Computer Methods and Programs in Biomedicine 23 (1986) 217-223 217 Elsevier

CPB 00804

A microcomputer-assisted analysis of drug resistance in bacteria

Jens K. Moiler

Institute of Medical Microbiology, Bartholin Building, University of Aarhus and Department of Clinical Bacteriology. Statens Seruminstitut, Aarhus Municipal Hospital, Aarhus, Denmark

A software package for analysis of antimicrobial drug resistance traits has been developed. It is written in PASCAL and implemented on a microcomputer. The microbiological data to be analysed is reduced to the different patterns of drug resistance found and the associated numbers of isolates. Based upon these patterns of drug resistance, the incidence of resistance to individual drugs and combinations is calculated. Furthermore, the extent and nature of the multiple drug resistance within a group of microorganisms is examined by looking at various aspects of the statistical association of drug resistance traits.

Clinical microbiology; Drug resistance; Microcomputer; PASCAL

1. Introduction

The registration of laboratory data in clinical mi- crobiology by means of computers enables the laboratory to survey nosocomial infections auto- matically [1,2]. Furthermore, it also makes availa- ble analyses of drug resistance in bacteria [3,4,5]. Drug resistance is taken as a measure for the ecological reaction of the bacteria against the im- pact of the antibacterial usage. Analyses of the occurrence of drug resistance and the consump- tion of antibiotics within the same environment (e.g. hospital) therefore constitute the rational ba- sis for a local antibiotic policy [6].

Analyses of drug resistance in bacteria should not be confined to the calculation of the per- centage of strains resistant to various drugs but also involve the examination of coexistence of drug resistance traits in the same bacterium. This

Correspondence: J.K. Moiler, Institute of Medical Microbi- ology, Bartholin Building, University of Aarhus, 8000 Aarhus. Denmark.

0169-2607/86/$03.50 © 1986 Elsevier Science Publishers B.V.

can be done by the registration of the different patterns of drug resistance observed. By analyses of the statistical association of drug resistance traits it may be possible to elucidate the genetic l inkage of the traits.

This paper reports a software package devel- oped for the analysis of drug resistance traits in a clinical material of bacteria and integrated in a microcomputer-assisted bacteriology reporting and information system [7].

2. Methods and comments

2.1. Collection o f data

The prerequisite for using the software package is a data-base consisting entirely of patterns of drug resistance and for each pattern the associated number of isolates. The patterns of drug resistance should be composed of the following 3 alterna- tives only: 0 (resistant to the drug), 1 (sensitive to the drug), and 9 (drug not tested). The method of

Biomedical Division)

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susceptibility testing employed is, in this context, insignificant as long as the results can be trans- formed to one of the alternatives above. In agar- diffusion tests the 'intermediate sensitive' thus have to be pooled with either the 'resistant' or the ' sensitive'.

To illustrate the software package, the patterns of drug resistance in a clinical material of bacteria isolated in the county of Aarhus were examined. The data used were kept in the main data-base in the microcomputer-assisted information system previously reported [7]. A secondary data-base consisting exclusively of the data on bacteriologi- cal specimens of interest, was created from this main data-base. Besides all the laboratory data, the new data-base contains all the data necessary to identify the patient, hospital ward and type of specimen. For the specific analyses of drug resis- tance traits, a tertiary data-base was created, which consists entirely of the different patterns of drug resistance observed and the corresponding num- bers of isolates. The programs used to create the new data-bases may employ any type of informa- tion recorded in the previous data-base to delimit a new set of data.

This stepwise reduction of the data saves stor- age space and makes repeated analyses on the selected data possible without a time-consuming run through the main data-base each time.

2.2. Analyses of drug resistance

Using a menu program, a series of programs for analyses of the drug resistance can be run to print

TABLE 1

Percentage of drug resistance (in E. coli)

out tables of interest. The number of drug resis- tance traits to be included may also be pro- grammed via the menu program.

2.2.1. Resistance to individual drugs and combina- tions The program RESTAB1 calculates the percentages of strains sensitive or resistant to the drugs ex- amined and the results are shown in Table 1. The program RESTAB2 ranks the patterns of drug resistance found according to their frequency of isolation. The patterns are shown in Table 2 to- gether with the number of strains observed and the corresponding percentages. Furthermore, the number of strains expected based upon a random distribution of the resistance to the individual drugs is also shown. There is no reason to believe that the formation of patterns of drug resistance in bacteria happens in a random fashion. There- fore, in order to estimate the degree of indepen- dent acquisition of drug resistance traits, an u- value (see Table 2) was calculated for each pattern using the expected and observed number of strains. The results are also included in Table 2.

2.2.2. Association of resistance to pairs of drugs The degree of statistical association of resistance to two or more antibiotics within a group of bacteria may throw light on the genetic linkage of the drug resistance traits. Resistance to two or more drugs in a bacterium could be coded for by separate genes or the same gene, The latter mecha- nism has been observed for related drugs such as

Drug Number of strains Percentage of strains

Resistant Sensitive N.T. Total Resistant Sensitive Tested

SU 972 3028 6 4016 24.2 75.8 99.9 SM 995 3 015 6 4016 24.8 75.2 99.9 AP 1005 3 005 6 4016 25.1 74.9 99.9 TP 373 3 637 6 4016 9.3 90.7 99.9 KM 176 3 834 6 4016 4.4 95.6 99.9 GM 9 4001 6 4016 0.2 99.8 99.9 MN 328 3678 10 4016 8.2 91.8 99.8

N.T. =not tested, SU=sulphonamides, SM=streptomycin, AP= ampicillin, TP= trimethoprim, KM=kanamycin, GM= gentamicin, MN ~ mecillinam.

T A B L E 2

Patterns of drug resistance (in E. coli) a

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Pattern No. o f s t ra ins u b

SU S M A P T P K M G M M N O b s e r v e d % Expected

2 4 9 2 62.1 1362 37.7

A P 230 5.7 455 11.2

SU S M A P 184 4.6 48 19.7

SU S M 175 4.4 144 2.7

SU S M A P T P 98 2.4 5 41.9

S M 97 2.4 449 17.6

SU S M A P M N 85 2.1 4 39.0

SU 73 1.8 436 18.4

SU S M T P 56 1.4 15 10.8

SU S M A P T P M N 54 1.3 0 80.8

a The 10 patterns isolated most frequently. See legend to Table 1 for drug abbreviations. b u = Iobserved -expected I/~/N x P x ( 1 - p ) .

N = n u m b e r o f strains examined, p = expected frequency of pattern, i.e. product of the frequency for each trait (resistance or sensitivity) in the pattern. Expected number of strains = N × p .

the aminoglycosides [8]. Several resistance genes are often present on plasmids but probably more rarely present in the chromosome as a conse- quence of mutations [9]. Therefore, a random dis- tribution of the individual resistance traits within a bacterial species probably points towards a chromosomal localisation of the resistance genes (mutations). In contrast, a non-random associa- tion of resistance traits within a species points towards a drug resistance determined by extrach-

romosomal elements (plasmids). It should be noted, however, that one or more drug resistance traits can be transposed into the chromosome from a plasmid [10]. Cross-resistance (one gene) should be suspected when a very high degree of association of resistance against biochemically re- lated drugs is found.

A calculation of the percentage of strains show- ing co-resistance is made by the program RE- STAB3. For each drug resistance trait, the per-

T A B L E 3

Co-resistance (in E. coli)

Basic drug resistance Percentage of strains showing co-resistance to given number of drugs

D r u g No . of 0 1 2 3 4 5 6 7

strains

Average no. of

co-resistances

SU 972 7.5 23.9 29.2 26.0 11.5 1.5 0.3 - 2.2

S M 995 9.7 23.5 28.8 25.0 11.1 1.5 0.3 - 2.1

A P 1005 22.9 13.6 25.6 25.1 11.0 1.5 0.3 - 1.9

T P 373 4.6 11.5 23.6 34.6 20.9 4.0 0.8 - 2.7

K M 176 0.0 5.7 14.8 36.4 33.0 8.5 1.7 - 3.3

G M 9 0.0 0 .0 22.2 0.0 11.1 33.3 33.3 - 4.6

M N 328 6.4 15.9 11.3 34.5 27.4 3.7 0.9 - 2.8

T ~ 4 0 1 6 62.3 10.9 8.8 8.1 6.6 2.8 0.4 0.1 1.0

a To ta l n u m b e r o f strains examined (regardless of drug resistance). See legend to Table 1 for abbreviations.

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Drug ^

Drug B

Resistant £ e n s i t i v e

R e s i s t a n t a b

S e n s i t i v e c d

Fig. 1. The four-fold table.

centage of strains showing co-resistance to one or more of the other drugs is calculated. The results are presented in Table 3.

The four-fold table in Fig. 1 represents all the possible results of a sensitivity testing of a pair of drugs. Cell a in the table includes the strains resistant to both drugs. The extent of the associa- tion between two drug resistance traits can be expressed in various ways: (1) as the fraction of strains examined for resis-

tance to two drugs which is resistant to both ( a / ( a + b + c + d)).

(2) as the ratio of the rates of incidence or the index of association for a four-fold table (Fig. 1) where the proportion of bacteria in each of the four cells is known. The one ratio of incidence is that of being resistant to drug B when resistant to drug A ( a / ( a + b)). The second is that of being resistant to drug B when sensitive (not resistant) to drug A ( c / ( c + d)). If the resistance to drug A and B is randomly distributed among the bacteria, the rates of incidence should equal each other and

TABLE 4

Percentage of strains resistant to pairs of antimicrobial drugs (in E. coli)

SU SM AP TP KM GM

SM 19.7 AP 15.4 15.6 TP 8.0 7.1 6.2 KM 3.5 3.9 3.8 1.5 GM a 0.2 0.2 0.1 0.1 MN 5.6 5.6 7.4 2.3

0.2 1.6 0.1

See legend to Table 1 for abbreviations. a Number of strains small ( < 10).

consequently, the index of association should be equal to one.

(3) As the fraction of strains resistant to one of the drugs which is also resistant to the other drug. Thus, for each pair of antimicrobial drugs (A and B), two fractions or percentages of drug resistance are calculated. One is the fraction of strains resistant to drug A which is in addition resistant to drug B ( a / ( a + b)). The second is the fraction of strains resistant to drug B which is in addition resistant to drug A ( a / ( a + c)).

The incidence of combined resistance to two drugs in a series of bacterial strains is revealed by (1), whereas the nature of the association of the resistance to the two drugs is analysed by (2) and (3). The calculation of (2) gives an estimate of the deviation from the hypotheses that two resistance

TABLE 5

Index of association for resistance to pairs of antimicrobial drugs (in E. coli)

Drug Drug B A

SU SM AP TP KM GM MN

SU 13.1 5.2 4.8 3.7 3.7 3.4 SM 12.0 5.1 3.9 4.1 4.1 3.3 AP 4.9 5.0 3.2 3.9 2.7 4.7 TP 19.3 9.7 6.0 4.1 7.3 3.7 KM 13.1 23.6 18.1 4.9 21.2 6.3 GM a 25.0 0.0 6.0 19.5 174.3 9.0 MN 6.7 6.5 27.6 3.8 5.2 5.5

See legend to Table 1 for abbreviations. a Number of strains small ( < 10).

TABLE 6

Percentage of strains resistant to drug B when resistant to drug A (in E. coli)

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Drug Drug B A SU SM AP TP KM GM MN

SU 81.2 63.4 33.0 14.6 0.8 23.0 SM 79.3 62.9 28.5 15.7 0.9 22.5 AP 61.3 62.3 24.8 15.0 0.6 29.5 TP 86.1 76.1 66.8 15.8 1.6 24.7 KM 80.7 88.6 85.8 33.5 4.5 35.8 GM a 88.9 100.0 66.7 66.7 88.9 44.4 MN 68.3 68.3 90.2 28.0 19.2 1.2

See legend to Table 1 for abbreviations. a Number of strains small.

traits are randomly distributed only. An index of association of greater than one implies an associa- tion of the resistance traits and vice versa. The association of two resistance traits might be one- sided due to an uneven distribution of resistance genes. One resistance gene might often be linked to the other resistance gene (on plasmids) whereas the other resistance gene probably most often will be found alone (chromosome/p lasmid) or at least in combination with other resistance genes on plasmids. This one-way or asymmetric association of drug resistance can be revealed by (2) and (3).

The analyses (1), (2), and (3) are made by RESTAB4, RESTAB5, and RESTAB6, respec- tively, and the results are shown in Tables 4, 5, and 6, respectively.

3. Example

The results of the susceptibility testing of Escherichia coli strains isolated in specimens of urine from hospital patients in 1985 in a labora- tory of clinical microbiology in the county of Aarhus have been used to illustrate the software package. An agar-diffusion test was used and the ' intermediate sensitive' pooled with the 'sensitive' ones. Identical isolates from the same patient were only counted once.

Table 1 shows the percentage of resistance to 7 antimicrobial drugs in E. coli. The 7 drugs be- longed to 3 different groups of antimicrobial

agents: synthetic inhibitors (sulphonamide and tri- methoprim), beta-lactam antibiotics (ampiciUin and mecillinam) and aminoglycosides (streptomy- cin, kanamycin and gentamicin). Within all 3 groups low and high percentages of drug resis- tance were found (Table 1). The 10 most frequent patterns of drug resistance are shown in Table 2. Together they comprise almost ninety percent of the E. coli strains tested. About two thirds of the strains are sensitive to all seven drugs tested. The patterns shown indicate that resistance to sulph- onamides and streptomycin often coincide with each other. All the u-values clearly support the conception that patterns of drug resistance are not formed at random. Table 3 shows the number of co-resistances associated with strains resistant to each one of the seven drugs tested. The average number of co-resistances for strains resistant to kanamycin or gentamicin is almost twice the fig- ure for streptomycin-resistant strains. It indicates that the use of kanamycin or gentamicin probably constitutes a greater risk for selection of multiple drug resistance in E. coli than streptomycin. Ta- bles 4, 5 and 6 give the association of resistance to pairs of antimicrobial drugs. In Table 4, the per- centage of combined resistance to sulphonamides and streptomycin among all strains examined was 19.7%. This is in accordance with the patterns shown in Table 2. The index of association for pairs of anitmicrobial drugs are shown in Table 5. Although the incidence of combined resistance to trimethoprim and sulphonamides is much lower

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than for streptomycin and sulphonamides, the statistical association of resistance between tri- methoprim and sulphonamides was found to be much stronger than between streptomycin and sulphonamides (indices of 19.3 and 12.0, respec- tively). Table 5 also shows that for many of the pairs of drugs, the association of resistance traits is asymmetric. This is further revealed by Table 6. As an example, 86% of the trimethoprim-resistant E. coli strains are also resistant to sulphonamides, whereas only about 33% of the sulphonamide-re- sistant E. coil strains exhibits resistance to tri- methoprim.

4. Discussion

The example above demonstrates that interesting aspects of the association of drug resistance are revealed by using the software package. However, the significance of a computer-assisted analysis lies in the easiness by which data from different periods of time can be compared when first com- piled by a computer-assisted reporting and infor- mation system. This makes a continuous surveil- lance of drug resistance possible but, even more important, the potential changes in the frequencies of drug resistance may be related to changes in environmental factors such as the usage of antimi- crobial drugs. Furthermore, the analysis of the association of drug resistance traits may even re- veal the prevalence of certain mechanisms of drug resistance. For instance, the asymmetric associa- tion of resistance to certain pairs of drugs suggests that more than one mechanism of resistance is involved for at least one of the drugs in each pair. Therefore, changes over time in the association of resistance to pairs of drugs indicate changes in the prevalence of the mechanisms responsible for the resistance against one or maybe both of the drugs. Such changes in prevalence of resistance mecha- nisms has in fact been seen, for example in the county of Aarhus, where the combined resistance to trimethoprim and sulphonamides in E. coli has tripled from 1977 to 1985, although the percentage of sulphonamide-resistant strains among all iso- lates has gone down at the same time (unpub- lished observations). Thus, if supplemented occa-

sionally by studies of the molecular nature of the combined drug resistance traits in the bacteria examined - e.g. by plasmid profiles [11,12] or analysis of drug inactivating enzymes [8] - the computer analysis above could also prove to be of value in the surveillance of specific drug resistance mechanisms.

5. Hardware and software

A multi-user microcomputer (SPC/1 (8 bits) from Danish Data Electronics, Herlev, Denmark) with 32K bytes of RAM memory for each user, two 280K byte floppy disc drives, and one 1M byte floppy disc drive was used [7]. MIKADOS was used as operating system and PASCAL as pro- gramming language.

6. Mode of availability

A listing of the PASCAL programs for the analy- sis of drug resistance will be supplied on request.

Acknowledgements

Head of the department, Dr. P. Biilow, Professor A. Stenderup, and lic. scient. Michael Vaeth, De- partment of Theoretical Statistics, are thanked for helpful discussions. This work was supported by grant No. 512-16174 from the Danish Medical Research Council.

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