Brendan Delaney

The Diagnosis of Urinary Tract infection in Young children (DUTY): a diagnostic prospective observational study to derive and validate a clinical algorithm for the diagnosis of urinary tract infection in children presenting to primary care with an acute illness.

BACKGROUNDnIt is not clear which young children presenting acutely unwell to primary care should be investigated for urinary tract infection (UTI) and whether or not dipstick testing should be used to inform antibiotic treatment.nnnOBJECTIVESnTo develop algorithms to accurately identify pre-school children in whom urine should be obtained; assess whether or not dipstick urinalysis provides additional diagnostic information; and model algorithm cost-effectiveness.nnnDESIGNnMulticentre, prospective diagnostic cohort study.nnnSETTING AND PARTICIPANTSnChildren <u20095 years old presenting to primary care with an acute illness and/or new urinary symptoms.nnnMETHODSnOne hundred and seven clinical characteristics (index tests) were recorded from the childs past medical history, symptoms, physical examination signs and urine dipstick test. Prior to dipstick results clinician opinion of UTI likelihood (clinical diagnosis) and urine sampling and treatment intentions (clinical judgement) were recorded. All index tests were measured blind to the reference standard, defined as a pure or predominant uropathogen cultured at ≥u200910(5) colony-forming units (CFU)/ml in a single research laboratory. Urine was collected by clean catch (preferred) or nappy pad. Index tests were sequentially evaluated in two groups, stratified by urine collection method: parent-reported symptoms with clinician-reported signs, and urine dipstick results. Diagnostic accuracy was quantified using area under receiver operating characteristic curve (AUROC) with 95% confidence interval (CI) and bootstrap-validated AUROC, and compared with the clinician diagnosis AUROC. Decision-analytic models were used to identify optimal urine sampling strategy compared with clinical judgement.nnnRESULTSnA total of 7163 children were recruited, of whom 50% were female and 49% were <u20092 years old. Culture results were available for 5017 (70%); 2740 children provided clean-catch samples, 94% of whom were ≥u20092 years old, with 2.2% meeting the UTI definition. Among these, clinical diagnosis correctly identified 46.6% of positive cultures, with 94.7% specificity and an AUROC of 0.77 (95% CI 0.71 to 0.83). Four symptoms, three signs and three dipstick results were independently associated with UTI with an AUROC (95% CI; bootstrap-validated AUROC) of 0.89 (0.85 to 0.95; validated 0.88) for symptoms and signs, increasing to 0.93 (0.90 to 0.97; validated 0.90) with dipstick results. Nappy pad samples were provided from the other 2277 children, of whom 82% were <u20092 years old and 1.3% met the UTI definition. Clinical diagnosis correctly identified 13.3% positive cultures, with 98.5% specificity and an AUROC of 0.63 (95% CI 0.53 to 0.72). Four symptoms and two dipstick results were independently associated with UTI, with an AUROC of 0.81 (0.72 to 0.90; validated 0.78) for symptoms, increasing to 0.87 (0.80 to 0.94; validated 0.82) with the dipstick findings. A high specificity threshold for the clean-catch model was more accurate and less costly than, and as effective as, clinical judgement. The additional diagnostic utility of dipstick testing was offset by its costs. The cost-effectiveness of the nappy pad model was not clear-cut.nnnCONCLUSIONSnClinicians should prioritise the use of clean-catch sampling as symptoms and signs can cost-effectively improve the identification of UTI in young children where clean catch is possible. Dipstick testing can improve targeting of antibiotic treatment, but at a higher cost than waiting for a laboratory result. Future research is needed to distinguish pathogens from contaminants, assess the impact of the clean-catch algorithm on patient outcomes, and the cost-effectiveness of presumptive versus dipstick versus laboratory-guided antibiotic treatment.nnnFUNDINGnThe National Institute for Health Research Health Technology Assessment programme.

A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model

PURPOSEnTo develop a model describing core concepts and principles of data flow, data privacy and confidentiality, in a simple and flexible way, using concise process descriptions and a diagrammatic notation applied to research workflow processes. The model should help to generate robust data privacy frameworks for research done with patient data.nnnMETHODSnBased on an exploration of EU legal requirements for data protection and privacy, data access policies, and existing privacy frameworks of research projects, basic concepts and common processes were extracted, described and incorporated into a model with a formal graphical representation and a standardised notation. The Unified Modelling Language (UML) notation was enriched by workflow and own symbols to enable the representation of extended data flow requirements, data privacy and data security requirements, privacy enhancing techniques (PET) and to allow privacy threat analysis for research scenarios.nnnRESULTSnOur model is built upon the concept of three privacy zones (Care Zone, Non-care Zone and Research Zone) containing databases, data transformation operators, such as data linkers and privacy filters. Using these model components, a risk gradient for moving data from a zone of high risk for patient identification to a zone of low risk can be described. The model was applied to the analysis of data flows in several general clinical research use cases and two research scenarios from the TRANSFoRm project (e.g., finding patients for clinical research and linkage of databases). The model was validated by representing research done with the NIVEL Primary Care Database in the Netherlands.nnnCONCLUSIONSnThe model allows analysis of data privacy and confidentiality issues for research with patient data in a structured way and provides a framework to specify a privacy compliant data flow, to communicate privacy requirements and to identify weak points for an adequate implementation of data privacy.

Improving the Diagnosis and Treatment of Urinary Tract Infection in Young Children in Primary Care: Results from the DUTY Prospective Diagnostic Cohort Study

PURPOSE Up to 50% of urinary tract infections (UTIs) in young children are missed in primary care. Urine culture is essential for diagnosis, but urine collection is often difficult. Our aim was to derive and internally validate a 2-step clinical rule using (1) symptoms and signs to select children for urine collection; and (2) symptoms, signs, and dipstick testing to guide antibiotic treatment. METHODS We recruited acutely unwell children aged under 5 years from 233 primary care sites across England and Wales. Index tests were parent-reported symptoms, clinician-reported signs, urine dipstick results, and clinician opinion of UTI likelihood (clinical diagnosis before dipstick and culture). The reference standard was microbiologically confirmed UTI cultured from a clean-catch urine sample. We calculated sensitivity, specificity, and area under the receiver operator characteristic (AUROC) curve of coefficient-based (graded severity) and points-based (dichotomized) symptom/sign logistic regression models, and we then internally validated the AUROC using bootstrapping. RESULTS Three thousand thirty-six children provided urine samples, and culture results were available for 2,740 (90%). Of these results, 60 (2.2%) were positive: the clinical diagnosis was 46.6% sensitive, with an AUROC of 0.77. Previous UTI, increasing pain/crying on passing urine, increasingly smelly urine, absence of severe cough, increasing clinician impression of severe illness, abdominal tenderness on examination, and normal findings on ear examination were associated with UTI. The validated coefficient- and points-based model AUROCs were 0.87 and 0.86, respectively, increasing to 0.90 and 0.90, respectively, by adding dipstick nitrites, leukocytes, and blood. CONCLUSIONS A clinical rule based on symptoms and signs is superior to clinician diagnosis and performs well for identifying young children for noninvasive urine sampling. Dipstick results add further diagnostic value for empiric antibiotic treatment.