Comparative effectiveness of antibiotics for uncomplicated urinary tract infections: Network meta-analysis of randomized trials

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1 Family Practice 2012; 29: doi: /fampra/cms029 Advance Access publication 19 April 2012 The Author Published by Oxford University Press. All rights reserved. For permissions, please Comparative effectiveness of antibiotics for uncomplicated urinary tract infections: Network meta-analysis of randomized trials Bart J Knottnerus a, *, Larissa Grigoryan b,c, Suzanne E Geerlings d, Eric P Moll van Charante a, Theo J M Verheij b, Alphons G H Kessels e and Gerben ter Riet a a Department of General Practice, Academic Medical Center, University of Amsterdam, b Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands, c Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX, USA, d Department of Infectious Diseases, Tropical Medicine & AIDS, Center for Infection and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam and e Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, The Netherlands. *Correspondence to Bart J Knottnerus, Department of General Practice, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, The Netherlands; b.j.knottnerus@amc.uva.nl Received 26 September 2011; Revised 2 March 2012; Accepted 12 March Background. The efficacies and adverse effects of different antibiotics for uncomplicated urinary tract infections (UTIs) have been studied by standard meta-analytic methods using pairwise direct comparisons of antimicrobial treatments: the effects of one treatment are compared to those of either another treatment or placebo. However, for clinical decisions, we need to know the effectiveness of each possible treatment in comparison with all relevant alternatives, not with just one. Objectives. To compare the efficacies and adverse effects of all relevant antibiotics for UTI treatment simultaneously by performing a network meta-analysis using direct and indirect treatment comparisons. Methods. Using logistic regression analysis, we performed a network meta-analysis of randomized controlled trials (RCTs) published after 1999 that compared different oral antibiotic or placebo regimens for UTI treatment in general practice or outpatient settings. We looked at five binary outcomes: early clinical, early bacteriological, late clinical and late bacteriological outcomes, as well as adverse effects. Consequently, a ranking of the antibiotic regimens could be composed. Results. Using a network structure, we could compare and rank nine treatments from 10 studies. Overall, ciprofloxacin and gatifloxacin appeared the most effective treatments, and amoxicillin clavulanate appeared the least effective treatment. In terms of adverse effects, there were no significant differences. Discussion. Network meta-analysis shows some clear efficacy differences between different antibiotic treatments for UTI in women. It provides a useful tool for clinical decision making in everyday practice. Moreover, the method can be used for meta-analyses of RCTs across primary care and beyond. Keywords. Anti-infective agents, meta-analysis, primary health care, urinary tract infections. Introduction Acute uncomplicated urinary tract infections (UTIs) are defined as infections of the lower urinary tract in otherwise healthy, non-pregnant adult women without known anatomical or functional abnormalities of the urinary tract. They comprise one of the most common types of bacterial infection. 1 Several antibiotics are used for the treatment of UTIs, of which trimethoprim sulfamethoxazole (TMP/SMX), fluoroquinolones, b-lactam antibiotics, nitrofurantoin and fosfomycin trometamol are the most common ones. 2 5 As bacterial resistance to frequently used antibiotics (especially TMP/SMX, fluoroquinolones and b-lactam antibiotics) is rising, there is an increasing need for evidence-based prescribing. 6,7 Standard meta-analytic methods have been used to make direct head-to-head comparisons between UTI treatments: the effects of one treatment are compared to those of either another treatment or placebo. 5,8 659

2 660 Family Practice The International Journal for Research in Primary Care However, for clinical decisions, we need to know the effectiveness of each possible treatment in comparison with all relevant alternatives, not with just one. As a result, physicians are forced to make the needed multiple comparisons in an informal way without explicit comparative evidence. This problem might be solved by comparing all relevant treatments simultaneously in one network metaanalysis, using both direct and indirect comparisons. Suppose we are interested in the treatment comparison A versus B. Direct evidence is lacking, but both A and B are compared with a common comparator C in different randomized controlled trials (RCTs). The evidence from these comparisons (A versus C and B versus C) allows us to make the comparison A versus B indirectly. 9,10 By expanding this concept to more treatments (D, E, F, etc.), it may be possible to compose a network structure in which all treatments can be compared to each other directly or indirectly. Although several possible approaches of network meta-analysis have been described, it is not yet widely used in primary health care research. This is surprising since it has some major advantages over traditional head-to-head meta-analysis. Apart from being a powerful tool to integrate all relevant treatments in one clinically applicable ranking, it serves as an upto-date addition to clinical guidelines and reveals future research needs. The objective of this study was to compare the relative efficacies and adverse effects of all relevant antibiotics for UTI treatment simultaneously by performing a network meta-analysis using direct and indirect treatment comparisons. Consequently, a ranking of the antibiotic regimens could be composed, in order to assist clinicians in selecting the best UTI treatment. Methods Outcomes Five binary outcomes were considered: short-term clinical, short-term bacteriological, long-term clinical and long-term bacteriological cure, as well as adverse effects. Eligibility criteria We included RCTs that were performed in general practice or outpatient settings and compared different oral antibiotic or placebo regimens for UTI symptoms (with or without bacteriuria) in female patients aged 12 years or older. We excluded studies that investigated complicated UTIs: men, children (<12 years), pregnant or breastfeeding women, signs of pyelonephritis, immunodeficiency (e.g. immunosuppressants, diabetes mellitus) and studies that included patients who had used antibiotics in the past 2 weeks, had undergone a urologic procedure in the past 2 weeks or had known anatomical or functional abnormalities of the urogenital tract. Since differences in bacterial resistance over time may influence the comparisons between older and newer studies, we excluded studies that were published before the year We made an exception for studies in which nitrofurantoin, fosfomycin and pivmecillinam were compared to each other as resistance against these antibiotics has not increased since their introduction Search strategy We developed a search strategy that covered the main subject areas of the review (UTI, antibiotics and RCT) and searched the following databases: MED- LINE, EMBASE, CINAHL, The Cochrane Central Register of Controlled Trials (CENTRAL), Controlled-trials.com and World Health Organization (WHO) International Clinical Trials Registry Platform Search Portal ( ). The search strategy is available on request. We checked the bibliographies of all retrieved articles and existing systematic reviews. Study selection One reviewer (BJK) assessed the titles and abstracts of all identified citations. The excluded citations were independently assessed by a second reviewer (LG) and reinstated in case of disagreement. Both reviewers independently evaluated the full text of the included articles. Quality assessment Two reviewers (BJK and LG) independently assessed the quality of included studies. In accordance with existing quality checklists, we assessed the method of randomization, concealment of treatment allocation, blinding, completeness of outcome data, baseline comparability and adherence to study medication. Discordant scores were resolved through consensus. Network construction For each of the five outcomes, we connected the treatment arms from the RCTs using their direct comparisons, yielding a network structure. Network metaanalysis makes it possible to compare all treatments within such a network structure with each other. To facilitate the formation of a network, we combined different regimens of the same antibiotic to become a single treatment arm in the network structure. Data extraction For each trial arm, one reviewer (BJK) extracted the number of patients for the five outcomes in contingency tables. Data were extracted for two different subpopulations: (i) all patients irrespective of the baseline culture (according to daily practice) and (ii) patients with a positive urine culture at baseline (to

3 Comparative effectiveness of different antibiotics for UTI 661 determine the antibiotic effect in bacteriologically proven UTI). As cut-off criteria for defining positive baseline and outcome cultures, we used the same cutoff criteria as the authors of the original studies. Six randomly selected studies were independently data extracted by a second reviewer (SEG or GtR). Discordant scores were resolved through consensus. Data synthesis and analysis Since our five outcomes were all binary, we used logistic regression for the analyses. In order to perform logistic regression, we used the previously made contingency tables to make a new data set, which contained individual patient data entries. See Appendix 1 for an example. Having created a data set with individual patient data entries for each of the five outcomes, we performed a logistic regression analysis on each data set. To preserve randomization within each trial, we included a dummy variable for each study. In each analysis, the outcome measure was the dependent variable and the study dummies and treatment dummies were the independent variables. Using the regression coefficients of the treatment dummies, we calculated the odds ratios of the available treatment contrasts for each outcome. Heterogeneity across studies was assessed by comparing the regression coefficients of the study dummies. To get an impression of the effect of withdrawals, we performed two types of analysis: one in which patients lost to follow-up were considered cures and one in which they were considered failures. Analyses were performed in Stata/SE, version Results Identification of studies Figure 1 summarizes the process of identifying eligible studies. In total, 12 of 153 studies were retained after full-text assessment The most frequent reason for exclusion was the inclusion of patients who had used antibiotics in the past 2 weeks. Study characteristics Table 1 shows details about regions, populations, interventions and outcomes for the 12 included studies. The most commonly used cut-off values for defining a positive baseline and outcome culture were 10 3 and 10 5 CFU/ml of a uropathogen. As time point for shortand long-term cure most studies used 1 week and 1 month after treatment, respectively. Table 2 shows the cure rates reported by the original head-to-head comparisons. Only four studies reported cure results for the subpopulation of all symptomatic patients (irrespective of baseline culture). 22,24,29,32 For the subpopulation of patients with a positive baseline culture, all studies reported adverse effects and short-term bacteriological cure. Adverse effects were reported as the percentage of patients experiencing adverse effects probably related to study medication, with the exception of Nicolle et al. 29 who reported adverse effects as the percentage of events being considered as probably related to study medication. Quality of reporting The average number of adequately reported aspects of study quality was 6.5 of 11 (Table 3). Blinding and completeness of outcome data were more often and more completely reported than the randomization procedure and concealment of treatment allocation. Treatment network Since only four studies reported cure results for the subpopulation of all symptomatic patients (irrespective of baseline culture), no connected network could be made for this subpopulation. For the subpopulation of patients with a positive baseline culture, we composed a network consisting of nine treatment arms from 10 studies (Fig. 2). Six of these studies did not report on all five outcomes but could still be used to make networks for the outcomes they did report on. 24,26,27,29,30,32 Only for short-term bacteriological outcome, all nine treatment arms could be connected. Two studies could not be used for network construction (Fig. 1): one study contained no treatment arms that could be connected to the network 23 and the other study did not report patient numbers but only percentages that could not be converted into numbers, which would have been needed to create contingency tables and, consequently, individual patient data entries. 22 Relative efficacies Figure 3 and Appendix 2 show the relative efficacies of the treatments in the network for the five outcomes. Ciprofloxacin was used as reference category since it was present in the network for all five outcomes. For short-term clinical cure, amoxicillin clavulanate was significantly less effective than all other treatments. For short-term bacteriological cure, TMP/SMX, nitrofurantoin, amoxicillin clavulanate and fosfomycin were significantly less effective than ciprofloxacin and gatifloxacin. For long-term clinical cure, amoxicillin clavulanate was significantly less effective than ciprofloxacin and gatifloxacin. For long-term bacteriological cure and adverse effects, there were no significant differences between the treatments. Thus, overall, ciprofloxacin and gatifloxacin appeared the most effective treatments and amoxicillin clavulanate appeared the least effective treatment.

4 662 Family Practice The International Journal for Research in Primary Care Title and abstract screening n= excluded main reasons: - publication before the year no RCT Full text assessment n=32 Fosfomycin was only present in the network for shortterm bacteriological cure, where it appeared less effective than the other treatments. Considering patients lost to follow-up either as cures or as failures did not result in important changes in the main results. Comparison of the regression coefficients of the study dummies suggested some heterogeneity across studies, which might have been investigated by making forest plots of all direct comparisons for each outcome and consequently compare them with the indirect comparisons. However, there were too few direct comparisons to make these forest plots. This scarcity of data was also the reason that we could not explore potential interactions between study-specific characteristics and the treatment coefficients, which would have been another way to explore potential sources of heterogeneity. 33 Discussion Data extraction n=12 Network meta-analysis n=10 We present a ranking of antibiotic regimens for UTI by comparing their relative efficacies using a network meta-analysis. Our results show some clear efficacy differences between different antibiotic treatments for UTI in women with a positive baseline culture. 20 excluded n=7: use of antibiotics < 2 weeks prior to inclusion n=4: no RCT n=2: males included and no sex-stratified results n=7: other reasons 2 excluded n=1: no connection with network n=1: creation of contingency table not possible FIGURE 1 Flow chart from the identification of studies to final inclusion. The figure summarizes the process of identifying eligible studies. In total, 12 of 153 studies were retained after full-text assessment. The most frequent reason for exclusion was the inclusion of patients who had used antibiotics in the past 2 weeks. Two eligible studies could not be used for network construction: one study contained no treatment arms that could be connected to the network and the other study did not report patient numbers but only percentages that could not be converted into numbers. Thus, 10 studies were used for network meta-analysis Ciprofloxacin and gatifloxacin appear the most effective treatments. However, since quinolones have increasing resistance rates and are associated with ecological adverse effects (collateral damage), 34 we do not recommend them to be used in primary care. The low efficacy of amoxicillin clavulanate in comparison with other antibiotics is remarkable. Hooton et al. 27 have already shown its inferiority to 3 days of ciprofloxacin, but after including it in our network, it also appears to be less effective than many other treatments. This is supported by previously described low eradication rates of 3-day b-lactam regimens 4 (with the exception of pivmecillinam 35 ). Our method provides a transparent approach to unifying evidence from different randomized trials while preserving randomization (which is essential in any meta-analysis 12 ). By presenting a ranking of treatments, clinicians may more easily choose the best therapy. We introduced a new approach for pooling information from different studies or treatment arms. Traditional meta-analyses usually use a two-stage approach, in which first each study is analysed separately to account for within-study variance and next the effect parameters from different studies are pooled, using either fixed-effects methods or random-effects methods to account for between-study variance. 36 Instead, we

5 Comparative effectiveness of different antibiotics for UTI 663 TABLE 1 Study characteristics First author (year) Region Population Treatment Duration Relevant outcomes Arredondo-García (2004) Latin America n = 285 Group 1: ciprofloxacin 250 mg bid 3 days Short-term: clinical and bacteriological cure 5 9 days after treatment Age >18 years Group 2: trimethoprim/sulfamethoxazole 160/800 mg 7 days Long-term: clinical and bacteriological cure 4 6 weeks after treatment Pre-menopausal Group 3: norfloxacin 400 mg bid 7 days Adverse effects during total study period Bjerrum (2009) Denmark n = 175 Group 1: pivmecillinam 400 mg tid 3 days Short-term: clinical and bacteriological cure after 7 10 days Age years Group 2: sulfamethizole 1 g bid 3 days Long-term: clinical relapse within 6 months after treatment Positive microscopy Adverse effects during total study period Cervigni (2003) Italy n = 231 Group 1: prulifloxacin 600 mg sd single dose Short-term: clinical and bacteriological cure after 5 7 days All ages Group 2: pefloxacin mg sd single dose Long-term: clinical and bacteriological cure 4 weeks after the end of the treatment Pyuria present Adverse effects during total study period Christiaens (2002) Belgium n = 78 Group 1: nitrofurantoin 100 mg qid 3 days Short-term: clinical and bacteriological cure after 7 days Age years Group 2: placebo qid 3 days Long-term: clinical and bacteriological cure after 14 days Pyuria present Adverse effects during total study period Ferry (2007) Sweden n = 1143 Group 1: pivmecillinam 200 mg tid 7 days Short-term: clinical and bacteriological cure after 8 10 days Age >18 years Group 2: pivmecillinam 200 mg bid 7 days Long-term: clinical and bacteriological cure after days Group 3: pivmecillinam 400 mg bid 3 days Adverse effects during total study period Group 4: placebo tid 7 days Gupta (2007) USA n = 308 Group 1: trimethoprim/sulfamethoxazole 160/800 mg 3 days Short-term: clinical and bacteriological cure 5 9 days after therapy Age years Group 2: nitrofurantoin 100 mg bid 5 days Long-term: clinical cure 30 days after therapy Adverse effects during total study period Hooton (2005) USA n = 322 Group 1: amoxicillin/clavulanate 500/125 mg bid 3 days Short-term: clinical and bacteriological cure 2 weeks after treatment Age years Group 2: ciprofloxacin 250 mg bid 3 days Long-term: clinical cure up to 4 months after treatment Adverse effects during total study period Naber (2004) Europe n = 1102 Group 1: gatifloxacin 400 mg sd single dose Short-term: clinical and bacteriological cure 7 9 days after therapy Age >18 years Group 2: gatifloxacin 200 mg od 3 days Long-term: clinical and bacteriological cure 4 6 weeks after therapy Pyuria present Group 3: ciprofloxacin 250 mg bid 3 days Adverse effects during total study period Nicolle (2002) Europe/Canada n = 901 Group 1: pivmecillinam 400 mg bid 3 days Short-term: clinical and bacteriological cure 9 13 days after start of Age years Group 2: norfloxacin 400 mg bid 3 days Lng-term: clinical and bacteriological cure days after start of Adverse effects during total study period Park (2007) Korea n = 65 Group 1: ciprofloxacin 500 mg qid 3 days Short-term: clinical and bacteriological cure 7 days after treatment

6 664 Family Practice The International Journal for Research in Primary Care TABLE 1 Continued First author (year) Region Population Treatment Duration Relevant outcomes 3 days No long-term outcomes Age years Group 2: trimethoprim/sulfamethoxazole 160/ 800 mg Adverse effects during total study period Richard (2002) USA n = 673 Group 1: gatifloxacin 400 mg sd single dose Short-term: clinical and bacteriological cure 5 9 days after treatment Age >18 years Group 2: gatifloxacin 200 mg od 3 days Long-term: clinical and bacteriological cure days after start of Group 3: ciprofloxacin 100 mg bid 3 days Adverse effects during total study period Van Pienbroek (1993) The Netherlands n = 231 Group 1: fosfomycin 3 g sd single dose Short-term: clinical and bacteriological cure 9 days after start of treatment Age >18 years Group 2: nitrofurantoin 50 mg qid 7 days Long-term: clinical and bacteriological cure 42 days after start of treatment Adverse effects until day 9 sd, single dose; od, once a day; bid, twice a day; tid, three times a day; qid, four times a day. The table shows the main characteristics of the included studies. Twelve RCTs were included. All studies were performed in general practice or outpatient settings and compared different oral antibiotic or placebo regimens for UTI symptoms (with or without bacteriuria) in female patients aged 12 years or older. Four studies used an additional inclusion criterion: Bjerrum: positive microscopy (defined as >1 rods per field of vision at 400 times magnification in uncentrifuged urine); Cervigni, Christiaens and Naber: pyuria. Time points of short- and long-term outcomes were comparable for all studies (±1 week and 1 month after treatment, respectively), with the exception of Hooton who reported short-term outcomes at 2 weeks after treatment. applied a one-stage approach, which uses data at the patient level and therefore accounts for all sources of variance simultaneously. In terms of the two-stage approach, our method would be more closely related to the random-effects model, which takes better account of between-trial heterogeneity than the fixed-effects model. However, random-effects methods increase the weight of small studies (which may be particularly prone to publication bias and lower methodological quality 37 ), which is a disadvantage our method does not have. We encountered the following limitations: Firstly, our results were derived from symptomatic women with a positive baseline culture. In daily practice, however, a clinician often has to decide whether or not to start antibiotic treatment without knowing a culture result. Due to a lack of direct comparisons, it was not possible to construct a network for this subpopulation. Secondly, we could use only 10 RCTs to compose a network. Due to this low number of studies, our network is somewhat fragile and most odds ratios could only be imprecisely estimated. Thirdly, the included RCTs were performed in different regions and periods, which might have had different bacterial resistance rates. For example, during the last 10 years, slightly higher resistance rates of Escherichia coli to ciprofloxacin have been reported in the USA compared to Germany. 15,17,38,39 Fourth, since our analysis was based on published papers, covariates were at the study-arm level (mean age) or the study level (geographical region); however, since our data were too sparse, we could not use any of these covariates sensibly. Ideally, patient-level covariates are used, which requires access to individual patient data (IPD) from the original studies. 10,40 Fifth, to facilitate the formation of networks, we combined different regimens of the same antibiotic to become a single treatment arm in the network structure. However, different treatment durations of the same antibiotic may yield different cure rates and adverse effects and might therefore not be considered a single treatment. As more evidence from randomized trials accumulates, more refined analyses may be possible in the future. Sixth, as in every intervention study, we cannot completely rule out the risk of reporting bias having an effect on our results, e.g. due to non-publication of relevant RCTs or selective reporting of outcomes by the original studies. 45 We have tried to minimize this risk by including in our search The Cochrane Central Register of Controlled Trials (CENTRAL), Controlled-trials.com and WHO International Clinical Trials Registry Platform Search Portal ( ), thereby assuring that we would identify all registered trials. In this context, we point out that in certain situations, indirect comparisons may provide

7 Comparative effectiveness of different antibiotics for UTI 665 TABLE 2 Outcomes reported by the original RCTs First author Treatment % Patients cured % Adverse effects Subpopulation: all patients Subpopulation: patients with positive baseline culture Short-term cure Long-term cure Short-term cure Long-term cure Clinical Bacteriological Clinical Bacteriological Clinical Bacteriological Clinical Bacteriological Arredondo-García Group 1: ciprofloxacin 250 mg bid 3 days na na na na na na na na Group 2: trimethoprim/sulfamethoxazole 960 mg bid 7 days Group 3: norfloxacin 400 mg bid 7 days na na na na Bjerrum Group 1: pivmecillinam 400 mg tid 3 days 95 na na na na 69 na na 14 Group 2: sulfamethizole 1 g bid 3 days 93 na na na na 78 na na 13 Cervigni Group 1: prulifloxacin 600 mg sd na na na na Group 2: pefloxacin mg sd na na na na Christiaens Group 1: nitrofurantoin 100 mg qid 3 days 88 na na na na na 23 Group 2: placebo qid 3 days 51 na na na na na 38 Ferry Group 1: pivmecillinam 200 mg tid 7 days na na na na Group 2: pivmecillinam 200 mg bid 7 days na na na na Group 3: pivmecillinam 400 mg bid 3 days na na na na Group 4: placebo tid 7 days na na na na Gupta Group 1: trimethoprim/sulfamethoxazole 960 mg bid 3 days na na na na na 31 Group 2: nitrofurantoin 100 mg bid 5 days na na na na na 28 na na na na na 27 Hooton Group 1: amoxicillin/clavulanate 625 mg bid 3 days Group 2: ciprofloxacin 250 mg bid 3 days na na na na na 19 Naber Group 1: gatifloxacin 400 mg sd na na na na Group 2: gatifloxacin 200 mg od 3 days na na na na Group 3: ciprofloxacin 250 mg bid 3 days na na na na Nicolle Group 1: pivmecillinam 400 mg bid 3 days 82 na 91 na na 75 na 82 9 Group 2: norfloxacin 400 mg bid 3 days 88 na 91 na na 91 na Park Group 1: ciprofloxacin 500 mg qid 3 days na na na na na na 28 Group 2: trimethoprim/sulfamethoxazole 960 mg bid 3 days na na na na na na 16 Richard Group 1: gatifloxacin 400 mg sd na na na na x Group 2: gatifloxacin 200 mg od 3 days na na na na x Group 3: ciprofloxacin 100 mg bid 3 days na na na na x Van Pienbroek Group 1: fosfomycin 3 g sd na 81 na Group 2: nitrofurantoin 50 mg qid 7 days na 90 na 87 8 sd, single dose; od, once a day; bid, twice a day; tid, three times a day; qid, four times a day; na, not applicable; x, numbers/percentages not exactly reported but comparable for all groups. The table shows the percentages of cure and adverse effects as reported by the original direct comparisons. Only four studies reported cure results for the subpopulation of all symptomatic patients (irrespective of baseline culture). For the subpopulation of patients with a positive baseline culture, all studies reported adverse effects and short-term bacteriological cure. Results for adverse effects were reported by all studies as the percentage of patients experiencing adverse effects probably related to study medication, with the exception of Nicolle et al., who reported adverse effects as the percentage of events being considered as being probably related to study medication.

8 666 Family Practice The International Journal for Research in Primary Care TABLE 3 Quality assessment First author (year) Method of randomization (max 1) Concealment of allocation (max 1) Blinding (max 3) Incomplete data addressed (max 2) Other sources of bias (max 4) Total (max 11) Arredondo-García (2004) Bjerrum (2009) Cervigni (2003) Christiaens (2002) Ferry (2007) Gupta (2007) Hooton (2005) Naber (2004) Nicolle (2002) Park (2007) Richard (2002) Van Pienbroek (1993) Quality scores per study: 1 = adequately described; 0 = not adequately described. The table shows the quality scores of the 12 included studies. The average study score was 6.5 of 11. Blinding and completeness of outcome data were more often and more completely reported than method of randomization and concealment of treatment allocation. FIGURE 2 Connections between treatment arms: network construction. The figure shows the network that could be composed for each of the five outcomes. Head-to-head comparisons are shown as lines. Each line colour represents an outcome: red = short-term clinical outcome, black = short-term bacteriological outcome, green = long-term clinical outcome, blue = long-term bacteriological outcome and orange = adverse effects. Only for short-term bacteriological outcome (black lines), all nine treatments could be connected less biased results than head-to-head RCTs, 46 which means that indirect comparisons, like our network meta-analysis, might compensate for possible publication bias from direct comparisons like the original RCTs. As in every meta-analysis, there may be heterogeneity in the framework of network meta-analysis also comprising inconsistency, 9,14,47 49 which means that the estimated treatment effects across trials differ more than can be accounted for by within-trial variability Although our results suggested some heterogeneity across studies, our data were too sparse to investigate this. However, between-trial differences are not necessarily linked to differences in treatment effects and may well be compatible with a constant relative treatment effect, such as the relative risk or odds ratio. 10,53,54 In conclusion, our network meta-analysis provides a useful tool for clinical decision making in everyday practice. Moreover, the method can be used for metaanalyses of RCTs across primary care and beyond. In our network, ciprofloxacin and gatifloxacin appear the most effective treatments and amoxicillin clavulanate appears the least effective treatment. More well-performed randomized trials on antibiotic treatment of uncomplicated UTIs are needed to build a more robust network and enable more refined analyses of different dosages within drug regimens.

9 Comparative effectiveness of different antibiotics for UTI 667

10 668 Family Practice The International Journal for Research in Primary Care FIGURE 3 Relative efficacies: (a) short-term clinical cure, (b) short-term bacteriological cure, (c) long-term clinical cure, (d) long-term bacteriological cure and (e) adverse effects. The figure shows the odds ratios (with 95% confidence intervals in red) of each treatment relative to the reference treatment (ciprofloxacin) for each of the five outcomes (short-term clinical cure, short-term bacteriological cure, long-term clinical cure, longterm bacteriological cure and adverse effects). The blue lines indicate an odds ratio of 1 (no effect relative to the reference treatment). An odds ratio >1 indicates better cure or more adverse effects (depending on the outcome) for the corresponding treatment compared to the reference treatment. An odds ratio <1 indicates worse cure or less adverse effects (depending on the outcome) for the corresponding treatment compared to the reference treatment. Non-overlap of confidence intervals of two different treatments means that the effectiveness of the treatments differs significantly for the corresponding outcome. Odds ratios could not be calculated for treatments that were not present in the network for the corresponding outcome (e.g. fosfomycin was not present in the network for short-term clinical cure). Ciprofloxacin was used as reference category since it was present in the network for all five outcomes Acknowledgements We thank Faridi van Etten Jamaludin (clinical librarian, Academic Medical Center, University of Amsterdam) for her help on developing and applying our search strategy. Declaration Funding: none. Ethical approval: none required. Conflict of interest: none. References 1 Foxman B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am J Med 2002; 113 (Suppl 1A): 5S 13S. 2 Grabe M, Bjerklund-Johansen TE, Botto H et al. EAU Guidelines on Urological infections, update March Arnhem, The Netherlands: EAU Guidelines Office, Nicolle L. Best pharmacological practice: urinary tract infections. Expert Opin Pharmacother 2003; 4: Warren JW, Abrutyn E, Hebel JR et al. Guidelines for antimicrobial treatment of uncomplicated acute bacterial cystitis and acute pyelonephritis in women. Clin Infect Dis 1999; 29: Zalmanovici TA, Green H, Paul M, Yaphe J, Leibovici L. Antimicrobial agents for treating uncomplicated urinary tract infection in women. Cochrane Database of Systemic Reviews 2010 issue 10. Art. No.: CD Gupta K, Hooton TM, Stamm WE. Increasing antimicrobial resistance and the management of uncomplicated communityacquired urinary tract infections. Ann Intern Med 2001; 135: Hooton TM, Besser R, Foxman B, Fritsche TR, Nicolle LE. Acute uncomplicated cystitis in an era of increasing antibiotic resistance: a proposed approach to empirical therapy. Clin Infect Dis 2004; 39: Falagas ME, Kotsantis IK, Vouloumanou EK, Rafailidis PI. Antibiotics versus placebo in the treatment of women with uncomplicated cystitis: a meta-analysis of randomized controlled trials. J Infec 2009; 58: Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 2005; 331 (7521): Glenny AM, Altman DG, Song F et al. Indirect comparisons of competing interventions. Health Technol Assess 2005; 9 (26): Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol 1997; 50 (6): Hasselblad V. Meta-analysis of multitreatment studies. Med Decis Making 1998; 18: Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 2004; 23: Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med 2002; 21: Kahlmeter G. An international survey of the antimicrobial susceptibility of pathogens from uncomplicated urinary tract infections: the ECO.SENS Project. J Antimicrob Chemother 2003; 51(1): Naber KG, Schito G, Botto H, Palou J, Mazzei T. Surveillance study in Europe and Brazil on clinical aspects and antimicrobial resistance Epidemiology in females with cystitis (ARESC): implications for empiric therapy. Eur Urol 2008; 54: Zhanel GG, Hisanaga TL, Laing NM et al. Antibiotic resistance in Escherichia coli outpatient urinary isolates: final results from the North American urinary tract infection Collaborative Alliance (NAUTICA). Int J Antimicrob Agents 2006; 27: Higgins JPT, Green S. (eds). Cochrane Handbook for Systematic Reviews of Interventions. Version [updated March 2011]. The Cochrane Collaboration, Available from (accessed on 13 April 2012). 19 Boutron I, Moher D, Tugwell P. A checklist to evaluate a report of a nonpharmacological trial (CLEAR NPT) was developed using consensus. J Clin Epidemiol 2005; 58: van Tulder M, Furlan A, Bombardier C, Bouter L. Updated method guidelines for systematic reviews in the cochrane collaboration back review group. Spine (Phila Pa 1976) 2003; 28: Arredondo-García JL, Figueroa DR, Rosas A et al. Comparison of short-term treatment regimen of ciprofloxacin versus longterm treatment regimens of trimethoprim/sulfamethoxazole or norfloxacin for uncomplicated lower urinary tract infections: a randomized, multicentre, open-label, prospective study. J Antimicrob Chemother 2004; 54: Bjerrum L, Gahrn-Hansen B, Grinsted P. Pivmecillinam versus sulfamethizole for short-term treatment of uncomplicated acute cystitis in general practice: a randomized controlled trial. Scand J Prim Health Care 2009; 27: Cervigni M, Orticelli G, Bologna M et al. Single-dose prulifloxacin versus single-dose pefloxacin in the treatment of acute uncomplicated urinary tract infection in women. 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11 Comparative effectiveness of different antibiotics for UTI Hooton TM, Scholes D, Gupta K et al. Amoxicillin-clavulanate vs ciprofloxacin for the treatment of uncomplicated cystitis in women: a randomized trial. JAMA 2005; 293: Naber KG, Allin DM, Clarysse L et al. Gatifloxacin 400 mg as a single shot or 200 mg once daily for 3 days is as effective as ciprofloxacin 250 mg twice daily for the treatment of patients with uncomplicated urinary tract infections. Int J Antimicrob Agents 2004; 23: Nicolle LE, Madsen KS, Debeeck GO et al. Three days of pivmecillinam or norfloxacin for treatment of acute uncomplicated urinary infection in women. Scand J Infect Dis 2002; 34: Park J, Min K, Kang D. The efficacy and safety of a once-daily extended-release ciprofloxacin tablet for the empirical treatment of symptomatic uncomplicated cystitis in Korean women. [Korean]. Korean J Urol 2007; 48: Richard GA, Mathew CP, Kirstein JM, Orchard D, Yang JY. 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Approach to create datasets with individual patient entries Since our five outcomes were all binary, we used logistic regression as the analysis method. In order to perform logistic regression, we needed datasets containing individual patient data entries. We took the following steps for each of the five outcomes: Step 1: Making contingency tables for each study As an example, let us consider the short-term clinical cure of Study 1 21 : Three different treatments were compared in this study: ciprofloxacin (which we call Treatment A), trimethoprim/sulfamethoxazol (Treatment B) and norfloxacin (Treatment C). The cure/failure ratios for each treatment (expressed as number of patients) were 86/11 (Treatment A), 70/11 (Treatment B) and 90/17 (Treatment C), yielding the following 3 2 table for Study 1: Now, we take a look at Study 5 27, which also contains Cure Number of patients Failure Treatment A Treatment B Treatment C Treatment A (ciprofloxacin). The other treatment arm (amoxicillin clavulanate) is new, so we give it a new name: Treatment D. Consequently, we make a 2 2 table for Study 5 in the same way as described above: Step 2: Creating a data set with individual data entries Cure Number of patients Failure Treatment A Treatment D

12 670 Family Practice The International Journal for Research in Primary Care Since we have no individual patient data, but only the frequencies of cures and failures for each treatment arm, we create N data entries (one for each simulated patient), using the known study-level data and previously made contingency tables. Each entry contains study number, treatment, binary outcome (cure = 1 and failure = 0) and mean age of treatment group. Inclusion of the study number assures preservation of randomization within each trial, which is essential in any meta-analysis. 12 For the given example, the data structure looks as follows: Step 3: Performing logistic regression analysis Study Treatment Cure Age 1 A A 1 32 / Another 84 similar entries (n = 86 in total) 1 A A 0 32 / Another 9 similar entries (n = 11 in total) 1 B B 1 32 / Another 68 similar entries (n = 70 in total) 1 B B 0 32 / Another 9 similar entries (n = 11 in total) 1 C C 1 32 / Another 88 similar entries (n = 90 in total) 1 C C 0 32 / Another 15 similar entries (n = 17 in total) 5 A A 1 22 / Another 157 similar entries (n = 159 in total) 5 A A 0 22 / Another similar entry (n = 3 in total) 5 D D 1 22 / Another 125 similar entries (n = 127 in total) 5 D D 0 22 / Another 31 similar entries (n = 33 in total) Now that we have a data set with individual patients, we can perform a logistic regression analysis. Cure is the dependent variable. The other variables in the data set are the independent variables. For study and treatment, we use dummy variables. We choose one treatment as reference and calculate the odds ratios with 95% confidence intervals of all treatments relative to this reference treatment. Appendix 2. Relative efficacies For each of the five outcomes (short-term clinical cure, short-term bacteriological cure, long-term clinical cure, long-term bacteriological cure and adverse effects), the odds ratio (with 95% confidence intervals in brackets) of each treatment relative to the reference treatment (ciprofloxacin) is shown. An odds ratio >1 indicates better cure or more adverse effects (depending on the outcome) for the corresponding treatment compared to the reference treatment. An odds ratio <1 indicates worse cure or less adverse effects (depending on the outcome) for the corresponding treatment compared to the reference treatment. Non-overlap of confidence intervals of two different treatments means that the effectiveness of the treatments differs significantly for the corresponding outcome. Odds ratios could not be calculated for treatments that were not present in the network for the corresponding outcome (e.g. fosfomycin was not present in the network for the outcome clinical cure). Ciprofloxacin was used as reference category since it was present in the network for all five outcomes. Treatment Short-term cure Long-term cure Adverse effects Clinical Bacteriological Clinical Bacteriological Ciprofloxacin 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) TMP/SMX 0.71 ( ) 0.36 ( ) 0.87 ( ) 0.87 ( ) 1.42 ( ) Norfloxacin 0.63 ( ) 0.81 ( ) 0.91 ( ) 0.86 ( ) 1.53 ( ) Nitrofurantoin 0.86 ( ) 0.27 ( ) 1.28 ( ) 1.07 ( ) Placebo 0.30 ( ) 0.03 ( ) 0.12 ( ) 1.24 ( ) Pivmecillinam 1.39 ( ) 0.40 ( ) 0.61 ( ) 1.36 ( ) Amoxicillin Clavulanate 0.07 ( ) 0.17 ( ) 0.31 ( ) 1.55 ( ) Gatifloxacin 0.93 ( ) 1.06 ( ) 0.93 ( ) 0.96 ( ) 1.16 ( ) Fosfomycin 0.12 ( ) ref, reference category.

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