This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

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2 588 Letters to the Editor F. Tubau Department of Microbiology, IDIBELL, Hosp. Univ. de Bellvitge, Feixa Llarga s/n, Barcelona, Spain C. Cabellos Laboratory of Experimental Infection, Infectious Diseases Service, IDIBELL, Hosp. Univ. de Bellvitge, Feixa Llarga s/n, Barcelona, Spain J. Cabo Department of Orthopaedic Surgery, IDIBELL, Hosp. Univ. de Bellvitge, Feixa Llarga s/n, Barcelona, Spain J. Ariza Laboratory of Experimental Infection, Infectious Diseases Service, IDIBELL, Hosp. Univ. de Bellvitge, Feixa Llarga s/n, Barcelona, Spain *Corresponding author. Infectious Diseases Service, Hospital Universitari de Bellvitge, Feixa Llarga s/n, L Hospitalet de Llobregat, Barcelona, Spain. Tel.: þ ; fax: þ Accepted 27 September 2012 ª 2012 The British Infection Association. Published by Elsevier Ltd. All rights reserved. Bronchoalveolar lavage lateral-flow device test for invasive pulmonary aspergillosis diagnosis in haematological malignancy and solid organ transplant patients Dear Sir, We read with interest the article of Park and colleagues who describe the use of bronchoalveolar lavage (BAL) fluids for IPA diagnosis based on Aspergillus galactomannan (GM) detection, a promising approach for early detection of pulmonary infections by this fungus. 1 Limitations of GM detection are assay turn-around time, which varies widely between centres (less than a day to several days), and the need for appropriately equipped laboratories that routinely test for this antigen. 2 These limitations are overcome by the Aspergillus Lateral-Flow Device (LFD), a novel point-of-care (POC) test for IPA diagnosis developed by Dr Thornton at the University of Exeter, UK. This simple, rapid (15 min), single-use test can be performed in rudimentary facilities using BAL or serum specimens. Recent studies have shown the immense potential of the LFD using human BAL and serum, 3e5 but have been limited by the small sample sizes, especially BALs. 3 More extensive clinical studies are therefore needed to evaluate this device in different patient cohorts. In this study, we evaluate the LFD using BAL specimens for IPA diagnosis in haematological malignancy and SOT patients. This retrospective study uses BAL samples from HM and SOT patients that were tested routinely for GM between June 2011 and July Lateral-Flow Device testing was performed at the Microbiology Laboratory, Department of Internal Medicine, Medical University of Graz. The LFD employs mab JF5 to detect an extracellular glycoprotein secreted during active growth of Aspergillus, 3 and test-line intensities range from strong (þþþ; Fig. 1) to weak (þ) positive or negative ( ). Regardless of intensity, all positive results using BAL samples indicate germination of spores and development of potentially pathogenic hyphae in the lungs. 3 For testing, 100 ml of neat BAL sample was applied to the LFD, with no pre-treatment. 3 Results were read after 15 min and interpreted in line with previous publications 3 by an investigator blinded to the corresponding GM results. Test results were compared to BAL GM and culture records. A threshold of 1.0 optical density index (ODI) was used for GM positivity, and IPA was graded in accordance with revised EORTC/MSG criteria. 6,7 The study adhered to Declaration of Helsinki, 1996, Good Clinical Practice, and the study Figure 1 LFD result using BAL fluid showing strong positive (þþþ) test (T) line. Internal control line (C) indicates that the test has run correctly.

3 Letters to the Editor 589 Table 1 Pat No. Demographic data, underlying diseases and test results of Study Cohort. Underlying disease Patient s age (years)/sex BAL GM value a BAL LFD result Fungal growth in BAL culture IPA according to EORTC 2008 criteria SOT patients 1 LTx /m 0.52 No No 2 HTx 2004, KTx /m 0.42 þ Aspergillus fumigatus Probable 3 KTx /m 1.22 þþ No Probable 4 LTx /m 0.7 þ Aspergillus fumigatus Probable 5 KTx /f 4.66 þþþ Aspergillus fumigatus Probable 6 LTx /m 0.73 þþ No Possible 7 LTx /m þþ Aspergillus fumigatus Probable 8 LTx /m Negative Lichtheimia corymbifera No 9 HTx /m Negative Candida albicans No 10 KTx /f Negative No No Haematological malignancy patients 11 AML 64/m Negative No No 12 NHL, HSCT 46/f 1.5 þ No Probable 13 MM, HSCT 52/m 19.3 þþþ No Probable 14 AML 36/m þþþ No Probable 15 NHL 72/m 2.8 þþ No Probable 16 AML 14/f 1.23 þ No Probable 17 NHL 62/m Negative No No 18 Autoimmune haemolytic 50/f Negative No No anaemia 19 MM, HSCT 69/m Negative No Possible Negative No No 20 MM 58/m Negative No No Negative No No 21 AML 56/m Negative No Possible 22 NHL 75/f Negative No No 23 NHL 71/m 22 þþ Aspergillus fumigatus Probable 24 ALL 49/f Negative No No 25 AML 41/f Negative þ No Possible 26 MM, HSCT 67/m 0.6 þ Candida glabrata Possible 27 AML 55/m Negative No No 28 Sezary Syndrome, HSCT 56/m 0.63 þ No Possible 29 AML, HSCT 43/m Negative þ No Possible 30 NHL 56/m 3.2 þþ C. guilliermondii, Probable Penicillium sp. 31 NHL 62/m Negative No No 32 MDS 21/m Negative No No 33 AML 51/f Negative No Possible 34 NHL 45/m Negative No Possible 35 Morbus Hodgkin 40/f 0.47 No No 36 AML 68/f Negative No No 37 AML 68/m Negative No No Note. ALL, acute lymphocytic leukaemia; AML, acute myeloid leukaemia; BAL, bronchoalveolar lavage; EORTC, European Organization for Research and Treatment of Cancer Invasive Fungal Infections Cooperative Group; f, female; GM, galactomannan; HSCT, haematopoietic stem cell transplantation; HTx, heart transplantation; IPA, invasive pulmonary aspergillosis; KTx, kidney transplantation; LFD, Lateral Flow Device; LTx, liver transplantation; m, male; MDS, myelodysplastic syndrome; MM, multiple myeloma; NHL, non-hodgkin s lymphoma. a Value displayed if >0.40, otherwise stated as negative in the table. The cut-off chosen in the study was, however, 1.0. protocol was approved by the local ethics committee, Medical University Graz, Austria (EC-number ex 10/11). Statistical analysis was performed using SPSS, version 19 (SPSS Inc., Chicago, IL, USA). Negative predictive value (NPV), positive predictive value (PPV), sensitivity and specificity were calculated, and continuous data (GM levels) are presented as medians (inter-quartile ranges [IQR]). A p-value of <0.05 was considered as statistically significant.

4 590 Letters to the Editor Thirty-nine BAL samples from 37 patients were tested (29 from 27 HM patients and 10 from 10 SOT patients). Demographic data, co-morbidities, EORTC/MSG diagnosis, as well as LFD, GM and culture test results are shown in Table 1, and a strong positive LFD test result is shown in Fig. 1. Sensitivity and specificity of BAL LFD tests for probable IPA were 100% and 81% (PPV 71%, NPV 100%), respectively. No false negatives were observed, but five patients with possible IPA had positive LFD results. In three cases, the corresponding GM values were 0.6, 0.63 and 0.7 ODI, and LFD results were weakly to moderately positive. Positive LFD results were also obtained in two cases of possible IPA, with GM values <0.4. All five patients with divergent results had received mould active antifungal therapy at the time of BAL sampling. For negative LFD results, all but two samples (GMI 0.47 and 0.42 respectively) had GMI results <0.4. All five samples that grew Aspergillus spp. gave positive LFD results, while one negative sample grew Lichtheimia corymbifera. GM levels in LFD negative BALs were significantly lower than in LFD positive BALs (n Z 22; median <0.4 ODI; [IQR] <0.4e<0.4 vs. n Z 17; median 1.50 ODI; [IQR] 0.72e11.33; p < , ManneWhitney U test). GM levels were also significantly lower in samples with weak LFD positives (n Z 8; median 0.97 ODI; [IQR] <0.4e1.23), than in moderate or strong positive LFD samples (n Z 9; median 4.66 ODI; [IQR] 2.8e19.3; p Z , ManneWhitney U test). While the NPV of the LFD was excellent (100%), the PPV was 81% due to five patients with possible IPA that did not fulfil mycological criteria. There are two possible explanations for this finding. First, even though the antigen targets of LFD and GM tests differ, a weak positive BAL LFD test result might correspond to a GM cut-off of 0.6 rather than to the chosen cut-off of 1.0. GM levels lay between 0.6 and 0.7 in three of these samples and were therefore markedly higher than the other GM negative samples that also gave negative LFD results. Also, Thornton previously reported a weak positive LFD result for a BAL sample with GM level of 0.9, while negative LFD BAL samples had GM levels of 0.25 or Second, all five patients had received antifungal treatment at the time of BAL sampling which may have caused false negative GM indexes. 8 Cross-reaction with other fungi may be a possible limitation of LFD as it resulted positive in two in which Candida spp., and in one of those a Penicillium sp. also, were detected in BAL culture. Cross-reaction in the former can be discounted as mab JF5 does not cross-react with Candida spp. 4 It is possible, however, that the Penicillium sp. may have contributed to the positive LFD in patient number 30, as mab JF5 does cross-react with certain Penicillium spp. 4 Despite this, the high NPV of the LFD may not only facilitate early diagnosis, but also prevent over-treatment with antifungals which has become commonplace, and which increases healthcare costs and may lead to resistance. 9,10 To conclude, the BAL LFD test is performed easily and provides accurate and rapid results. This new POC assay is a very promising test for IPA diagnosis using BAL specimens from HM and SOT patients. For routine clinical use, multicenter studies should be conducted with larger sample sizes from other patient collectives. Transparency declaration LFD tests used in this study were provided by Dr C. Thornton, University of Exeter. No other funding was obtained for this study. Potential conflicts of interest M. Hoenigl received research grant from Merck; speakers fee from Astellas and Merck. All other authors no conflict. Acknowledgement We acknowledge the help of Brigitta Waitzl in sample organization and preparation. References 1. Park SY, Lee SO, Choi SH, Sung H, Kim MN, Choi CM, et al. Aspergillus galactomannan antigen assay in bronchoalveolar lavage fluid for diagnosis of invasive pulmonary aspergillosis. J Infect 2010 Dec;61(6):492e8. 2. Hoenigl M, Salzer HJ, Raggam RB, Valentin T, Rohn A, Woelfler A, et al. Impact of galactomannan testing on the prevalence of invasive aspergillosis in patients with hematological malignancies. Med Mycol 2012 Apr;50(3):266e9. 3. Thornton C, Johnson G, Agrawal S. Detection of invasive pulmonary aspergillosis in haematological malignancy patients by using lateral-flow technology. J Vis Exp 2012 Mar 22;(61). pii: Thornton CR. Development of an immunochromatographic lateral-flow device for rapid serodiagnosis of invasive aspergillosis. Clin Vaccine Immunol 2008 Jul;15(7):1095e Wiederhold NP, Thornton CR, Najvar LK, Kirkpatrick WR, Bocanegra R, Patterson TF. Comparison of lateral flow technology and galactomannan and (1->3)-beta-D-glucan assays for detection of invasive pulmonary aspergillosis. Clin Vaccine Immunol 2009 Dec;16(12):1844e6. 6. Hoenigl M, Strenger V, Buzina W, Valentin T, Koidl C, Wolfler A, et al. European Organization for the Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG) host factors and invasive fungal infections in patients with haematological malignancies. J Antimicrob Chemother 2012 Aug;67(8): 2029e De Pauw B, Walsh TJ, Donnelly JP, Stevens DA, Edwards JE, Calandra T, et al. Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group. Clin Infect Dis 2008 Jun 15;46(12):1813e Becker MJ, Lugtenburg EJ, Cornelissen JJ, Van Der Schee C, Hoogsteden HC, De Marie S. Galactomannan detection in computerized tomography-based broncho-alveolar lavage fluid and serum in haematological patients at risk for invasive pulmonary aspergillosis. Br J Haematol 2003 May;121(3):448e Azoulay E, Dupont H, Tabah A, Lortholary O, Stahl JP, Francais A, et al. Systemic antifungal therapy in critically ill patients without invasive fungal infection*. Crit Care Med 2012 Mar;40(3):813e van der Linden JW, Snelders E, Kampinga GA, Rijnders BJ, Mattsson E, Debets-Ossenkopp YJ, et al. Clinical implications

5 Letters to the Editor 591 of azole resistance in Aspergillus fumigatus, The Netherlands, 2007e2009. Emerg Infect Dis 2011 Oct;17(10):1846e54. Martin Hoenigl* address: Christoph Koidl Institute of Hygiene, Microbiology and Environmental Medicine, Wiebke Duettmann Katharina Seeber Jasmin Wagner Walter Buzina Institute of Hygiene, Microbiology and Environmental Medicine, Albert W olfler Division of Haematology, Medical University of Graz, Graz, Austria Reinhard B. Raggam Clinical Institute of Medical and Chemical Laboratory Diagnostics, Christopher R. Thornton Biosciences, University of Exeter, Exeter, United Kingdom Robert Krause *Corresponding author. Tel.: þ ; fax: þ Accepted 5 October 2012 ª 2012 The British Infection Association. Published by Elsevier Ltd. All rights reserved. A novel infection screening method using a neural network and k-means clustering algorithm which can be applied for screening of unknown or unexpected infectious diseases Dear Editor, We recently reported in this journal on a novel mass screening method for influenza patients using a noncontact screening system. 1 The method enabled the screening of infection within tens of seconds using noncontact monitored parameters, i.e., heart rate, respiratory rate and average facial temperature via linear discriminant analysis (LDA). The method achieved higher sensitivity (88%) than conventional methods using only thermography. 2 However, our method using LDA indicated two limitations. The one was a limitation derived from being a linear method, the other was the fact that LDA could not respond to unknown or unexpected infectious diseases. In the present paper, we developed a non-linear screening method which determines the perfect screening condition using a neural network (self-organizing map) and k-means clustering algorithm (a non-linear clustering algorithm). The limitations to detect infected individuals using LDA are the following: 1) LDA finds the optimal discriminant function by maximizing the between-class distances and minimizing the within-class distances. 3 In some situations, LDA gives unwanted clustering results when the number of monitored parameters increases. 4 The sensitivity of the screening system using LDA was less than 90% (88%) reported in our previous study. 2) LDA is a supervised classification method. LDA can be used only when the clustering information is previously known. In order to distinguish the influenza patients from the normal subjects, LDA requires a preliminary statistical calculation for determining the discriminant function. In this sense, LDA cannot be used for the screening of unknown or unexpected infectious diseases. There is a real need for an unsupervised screening method in order to discriminate unknown or unexpected infectious diseases. We proposed a discriminant method using a neural network, i.e., Kohonen s self-organizing map 5 (SOM) combined with a k-means clustering algorithm. 6 With the help of the k-means clustering algorithm, we determined whether the SOM can discriminate the influenza patients from the normal subjects without being preliminarily taught by any medical or personal backgrounds. The proposed method can optimize the current discriminant function autonomously without using any previously determined clustering condition. As a first step, we calculated the SOM using a software written in MATLAB (Math- Works, Natick, MA, USA) programming language from non-contact derived parameters. The SOM clustering result is visualized on a two-dimensional color-coded map using the unified distance matrix (U-matrix). The U-matrix shows distances between neighboring map units using a color tone. This classification is composed of various clusters corresponding to a variety of U-matrix distances (color tone). As a second step, the k-means clustering algorithm in MAT- LAB was employed to reduce the SOM clusters into two clusters ( Influenza group and Normal group ). We tested the SOM with the k-means clustering algorithm (a two-step method) using the same clinical data which had been analyzed in our previous study using LDA. 1 A total of 92 subjects were recruited, consisting of 57 hospitalized influenza patients and 35 normal control subjects. The screening results of the SOM with the k-means clustering algorithm are shown in Fig. 1 (left; SOM alone, right; SOM with k-means clustering algorithm). Normal (NOR1, NOR2,.) and influenza (INF1, INF2,.) subjects tend to be distributed in the upper part and the lower part of the left figure, respectively. In order to optimally classify SOM clusters into two clusters, the k-means clustering algorithm was applied to the SOM classification of the clinical data as shown in the right figure. A total of 56 out