A simple mathematical model to determine the ideal ...

Our reference: BJID 304

P-authorquery-v9

AUTHOR QUERY FORM Journal: BJID

Please e-mail or fax your responses and any corrections to: E-mail: [email protected]

Article Number: 304

Fax: +34 932 091 136

Dear Author, Please check your proof carefully and mark all corrections at the appropriate place in the proof (e.g., by using on-screen annotation in the PDF file) or compile them in a separate list. Note: if you opt to annotate the file with software other than Adobe Reader then please also highlight the appropriate place in the PDF file. To ensure fast publication of your paper please return your corrections within 48 hours. For correction or revision of any artwork, please consult http://www.elsevier.com/artworkinstructions. Any queries or remarks that have arisen during the processing of your manuscript are listed below and highlighted by flags in the proof. Click on the ‘Q’ link to go to the location in the proof. Location in article Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13

Query / Remark: click on the Q link to go Please insert your reply or correction at the corresponding line in the proof Please confirm that given names and surnames have been identified correctly. The number of keywords provided exceeds the maximum allowed by this journal. Please delete one keyword. Please check the edit made in the sentence “For community-acquired infections, protocols or consensus....”, and correct if necessary. Please check the edit made in the sentence “Additionally, three to four drugs may be required....”, and correct if necessary. Supplement table is cited in the article. But the corresponding table was not provided. Please check. Please check the edit made in the sentence “The susceptibility to antimicrobials with....”, and correct if necessary. Please check the edit made in the sentence “Tigecycline was not included in this analysis....”, and correct if necessary. Is the word “descalonate” spelled correctly? Please check, and amend if necessary. Please check the edit made in the sentence “Descalonation may be performed as soon....”, and correct if necessary. Please check the edit made in the sentence “Other questions about antibiotic prescription....”, and correct if necessary. Please clarify the meaning of the sentence that begins with “Falagas et al. published an interesting article.”. Please check the edit made in the sentence “Some bias probably may have....”, and correct if necessary. Please supply the name of the city of publication for Ref. 2.

Please check this box or indicate your approval if you have no corrections to make to the PDF file

Page 1 of 2

Location in article

Query / Remark: click on the Q link to go Please insert your reply or correction at the corresponding line in the proof

Thank you for your assistance.

ARTICLE IN PRESS

BJID 304 1–4

b r a z j i n f e c t d i s . 2 0 1 4;x x x(x x):xxx–xxx

The Brazilian Journal of

INFECTIOUS DISEASES www.elsevier.com/locate/bjid

Original article

1

A simple mathematical model to determine the ideal empirical antibiotic therapy for bacteremic patients

2

3

4

Q1

Felipe F. Tuon a,b,∗,1 , Jaime L. Rocha c,1 , Talita M. Leite b,1 , Camila Dias b,1

5

a

6

b

7

c

Division of Infectious Diseases, Universidade Federal do Parana, Curitiba, PR, Brazil Division of Infectious and Parasitic Diseases, Hospital Evangelico de Curitiba, Curitiba, PR, Brazil Division of Microbiology, Frischmann Aisengart/DASA Medicina Diagnóstica, Curitiba, PR, Brazil

8

9

a r t i c l e

i n f o

a b s t r a c t

10 11

Article history:

Background: Local epidemiological data are always helpful when choosing the best antibiotic

12

Received 2 September 2013

regimen, but it is more complex than it seems as it may require the analysis of multiple com-

13

Accepted 6 November 2013

binations. The aim of this study was to demonstrate a simplified mathematical calculation

14

Available online xxx

to determine the most appropriate antibiotic combination in a scenario where monotherapy is doomed to failure.

15 16

Keywords:

Methods: The susceptibility pattern of 11 antibiotics from 216 positive blood cultures from

17

Mathematical model

January 2012 to January 2013 was analyzed based on local policy. The length of hospitaliza-

18

Infection

tion before bacteremia and the unit (ward or intensive care unit) were the analyzed variables.

19

Antibiotic

Bacteremia was classified as early, intermediate or late. The antibiotics were combined

20 Q2

Treatment

according to the combination model presented herein.

21

Bacteremia

Results: A total of 55 possible mathematical associations were found combining 2 by 2, 165 associations with 3 by 3 and 330 combinations with 4 by 4. In the intensive care unit, monotherapy never reached 80% of susceptibility. In the ward, only carbapenems covered more than 90% of early bacteremia. Only three drugs combined reached a susceptibility rate higher than 90% anywhere in the hospital. Several regimens using four drugs combined reached 100% of susceptibility. Conclusions: Association of three drugs is necessary for adequate coverage of empirical treatment of bacteremia in both the intensive care unit and the ward. © 2014 Published by Elsevier Editora Ltda.

Introduction Antibiotic therapy is essential for the proper treatment of 1 23Q3 infections. For community-acquired infections, protocols or consensus guidelines are extremely useful, since the suscep24 tibility profile of bacteria may be quite similar in many areas 22

of the world. Even when there are differences in antimicrobial susceptibility, administration of a large-spectrum antibiotic is sufficient to overcome this problem. In different hospitals, the ideal choice of antibiotics is hampered by extensive variability of bacteria and susceptibility profiles. Thus, the establishment of a consensus by the medical societies, even for regional

∗ Corresponding author at: Division of Infectious and Parasitic Diseases, Hospital Universitário Evangélico de Curitiba, Alameda Augusto Stellfeld 1908, 3◦ . andar – SCIH – Bigorrilho, CEP Number 80730-150, Curitiba, Brazil. E-mail address: fl[email protected] (F.F. Tuon). 1 All authors contributed for this manuscript in all stages, including data collection and manuscript writing. 1413-8670/$ – see front matter © 2014 Published by Elsevier Editora Ltda. http://dx.doi.org/10.1016/j.bjid.2013.11.006

Please cite this article in press as: Tuon FF, et al. A simple mathematical model to determine the ideal empirical antibiotic therapy for bacteremic BJID 304 1–4 patients. Braz J Infect Dis. 2014. http://dx.doi.org/10.1016/j.bjid.2013.11.006

25 26 27 28 29

BJID 304 1–4

2

ARTICLE IN PRESS b r a z j i n f e c t d i s . 2 0 1 4;x x x(x x):xxx–xxx

entities, is rather challenging. Therefore, each hospital must assess its microbiological profile and propose treatment rec32 ommendations and protocols based on local data. 33 The knowledge of the local susceptibility profile can be 34 used for choosing monotherapy. However, selecting the best 35 antibiotic combination is far more complex since it involves 36Q4 dynamic calculations of the combination. Additionally, three 37 to four drugs may be required to achieve an adequate coverage 38 in high resistance scenarios, and this will generate hundreds 39 of possible combinations. 40 The aim of this study was to demonstrate a simplified 41 mathematical calculation to determine the most appropriate 42 antibiotic association, considering dynamic but easily acces43 sible epidemiological variables. 30 31

Materials and methods 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85

A cross-sectional study was carried out at the Hospital Universitário Evangélico de Curitiba, a 660-bed tertiary-care university hospital in Curitiba, Brazil. This hospital is reference for renal transplantion, trauma and burn. All patients aged ≥18 years with a positive blood culture collected from January 2012 to January 2013 were included in the study. Only the first episode per patient was analyzed. Blood cultures yielding coagulase-negative Staphylococcus were considered contaminated and thus excluded. Data were collected from hospital computer system databases. Blood cultures were collected according to the standard protocol used in the hospital and were processed using the BACT/Alert® (BioMerieux, Durham, USA). Bacteria were identified by Vitek 2 (Biomérieux, Marcy-L’Étoile, France). Susceptibility testing was performed using the disk diffusion method according to the CLSI guidelines.2 Molecular confirmation of extended and pan-resistant strains was routinely done. The susceptibility pattern of 11 antibiotics was analyzed. The choice was based on local policy: aminoglycosides (amikacin and gentamycin), semi-synthetic penicillin with beta-lactamases inhibitor (ampicillin/sulbactam), third and fourth generation of cephalosporins (ceftriaxone, ceftazidime and cefepime), fluoroquinolones (ciprofloxacin and levofloxacin), ureidopenicillin with beta-lactamase inhibitor (piperacillin/tazobactam), glycopeptides (vancomycin and teicoplanin), polymyxin, carbapenems (meropenem and imipenem) and tetracyclines (tigecycline). For statistical analysis, aminoglycosides and carbapenems were considered to be resistant if any of the tested antibiotics in the same class was identified as resistant (e.g. if amikacin was susceptible and gentamycin resistant, the aminoglycoside group was considered resistant). Ceftazidime and ceftriaxone were evaluated separately. Tigecycline was included in the analysis once the incidence of KPCproducing Klebsiella pneumoniae was high in this hospital.3 The length of hospitalization before bacteremia and the unit (ward or intensive care unit – ICU) were the variables analyzed. Bacteremia was classified as early (14 days). This classification was created just for clinical purposes in the hospital.

For assessing combinations, the antibiotics were combined 2 by 2, 3 by 3, and 4 by 4 according to the following combination formula:

s

n

=

n! s!(n − s)!

86 87 88

89

where “n” is the number of antibiotics and “s” the number of combined antibiotics. A large table was assembled to evaluate the percentage of antibiotic susceptibility for each bacterium to each group of antibiotic and for each combination of antibiotics (2 by 2, 3 by 3, and 4 by 4) (supplement table). When combinations were Q5 analyzed, the bacteria were considered susceptible for that combination if at least one antibiotic was active. The percentages in the table are the susceptibility rates against all bacteria during the period (100% for total susceptibility and 0% for no susceptibility).

Statistical analysis

90 91 92 93 94 95 96 97 98 99 100

101

Continuous data are expressed as mean ± standard deviation (SD) or median with ranges. Frequencies are expressed as percentages. Dichotomous variables were compared using 2 test and Mann–Whitney test was used for continuous variables. Significance level was set at 0.05. All data were recorded using the software Excel (Microsoft, New York, USA) and the statistical analysis was performed using the software SPSS 16 (SPSS, Chicago, USA). GraphPad Prism 5.0 (GraphPad, La Jolla, USA) was used for graphics.

102 103 104 105 106 107 108 109 110

Results A total of 216 bacteremias were evaluated. Staphylococcus aureus was the most common bacteria found in the study (31.5%), followed by Klebsiella spp. (13.4%). The species distribution is detailed in Table 1. Early bacteremia (44.0%) was more frequent than late (34.7%) and intermediate (21.3%) (p < 0.05). Bacteremia was more frequent in the ward (67.6%) than in the intensive care unit (32.4%) (p < 0.05).

Table 1 – Bacterium species of 216 bacteremia in a hospital during January 2012 to January 2013. Bacteria

n

%

Staphylococcus aureus MSSA Klebsiella spp. Acinetobacter baumannii Staphylococcus aureus MRSA Streptococcus spp. Enterobacter spp. Escherichia coli Serratia spp. Pseudomonas aeruginosa Enterococcus spp. Citrobacter spp. Proteus spp. Haemophilus spp. Providencia spp. Stenotrophomonas maltophila

46 29 24 22 22 16 16 14 11 7 3 3 1 1 1

21.5 13.4 11.0 10.0 10.2 7.4 7.4 6.5 5.1 3.2 1.4 1.4 0.5 0.5 0.5

216

100.0

Total


111 112 113 114 115 116 117

ARTICLE IN PRESS

BJID 304 1–4

3

b r a z j i n f e c t d i s . 2 0 1 4;x x x(x x):xxx–xxx

Table 2 – Susceptibility pattern of each antibiotic for bacteria recovered from 216 bacteremia classified as early (< 5 days), intermediate (5 – 14 days) and late (>14 days). The bacteremia cases were categorized as from the intensive care unit or from the clinical ward. Antibiotic

Ward (n = 146) Early (%)

Aminoglycoside Ampicillin–sulbactam Cefepime Ceftazidime Ceftriaxone Fluoroquinolone Carbapenem Piperacillin–tazobactam Polymyxin Glycopeptide Tigecycline

26.3 75.0 84.2 22.4 75.0 81.6 92.1 80.3 26.3 67.1 84.2

Intermediate (%) 33.3 28.6 44.4 0.0 14.8 48.5 59.2 37.0 37.0 44.4 88.9

Using the combination formula, 55 associations were found combining 2 by 2, 165 associations with 3 by 3, and 330 combi120 nations with 4 by 4. A total of 561 options for treatment were 121 available. 122 The susceptibility to antimicrobials with respect to the Q6 123 unit of admission and length of hospitalization is detailed in 124 Table 2. In the ICU, no antibiotic reached 80% of susceptibil125 ity, with the exception of tigecycline (87%). This drug is not 126 indicated for severely ill patients, and we excluded it from the 127 analysis. Based on these data, one antibiotic is not enough as 128 empirical treatment for bacteremia in the ICU setting. In the 129 ward, early bacteremia showed better susceptibility pattern, 130 with four antibiotics (cefepime, piperacillin/tazobactam, fluo131 roquinolone, and tigecycline) having more than 80–90%, and 132 one (carbapenems) having 92% coverage. However, suscepti133 bility rate was lower than 60% for all antibiotics (excluding 134 tigecycline) in case of intermediate and late bacteremia. 135 Detailed rates of susceptibility for combined therapy are 136 available as supplement material or at the site INFECTOPE137 DIA (http://infectopedia.com/dados-estatisticos/category/6138 dados-estatisticos). In the table there are redundant antibi139 otic combinations such as cefepime with ceftriaxone. These 140 combinations were included in the table just for statistical 141 analysis, but must not be considered as clinical options for 142 therapy. 143 In the ward, monotherapy for intermediate and late 144 bacteremia had low coverage (up to 59% and up to 56%, 145 respectively). Combination of two antibiotics reached a maxi146 mum of 85% susceptibility with carbapenem plus glycopeptide 147 for intermediate bacteremia, and 83% for late bacteremia 148 with carbapenem plus polymyxin. Only three drugs combined 149 reached a coverage higher than 90%, using polymyxin plus 150 glycopeptide associated with one of three options (cefepime, 151 fluorquinolone or aminoglycoside). 152 For intermediate bacteremia in the ICU, two combined 153 antibiotics reached a susceptibility rate of 89% for glycopep154 tide plus polymyxin, 89% for carbapenem plus polymyxin, and 155 Q7 95% for polymyxin plus fluoroquinolone. Tigecycline was not 156 included in this analysis as it was not indicated for use in bac157 teremic patients. For late bacteremia, the same regimen using 158 carbapenem plus polymyxin reached 91%, which increased 159 to 97% by adding a glycopeptide. Several regimens using four drugs combined reached 100% susceptibility. 118 119

Intensive care unit (n = 70) Late (%)

Early (%)

27.9 34.8 39.6 13.9 23.5 39.5 48.8 41.8 55.8 25.5 81.4

21.10 47.40 52.60 10.50 36.80 63.20 68.40 63.20 42.10 42.10 84.20

Intermediate (%) 26.30 31.60 47.40 15.80 36.80 47.40 47.40 36.80 57.90 31.60 78.90

Late (%) 34.40 50.00 50.00 15.60 31.30 50.00 71.90 56.30 37.50 28.10 93.80

Discussion 160 There are several publications showing the importance of an 161 adequate antibiotic for the treatment of severe infections.4–7 162 Inadequate antibiotic therapy can increase mortality to a vary163 ing extent according to the population studied, severity of 164 infection, clinical underlying conditions, and other epidemio165 logical variables.8 However, other studies have not confirmed 166 these findings, mainly in case of bacteremia in the ICU setting 167 with comorbidities and advanced age.3,9,10 168 However, none of these studies defined the ideal rate of 169 antibiotic coverage, regardless of the patient’s clinical con170 ditions. The question is: what should be the acceptable 171 margin of error in the prescription of antibiotics for bac172 teremic patients? The ideal is 100% adequacy, but this figure 173 can only be reached with at least three drugs, a suggestion 174 not found in guidelines, including the IDSA guidelines for 175 developing an institutional program to enhance antimicrobial 176 stewardship.11 177 One could extensively discuss this issue. Several factors 178 influence the decision of the physician when prescribing the 179 antibiotic therapy, such as disease severity, patient age, comor180 bidities, organ dysfunction, previous colonization/infection, 181 and probable site of infection. In some cases, it might be 182 possible to delay the choice of antibiotics until the final 183 result of cultures. However, in patients with severe sepsis, 184 the decision must be taken in the first hours.12 Labora185 tory tests to identify bacteria and their susceptibility pattern 186 to antibiotics in this window of time are not a reality 187 yet.13 Nonetheless, when properly collected, culture results will allow the clinician to descalonate all drugs to a broad Q8 188 spectrum specific therapy. Descalonation may be performed Q9 189 190 as soon as the result of Gram-positive blood culture is 191 obtained, and it may be performed again after final identifi192 cation. Other questions about antibiotic prescription are: to deter- Q10 193 194 mine if the patient is really infected; to consider candidemia 195 or other fungi; how to descalonate when blood cultures are 196 negative; additional epidemiological variables may improve 197 empirical decisions. 198 This simulation must be validated considering the follow199 ing facts: (1) most cases of “fever” and laboratory alterations


BJID 304 1–4

4

ARTICLE IN PRESS b r a z j i n f e c t d i s . 2 0 1 4;x x x(x x):xxx–xxx 243

(mainly leukocytosis with immature cells) are not infection; 201 (2) the infection is not severe, and we can wait for culture 202 results; (3) the need for and effectiveness of antibiotic ther203 apy in terminal patients; (4) side effects of extremely toxic 204 associations (e.g. vancomycin, polymyxin, aminoglycoside); 205 and (5) resistance induction and microbiote modification with 206 further superinfection or Clostridium difficile colitis. Although 207 combined therapy may be used for a short period, constant 208 vigilance and follow up by an infectious diseases specialist 209 Q11 is mandatory. Falagas et al. published an interesting article 210 where they suppose that 50% of fever is not infection and they 211 determined the necessity to use empirically polymyxin based 212 on epidemiological data.14 213 The current results are the epidemiological panorama of a 214 university hospital in a developing country, which cannot be 215 applied to other health services and hospitals. The combina216 tion of three or four drugs can be used as a policy of antibiotic 217 restriction. Carbapenems may be avoided with a regimen of 218 ciprofloxacin + piperacillin/tazobactam. This can be an option 219 in hospitals that practice antibiotic cycling. 220 Some bias probably may have occurred in this study, once Q12 221 patients with bacteremia in the ward had been previously 222 admitted at the ICU, falsely increasing the rate of resistant 223 in the ward. 224 The current study does not have the intention to con225 vince clinicians to use bizarre and threatening antibiotic 226 combinations, but rather to consider revision of current local 227 guidelines and consider the mathematical formula of combi228 nation to construct an ideal regimen in that context. The same 229 approach can be used for other infections, including urinary 230 tract and respiratory infections, although one should consider 231 cultures obtained from these sites. After this study, our hospi232 tal changed the antibiotic protocol for treating bacteremia, and 233 other studies will be conducted in different sites. An internal 234 validation must be performed followed by an external vali235 dation, if possible. 236 Antibiotic regimens with two, three or even four antibiotics 237 seem so remote from what is currently considered to be good 238 antibiotic prescribing practice that it may be difficult to be 239 applied in clinical practice. 200

Conflict of interest 240 241 242

Felipe F. Tuon received grants from Bayer, Astellas, Merck, Pfizer, Novartis, AztraZeneca and United Medical (Gilead). Jaime L. Rocha received grants from Bayer, Merck, Pfizer, Novartis, Sanofi and AztraZeneca.

references 244

245 1. Tumbarello M, De PG, Trecarichi EM, et al. Clinical outcomes 246 of Pseudomonas aeruginosa pneumonia in intensive care unit 247 patients. Intensive Care Med. 2013;39:682–92. 2. CLSI. Performance standards for antimicrobial susceptibility Q13 248 249 testing. Clinical and Laboratory Standards Institute; 2010. 250 3. Tuon FF, Rocha JL, Toledo P, et al. Risk factors for 251 KPC-producing Klebsiella pneumoniae bacteremia. Braz J Infect 252 Dis. 2012;16:416–9. 253 4. Paul M, Kariv G, Goldberg E, et al. Importance of appropriate 254 empirical antibiotic therapy for methicillin-resistant 255 Staphylococcus aureus bacteraemia. J Antimicrob Chemother. 256 2010;65:2658–65. 257 5. Chen HC, Lin WL, Lin CC, et al. Outcome of inadequate 258 empirical antibiotic therapy in emergency department 259 patients with community-onset bloodstream infections. J 260 Antimicrob Chemother. 2012. 261 6. Piskin N, Aydemir H, Oztoprak N, et al. Inadequate treatment 262 of ventilator-associated and hospital-acquired pneumonia: 263 risk factors and impact on outcomes. BMC Infect Dis. 264 2012;12:268. 265 7. Retamar P, Portillo MM, Lopez-Prieto MD, et al. Impact of 266 inadequate empirical therapy on the mortality of patients 267 with bloodstream infections: a propensity score-based 268 analysis. Antimicrob Agents Chemother. 2012;56:472–8. 269 8. Tuon FF, Rymsza AM, Penteado-Filho SR, Pilonetto M, Arend 270 LN, Levin AS. Should polymyxin be used empirically to treat 271 infections in patients under high risk for 272 carbapenem-resistant Acinetobacter? J Infect. 2011;62:246–9. 273 9. Tuon FF, Bianchet LC, Penteado-Filho SR. Epidemiology of 274 extended spectrum beta-lactamase producing Enterobacter 275 bacteremia in a brazilian hospital. Rev Soc Bras Med Trop. 276 2010;43:452–4. 277 10. Tuon FF, Kruger M, Terreri M, Penteado-Filho SR, Gortz L. 278 Klebsiella ESBL bacteremia-mortality and risk factors. Braz J 279 Infect Dis. 2011;15:594–8. 280 11. Dellit TH, Owens RC, McGowan Jr JE, et al. Infectious Diseases 281 Society of America and the Society for Healthcare 282 Epidemiology of America guidelines for developing an 283 institutional program to enhance antimicrobial stewardship. 284 Clin Infect Dis. 2007;44:159–77. 285 12. Dellinger RP, Levy MM, Rhodes A, et al. Surviving sepsis 286 campaign: international guidelines for management of severe 287 sepsis and septic shock, 2012. Intensive Care Med. 288 2013;39:165–228. 289 13. Motoshima M, Yanagihara K, Morinaga Y, et al. Identification of bacteria directly from positive blood culture samples by 290 DNA pyrosequencing of the 16S rRNA gene. J Med Microbiol. 291 2012;61:1556–62. 292 14. Falagas ME, Rafailidis PI. When to include polymyxins in the 293 empirical antibiotic regimen in critically ill patients with 294 fever? A decision analysis approach. Shock. 2007;27:605–9. 295
