doi.org/10.1016/j.rmed.2012.08.008. 10. Carr IM, Koegelenberg CFN, von Groote-Bidlingmaier F, Mowlana A, Silos K, Haverman T, Diacon AH, Bolliger CT. 2012. Blood loss during flexible bronchoscopy: a prospective observational study. Respiration 84: 312–318. http://dx.doi.org/10.1159/000339507. 11. Facciolongo N, Patelli M, Gasparini S, Lazzari Agli L, Salio M, Simonassi C, Del Prato B, Zanoni P. 2009. Incidence of complications in bronchoscopy. Multicentre prospective study of 20,986 bronchoscopies. Monaldi Arch Chest Dis 71:8 –14. 12. Jin F, Mu D, Chu D, Fu E, Xie Y, Liu T. 2008. Severe complications of bronchoscopy. Respiration 76:429 – 433. http://dx.doi.org/10.1159 /000151656. 13. Pue CA, Pacht ER. 1995. Complications of fiberoptic bronchoscopy at a university hospital. Chest 107:430 – 432. http://dx.doi.org/10.1378/chest .107.2.430. 14. Leeflang MM, Debets-Ossenkopp YJ, Visser CE, Scholten RJ, Hooft L, Bijlmer HA, Reitsma JB, Bossuyt PM, Vandenbroucke-Grauls CM.
Journal of Clinical Microbiology
jcm.asm.org
573
de Heer et al.
15.
16.
17.
18.
19. 20. 21.
22.
23.
24.
25.
26. 27.
28.
29.
30.
574
2008. Galactomannan detection for invasive aspergillosis in immunocompromized [sic] patients. Cochrane Database Syst Rev (4):CD007394. Hebart H, Klingspor L, Klingebiel T, Loeffler J, Tollemar J, Ljungman P, Wandt H, Schaefer-Eckart K, Dornbusch HJ, Meisner C, Engel C, Stenger N, Mayer T, Ringden O, Einsele H. 2009. A prospective randomized controlled trial comparing PCR-based and empirical treatment with liposomal amphotericin B in patients after allo-SCT. Bone Marrow Transplant 43:553–561. http://dx.doi.org/10.1038/bmt.2008.355. Cordonnier C, Pautas C, Maury S, Vekhoff A, Farhat H, Suarez F, Dhdin N, Isnard F, Ades L, Kuhnowski F, Foulet F, Kuentz M, Maison P, Bretagne S, Schwarzinger M. 2009. Empirical versus preemptive antifungal therapy for high-risk, febrile, neutropenic patients: a randomized, controlled trial. Clin Infect Dis 48:1042–1051. http://dx.doi.org/10.1086 /597395. de Heer K, van der Schee MP, Zwinderman K, van den Berk IA, Visser CE, van Oers R, Sterk PJ. 2013. Electronic nose technology for detection of invasive pulmonary aspergillosis in prolonged chemotherapy-induced neutropenia: a proof-of-principle study. J Clin Microbiol 51:1490 –1495. http://dx.doi.org/10.1128/JCM.02838-12. Koo S, Thomas HR, Daniels SD, Lynch RC, Fortier SM, Shea MM, Rearden P, Comolli JC, Baden LR, Marty FM. 2014. A breath fungal secondary metabolite signature to diagnose invasive aspergillosis. Clin Infect Dis 59:1733–1740. http://dx.doi.org/10.1093/cid/ciu725. Röck F, Barsan N, Weimar U. 2008. Electronic nose: current status and future trends. Chem Rev 108:705–725. http://dx.doi.org/10.1021/cr068121q. Wilson AD. 2015. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites 5:140 –163. http://dx.doi.org/10.3390/metabo5010140. Pauling L, Robinson AB, Teranishi R, Cary P. 1971. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A 68:2374 –2376. http://dx.doi.org/10.1073/pnas .68.10.2374. Fens N, Zwinderman AH, van der Schee MP, de Nijs SB, Dijkers E, Roldaan AC, Cheung D, Bel EH, Sterk PJ. 2009. Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am J Respir Crit Care Med 180:1076 –1082. http://dx.doi.org/10 .1164/rccm.200906-0939OC. Fens N, Roldaan AC, van der Schee MP, Boksem RJ, Zwinderman AH, Bel EH, Sterk PJ. 2011. External validation of exhaled breath profiling using an electronic nose in the discrimination of asthma with fixed airways obstruction and chronic obstructive pulmonary disease. Clin Exp Allergy 41:1371–1378. http://dx.doi.org/10.1111/j.1365-2222.2011.03800.x. Dragonieri S, Schot R, Mertens BJ, Le Cessie S, Gauw SA, Spanevello A, Resta O, Willard NP, Vink TJ, Rabe KF, Bel EH, Sterk PJ. 2007. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 120:856 – 862. http://dx.doi.org/10.1016/j.jaci .2007.05.043. Dragonieri S, Annema JT, Schot R, van der Schee MP, Spanevello A, Carratu P, Resta O, Rabe KF, Sterk PJ. 2009. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer 64:166 –170. http://dx.doi.org/10.1016/j.lungcan.2008.08 .008. van der Schee MP, Paff T, Brinkman P, van Aalderen WMC, Haarman EG, Sterk PJ. 2015. Breathomics in lung disease. Chest 147:224 –231. http://dx.doi.org/10.1378/chest.14-0781. Machado RF, Laskowski D, Deffenderfer O, Burch T, Zheng S, Mazzone PJ, Mekhail T, Jennings C, Stoller JK, Pyle J, Duncan J, Dweik RA, Erzurum SC. 2005. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 171:1286 –1291. http://dx.doi .org/10.1164/rccm.200409-1184OC. Di Natale C, Macagnano A, Martinelli E, Paolesse R, D’Arcangelo G, Roscioni C, Finazzi-Agro A, D’Amico A. 2003. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 18:1209 –1218. http://dx.doi.org/10.1016 /S0956-5663(03)00086-1. Peng G, Hakim M, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A, Tisch U, Haick H. 2010. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br J Cancer 103:542–551. http://dx.doi.org/10.1038/sj.bjc.6605810. Friedrich MJ. 2009. Scientists seek to sniff out diseases: electronic “noses” may someday be diagnostic tools. JAMA 301:585–586. http://dx.doi.org /10.1001/jama.2009.90.
jcm.asm.org
31. Collins FS. 1992. Cystic fibrosis: molecular biology and therapeutic implications. Science 256:774 –779. http://dx.doi.org/10.1126/science.1375392. 32. Milla CE, Wielinski CL, Regelmann WE. 1996. Clinical significance of the recovery of Aspergillus species from the respiratory secretions of cystic fibrosis patients. Pediatr Pulmonol 21:6 –10. http://dx.doi.org/10.1002 /(SICI)1099-0496(199601)21:1⬍6::AID-PPUL1⬎3.0.CO;2-R. 33. Skov M, McKay K, Koch C, Cooper PJ. 2005. Prevalence of allergic bronchopulmonary aspergillosis in cystic fibrosis in an area with a high frequency of atopy. Respir Med 99:887– 893. http://dx.doi.org/10.1016/j .rmed.2004.11.018. 34. Valenza G, Tappe D, Turnwald D, Frosch M, Konig C, Hebestreit H, Abele-Horn M. 2008. Prevalence and antimicrobial susceptibility of microorganisms isolated from sputa of patients with cystic fibrosis. J Cyst Fibros 7:123–127. http://dx.doi.org/10.1016/j.jcf.2007.06.006. 35. Nelson LA, Callerame ML, Schwartz RH. 1979. Aspergillosis and atopy in cystic fibrosis. Am Rev Respir Dis 120:863– 873. 36. Bakare N, Rickerts V, Bargon J, Just-Nubling G. 2003. Prevalence of Aspergillus fumigatus and other fungal species in the sputum of adult patients with cystic fibrosis. Mycoses 46:19 –23. http://dx.doi.org/10.1046/j .1439-0507.2003.00830.x. 37. Mroueh S, Spock A. 1994. Allergic bronchopulmonary aspergillosis in patients with cystic fibrosis. Chest 105:32–36. http://dx.doi.org/10.1378 /chest.105.1.32. 38. Skov M, Koch C, Reimert CM, Poulsen LK. 2000. Diagnosis of allergic bronchopulmonary aspergillosis (ABPA) in cystic fibrosis. Allergy 55:50 – 58. http://dx.doi.org/10.1034/j.1398-9995.2000.00342.x. 39. Boyle MP. 2003. Nonclassic cystic fibrosis and CFTR-related diseases. Curr Opin Pulm Med 9:498 –503. http://dx.doi.org/10.1097/00063198 -200311000-00009. 40. De Boeck K, Wilschanski M, Castellani C, Taylor C, Cuppens H, Dodge J, Sinaasappel M, Diagnostic Working Group. 2006. Cystic fibrosis: terminology and diagnostic algorithms. Thorax 61:627– 635. http://dx.doi .org/10.1136/thx.2005.043539. 41. de Hoog GS, Guarro J, Gené J, Figueras MJ. 2001. Atlas of clinical fungi, 2nd edition. ASM Press, Washington, DC. 42. Broadhurst D, Kell DB. 2006. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2:171–196. 43. Moens M, Smet A, Naudts B, Verhoeven J, Ieven M, Jorens P, Geise HJ, Blockhuys F. 2006. Fast identification of ten clinically important microorganisms using an electronic nose. Lett Appl Microbiol 42:121–126. http: //dx.doi.org/10.1111/j.1472-765X.2005.01822.x. 44. Fend R, Kolk AH, Bessant C, Buijtels P, Klatser PR, Woodman AC. 2006. Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum. J Clin Microbiol 44:2039 –2045. http://dx.doi.org/10.1128/JCM.01591-05. 45. Dutta R, Hines EL, Gardner JW, Boilot P. 2002. Bacteria classification using Cyranose 320 electronic nose. Biomed Eng Online 1:4. http://dx.doi .org/10.1186/1475-925X-1-4. 46. Chambers ST, Syhre M, Murdoch DR, McCartin F, Epton MJ. 2009. Detection of 2-pentylfuran in the breath of patients with Aspergillus fumigatus. Med Mycol 47:468 – 476. http://dx.doi.org/10.1080/13693780802475 212. 47. Syhre M, Scotter JM, Chambers ST. 2008. Investigation into the production of 2-pentylfuran by aspergillus fumigatus and other respiratory pathogens in vitro and human breath samples. Med Mycol 46:209 –215. http://dx.doi.org/10.1080/13693780701753800. 48. Heddergott C, Calvo AM, Latge JP. 2014. The volatome of Aspergillus fumigatus. Eukaryot Cell 13:1014 –1025. http://dx.doi.org/10.1128/EC .00074-14. 49. Bushdid C, Magnasco MO, Vosshall LB, Keller A. 2014. Humans can discriminate more than 1 trillion olfactory stimuli. Science 343:1370 – 1372. http://dx.doi.org/10.1126/science.1249168. 50. Boots AW, Bos LD, van der Schee MP, van Schooten FJ, Sterk PJ. 2015. Exhaled molecular fingerprinting in diagnosis and monitoring: validating volatile promises. Trends Mol Med 21:633– 644. http://dx.doi.org/10 .1016/j.molmed.2015.08.001. 51. Peetermans M, Goeminne P, De Boeck C, Dupont LJ. 2014. IgE sensitization to Aspergillus fumigatus is not a bystander phenomenon in cystic fibrosis lung disease. Chest 146:e99 – e100. http://dx.doi.org/10.1378 /chest.14-0635. 52. Baxter CG, Moore CB, Jones AM, Webb AK, Denning DW. 2013. IgE-mediated immune responses and airway detection of Aspergillus and
Journal of Clinical Microbiology
March 2016 Volume 54 Number 3
A. fumigatus Colonization Detection in CF Using eNose
Candida in adult cystic fibrosis. Chest 143:1351–1357. http://dx.doi.org /10.1378/chest.12-1363. 53. Joensen O, Paff T, Haarman EG, Skovgaard IM, Jensen P, Bjarnsholt T, Nielsen KG. 2014. Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections. PLoS One 9:e115584. http://dx.doi.org/10.1371/journal.pone .0115584. 54. Paff T, van der Schee MP, Daniels JMA, Pals G, Postmus PE, Sterk PJ, Haarman EG. 2013. Exhaled molecular profiles in the assessment of cystic fibrosis and primary ciliary dyskinesia. J Cyst Fibros 12:454 – 460. http: //dx.doi.org/10.1016/j.jcf.2012.12.010. 55. Bos LDJ, Martin-Loeches I, Kastelijn JB, Gili G, Espasa M, Povoa P, Kolk AHJ, Janssen HG, Sterk PJ, Artigas A, Schultz MJ. 2014. The
March 2016 Volume 54 Number 3
volatile metabolic fingerprint of ventilator-associated pneumonia. Intensive Care Med 40:761–762. http://dx.doi.org/10.1007/s00134-014 -3260-5. 56. Maertens J, Theunissen K, Verhoef G, Verschakelen J, Lagrou K, Verbeken E, Wilmer A, Verhaegen J, Boogaerts M, Van Eldere J. 2005. Galactomannan and computed tomography-based preemptive antifungal therapy in neutropenic patients at high risk for invasive fungal infection: a prospective feasibility study. Clin Infect Dis 41:1242–1250. http://dx.doi .org/10.1086/496927. 57. Maertens J, Van Eldere J, Verhaegen J, Verbeken E, Verschakelen J, Boogaerts M. 2002. Use of circulating galactomannan screening for early diagnosis of invasive aspergillosis in allogeneic stem cell transplant recipients. J Infect Dis 186:1297–1306. http://dx.doi.org/10.1086/343804.
Journal of Clinical Microbiology
jcm.asm.org
575
