Annals of Clinical Microbiology, The official Journal of the Korean Society of Clinical Microbiology
, Eun-Young Kim1*, Hyun Jin Choi1, Si Hyun Kim2, Eunkyoung You1, Ja Young Lee1, Jihyun Cho3, Seok Hoon Jeong4, Dokyun Kim4, Hyun Soo Kim5, Soo Hyun Kim6, Young Ah Kim7, Young Ree Kim8, Nam Hee Ryoo9, Jong Hee Shin10, Kyeong Seob Shin11, Young Uh12, Jeong Hwan Shin1 1Department of Laboratory Medicine and Paik Institute for Clinical Research, Inje University College of Medicine, Busan, Korea
2Department of Biomedical Laboratory Science, Inje University, Gimhae, Korea
3Department of Laboratory Medicine, Wonkwang University Medical School, Iksan, Korea
4Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Korea
5Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
6Department of Microbiology and Laboratory Medicine, Chonnam National University Medical School, Gwangju, Korea
7Department of Laboratory Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Korea
8Department of Laboratory Medicine, Jeju National University College of Medicine, Jeju, Korea
9Department of Laboratory Medicine, Keimyung University School of Medicine, Daegu, Korea
10Department of Laboratory Medicine, Chonnam National University Medical School, Gwangju, Korea
11Department of Laboratory Medicine, Chungbuk National University College of Medicine, Cheongju, Korea
12Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
*These authors contributed equally to this work.
Correspondence to Jeong Hwan Shin E-mail: jhsmile@paik.ac.kr
Ann Clin Microbiol 2025;28(3):13. https://doi.org/10.5145/ACM.2025.28.3.2
Received on 1 July 2025, Revised on 30 July 2025, Accepted on 3 August 2025, Published on 2 September 2025.
Copyright © Korean Society of Clinical Microbiology.
This is an Open Access article which is freely available under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Background: Haemophilus is an important pathogen in community-acquired pneumonia and invasive diseases, such as sepsis and meningitis. We aimed to evaluate the VITEK 2 system and VITEK MS system for the identification of Haemophilus strains isolated from clinical specimens in Korea during 2023.
Methods: In total, 118 Haemophilus strains isolated from respiratory specimens (n = 107) and blood samples (n = 11) from 10 sentinel hospitals in Kor-GLASS were included in this study. All Haemophilus strains were evaluated using the VITEK 2 and VITEK MS systems. Real-time PCR and 16S rRNA sequencing were used to identify specific species.
Results: Among the 118 Haemophilus isolates, 115 were identified as H. influenzae by realtime PCR using hpd gene, and the remaining three strains were identified as H. parainfluenzae by 16S rRNA sequencing. Eighty-eight of the 115 (76.5%) and two of three (66.7%) isolates were correctly identified as H. influenzae and H. parainfluenzae, respectively, using the VITEK 2 system. The VITEK 2 system showed low discrimination (n = 22), misidentification (n = 4), and unidentified organisms (n = 2) in the 28 Haemophilus strains. The VITEK MS system achieved 100% sensitivity and specificity in identifying all 115 H. influenzae and three H. parainfluenzae isolates.
Conclusion: The VITEK MS system showed excellent performance in the identification of H. influenzae and H. parainfluenzae, whereas the VITEK 2 system showed relatively low concordance.
Haemophilus, Haemophilus influenzae, Haemophilus parainfluenzae, Identification, Matrix-assisted laser desorption-ionization mass spectrometry
Haemophilus influenzae is an important pathogen that causes community-acquired pneumonia and invasive diseases such as sepsis and meningitis [1–3]. It can also cause other infections including acute otitis media, sinusitis, and acute exacerbations of chronic obstructive pulmonary disease [4,5].
Automated identification systems such as VITEK 2 system (VITEK® 2, bioMérieux), Microscan Walkaway plus Microbiology system (Siemens Healthcare Diagnostics), and BD Phoenix M50 (BD Biosciences) have a long history of use in clinical microbiology laboratories. However, these systems show limited accuracy and reproducibility for specific bacterial groups [6,7]. Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has become a valuable tool for microbial identification in clinical microbiology laboratory for rapid, cost-effective, and highly accurate identification of the most common pathogens. Unlike traditional biochemical methods, which can be affected by variability in metabolic expression among strains, MALDI-TOF MS provides consistent and reproducible results, even for closely related species [8,9].
We aimed to evaluate VITEK 2 system and VITEK MS system (VITEK® MS PRIME) for the identification of Haemophilus strains isolated from clinical specimens.
This was a retrospective diagnostic accuracy study using the VITEK 2 and VITEK MS systems as index tests and hpd real-time polymerase chain reaction (PCR) and 16S rRNA sequencing as reference tests. This study was performed in accordance with the STARD statement available at https://www.equator-network.org/reporting-guidelines/stard/.
A total of 118 non-duplicate Haemophilus strains isolated from respiratory specimens (n = 107) and blood (n = 11) from the Kor-GLASS (Global Antimicrobial Resistance Surveillance System in Korea) collection network between January 2023 to December 2023 were included in this study. The Kor-GLASS, compatible with the GLASS platform, was established in 2016, with the third phase (2023–2025) involving 10 sentinel hospitals (Gangnam Severance Hospital, National Health Insurance Service Ilsan Hospital, Wonju Severance Christian Hospital, Chungbuk National University Hospital, Chonnam National University Hospital, Inje University Busan Paik Hospital, Hallym University Dongtan Sacred Heart Hospital, Jeju National University Hospital, Keimyung University Dongsan Hospital, Wonkwang University Hospital) across 10 regions [10,11]. These strains were stored at -80°C in skim milk containing 10% glycerol until use.
The sample size of 118 strains was determined by the total number of available non-duplicate isolates collected in the Kor-GLASS system during the study period. Although no formal sample size calculation was performed, the study included all available isolates in order to minimize selection bias.
All Haemophilus strains were identified using the VITEK 2 system with a VITEK 2 Neisseria-Haemophilus (NH) identification card according to the manufacturer’s instructions. The bacterial suspension prepared in 0.45% aqueous NaCl was adjusted to a McFarland standard of 3 using the VITEK 2 DensiCheck instrument (bioMérieux). A 64-well NH card was placed on the VITEK 2 system, and VITEK 2 software version 9.02 was used for data analysis. H. influenzae ATCC 49247 was used as a quality control strain.
VITEK MS system (VITEK® MS PRIME, bioMérieux) was used to identify all Haemophilus strains according to the manufacturer’s instructions. A fresh colony was smeared onto a 48-well target plate and covered with 1 µL of α-cyano-4-hydroxycinnamic acid matrix solution. After drying, the target plate was loaded onto a VITEK MS system [12]. The results were interpreted using the VITEK MS database version 3.2. Escherichia coli ATCC 8739 was used as the calibrant and quality control strain. The final result was reported as good identification when one organism (or organism group) was identified with a probability of 60.0%–99.9%.
Real-time PCR of hpd gene was performed for all 118 Haemophilus strain isolates as the gold standard for identification of H. influenzae. Bacterial genomic DNA was extracted using Instagene matrix (Bio-Rad Laboratories). PCR was performed on a 7500 Real-Time PCR System (Applied Biosystems). The primer and probe sequences were described in Table 1 [13]. Real-time PCR was performed under the following conditions: 50°C for 2 min and 95°C for 10 min, followed by 50 cycles of 95°C for 15 s and 60°C for 60 s. H. influenzae ATCC 49247 was used as a positive control.
When the result of real-time PCR using hpd was negative, we performed 16S rRNA gene sequencing to identify Haemophilus species other than H. influenzae. The 16S rRNA gene was amplified using universal primers 27F and 1492R and sequencing was performed using a total of four primers: 27F, 785F, 907R, and 1492R (Table 1).
Table 1. Primers and probe list used in the study
| Target genes | Primer or probe | Sequences (5′–3′) |
|---|---|---|
| hpd | hpd822F | GGT TAA ATA TGC CGA TGG TGT TG |
| hpd952R | TGC ATC TTT ACG CAC GGT GTA | |
| Pb896i | (FAM) TTG TGT ACA CTC CGT TGG T (BHQ1) | |
| 16S rRNA | 27F | AGA GTT TGA TCM TGG CTC AG |
| 1492R | GGT TAC CTT GTT ACG ACT T | |
| 785F | GGA TTA GAT ACC CTG GTA | |
| 907R | CCG TCA ATT CMT TTR AGT TT |
The investigators performing the VITEK 2 and VITEK MS tests were blinded to the results of the reference standard assays. Likewise, personnel conducting PCR and sequencing were blinded to the results of the index tests to minimize interpretation bias.
We calculated the percentages of accurate identification, low discrimination, misidentification, and no identification results from the VITEK 2 and VITEK MS systems based on hpd real-time PCR and 16S rRNA sequencing. We precisely analyzed the discrimination results.
Among the 118 Haemophilus isolates, 115 were identified as H. influenzae by real-time PCR using hpd. The remaining three strains were identified as H. parainfluenzae by 16S rRNA sequencing.
Of the 118 Haemophilus isolates, 115 (97.5%) were identified as H. influenzae, and three strains (2.5%) were correctly identified as H. parainfluenzae using the VITEK MS system. The VITEK MS system exhibited 100% accuracy in identifying all 115 H. influenzae and three H. parainfluenzae isolates (Table 2).
Table 2. Identification results from VITEK MS and VITEK 2 systems for 118 Haemophilus strains
| VITEK MS (n, %) | VITEK 2 (n, %) | hpd real-time PCR and 16S rRNA sequencing |
|---|---|---|
| H. influenzae (115, 100) | H. influenzae (88, 76.5) | H. influenzae |
| Low discrimination (22, 19.1) | ||
| H. parainfluenzae (3, 2.6) | ||
| Unidentified organism (2, 1.7) | ||
| H. parainfluenzae (3, 100) | H. parainfluenzae (2, 66.7) | H. parainfluenzae |
| H. influenzae (1, 33.3) |
Abbreviation: PCR, polymerase chain reaction.
For the 115 H. influenzae isolates, 88 strains (76.5%) were correctly identified as H. influenzae, and 22 strains (19.1%) were reported as “low discrimination” by the VITEK 2 system. The remaining two strains (1.7%) showed “unidentified organism” results and three strains (2.6%) were incorrectly identified as H. parainfluenzae. Among the three H. parainfluenzae isolates, two were correctly identified as H. parainfluenzae, whereas one isolate was misidentified as H. influenzae by the VITEK 2 system. The VITEK 2 system correctly identified 76.3% of the 88 H. influenzae and two H. parainfluenzae isolates (Table 2).
All 22 strains categorized as having “low discrimination” showed mixed results with H. influenzae along with other Haemophilus and Actinobacillus species (Table 3). Mixed identification results were observed in six strains of H. influenzae/H. haemolyticus, five strains of H. influenzae/A. ureae, three strains each of H. influenzae/H. parainfluenzae and H. influenzae/H. parahaemolyticus, and one strain of H. influenzae/A. pleuropneumoniae. The remaining three strains exhibited mixed identification results involving three distinct bacterial species.
Table 3. Low discrimination results of 22 Haemophilus influenzae strains by VITEK 2 system
| VITEK 2 results of identification | Strains (n) |
|---|---|
| H. influenzae / H. haemolyticus | 6 |
| H. influenzae / A. urae | 5 |
| H. influenzae / H. parainfluenzae | 3 |
| H. influenzae / H. parahaemolyticus | 3 |
| H. influenzae / A. pleuropneumoniae | 1 |
| H. influenzae / A. urae / H. haemolyticus | 1 |
| H. influenzae / H. haemolyticus / H. parainfluenzae | 1 |
| H. influenzae / H. parahaemolyticus / A. pleuropneumoniae | 1 |
| Total | 22 |
Although widely used in clinical laboratories, the VITEK 2 system showed a limited ability to identify H. influenzae. In addition, H. influenzae is sometimes misidentified as H. parainfluenzae. The use of molecular methods such as real-time PCR, 16S rRNA sequencing, and MALDI-TOF should be considered to ensure correct identification of H. influenzae when the VITEK 2 system yields “low discrimination” or “unidentified organisms”.
Automated identification systems have been used in clinical microbiology laboratories for several years. In a report by Janda et al., MicroScan using NH identification panel with 132 Haemophilus strains showed good performance for H. influenzae (98.8%) and H. parainfluenzae (97.1%) [14]. Munson et al. evaluated the performance of the VITEK system for identification of Haemophilus and obtained accuracies of 98.2% and 85.7% for 174 H. influenzae and 154 H. parainfluenzae, respectively [15].
Valenza et al. reported that the VITEK 2 system showed correct identification results for 84% and 92.5% for 25 H. influenzae and 27 H. parainfluenzae isolates, respectively [16]. Low discrimination was observed for two H. influenzae isolates (H. influenzae–H. haemolyticus), reflecting uncertainty in distinguishing between these closely related species. The other two H. influenzae isolates were labeled as unidentified organisms. Incorrect results for the two H. parainfluenzae isolates showed low discrimination (H. parainfluenzae–H. segnis) and unidentified results. Rennie et al. reported higher identification accuracies of 93% for H. influenzae and 83% for H. parainfluenzae using 90 H. influenzae and 41 H. parainfluenzae strains evaluated using the VITEK 2 system [17]. In contrast, our study showed a lower correct identification rate (76.5%) for H. influenzae using this system, with a large proportion of low discrimination (19.1%), misidentification as H. parainfluenzae (2.6%), and unidentified organisms (1.7%). However, all results considered as low discrimination by the VITEK 2 system still included H. influenzae as a possible organism.
Unlike the VITEK 2 system, which uses traditional biochemical methods, VITEK MS is a laboratory instrument based on MALDI-TOF MS. Currently, VITEK MS is widely adopted in clinical laboratories because of its high accuracy and rapid identification capabilities [18,19]. Bruin et al. evaluated the Bruker MALDI Biotyper® System (Bruker Daltonics) using 244 H. influenzae and 33 H. haemolyticus [20]. The accuracy rates were 100% for H. influenzae and 87.9% H. haemolyticus. Frickmann et al. [21] reported that 88% of H. influenzae isolates were correctly identified using the Bruker system. In our study, all H. influenzae and H. parainfluenzae strains were correctly identified at the species level using the VITEK MS system. Another report by Nürnberg et al. showed similar results in identifying H. influenzae [22]. However, they reported that 42% of H. haemolyticus strains were identified as H. influenzae. These findings highlight the persistent difficulties in differentiating closely related species, particularly H. haemolyticus, even when using VITEK MS. In our study, only a few H. parainfluenzae and no H. haemolyticus were included; thus, further evaluation of other Haemophilus species is necessary.
This study had several limitations. There were only a few H. parainfluenzae except H. influenzae. No other Haemophilus species were examined. We evaluated only the VITEK 2 and VITEK MS systems, although other commercial identification systems are used in clinical laboratories.
The VITEK 2 system showed relatively low performance in identifying H. influenzae, although results were generally satisfactory. The VITEK MS system is appropriate for identifying H. influenzae in clinical laboratories.
This study was approved by the Institutional Review Board of Inje University Busan Paik Hospital (BPIRB NON2024-002), and the requirement for patient consent was waived.
Soo Hyun Kim has been an editorial board member since 2011 and Young Uh has been a statistical editor of the Annals of Clinical Microbiology since 2024. However, they were not involved in the review process of this article. No potential conflict of interest relevant to this article was reported.
This research was supported by the Research Program funded by the Korea Disease Control and Prevention Agency (grant 2023-10-002) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR21C1003).
The data sets generated in this study are available from the corresponding author upon request.
We appreciate the dedication and contributions of all Kor-GLASS participants and government officials from relevant authorities.
1. Shoar S, Centeno FH, Musher DM. Erratum to: clinical features and outcomes of community-acquired pneumonia caused by Haemophilus influenzae. Open Forum Infect Dis 2021;8:ofab226.
2. Ulanova M, Tsang RS. Invasive Haemophilus influenzae disease: changing epidemiology and host-parasite interactions in the 21st century. Infect Genet Evol 2009;9:594-605.
3. Klibanov OM, Kehr H, Jeter Z, Ekwonu T. Fatal meningitis and sepsis caused by nontypeable Haemophilus influenzae. J Med Cases 2022;13:396-401.
4. Turk DC. The pathogenicity of Haemophilus influenzae. J Med Microbiol 1984;18:1-16.
5. Murphy TF. Respiratory infections caused by non-typeable Haemophilus influenzae. Curr Opin Infect Dis 2003;16:129-34.
6. Jin WY, Jang SJ, Lee MJ, Park G, Kim MJ, Kook JK, et al. Evaluation of VITEK 2, MicroScan, and Phoenix for identification of clinical isolates and reference strains. Diagn Microbiol Infect Dis 2011;70:442-7.
7. Donay JL, Mathieu D, Fernandes P, Pregermain C, Bruel P, Wargnier A, et al. Evaluation of the automated phoenix system for potential routine use in the clinical microbiology laboratory. J Clin Microbiol 2004;42:1542-6.
8. Singhal N, Kumar M, Kanaujia PK, Virdi JS. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front Microbiol 2015;6:791.
9. Wieser A, Schneider L, Jung J, Schubert S. MALDI-TOF MS in microbiological diagnosticsidentification of microorganisms and beyond (mini review). Appl Microbiol Biotechnol 2012;93:965-74.
10. Tornimbene B, Eremin S, Escher M, Griskeviciene J, Manglani S, Pessoa-Silva CL. WHO Global Antimicrobial Resistance Surveillance System early implementation 2016-17. Lancet Infect Dis 2018;18:241-2.
11. Lee H, Yoon EJ, Kim D, Jeong SH, Shin JH, Shin JH, et al. Establishment of the South Korean national antimicrobial resistance surveillance system, Kor-GLASS, in 2016. Euro Surveill 2018;23:1700734.
12. Guo L, Ye L, Zhao Q, Ma Y, Yang J, Luo Y. Comparative study of MALDI-TOF MS and VITEK 2 in bacteria identification. J Thorac Dis 2014;6:534-8.
13. Wang X, Mair R, Hatcher C, Theodore MJ, Edmond K, Wu HM, et al. Detection of bacterial pathogens in Mongolia meningitis surveillance with a new real-time PCR assay to detect Haemophilus influenzae. Int J Med Microbiol 2011;301:303-9.
14. Janda WM, Bradna JJ, Ruther P. Identification of Neisseria spp., Haemophilus spp., and other fastidious gram-negative bacteria with the MicroScan Haemophilus-Neisseria identification panel. J Clin Microbiol 1989;27:869-73.
15. Munson E, Pfaller M, Koontz F, Doern G. Comparison of porphyrin-based, growth factor-based, and biochemical-based testing methods for identification of Haemophilus influenzae. Eur J Clin Microbiol Infect Dis 2002;21:196-203.
16. Valenza G, Ruoff C, Vogel U, Frosch M, Abele-Horn M. Microbiological evaluation of the new VITEK 2 Neisseria-Haemophilus identification card. J Clin Microbiol 2007;45:3493-7.
17. Rennie RP, Brosnikoff C, Shokoples S, Reller LB, Mirrett S, Janda W, et al. Multicenter evaluation of the new Vitek 2 Neisseria-Haemophilus identification card. J Clin Microbiol 2008;46:2681-5.
18. Hou TY, Chiang-Ni C, Teng SH. Current status of MALDI-TOF mass spectrometry in clinical microbiology. J Food Drug Anal 2019;27:404-14.
19. Tsuchida S, Nakayama T. MALDI-based mass spectrometry in clinical testing: focus on bacterial identification. Appl Sci 2022;12:2814.
20. Bruin JP, Kostrzewa M, van der Ende A, Badoux P, Jansen R, Boers SA, et al. Identification of Haemophilus influenzae and Haemophilus haemolyticus by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Eur J Clin Microbiol Infect Dis 2014;33:279-84.
21. Frickmann H, Christner M, Donat M, Berger A, Essig A, Podbielski A, et al. Rapid discrimination of Haemophilus influenzae, H. parainfluenzae, and H. haemolyticus by fluorescence in situ hybridization (FISH) and two matrix-assisted laser-desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) platforms. PLoS One 2013;8:e63222.
22. Nurnberg S, Claus H, Krone M, Vogel U, Lam TT. Discriminative potential of the Vitek MS in vitro diagnostic device regarding Haemophilus influenzae and Haemophilus haemolyticus. J Clin Microbiol 2020;58:e00278-20.
1. Shoar S, Centeno FH, Musher DM. Erratum to: clinical features and outcomes of community-acquired pneumonia caused by Haemophilus influenzae. Open Forum Infect Dis 2021;8:ofab226.
2. Ulanova M, Tsang RS. Invasive Haemophilus influenzae disease: changing epidemiology and host-parasite interactions in the 21st century. Infect Genet Evol 2009;9:594-605.
3. Klibanov OM, Kehr H, Jeter Z, Ekwonu T. Fatal meningitis and sepsis caused by nontypeable Haemophilus influenzae. J Med Cases 2022;13:396-401.
4. Turk DC. The pathogenicity of Haemophilus influenzae. J Med Microbiol 1984;18:1-16.
5. Murphy TF. Respiratory infections caused by non-typeable Haemophilus influenzae. Curr Opin Infect Dis 2003;16:129-34.
6. Jin WY, Jang SJ, Lee MJ, Park G, Kim MJ, Kook JK, et al. Evaluation of VITEK 2, MicroScan, and Phoenix for identification of clinical isolates and reference strains. Diagn Microbiol Infect Dis 2011;70:442-7.
7. Donay JL, Mathieu D, Fernandes P, Pregermain C, Bruel P, Wargnier A, et al. Evaluation of the automated phoenix system for potential routine use in the clinical microbiology laboratory. J Clin Microbiol 2004;42:1542-6.
8. Singhal N, Kumar M, Kanaujia PK, Virdi JS. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front Microbiol 2015;6:791.
9. Wieser A, Schneider L, Jung J, Schubert S. MALDI-TOF MS in microbiological diagnosticsidentification of microorganisms and beyond (mini review). Appl Microbiol Biotechnol 2012;93:965-74.
10. Tornimbene B, Eremin S, Escher M, Griskeviciene J, Manglani S, Pessoa-Silva CL. WHO Global Antimicrobial Resistance Surveillance System early implementation 2016-17. Lancet Infect Dis 2018;18:241-2.
11. Lee H, Yoon EJ, Kim D, Jeong SH, Shin JH, Shin JH, et al. Establishment of the South Korean national antimicrobial resistance surveillance system, Kor-GLASS, in 2016. Euro Surveill 2018;23:1700734.
12. Guo L, Ye L, Zhao Q, Ma Y, Yang J, Luo Y. Comparative study of MALDI-TOF MS and VITEK 2 in bacteria identification. J Thorac Dis 2014;6:534-8.
13. Wang X, Mair R, Hatcher C, Theodore MJ, Edmond K, Wu HM, et al. Detection of bacterial pathogens in Mongolia meningitis surveillance with a new real-time PCR assay to detect Haemophilus influenzae. Int J Med Microbiol 2011;301:303-9.
14. Janda WM, Bradna JJ, Ruther P. Identification of Neisseria spp., Haemophilus spp., and other fastidious gram-negative bacteria with the MicroScan Haemophilus-Neisseria identification panel. J Clin Microbiol 1989;27:869-73.
15. Munson E, Pfaller M, Koontz F, Doern G. Comparison of porphyrin-based, growth factor-based, and biochemical-based testing methods for identification of Haemophilus influenzae. Eur J Clin Microbiol Infect Dis 2002;21:196-203.
16. Valenza G, Ruoff C, Vogel U, Frosch M, Abele-Horn M. Microbiological evaluation of the new VITEK 2 Neisseria-Haemophilus identification card. J Clin Microbiol 2007;45:3493-7.
17. Rennie RP, Brosnikoff C, Shokoples S, Reller LB, Mirrett S, Janda W, et al. Multicenter evaluation of the new Vitek 2 Neisseria-Haemophilus identification card. J Clin Microbiol 2008;46:2681-5.
18. Hou TY, Chiang-Ni C, Teng SH. Current status of MALDI-TOF mass spectrometry in clinical microbiology. J Food Drug Anal 2019;27:404-14.
19. Tsuchida S, Nakayama T. MALDI-based mass spectrometry in clinical testing: focus on bacterial identification. Appl Sci 2022;12:2814.
20. Bruin JP, Kostrzewa M, van der Ende A, Badoux P, Jansen R, Boers SA, et al. Identification of Haemophilus influenzae and Haemophilus haemolyticus by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Eur J Clin Microbiol Infect Dis 2014;33:279-84.
21. Frickmann H, Christner M, Donat M, Berger A, Essig A, Podbielski A, et al. Rapid discrimination of Haemophilus influenzae, H. parainfluenzae, and H. haemolyticus by fluorescence in situ hybridization (FISH) and two matrix-assisted laser-desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) platforms. PLoS One 2013;8:e63222.
22. Nurnberg S, Claus H, Krone M, Vogel U, Lam TT. Discriminative potential of the Vitek MS in vitro diagnostic device regarding Haemophilus influenzae and Haemophilus haemolyticus. J Clin Microbiol 2020;58:e00278-20.