Evaluation of dipstick analysis among elderly residents to detect bacteriuria: a cross-sectional study in 32 nursing homes

Background: Up to half the residents of nursing homes for the elderly have asymptomatic bacteriuria (ABU), which should not be treated with antibiotics. Thus, it is difficult to know if new symptoms in residents with bacteriuria are caused by urinary tract infection (UTI), or if bacteriuria only represents an ABU. This is especially difficult in the presence of non-urinary tract specific symptoms. The diagnostic uncertainty is likely to generate significant overtreatment with UTI antibiotics. Aim: The general aim was to clarify the association between symptoms, bacteriuria, dipstick urinalysis and urine Interleukin-6 (IL-6) among nursing home residents to improve the diagnostic procedure of a suspected lower UTI. Methods: In 2003 a study protocol including newly onset symptoms was completed, and single voided urine specimens collected for dipstick urinalysis and cultures from 651 residents of 32 participating Swedish nursing homes for the elderly. This data was used for a study of dipstick urinalysis (Paper I) and for a study of nonspecific symptoms and bacteriuria (Paper II). In 2012, similar data was collected for 421 elderly residents of 22 nursing homes, which also included an analysis of IL-6 in urine and urine specimens from another 59 residents with urinary catheters. The association between bacteriuria, IL-6 in urine, dipstick urinalysis and newly onset symptoms was analysed (Paper III). Antimicrobial resistance rates were described among residents of nursing homes in 2012 and compared with those from 2003 (Paper IV). Results: Paper I: The negative predictive value for predicting absence of bacteriuria was 88 (84-92)% when dipstick urinalysis for nitrite and leukocyte esterase were simultaneously negative. A positive dipstick or any combination thereof could not sufficiently predict bacteriuria. Papers II-III: New or increased nonspecific symptoms were common among elderly residents of nursing homes. Residents without nonspecific symptoms had positive urine cultures as often as those with nonspecific symptoms with a duration of up to one month. Paper III: Residents with positive urine cultures had higher concentrations of IL-6 in the urine. However, among residents with positive urine cultures there were no differences in IL-6 concentrations or dipstick findings between those with or without nonspecific symptoms. Paper IV: The average rates of antimicrobial resistance were low and did not increase between 2003 and 2012 in Escherichia coli (E. coli) urinary isolates among Swedish nursing home residents. Any antibiotic treatment during the last month and hospitalization during the last six months predicted higher resistance rates among E. coli. Conclusions: Nonspecific symptoms among elderly residents of nursing homes are unlikely to be caused by bacteria in the urine. Therefore, dipstick urinalysis, IL-6 in the urine and urine cultures are of little or no value in clarifying the aetiology of nonspecific symptoms. If there is a reason for testing for bacteriuria, dipstick urinalysis for nitrite and leukocyte esterase can rule out but cannot reliably rule in bacteriuria. Antimicrobial resistance in urinary pathogens among Swedish nursing home residents remained low. It is important to use antibiotics rationally to preserve the effectiveness of antibiotics

A decision aid to rule out pneumonia and reduce unnecessary prescriptions of antibiotics in primary care patients with cough and fever

BACKGROUND: Physicians fear missing cases of pneumonia and treat many patients with signs of respiratory infection unnecessarily with antibiotics. This is an avoidable cause for the increasing worldwide problem of antibiotic resistance. We developed a user-friendly decision aid to rule out pneumonia and thus reduce the rate of needless prescriptions of antibiotics. METHODS: This was a prospective cohort study in which we enrolled patients older than 18 years with a new or worsened cough and fever without serious co-morbidities. Physicians recorded results of a standardized medical history and physical examination. C-reactive protein was measured and chest radiographs were obtained. We used Classification and Regression Trees to derive the decision tool. RESULTS: A total of 621 consenting eligible patients were studied, 598 were attending a primary care facility, were 48 years on average and 50% were male. Radiographic signs for pneumonia were present in 127 (20.5%) of patients. Antibiotics were prescribed to 234 (48.3%) of patients without pneumonia. In patients with C-reactive protein values below 10 μg/ml or patients presenting with C-reactive protein between 11 and 50 μg/ml, but without dyspnoea and daily fever, pneumonia can be ruled out. By applying this rule in clinical practice antibiotic prescription could be reduced by 9.1% (95% confidence interval (CI): 6.4 to 11.8). CONCLUSIONS: Following validation and confirmation in new patient samples, this tool could help rule out pneumonia and be used to reduce unnecessary antibiotic prescriptions in patients presenting with cough and fever in primary care. The algorithm might be especially useful in those instances where taking a medical history and physical examination alone are inconclusive for ruling out pneumonia

Predicting streptococcal pharyngitis in adults in primary care: a systematic review of the diagnostic accuracy of symptoms and signs and validation of the Centor score

Background: Stratifying patients with a sore throat into the probability of having an underlying bacterial or viral cause may be helpful in targeting antibiotic treatment. We sought to assess the diagnostic accuracy of signs and symptoms and validate a clinical prediction rule (CPR), the Centor score, for predicting group A ?-haemolytic streptococcal (GABHS) pharyngitis in adults (> 14 years of age) presenting with sore throat symptoms. Methods: A systematic literature search was performed up to July 2010. Studies that assessed the diagnostic accuracy of signs and symptoms and/or validated the Centor score were included. For the analysis of the diagnostic accuracy of signs and symptoms and the Centor score, studies were combined using a bivariate random effects model, while for the calibration analysis of the Centor score, a random effects model was used. Results: A total of 21 studies incorporating 4,839 patients were included in the meta-analysis on diagnostic accuracy of signs and symptoms. The results were heterogeneous and suggest that individual signs and symptoms generate only small shifts in post-test probability (range positive likelihood ratio (+LR) 1.45-2.33, -LR 0.54-0.72). As a decision rule for considering antibiotic prescribing (score ? 3), the Centor score has reasonable specificity (0.82, 95% CI 0.72 to 0.88) and a post-test probability of 12% to 40% based on a prior prevalence of 5% to 20%. Pooled calibration shows no significant difference between the numbers of patients predicted and observed to have GABHS pharyngitis across strata of Centor score (0-1 risk ratio (RR) 0.72, 95% CI 0.49 to 1.06; 2-3 RR 0.93, 95% CI 0.73 to 1.17; 4 RR 1.14, 95% CI 0.95 to 1.37). Conclusions: Individual signs and symptoms are not powerful enough to discriminate GABHS pharyngitis from other types of sore throat. The Centor score is a well calibrated CPR for estimating the probability of GABHS pharyngitis. The Centor score can enhance appropriate prescribing of antibiotics, but should be used with caution in low prevalence settings of GABHS pharyngitis such as primary care

Changes in diagnostic decision-making after a computerized decision support consultation based on perceptions of need and helpfulness: a preliminary report.

We examined the degree to which attending physicians, residents, and medical students' stated desire for a consultation on difficult-to-diagnose patient cases is related to changes in their diagnostic judgments after a computer consultation, and whether, in fact, their perceptions of the usefulness of these consultations are related to these changes. The decision support system (DSS) used in this study was ILIAD (v4.2). Preliminary findings based on 16 subjects' (6 general internists, 4 second-year residents in internal medicine, and 6 fourth-year medical students) workup of 136 patient cases indicated no significant main effects for 1) level of experience, 2) whether or not subjects indicated they would seek a diagnostic consultation before using the DSS, or 3) whether or not they found the DSS consultation in fact to be helpful in arriving at a diagnosis (p > .49 in all instances). Nor were there any significant interactions. Findings were similar using subjects or cases as the unit of analysis. It is possible that what may appear to be counter-intuitive, and perhaps irrational, may not necessarily be so. We are currently examining potential explanatory hypotheses in our ongoing current, larger study

Effects of a decision support system on the diagnostic accuracy of users: a preliminary report.

OBJECTIVES: To assess the effects of incomplete data upon the output of a computerized diagnostic decision support system (DSS), to assess the effects of using the system upon the diagnostic opinions of users, and to explore if these effects vary as a function of clinical experience. DESIGN: Experimental pilot study. Four clusters of nine cases each were constructed and equated for case difficulty. Definitive findings were omitted from the case abstracts. Subjects were randomly assigned to one of four clusters and were trained on the DSS prior to use. SUBJECTS: The study involved 16 physicians at three levels of clinical experience (six general internists, four residents in internal medicine, and six fourth-year medical students), from three academic medical centers. PROCEDURE: Each subject worked up nine cases, first without and then with ILIAD consultation. They were asked to offer up to six potential diagnoses and to list up to three steps that should be the next items in the diagnostic workup. Effects of DSS consultation were measured by changes in the position of the correct diagnosis in the lists of differential diagnoses, pre- and post-consultation. RESULTS: The DSS lists of diagnostic possibilities contained the correct diagnosis in 38% of cases, about midway between the levels of accuracy of residents and attending general internists. In over 70% of cases, the DSS output had no effect on the position of the correct diagnosis in the subjects' lists. The system's diagnostic accuracy was unaffected by the clinical experience of the users

Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital.

Background Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. Methods Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. Discussion Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation