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Article

Performance of MALDI–TOF Mass Spectrometry in the Philippines

1
School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4, Sakamoto, Nagasaki 852-8102, Japan
2
San Lazaro Hospital, Quiricada St., Sta. Cruz, Manila 1003, Metro Manila, Philippines
3
San Lazaro Hospital-Nagasaki University Collaborative Research Office, San Lazaro Hospital, Quiricada St., Sta. Cruz, Manila 1003, Philippines
4
Institute of Tropical Medicine, Nagasaki University, 1-12-4, Sakamoto, Nagasaki 852-8523, Nagasaki, Japan
5
Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
6
Department of Microbiology, Faculty of Medicine, Oita University, Yufu, Oita 870-1192, Japan
7
Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel St., Bloomsbury, London WC1E 7HT, UK
*
Author to whom correspondence should be addressed.
Trop. Med. Infect. Dis. 2021, 6(3), 112; https://doi.org/10.3390/tropicalmed6030112
Submission received: 26 May 2021 / Revised: 20 June 2021 / Accepted: 23 June 2021 / Published: 26 June 2021

Abstract

:
Identification of the causative pathogen in infectious diseases is important for surveillance and to guide treatment. In low- and middle-income countries (LMIC), conventional culture and identification methods, including biochemical methods, are reference-standard. Biochemical methods can lack sensitivity and specificity and have slow turnaround times, causing delays in definitive therapy. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI–TOF MS) is a rapid and accurate diagnostic method. Most studies comparing MALDI–TOF MS and biochemical methods are from high-income countries, with few reports from LMIC with tropical climates. The aim of this study was to assess the performance of MALDI–TOF MS compared to conventional methods in the Philippines. Clinical bacterial or fungal isolates were identified by both MALDI–TOF MS and automated (VITEK2) or manual biochemical methods in the San Lazaro Hospital, Metro Manila, the Philippines. The concordance between MALDI–TOF MS and automated (VITEK2) or manual biochemical methods was analyzed at the species and genus levels. In total, 3530 bacterial or fungal isolates were analyzed. The concordance rate between MALDI–TOF MS and biochemical methods was 96.2% at the species level and 99.9% at the genus level. Twenty-three isolates could not be identified by MALDI–TOF MS. In this setting, MALDI–TOF MS was accurate compared with biochemical methods, at both the genus and the species level. Additionally, MALDI–TOF MS improved the turnaround time for results. These advantages could lead to improved infection management and infection control in low- and middle-income countries, even though the initial cost is high.

1. Introduction

When giving treatment for bacterial infection with antibiotics, accurate identification of the causative pathogen is essential to guide their appropriate use. There are several ways to identify causative bacteria and fungi, including biochemical methods, antigen and gene detection techniques [1]. Biochemical methods, by manual tests and/or using automated equipment such as VITEK2, have been the reference-standard for the identification of bacteria in resource-limited settings. The VITEK2 system can identify bacteria automatically by reading fluorescence, turbidity and colorimetric signals. Biochemical methods usually take at least 24–48 h, including conventional culture, to identify those bacteria or fungi and can lead to delayed treatment. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI–TOF MS), developed by Koichi Tanaka in 1988 [2], has become the reference-standard in many high-income laboratories [3]. MALDI–TOF MS has a high level of accuracy and provides a rapid identification (10–15 min) of microbes compared with biochemical methods [4,5,6]. MALDI–TOF MS can differentiate with high accuracy species that are difficult to be identified by biochemical methods such as Haemophilus, Aggregatibacter, Cardiobacterium, Eikenella and Kingella (HACEK) groups, coagulase-negative Staphylococci or nutritionally variant Streptococci [7,8,9]. MALDI–TOF MS has also been shown to be cost-effective by reducing the length of hospital admission and costs [10,11,12]. Moreover, MALDI–TOF MS has been shown to be able to predict antimicrobial resistance in bacteria [13].
Most studies comparing MALDI–OF MS and biochemical methods are from high-income countries [4,5,6], with few reports from low–middle-income countries or countries (LMIC) with tropical climates [4,14,15]. In 2015, a MALDI–TOF MS was installed in the San Lazaro Hospital (SLH)-Nagasaki Collaborative Research Laboratory and analyzed over 13,000 bacterial and fungal isolates in 5 years. The aim of this study was to assess the performance of MALDI–TOF MS compared to conventional methods in the Philippines.

2. Materials and Methods

This is a retrospective study using secondary data, which were collected from microbiological specimens in the San Lazaro Hospital (SLH), Metro Manila, the Philippines between 1 January 2018 and 15 January 2020. All data were de-identified to respect patient confidentiality and assigned a new code by the laboratory staff in SLH prior to being provided to the investigators.

2.1. Identification of Bacteria by Conventional Biochemical Methods

Bacteria or fungi cultured from clinical samples were sub-cultured for purity where necessary and examined by Gram staining and colonial morphology. Further identification was conducted using the VITEK2 compact system (version 8.01 bioMe’rieux, Marcy l’Etoile, France). In cases where the isolates could not be identified, biochemical tests that help differentiate bacteria through the characterization of their abilities in enzyme production, carbohydrate, protein, and lipid metabolism and compound utilization were performed according to standardized microbiology protocols [16].

2.2. Identification of Bacteria by MALDI–TOF MS

All the isolates were identified by the MALDI Biotyper 3.1 MSP database 5627 (Bruker Daltonik GmbH, Bremen, Germany). The detected spectrum was compared with reference data and evaluated by calculating a score by a unique algorithm. If the score was 2.0 or more, it was considered highly reliable at the species level, if the score was 1.7 or more and less than 2.0, it was highly reliable at the genus level, if it was less than 1.7, it was considered less reliable, and if it could not be identified, the result was ‘No identification’ returned.

3. Results

In total, 3530 sample isolates were tested with conventional biochemical methods. Of these, 1809 samples were tested by VITEK2, and 1721 were tested by manual methods. The concordance was calculated at the species and genus levels. Figure 1 shows the result of concordance between MALDI–TOF MS and VITEK2 or manual tests. Table 1 shows the concordance between MALDI–TOF MS and biochemical methods. The concordance was 95.8% (species level) and 99.8% (genus level) compared with VITEK2, and 96.6% (species level) and 99.9% (genus level) compared with manual biochemical testing. The total concordance was 96.2% (species) and 99.9% (genus).
The concordance of Gram-positive cocci was 100% (genus) and 97.8% (species). The concordance of Gram-negative rods was 99.8% (genus) and 95.1% (species). Among Gram-positive rods, only Corynebacterium diphtheriae was identified by biochemical methods. For Corynebacterium diphtheriae, MALDI–TOF MS had 100% concordance with the biochemical methods. Concordance of Gram-negative cocci was 100% for both genus and species. The concordance of fungi was 100% (genus) and 94.7% (species).
There were 23 isolates that could not be identified by MALDI–TOF MS. Of these, six were regarded as contaminants based on colony morphology and were not tested by biochemical methods. The remaining 17 were identified by biochemical methods: 1 was Streptococcus pneumoniae, three were Pseudomonas (1 aeruginosa, 1 alcaligenes and 1 putida), 1 was Klebsiella pneumoniae, 4 were Cryptococcus (3 neoformans and 1 laurentii), 7 were Candida (3 albicans, 1 krusei, 1 lipolytica, 1 parapsilosis and 1 tropicalis) and 1 was an unidentified fungus.

4. Discussion

This study is the first to compare the performance of MALDI–TOF MS with that of standard biochemical methods in the Philippines. MALDI–TOF MS had high concordance with biochemical methods in the identification of microorganisms at both the species and the genus levels (96.2%, 99.9%). Several studies have shown that MALDI–TOF MS is an accurate and rapid diagnostic test for not only bacteria but also fungi and acid-fast bacilli in high-income countries [17,18,19]. The finding that MALDI–TOF MS is almost equivalent to biochemical methods strongly supports its role in identifying pathogenic microorganisms in this setting. Additional methods are still sometimes needed, including biochemical methods, because MALDI–TOF MS is not good at differentiating and sometimes misidentifies closely related species [20,21,22,23]. For example, Burkholderia pseudomallei, mallei, and thailandensis cannot be differentiated [21], and the same is true for Streptococcus pneumoniae and mitis [16], and Neisseria meningitidis [23]. It may be necessary to modify the sample preparation protocol in such instances [24].
Another limitation of this method is that the accuracy of the results is highly dependent on the spectrum of the database. If an organism is not included in the database, then MALDI–TOF MS cannot identify it or sometimes misidentifies it. It is necessary that the database is up to date. Unfortunately, MALDI–TOF MS databases are proprietary, and regular database updating may not be sustainable for many laboratories, especially in LMICs. One potential solution to this is the creation of a publicly available online platform with a universal database of reference mass spectra [25]. Refining criteria, such as lowering cutoff values, for distinguishing closely related species may also be a workaround to this problem. Supplemental nucleic acid sequencing of the 16S rRNA gene may also help resolve unidentifiable or undifferentiable isolates [26].
In our study, 23 bacteria were not detected by MALDI–TOF MS. These results would be caused by different reasons, for example, the culture not being fresh, the colony being too small, an inadequate sample inoculated into the target plate, or the sample in the target plate being contaminated with culture media/agar.
The other disadvantage of MALDI–TOF MS is its relatively high initial cost. It may be difficult for institutions in LMIC to install this equipment, even in referral hospitals. MALDI–TOF could potentially reduce healthcare-associated costs and reduce the turnaround time for culture results, thereby allowing clinicians to initiate early targeted therapy. A previous study reported that MALDI–TOF MS is cost-effective for the identification of bacteria in an LMIC setting [14].
Even though MALDI–TOF MS displayed high accuracy for the identification of bacteria and fungi, it does not provide an answer in all circumstances. It is important to retain skills in traditional microbiological methods, and, for some microorganisms, molecular methods, such as nucleic acid sequencing, may be the best route to their identification. Furthermore, at present, MALDI–TOF MS does not provide antimicrobial sensitivity test results, and other methods to determine this property will continue to be required.

5. Conclusions

MALDI–TOF MS appears to be an accurate and rapid diagnostic method compared with biochemical methods at not only genus level but also species level. Additionally, with a result available in 10–15 min, MALDI–TOF MS can improve the turnaround time of results. Those advantages could lead to improved infection management and infection control in low- and middle-income countries.

Author Contributions

Conceptualization, M.O., C.S.; methodology, J.M., A.C., M.C., A.M.G.V., C.M.P., N.S.; formal analysis, M.O., C.S.; investigation, M.O., N.S., C.M.P., C.S.; resources, M.C.B., Z.D.M.; data curation, M.C.B.; writing—original draft preparation, M.O.; writing—review and editing, M.O., C.S., C.M.P., A.M.G.V., S.S., S.T., B.G.D.; supervision, C.S., K.A., E.F.O.T., D.V.U.; project administration, S.S., T.U.; funding acquisition, C.S., K.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All data were organized by laboratory staff in SLH, were de-identified in SLH to remove information such as hospital ID, name, address by laboratory staff prior to being provided to the investigators and were assigned a code to respect patient confidentiality. Ethical approval was obtained from the ethical committee of both School of Tropical Medicine and Global Health, Nagasaki University (Ref. No. 103), and San Lazaro Hospital (protocol number: SLH-RERU-2020-009-E).

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart of concordance between MALDI–TOF MS and VITEK2 or manual biochemical testing.
Figure 1. Flow chart of concordance between MALDI–TOF MS and VITEK2 or manual biochemical testing.
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Table 1. Performance of the MALDI Biotyper in comparison to conventional methods.
Table 1. Performance of the MALDI Biotyper in comparison to conventional methods.
PathogenNo. of Tests Performed with Conventional Method *No. of MALDI Biotyper with ScoreConcordance with Conventional Method to Indicated Level (%)
≤1.6991.700–1.999≥2.000GenusSpecies
Gram-positive cocci94239084810097.8
Enterococcus faecalis23012210095.7
Enterococcus faecium290029100100
Staphylococcus aureus411019391100100
Staphylococcus capitis3003100100
Staphylococcus cohnii1010100100
Staphylococcus epidermidis170413100100
Staphylococcus haemolyticus370532100100
Staphylococcus hominis53015210098.9
Staphylococcus kloosi1100100100
Staphylococcus lugdunensis1001100100
Staphylococcus saprophyticus7006100100
Staphylococcus warneri1001100100
Streptococcus agalactiae170017100100
Streptococcus anginosus4013100100
Streptococcus dysagalactiae21002110095.5
Streptococcus gallolyticus1101100100
Streptococcus intermedius1001100100
Streptococcus mitis144118125100100
Streptococcus mitis spp1010100100
Streptococcus oralis6702047100100
Streptococcus oralis spp1010100100
Streptococcus parasanguinis7043100100
Streptococcus peroris2011100100
Streptococcus pneumoniae480103810062.5
Streptococcus pyogenes310130100100
Streptococcus salivarius130211100100
Gram-positive rod12219112100100
Clostridium tertium1001100100
Corynebacterium diphtheriae11909110100100
Corynebacterium jeikeium1001100100
Rhodococcus equi1100100100
Gram-negative cocci340034100100
Moraxella catarrhalis210021100100
Moraxella equi1001100100
Moraxella osloensis1001100100
Neisseria gonorrhoeae2002100100
Neisseria meningitidis9009100100
Gram-negative rod216121139199999.895.1
Achromobacter xylosoxidans18131494.477.8
Acinetobacter baumannii3311932110099.4
Acinetobacter baylyi503210020
Acinetobacter calcoaceticus211010050
Acinetobacter guillouiae2011100100
Acinetobacter haemolyticus3003100100
Acinetobacter junii1304910069.2
Acinetobacter nosocomialis22012110036.4
Acinetobacter pittii17011610047.1
Acinetobacter radioresistens1100100100
Acinetobacter ursingii12001210083.3
Aeromonas caviae3003100100
Aeromonas hydrophila2002100100
Aeromonas veronii10011000
Burkholderia cenocepacia300310033.3
Burkholderia cepacia9018100100
Burkholderia seminalis200210050
Burkholderia thailandensis302110033.3
Cedecea neteri10011000
Citrobacter amalonaticus1100100100
Citrobacter freundii8017100100
Citrobacter koseri100010100100
Citrobacter sedlakii200210050
Cronobacter sakazakii1010100100
Delftia acidovorans3003100100
Enterobacter asburiae21031810057.1
Enterobacter cloacae **81137610097.5
Enterobacter gergoviae1001100100
Enterobacter kobei812510062.5
Escherichia coli16604162100100
Haemophilus haemolyticus28142310092.9
Haemophilus influenzae1231311910099.2
Haemophilus parahaemolyticus35113310065.7
Haemophilus parainfluenzae88058210092
Enterobacter aerogenes120111100100
Klebsiella oxytoca401310075
Klebsiella pneumoniae52624847610099.6
Leclercia adecarboxylata100100
Morganella morganii5005100100
Pantoea septica10101000
Pasteurella multocida5005100100
Proteus mirabilis420141100100
Proteus vulgaris1002810090
Providencia rettgeri4004100100
Providencia stuartii4013100100
Pseudomonas aeruginosa40051338210099.3
Pseudomonas anguilliseptica110000
Pseudomonas fluorescens1001100100
Pseudomonas fulva10011000
Pseudomonas libanensis10011000
Pseudomonas mendocina1010100100
Pseudomonas monteilii10011000
Pseudomonas mosselii2110100100
Pseudomonas otitidis301210033.3
Pseudomonas putida302110066.7
Pseudomonas rhodesiae10101000
Pseudomonas stutzeri900910088.9
Ralstonia insidiosa1001100100
Ralstonia mannitolytica1001100100
Raoultella ornithinolytica210110050
Serratia liquefaciens2002100100
Serratia marcescens11038100100
Serratia rubidaea1001100100
Serratia ureilytica20021000
Shewanella algae1001100100
Shewanella putrefaciens1001100100
Stenotrophomonas maltophilia74186598.698.6
Vibrio parahaemolyticus1010100100
Fungi270158716910094.7
Candida albicans1133347610099.1
Candida dubliniensis511310080
Candida glabrata612310083.3
Candida guilliermondii2200100100
Candida krusei4103100100
Candida lusitaniae1010100100
Candida orthopsilosis1010100100
Candida parapsilosis1010100100
Candida pararugosa1001100100
Candida tropicalis531242810094.3
Cryptococcus neoformans8362255100100
Trichosporon inkin10101000
Total353040325316499.996.2
Species highlighted in gray are considered clinically important microorganisms. * Includes isolates that were tested using both VITEK2 and manual biochemical methods, hence the total number may not meet. ** There was one isolate that lacked data.
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Osa, M.; Belo, M.C.; Dela Merced, Z.; Villanueva, A.M.G.; Mauhay, J.; Celis, A.; Catli, M.; Suzuki, S.; Ukawa, T.; Tamaki, S.; et al. Performance of MALDI–TOF Mass Spectrometry in the Philippines. Trop. Med. Infect. Dis. 2021, 6, 112. https://doi.org/10.3390/tropicalmed6030112

AMA Style

Osa M, Belo MC, Dela Merced Z, Villanueva AMG, Mauhay J, Celis A, Catli M, Suzuki S, Ukawa T, Tamaki S, et al. Performance of MALDI–TOF Mass Spectrometry in the Philippines. Tropical Medicine and Infectious Disease. 2021; 6(3):112. https://doi.org/10.3390/tropicalmed6030112

Chicago/Turabian Style

Osa, Morichika, Maria Cecilia Belo, Zita Dela Merced, Annavi Marie G. Villanueva, Jaira Mauhay, Alyannah Celis, Melissa Catli, Shuichi Suzuki, Tatsuya Ukawa, Shingo Tamaki, and et al. 2021. "Performance of MALDI–TOF Mass Spectrometry in the Philippines" Tropical Medicine and Infectious Disease 6, no. 3: 112. https://doi.org/10.3390/tropicalmed6030112

APA Style

Osa, M., Belo, M. C., Dela Merced, Z., Villanueva, A. M. G., Mauhay, J., Celis, A., Catli, M., Suzuki, S., Ukawa, T., Tamaki, S., Dhoubhadel, B. G., Ariyoshi, K., Telan, E. F. O., Umipig, D. V., Parry, C. M., Saito, N., & Smith, C. (2021). Performance of MALDI–TOF Mass Spectrometry in the Philippines. Tropical Medicine and Infectious Disease, 6(3), 112. https://doi.org/10.3390/tropicalmed6030112

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