Dataset Information

Reevaluating the true diagnostic accuracy of dipstick tests to diagnose urinary tract infection using Bayesian latent class analysis.

ABSTRACT:

Objective

Previous studies on diagnostic accuracy of dipstick testing for leukocyte esterase (LE) and nitrite to diagnose urinary tract infection (UTI) had used urine culture, which is an imperfect gold standard. Estimates of diagnostic accuracy obtained using the classical gold standard framework might not reflect the true diagnostic accuracy of dipstick tests.

Methods

We used the dataset from a prospective, observational study conducted in the emergency department of a teaching hospital in southern India. Patients with a clinical suspicion of UTI underwent dipstick testing for LE and nitrite, urine microscopy, and urine culture. Based on the results of urine microscopy and culture, UTI was classified into definite, probable, and possible. Patients with microscopic pyuria and a positive urine culture were adjudicated as definite UTI. Unequivocal imaging evidence of emphysematous pyelonephritis or perinephric collections was also considered definite UTI. We estimated the diagnostic accuracy of LE and nitrite tests using the classical analysis (assuming definite UTI as gold standard) and two different Bayesian latent class models (LCMs; 3-tests in 1-population and 2-tests in 2-populations models).

Results

We studied 149 patients. Overall, 64 (43%) patients had definite, 76 (51%) had probable, and 2 (1.3%) had possible UTI; 7 (4.6%) had alternate diagnoses. In classical analysis, LE was more sensitive than nitrite (87.5% versus 70.5%), while nitrite was more specific (24% versus 58%). The 3-tests in 1-population Bayesian LCM indicated a substantially better sensitivity and specificity for LE (98.1% and 47.6%) and nitrite (88.2% and 97.7%). True sensitivity and specificity of urine culture as estimated by the model was 48.7% and 73.0%. Estimates of the 2-tests in 2-populations model were in agreement with the 3-tests in 1-population model.

Conclusions

Bayesian LCMs indicate a clinically important improvement in the true diagnostic accuracy of urine dipstick testing for LE and nitrite. Given this, a negative dipstick LE would rule-out UTI, while a positive dipstick nitrite would rule-in UTI in our study setting. True diagnostic accuracy of urine dipstick testing for UTI in various practice settings needs reevaluation using Bayesian LCMs.

SUBMITTER: Bafna P

PROVIDER: S-EPMC7774958 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Reevaluating the true diagnostic accuracy of dipstick tests to diagnose urinary tract infection using Bayesian latent class analysis.

Bafna Prashant P Deepanjali Surendran S Mandal Jharna J Balamurugan Nathan N Swaminathan Rathinam P RP Kadhiravan Tamilarasu T

PloS one 20201231 12

<h4>Objective</h4>Previous studies on diagnostic accuracy of dipstick testing for leukocyte esterase (LE) and nitrite to diagnose urinary tract infection (UTI) had used urine culture, which is an imperfect gold standard. Estimates of diagnostic accuracy obtained using the classical gold standard framework might not reflect the true diagnostic accuracy of dipstick tests.<h4>Methods</h4>We used the dataset from a prospective, observational study conducted in the emergency department of a teaching ...[more]

PMID: 33382863

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Project description:INTRODUCTION:Dipstick urinalysis is a widely used screening tool in the evaluation of men with lower urinary tract symptoms (LUTS) suggestive of benign prostatic hyperplasia (BPH). As limited data support the use of dipstick urinalysis, we have used data from three recently published studies to assess clinical outcomes in those who had dipstick urinalysis findings for blood, glucose, and/or leukocytes. METHODS:We analyzed data from three observational studies involving men interested in using over-the-counter tamsulosin: a self-selection study (SSS) and two actual-use studies of 8-week (AUS8) and 24-week (AUS24) durations. Subgroup analyses focused on pooled data from participants not using α-blockers or other prescription medication for LUTS suggestive of BPH (nonRx users) and who had urine dipstick findings. Data from participants using α-blockers (AUS8) or any prescription BPH medications (SSS and AUS24) are presented as reference. RESULTS:Overall, 2488 nonRx users underwent dipstick urinalysis and 680 (27.3%) had positive findings including traces of blood (332; 13.3%), glucose (259; 10.4%), and/or leukocytes (245; 9.8%). Among users of prescription medicines, 21.6% (37/171) in SSS, 27.4% (23/84) in AUS8, and 31.1% (47/151) in AUS24 had urine dipstick findings. The 200 dipstick-positive nonRx users in SSS underwent per protocol urological assessment: 26 (13.0%) had a newly diagnosed condition causing/contributing to urinary symptoms of which 2.9% were identified as medically important conditions. Among nonRx users with or without a dipstick finding, medically important conditions reported included prostate cancer (1.0% vs. 1.0%, respectively) and urolithiasis (1.0% vs. 0.3%, respectively). The proportion of men with dipstick urinalysis findings was similar between men who regularly visited their physician and those who did not. CONCLUSION:Dipstick urinalysis did not markedly increase the detection of undiagnosed medically important conditions that cause/contribute to urinary symptoms, suggesting that this test may not be a very effective screening tool for men with LUTS. FUNDING:Boehringer Ingelheim Pharmaceuticals, Inc.

Project description:BackgroundEvaluating the accuracy of extrapulmonary tuberculosis (TB) tests is challenging due to lack of a gold standard. Latent class analysis (LCA), a statistical modeling approach, can adjust for reference tests' imperfect accuracies to produce less biased test accuracy estimates than those produced by commonly used methods like composite reference standards (CRSs). Our objective is to illustrate how Bayesian LCA can address the problem of an unavailable gold standard and demonstrate how it compares to using CRSs for extrapulmonary TB tests.MethodsWe re-analyzed a dataset of presumptive extrapulmonary TB cases in New Delhi, India, for three forms of extrapulmonary TB. Results were available for culture, smear microscopy, Xpert MTB/RIF, and a non-microbiological test, cytopathology/histopathology, or adenosine deaminase (ADA). A diagram was used to define assumed relationships between observed tests and underlying latent variables in the Bayesian LCA with input from an inter-disciplinary team. We compared the results to estimates obtained from a sequence of CRSs defined by increasing numbers of positive reference tests necessary for positive disease status.ResultsData were available from 298, 388, and 230 individuals with presumptive TB lymphadenitis, meningitis, and pleuritis, respectively. Using Bayesian LCA, estimates were obtained for accuracy of all tests and for extrapulmonary TB prevalence. Xpert sensitivity neared that of culture for TB lymphadenitis and meningitis but was lower for TB pleuritis, and specificities of all microbiological tests approached 100%. Non-microbiological tests' sensitivities were high, but specificities were only moderate, preventing disease rule-in. CRSs' only provided estimates of Xpert and these varied widely per CRS definition. Accuracy of the CRSs also varied by definition, and no CRS was 100% accurate.ConclusionUnlike CRSs, Bayesian LCA takes into account known information about test performance resulting in accuracy estimates that are easier to interpret. LCA should receive greater consideration for evaluating extrapulmonary TB diagnostic tests.

Dataset Information

Reevaluating the true diagnostic accuracy of dipstick tests to diagnose urinary tract infection using Bayesian latent class analysis.

Objective

Methods

Results

Conclusions

Publications

Reevaluating the true diagnostic accuracy of dipstick tests to diagnose urinary tract infection using Bayesian latent class analysis.

Similar Datasets

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