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Open Access Highly Accessed Research article

Does clinical examination aid in the diagnosis of urinary tract infections in women? A systematic review and meta-analysis

David Medina-Bombardó13* and Antoni Jover-Palmer2

Author Affiliations

1 Manacor Health Center. Majorca Primary Care Department. Balearic Institute of Health Manacor, Simó Tort s/n, 07500 Manacor, Balearic Islands, Spain

2 Arquitecte Bennasar Health Center, Majorca Primary Care Department. Balearic Institute of Health, Avda Gaspar Bennàzar 9, 07004 Palma, Balearic Islands, Spain

3 Unit of Reseach, Majorca Primary Care Department. Balearic Institute of Health, Reina Esclaramunda 9, 07003 Palma, Balearic Islands, Spain

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BMC Family Practice 2011, 12:111  doi:10.1186/1471-2296-12-111


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2296/12/111


Received:26 August 2010
Accepted:10 October 2011
Published:10 October 2011

© 2011 Medina-Bombardó and Jover-Palmer; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Clinicians should be aware of the diagnostic values of various symptoms, signs and antecedents. This information is particularly important in primary care settings, where sophisticated diagnostic approaches are not always feasible. The aim of the study is to determine the probability that various symptoms, signs, antecedents and tests predict urinary tract infection (UTI) in women.

Methods

We conducted a systematic search of the MEDLINE and EMBASE databases to identify articles published in all languages through until December 2008. We particularly focused on studies that examined the diagnostic accuracy of at least one symptom, sign or patient antecedent related to the urinary tract. We included studies where urine culture, a gold standard, was preformed by primary care providers on female subjects aged at least 14 years. A meta-analysis of the likelihood ratio was performed to assess variables related to the urinary tract symptoms.

Results

Of the 1, 212 articles identified, 11 met the selection criteria. Dysuria, urgency, nocturia, sexual activity and urgency with dysuria were weak predictors of urinary tract infection, whereas increases in vaginal discharge and suprapubic pain were weak predictors of the absence of infection. Nitrites or leukocytes in the dipstick test are the only findings that clearly favored a diagnosis of UTI.

Conclusions

Clinical findings do not aid in the diagnosis of UTI among women who present with urinary symptoms. Vaginal discharge is a weak indicator of the absence of infection. The urine dipstick test was the most reliable tool for detecting UTI.

Background

Clinicians should be aware of the diagnostic values of various symptoms, signs and antecedents. This information is particularly important in primary care settings, where sophisticated diagnostic approaches are not always feasible.

Urinary tract infection (UTI) is one of the most common bacterial infections seen in primary care, second only to infections of the respiratory tract [1,2]. Infections of the urinary tract can present with various symptoms and signs [3] and are particularly common among women, with an incidence of about 3-9% in young women [4,5] and 20% in women aged more than 65 years [6]. Approximately 2.692 of every 100, 000 American individuals were diagnosed with UTIs in the year 2000 [7]. In the United States, UTIs account for 2-3% of all visits to the general practitioner [8] (i.e., 7-8 million annual visits [1,9]) and 2% of all prescriptions [10]. This results in an annual cost of nearly 1.6 billion dollars [11,12]. Sixty-one percent of all UTIs are managed in the primary care setting [1,13].

There is an ongoing debate about the best way to diagnose UTIs in the primary health care setting [14]. This condition is often challenging to diagnose [15] because the clinician has to decide on the proper diagnostic tools and mode of interpretation [16] according to the diagnostic accuracy of clinical findings. Time and resources are scarce for primary health care professionals, whose services are in high demand. Thus, there is a need for studies of the effectiveness of diagnostic and therapeutic tools. A systematic review and meta-analysis of studies on the use of diagnostic tools in primary health care will help identify clinical findings that are useful in the diagnosis of UTIs.

Two meta-analysis [17,18] were performed to determine the usefulness of clinical findings in the diagnosis of UTI. Bent [17] analysis included data from a variety of settings such as hospitals, emergency departments, and specialty clinics. The findings revealed that the prevalence (i.e., a priori probability) of UTI differed depending of the clinical spectrum of patients with UTI. Giesen [18] estimated post-test values of some clinical findings used to diagnose UTI in primary care settings across three different threshold reference standards (102 or 103 or 105 CFU/ml). We reviewed the accuracy of various symptoms, signs, antecedents and tests performed in the primary care physician's office for the diagnosis of UTI.

Methods

We conducted a systematic search of the MEDLINE (i.e., literature dating from 1966), and EMBASE (i.e., literature dating from 1974) databases for abstracts of articles published in all languages through December 2008. The selection of publications was made using the following six steps. First, we used an automated system to retrieve abstracts and references that contained the keywords indicated in Table 1. Second, we selected articles whose abstracts were consistent with a previously defined selection protocol. When data in the abstract were insufficient to determine if the article should be included in our study, or when the abstract was not available, the decision to include study was deferred to the next step. Third, we obtained full-text versions of the articles selected in the second step, including articles without abstracts or with insufficient data in the abstracts, and these publications were independently assessed by the two authors using a specific protocol to determine if they should be included in this review. At this step, a secondary review was performed using bibliography of each of the selected articles as a starting point, which included other studies. Fourth, we compared the decisions made by the reviewers during the third step, and discrepancies were discussed until a consensus was attained. Fifth, an external expert assessed those articles for which an agreement had not been reached. The two concordant decisions were those considered for inclusion or exclusion of the study. Sixth, we contacted the authors of articles with missing data or unclear findings to obtain further explanations. If the necessary data were not available, the article was excluded from our study. Table 2 describes the data of all included articles [14,19-28]. Our automated search was complemented by a manual search for papers that were not found in the databases, but that fulfilled our inclusion criteria. These papers were then retrieved using the references of the articles that had been found.

Table 1. Keywords used in the automated searches of Medline and EMBASE

Table 2. Description of studies

Inclusion criteria

All articles included in the review provided implicit or explicit evidence obtained from women aged 14 years and older who presented to their primary health care physician with urinary tract complaints of recent onset that had not yet been treated. We included original, observational, prospective, diagnostic studies of the accuracy of clinical findings (i.e., symptoms, signs and antecedents) with regards to the diagnosis of UTI. The studies included consecutive cases of primary care patients in whom UTI was suspected. Review articles and meta-analyses were considered only as sources of references for the original studies. All clinical findings were collected systematically, according to defined standards (i.e., check-list protocol or similar). The variables assessed in the studies were consistent with clinical parameters related to UTI. The cut-off values for urine cultures, our gold standard, were provided (in CFU/mL) for the infecting pathogen, based on usual agar plate urine culture. Data needed to calculate the sensitivity, specificity, and/or likelihood ratio for the predictive value of symptoms, signs, antecedents and/or results of the urine dipstick test was available for all studies included in this report. Finally, the studies included a sufficient period of follow-up to assure that patients' illness is attributable to UTI.

Exclusion criteria

Studies were excluded from our analysis if the study population included fewer than 50 subjects; a poorly defined population that is, when context, gender, age or study subjects inclusion criteria were not specified in the methods section. When population was recruited from a hospital or a specialty practice, patients younger than 14 years, or patients whose urinary symptoms were not of recent onset (more than one month since onset of symptoms). Studies were also excluded for non-systematic assessment of clinical findings that is, when authors did not describe specifications of materials and methods involved, including how and when measurements were taken. Also were excluded those studies with non-consecutive or non-randomized recruitment of patients into the study, or unoriginal data.

After studies selection we identified that most of studies used as cut off > 105 CFU/mL. In order to avoid heterogeneity of the study population and disease definition as well as statistical power both authors agreed to exclude bacterial count cut off < 105 CFU/mL.

Quality assessment

The QUADAS assessment tool [29] was applied by one of the reviewers (DMB) in order to evaluate the quality of included studies.

Analysis

The likelihood ratio (LR) is the ratio of two probabilities, namely the probability that a specific test result is obtained in patients with the disease divided by the probability of a test result in patients without the disease. Positive likelihood ratios (PLRs) are calculated for positive test results and negative likelihood ratios (NLRs) are calculated for negative test results. The diagnostic odds ratio (DOR) describes how well the test works in subjects with disease compared with subjects without disease, as well as the discriminatory properties of positive and negative test results (PLRs and NLRs, respectively). Every clinical exam finding was considered a different test to aid in the diagnosis of urinary tract infections. Sensitivity (S), specificity (Sp), positive and negative likelihood ratios (PLR and NLR) and (DOR), as well as their corresponding standard errors and 95% confidence intervals (CI95%), were calculated for every symptom or sign. Results were analyzed for all possible synonyms of each variable, as well as the more commonly used synonyms. Summary or pooled LR (PLRp, NLRp) and pooled DOR (DORp) indices, as well as their corresponding standard errors and CI95%, were calculated when two or more studies described the same clinical finding. The statistical heterogeneity of the LR and DOR indices was analyzed. When heterogeneity was found, the threshold effect was analyzed using the Moses-Shapiro-Littenberg method. If heterogeneity could not be explained by the threshold effect, the data were analyzed using a meta-regression model that included prevalence as an independent variable. When the summary likelihood ratio was estimated (i.e., when clinical data were obtained from different studies), heterogeneity was assessed via Chi-square tests and I2 inconsistency tests. Indices were pooled for the fixed effects method, when possible, and heterogeneity was rejected. If heterogeneity or inconsistency could not be explained, the pooled likelihood ratio was interpreted cautiously. The summary LR for random effects is usually recommended for the analysis of accuracy studies when an estimate of between study variation can be incorporated. All statistics were calculated using the Meta-DiSc [30] and RevMan 4.2 [31] software.

Results

A total of 1, 212 articles were retrieved via an automated search (1, 059 from the MEDLINE database, 242 from the EMBASE database, 89 found in both databases). We excluded studies of urinary tract treatments, reviews, diagnostic studies performed in pediatric settings, studies including men, editorials, and articles addressing issues other than UTI. Seventy-eight articles were preselected during the initial screening of abstracts.

The second screening (of full-text articles) revealed eight studies [14,18-23,26,28] that satisfied the inclusion criteria. Three additional articles were cited in reviews or in the references of other articles and were subsequently included in our study [24,25,27] (Figure 1). Reasons for exclusion are shown in Figure 1[1,3-6,9,10,15,18,28,32-92]. We contacted eight authors to obtain missing data [2,21,23,26,83,85,86,90], but only four authors replied [21,23,26,85].

thumbnailFigure 1. Summary of exclusion of studies.

The quality assessment of included studies is presented in Table 3. The index test was not described in sufficient detail to permit replication in all studies included and the reference standard in seven. Blinding of both index test results and reference test was poorly reported in the 10 of the 11 studies considered. In all studies patients received the same reference standard.

Table 3. Quality analysis by QUADAS tool of all studies included

We performed a meta-analysis of the studies shown in Table 2 to determine the likelihood ratios (LR) for symptoms such as dysuria, urination frequency, urinary urgency, nocturia, back pain, suprapubic pain, fever, increased vaginal discharge, vaginal irritation, history of UTI, sexual activity and the presence of nitrites or leukocytes in the urine dipstick test, see Table 4. Figures 2, 3 and 4 show the comparative PLRp, NLRp and DORp, respectively, for all clinical findings.

Table 4. Pooled positive and negative likelihood ratio and diagnostic odds ratio whith theirs 95% conficence interval, inconsitency index and heterogeneity chi-squared significance for all clinical findings analized

thumbnailFigure 2. Comparative estimate of the number of times a woman with a urinary tract infection (UTI) is more likely than a woman without a UTI to present with certain clinical findings (i.e., pooled positive likelihood ratios - PLRp - and confidence intervals for all clinical findings).

thumbnailFigure 3. Comparative estimate of the number of times a woman with a urinary tract infection (UTI) is more likely than a woman without a UTI to lack certain clinical findings (i.e., pooled negative likelihood ratios - NLRp - and confidence intervals for all clinical findings).

thumbnailFigure 4. Comparative usefulness of clinical findings in the diagnosis of urinary tract infections. The pooled diagnostic odds ratio (DORp) values estimate the accuracy of the test (i.e., clinical finding) in patients with and without disease, as well as the discriminatory properties of positive and negative test results (PLR and NLR, respectively).

Dysuria, urgency, nocturia, sexual activity and the simultaneous presence of urgency and dysuria were weak diagnostic indicators of UTI. However, an increase in vaginal discharge and suprapubic pain were weak predictors of the absence of infection. Frequency of urination, back pain, fever, vaginal irritation, history of UTI, as well as the simultaneous presence of dysuria with urgency were not significant indicators of UTI. Nitrites or leukocytes in the dipstick test were the only findings that clearly indicated the presence of UTI.

The studies included in our meta-analysis were statistically homogeneous, with regards to the PLRp of all variables except frequency of urination, suprapubic pain, sexual activity and the presence of leukocytes in the urine, as indicated by the dipstick test. NLRp was homogeneous for nine variables (i.e., dysuria, frequency, nocturia, back or suprapubic pain, fever, history of UTI, vaginal irritation and the presence of nitrites in the urine, as indicated by the dipstick test).

The threshold effect can explain LR heterogeneity indexes with regards to urgency, frequency of urination, suprapubic pain, and the presence of leukocytes in the urine, as indicated by the dipstick test. We were unable to analyze threshold effect of data on sexual activity, vaginal irritation, and the simultaneous presence of dysuria with urgency or frequency because only two studies assessed both of these variables. Prevalence was not a source of heterogeneity in the metaregression model.

Only two studies studied nocturia (415 cases), vaginal irritation (361 cases), dysuria with frequency (284 cases) or urgency (1, 149 cases), or sexual intercourse (584), consequently, it was not possible to explore their threshold effect by Moses-Shapiro-Littenberg method. There is also considerable inconsistency among studies with regards to the PLRp and NLRp values for sexual activity (I2 = 75.0% PLRp and 89.8% NLRp) and for the simultaneous presence of dysuria and urgency (94.2% PLRp and 89.9% NLRp). However, there were inconsistencies in the analysis of PLRp in the cases presence of leukocyes (87.8%), frequency (63.8%) and suprapubic pain (63.4%), as well as in the analysis of NLRp in the cases of urgency (92.8%), vaginal discharge (78.7%) or the simultaneous presence of dysuria and frequency (77.2%).

Discussion

Our results show that exploratory clinical findings may suggest a diagnosis of UTI in women; however, nitrituria is clearly the most useful diagnostic indicator. Some clinical findings, alone or in combination with others, can indicate the presence of UTI. Consistent with the results of a meta-analysis by Bent [17], our results show that dysuria and the presence of nitrites or leukocytes in the urine, as indicated by the dipstick test, are useful in the diagnosis of UTI. Furthermore, increased vaginal discharge indicates the absence of infection. The presence or absence of back pain provides little diagnostic information. However, contrary to our results, Bent [17] found that frequent urination or back pain increased the likelihood of UTI, whereas vaginal irritation was not associated with UTI. Urgency, nocturia, sexual activity and a history of UTI were not considered by Bent, whereas costovertebral angle tenderness was not considered in our study. Contrary to our analysis, Bent calculated separate pooled indexes for back pain and flank pain. Both metaanalyses share several of the above mentioned shortcomings; thus, these results should be interpreted with caution. Bent's meta-analysis [17] included a considerably more heterogeneous population than did our study, but did not include any analysis of heterogeneity. Bent's pooled LR indexes were calculated using a random effects model, whereas ours were calculated using a fixed effects model with corresponding differences in confidence intervals.

On the other hand in Giesen meta-analysis [18] only dysuria and urgency pooled LR were favorable at UTI diagnostic and fever were indifferent as observed in our results, using the same threshold in urinoculture. Other results were not in accordance with our finding. That is, lower abdominal pain PLR and NLR were indiferent in Giesen meta-analisis and PLR favourable and NRL indiferent for us. Giesen's pooled LR indexes conficence intervals were calculated using random effects model and we used fixed effects.

We would like to emphasize the fact that we analyzed only those variables for which results were found in more than one study. The studies included in our analysis were of adequate quality with regards to all concepts usually assessed [17,93] with the exception of one study that included a sample size of 48 subjects. However, that study met all other quality requirements [20]. Although we may have lost some relevant clinical information by excluding studies with sample sizes less than 50, we gained statistical precision and our data demonstrated minor variability when the random effects method was applied. In addition, our quality analysis revealed a decrease in heterogeneity.

We included studies that recruited consecutive samples, which is the recommended method [93] to reflect clinical conditions. Randomized samples would be ideal; however, there have not been published studies of diagnostic tests based on randomized samples.

Variability in the results of a diagnostic test can be attributed to several factors. The sensitivity and specificity of a test, as well as the LR, may change during the course of the disease [93-95] as its manifestations become more or less pronounced. Validity test parameters such as sensitivity, specificity and likelihood ratio remain constant only if the test is used on a population with similar characteristics as the subjects whose parameters were originally estimated. However, post-test probability depends on pre-test probability. This may be related to the variability encountered when the same diagnostic tool is used in different levels of health care, a phenomenon known as the referral filter bias [96,97]. This concept is independent of the variability that results from differences in prevalence at one stage of the natural history of the disease [93,94,96,97], and it may also affect the predictive value of the test when used in that context. Therefore, we only included studies performed in the primary care setting. This may partially explain the differences between our results and those reported by Bent [17], whose population was more heterogeneous.

Another source of variability is interobserver variability, due to differences in the understanding of terms and in the examination techniques used by different field researchers. In many instances, variables are not clearly defined (i.e., different names may refer to the same concept, whereas one name may refer to quite different things), making it difficult to understand what the study is attempting to measure. Interobserver variability is a source of potential bias and an important limitation to studies that aim to estimate the validity of diagnostic tools. It is also of particular concern in studies that focus on symptoms. There were no clear definitions of variables in the studies included in our analysis. For example, pain in the lumbar region was variously referred to as back pain [20,21,27], low back pain [18], loin pain [22,87,88], flank pain [23] or kidney/flank pain [90] while other authors [17] have distinguished flank pain from back pain. Fever was referred to as fever [26,27,87,88], pyrexia [24] or temperature [22]. However, for the poorly defined symptom dysuria, the overall PLRp and NLRp values obtained for dysuria [18,21,23,24,27] were 1.06 (CI95% 1.00-1.13) and 0.84 (CI95% 0.68-1.03), respectively. These findings do not differ greatly from the results obtained when we include some of the terms usually considered synonymous for dysuric syndrome [98,99], such as pain on micturition [20,26] or burning on micturition [22]. The PLRp and NLRp values would then be 1.09 (CI95% 1.03-1.16) and 0.80 (CI95% 0.68-0.94), respectively.

The evidence power analysis further revealed homogeneous DORp values for urgency, vaginal discharge, leukocytes in the urine, and simultaneous dysuria and frequency. However, the DORp values were heterogeneous for frequency, sexual activity and simultaneous dysuria and urgency. In all cases, the threshold effect could be explained by heterogeneity, except for history of UTI. We included studies that showed wide differences in the pre-test probability of UTI (i.e., range: 28% [27] to 83% [23]) because the target populations in all of these studies were similar (i.e., primary care patients). An unknown factor might explain these differences, but pre-test probability could not account for the heterogeneity observed in a metaregression model. The available data do not allow us to classify our results according to age for the comparison of the pre-test probabilities of UTI [7,100-102]. Poor descriptions of methods and incomplete data made it difficult to assess the methodological quality of the studies included in our analysis, to compare our inclusion and exclusion criteria, and to identify and control for other characteristics that may not have been reflected in the publication. The validity of our meta-analysis is limited by quality analysis of selected studies. Our most important limitations are the poor definition of the index test, which is clinical symptoms and signs, in all studies. Furthermore, unclear specification of blind interpretation of the index test or reference standard results. Consensus tools like QUADAS [29,103] are needed to assess the quality of studies included in meta-analysis such as this and necessary to performing diagnostic test studies designs. Therefore, it is difficult to exclude the possibility of work-up bias, diagnostic-review bias, test-review bias or incorporation bias [104], all of which could have influenced the selection or classification bias of the studies included in our analysis. Moreover, these problems limit our capacity to detect a bias that is particularly difficult to control in systematic reviews of diagnostic tests; namely, publication bias [105,106]. However, the existence of publication bias can be suspected. An interesting project [92] promises to assess the validity of clinical findings and improve their use in diagnosing UTI.

Conclusions

Implications for practice

Clinical findings are not 'per se' predictive of UTI. In women who present to their primary care provider with urinary symptoms, an office urine dipstick test could be helpful to guide UTI diagnosis, and identification of nitrites or leukocytes is a good predictor of UTI.

Implications for research

The quality of meta-analyses and systematic reviews of diagnostic tests could be improved by the development of consensus tools. These tools would aid in the design of better studies and in the analysis of clinical findings, which would ultimately improve the diagnosis of UTI.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DMB participated in the coordination and design of the study, in search, review and selection of articles, perfomed the statistical analysis and drafted the manuscript. AJP participated in the design of the study, in review an selection of articles and helped to draft the manuscript. Both authors read and approved the final manuscript.

Acknowledgements

The authors are grateful to Xavier Bonfill Gosp MD, PhD; Magdalena Esteva Cantó MD, PhD; Maria Ramos Monserrat MD, PhD; Carlos. Campillo Artero MD, PhD; Francisco Campoamor Landín MD and Victor Abraira Santos MD, PhD; for their helpful insights in critically revising and the manuscript for important intellectual content, but does not meet criteria for authorship. A portion of this study was completed as part of the first author's PhD dissertation at the Universitat Autònoma de Barcelona under XBG direction.

This research was funded by an Evidence-Based Healthcare (ASBE) grant awarded by the Iberoamerican Cochrane Center at Universitat Autònoma de Barcelona. This study was also supported by the University Institute of Health Sciences and by the Preventive Activities and Health Promotion Network grant RD06/0018/0036 of the Ministry of Health and Social Participation.

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