Novel in-house computerized program for bacterial identification
Aduldech Tammametakarn 1,*, Ekawat Pasomsub1, Pitak Santanirand1, #
1
Department of pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol
University, Thailand
*e-mail: dreamdavil@gmail.com, # e-mail: pitak.san@mahidol.ac.th
Abstract
In routine clinical microbiology laboratories, identification of pathogenic
bacteria is generally using biochemical tests in which accurate identification of
microorganism relies mainly on microbiologist’s skill and experience. In addition,
the large amount of data as well as the similar characters among organisms can also
cause mis-identification. This can lead to improper treatment of the patients.
Therefore, this study was designed to use a new computerized program in order to
decrease problems in routine process and support workflow of identification
algorithms in microbiology laboratories. The program has included over 120 species
of Gram positive cocci in the genus of Staphylococcus, Enterococcus and
Streptococcus and 136 Enterobacteriaceae species in Gram negative group. This
model has been proved to be useful not only for identification purpose but also it
provides the additional test for those isolates which cannot be distinguished in the
first step.
Keyword: Microbiology, Computerized program, Bacterial identification
Introduction
Identification of bacteria isolated from clinical specimen is essential for
confirmation of bacterial infection and useful for antimicrobial treatment. There are
many methods for identification of these microorganisms, including phenotypic,
genotypic and phylogenetic analysis. The conventional techniques in routine
laboratories can classify microorganism based on phenotypic analysis that is
characterized by gram staining, growth characteristics, biochemical test results and
antibiotic susceptibility patterns (Gobernado M, 2003). However, in slow growing
and fastidious bacteria, these phenotypic methods may not be reliable. In recent
year, genotypic and phylogenetic methods have been developed and showed to solve
some limitations of phenotypic technique. Analysis of 16S rRNA gene, assumption
of phylogenetic tree relationship by 16S rRNA sequencing and DNA based typing
methods has been demonstrated as more reliable technique (Prakash O, 2007). The
advantages of genotypic and phylogenetic system are rapid detection and
classification of various microorganisms including slow growing and fastidious
bacteria. Although these techniques can directly detection microorganism in clinical
specimen, the price is more expensive than conventional methods. Therefore, the
conventional techniques remain the most common use for identification of bacteria
in clinical microbiology laboratories.
Fault diagnosis of routine process is actually one problem in clinical
microbiology laboratories. It depends on several factors such as variety of
organisms, similarity of several species, identification techniques as well as numbers
of tests using for identification. However, skill and experience of microbiologist also
plays a major role in this manner. Hence, the present proposal is designed to develop
a new computerized program which can assist for bacterial identification as well as
guiding of effective and accurate additional tests for accurate identification.
Objective
To develop a new computerized program that assisted microbiologist or user on
bacterial identification process.
Materials and methods
Bacterial database
Three genus of gram positive cocci including 46 Staphylococcus species, 32
Enterococcus species and 47 Streptococcus species and a single family of gram
negative bacteria, Enterobacteriaceae, containing 136 species were used in this
study. The Algorithm of phenotypic characteristics that used to identification of all
strains in the collection has been created and documented. Database of tested
organisms were applied from Manual of Clinical Microbiology Book (Murray, P.R.,
th
Baron, E. J., Jorgensen, J.J., Pfaller, M.A., and Yolken, R.H.), 8 ed. ASM Press:
Washington, DC, 2003. The data of bacterial characters included Gram stain, cell
morphology, cell arrangement, pigmentation and two basic tests, catalase and
oxidase tests, were used as screening characters prior to further biochemical tests. In
gram positive cocci group, another 24 biochemical tests were added for confirmative
species identification while 47 additional testes were used in gram negative
identification.
Operating application
The computer program was created using Microsoft Excel program.
Theory
Bayes’theorem was used in this study for calculate probabilities of gram negative
bacilli by
Where: P(ti|R) is the probability that an unknown isolate, giving a pattern of test
results R, is a member of taxon ti and P(R|ti) is the probability that the unknown has
a pattern R given that it is a member of taxon ti. Bayes theorem incorporates prior
probabilities; these are the expected prevalence of strains included in the
identification matrix.
The principle to find the recombination biochemical test set
In process to identification of gram negative bacilli was required additional
test to separate the possibility organism. For this program, the additional
biochemical test has been set at 2 and 3 biochemical tests. The program has been set
to find pattern of combination set of 2-3 biochemical tests from database (Table 1
and Table 2).
Pattern
1
Biochemical 1
Biochemical 2
+
+
2
+
-
3
-
+
4
-
-
Table 1 Combination set of 2 Biochemical tests. Possibility result of 2 Biochemical tests, which can
separate the 4 organisms is 2² = 4 pattern.
Pattern
Biochemical 1
Biochemical 2
Biochemical 3
1
+
+
+
2
+
+
-
3
+
-
+
4
+
-
-
5
-
+
+
6
-
+
-
7
-
-
+
8
-
-
-
Table 2 Combination set of 3 Biochemical tests. Possibility result of 3 Biochemical tests, which can
separate the 4 organisms is 23 = 8 pattern.
Results
This novel computerized program was designed to support the bacterial
identification scheme and to suggest for further tests that could be used for definite
identification. The program consisted of four parts. The first part worked as
screening tests. This part contained basic bacterial characters including Gram
reaction, bacterial cell morphology and arrangement, pigmentation, catalase and
oxidase tests. By entering each individual character of the bacteria, the program had
calculated and showed the possible organisms in real time manner. (Figure 1)
After entering the first part, the program was then moved into the second
part. Two pages were included in this part. However, only one page was showed
depending on the gram stain result. For easier understanding, the term 2.1 and 2.2
parts were used for Gram positive cocci and Gram negative bacilli groups,
respectively. The 2.1 part contained 24 biochemical tests. It also worked as same as
the first part which calculated the result immediately after each biochemical result
was entered. The final identification result of gram positive was demonstrated in this
part. (Figure 2). The 2.2 part, however, worked differently. This part contained only
9 biochemical tests including Triple Iron Sugar (TSI) test, H2S production, motility,
indole, ornithine decarboxylase, lysine decarboxylase, phenylalanine deaminase,
urease and citrate utility tests. These biochemical tests were generally used as basic
routine tests for identification of Enterobacteriaceae members. After entering the
result of these biochemical tests, the program then calculated and presented the list
of possible organisms with percentage of possibility. The acceptable result was set at
85% possibility. Any organism which showed the possibility percentage over 85%,
no further test was required. However, if the percentages of possibility showed
lower than 85%, the program then passed into part 3 of the program (Figure3).
In part 3, the program was set to select all biochemical tests of 4
possibility organisms listed in part 2.2 and searched for combinations of 2-3
biochemical tests which showed the most distinguishable characters and listed those
as suggestive tests. After choosing a suitable set and entering the result, the program
then re-calculated the results based on those 4 listed organisms from part 2.2.
(Figure 4 and 5).
Figure 1 The program run on Microsoft office. After you click to run the program, you will see this
window on your screen 1A.In part 1 has 8 characters (blue box in left side) and drop down results of
each character (1A-1B). After select tests, the program will immediately filter and show possibility
organism (1C). If you select more results, the program will filter the result of that characters (1D).
Figure 2 In part 2.1 is part 2 of Gram positive (2A).Part 2.1 has list of relevant biochemical tests and
list of possibility organisms from part 1 (2B).Biochemical test results can choose from drop down
reside each biochemical test (2C). The program will filter only the results of that character, if you
enter more results (2D).
Figure 3 In part 2.2 is part 2 of Gram negative. Part 2.2 has list of common used biochemical tests
for Gram negative (3A). Biochemical test results can choose from drop down reside each biochemical
test (3B). (3C)The program present possibility organism after calculated that percentage of possibility
over 85%. (3D)The program present possibility organism after calculated that percentage of
possibility lower 85%.
Figure 4 (4A) Part 3 is part of recommend biochemical tests set for additional tests in Gram negative.
(4B) The program arrangement and show top of separated percent of biochemical test sets in set of 2
(red box) and set of 3(yellow box).(4C)User can individual choose set of biochemical test in custom
box.(4D)The program will show “Not found” , if you choose but the set is not match with
recommend biochemical test list.
Figure 5 Part 4 has biochemical tests that you customize choose from part 3 and possibility organisms (5A-5B).
Biochemical test results can choose from drop down reside each biochemical test (5C). After you choose result
and click “CALCULATE”, the program will calculate the percentage possibility again (5D).
Discussion and conclusion
Our program was designed to calculate the probability for bacterial identification
in microbiology laboratory in Ramathibodi Hospital that was created by published
characteristics database of microorganism from Clinical microbiology textbook (Murray P.R.
et al,2003). This program is a trial version that was produces for identification in the four
major groups of bacteria such as Staphylococcus, Streptococcus, Enterococcus and
Enterobacteriaceae species. However, our program showed that simple to use and can
provide the suggest test to distinct the organism for users. Thus, the program is the important
tool to assist the microbiologist in identification process.
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