The Diagnostic Accuracy of Procalcitonin, Soluble Urokinase-Type Plasminogen Activator Receptors, and C-Reactive Protein in Diagnosing Urinary Tract Infections in the Emergency Department-A Diagnostic Accuracy Study.

Background: Urinary tract infections (UTIs) are a leading bacterial infection in the emergency department (ED). Diagnosing UTIs in the ED can be challenging due to the heterogeneous presentation; therefore, fast and precise tests are needed. We aimed to evaluate the diagnostic precision of procalcitonin (PCT), soluble urokinase plasminogen activator receptors (suPARs), and C-reactive protein (CRP) in diagnosing UTIs, grading the severity of UTIs, and ruling out bacteremia. Methods: We recruited adults admitted to three Danish EDs with suspected UTIs. PCT, suPAR, and CRP were used in index tests, while blood cultures, expert panel diagnosis, and severity grading were used in the reference tests. Logistic regression and area under the receiver operator characteristic curves (AUROCs) were utilized to evaluate the models and determine the optimal cut-offs. Results: We enrolled 229 patients. PCT diagnosed UTI with an AUROC of 0.612, detected severe disease with an AUROC of 0.712, and ruled out bacteremia with an AUROC of 0.777. SuPAR had AUROCs of 0.480, 0.638, and 0.605, while CRP had AUROCs of 0.599, 0.778, and 0.646. Conclusions: The diagnostic performance of PCT, suPAR, or CRP for UTIs or to rule out severe disease was poor. However, PCT can safely rule out bacteremia in clinically relevant numbers in ED patients suspected of UTI.

Monitoring performance of clinical artificial intelligence: a scoping review protocol.

OBJECTIVE: The objective of this scoping review is to describe the scope and nature of research on the monitoring of clinical artificial intelligence (AI) systems. The review will identify the various methodologies used to monitor clinical AI, while also mapping the factors that influence the selection of monitoring approaches. INTRODUCTION: AI is being used in clinical decision-making at an increasing rate. While much attention has been directed toward the development and validation of AI for clinical applications, the practical implementation aspects, notably the establishment of rational monitoring/quality assurance systems, has received comparatively limited scientific interest. Given the scarcity of evidence and the heterogeneity of methodologies used in this domain, there is a compelling rationale for conducting a scoping review on this subject. INCLUSION CRITERIA: This scoping review will include any publications that describe systematic, continuous, or repeated initiatives that evaluate or predict clinical performance of AI models with direct implications for the management of patients in any segment of the health care system. METHODS: Publications will be identified through searches of the MEDLINE (Ovid), Embase (Ovid), and Scopus databases. Additionally, backward and forward citation searches, as well as a thorough investigation of gray literature, will be conducted. Title and abstract screening, full-text evaluation, and data extraction will be performed by 2 or more independent reviewers. Data will be extracted using a tool developed by the authors. The results will be presented graphically and narratively. REVIEW REGISTRATION: Open Science Framework https://osf.io/afkrn.

Urine Flow Cytometry and Dipstick Analysis in Diagnosing Bacteriuria and Urinary Tract Infections among Adults in the Emergency Department-A Diagnostic Accuracy Trial.

Urinary tract infections (UTIs) are a leading infectious cause of emergency department admission. Early UTI diagnosis is challenging, and a faster, preferably point-of-care urine analysis is necessary. We aimed to evaluate the diagnostic accuracy of urine flow cytometry (UFC) and urine dipstick analysis (UDA) in identifying bacteriuria and UTIs. This study included adults suspected of an infection admitted to three Danish emergency departments. UFC and UDA were the index tests, and urine culture and an expert panel diagnosis were the reference tests. We used logistic regression and receiver operator characteristics curves to find each test's optimal model and cut-off. We enrolled 966 patients and performed urine cultures on 786. Urine culture was positive in 337, and 200 patients were diagnosed with a UTI. The UFC model ruled out bacteriuria in 10.9% with a negative predictive value (NPV) of 94.6% and ruled out UTI in 38.6% with an NPV of 97.0%. UDA ruled out bacteriuria in 52.1% with an NPV of 79.2% and UTI in 52.8% with an NPV of 93.9%. Neither UFC nor UDA performed well in ruling out bacteriuria in our population. In contrast, both tests ruled out UTI safely and in clinically relevant numbers.

POCT urine dipstick versus central laboratory analyses: Diagnostic performance and logistics in the medical emergency department.

OBJECTIVES: The aim of this study was to evaluate the logistics and diagnostic performances of dipstick analyses compared to their counterpart central laboratory analyses for detection of bacteriuria, proteinuria, hyperglycemia, ketosis and hematuria. DESIGN AND METHODS: Urine dipstick results, urine culture results, flow cytometric cell counts, U-albumin-to-creatinine ratio, P-glucose and P-beta-hydroxybutyrate were retrospectively reviewed in a cohort of consecutive patients admitted to the medical emergency departments of two Danish hospitals. Sensitivity, specificity and predictive values of traditional dipstick analysis were estimated and dipstick was compared to flow cytometry for detection of significant bacteriuria using logistic regression. Turn-around-time for central laboratory analyses were assessed. RESULTS: For each comparison, 1,997 patients or more were included. Traditional dipstick analyses for proteinuria, bacteriuria and ketosis reached sensitivities of up to 90%, while sensitivity for hyperglycemia was 59%. Flow cytometry outperformed traditional dipstick analysis for detection of bacteriuria with a difference in the area under the ROC-curve of 0.07. Turn-around-times for 95% delivery of central laboratory analysis results ranged from approximately 1½ to 2 h. CONCLUSIONS: For the detection of bacteriuria and albuminuria, central laboratory analyses reach better performance than dipstick analysis while achieving acceptable turn-around-times and are thus viable alternatives to dipstick analysis. For detection of ketosis and hyperglycemia, dipstick analysis does not perform adequately, but as very short turn-around-time is often required, these conditions may be best diagnosed by point-of-care blood test rather than dipstick or central laboratory analyses. Dipstick hemoglobin analysis, flow cytometry and microscopic evaluation may serve each their distinct purposes, and thus are relevant in the emergency department.

Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report.

BACKGROUND: Mouth pain has been associated with abnormal vitamin B6 levels. Hypophosphatasia is a rare genetic disease, which causes imbalances between B6 vitamers. We report the case of a patient with hypophosphatasia and burning mouth pain. CASE PRESENTATION: A 39-year old Caucasian male with chronic burning mouth pain underwent extensive investigations with no cause of the pain being found. During the course of the investigation, an elevated vitamin B6 (pyridoxal phosphate) level was detected, which led to the diagnosis of hypophosphatasia. We hypothesize that the patient's mouth pain stems from hypophosphatasia through a B6 dependent mechanism. CONCLUSIONS: Mouth pain may, in some cases, be a symptom of hypophosphatasia and when investigating B6 in relation to mouth pain, attention should be paid to the exact B6 vitamer measured. The case underlines the importance of low alkaline phosphatase results, especially in patients with unexplained pain, as this should prompt suspicion of hypophosphatasia.

Deep learning detects and visualizes bleeding events in electronic health records.

BACKGROUND: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection. OBJECTIVES: To develop a deep learning model that detects and visualizes bleeding events in electronic health records. PATIENTS/METHODS: Three hundred electronic health records with International Classification of Diseases, Tenth Revision diagnosis codes for bleeding or leukemia were extracted. Each sentence in the electronic health record was annotated as positive or negative for bleeding. The annotated sentences were used to develop a deep learning model that detects bleeding at sentence and note level. RESULTS: On a balanced test set of 1178   sentences, the best-performing deep learning model achieved a sensitivity of 0.90, specificity of 0.90, and negative predictive value of 0.90. On a test set consisting of 700 notes, of which 49 were positive for bleeding, the model achieved a note-level sensitivity of 1.00, specificity of 0.52, and negative predictive value of 1.00. By using a sentence-level model on a note level, the model can explain its predictions by visualizing the exact sentence in a note that contains information regarding bleeding. Moreover, we found that the model performed consistently well across different types of bleedings. CONCLUSIONS: A deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.

Pneumatic tube transportation of urine samples.

OBJECTIVES: Pneumatic tube transportation of samples is an effective way of reducing turn-around-time, but evidence of the effect of pneumatic tube transportation on urine samples is lacking. We thus wished to investigate the effect of pneumatic tube transportation on various components in urine, in order to determine if pneumatic tube transportation of these samples is feasible. METHODS: One-hundred fresh urine samples were collected in outpatient clinics and partitioned with one partition being carried by courier to the laboratory, while the other was sent by pneumatic tube system (Tempus600). Both partitions were then analysed for soluble components and particles, and the resulting mean difference and limits of agreement were calculated. RESULTS: Albumin, urea nitrogen, creatinine, protein and squamous epithelial cells were unaffected by transportation in the Tempus600 system, while bacteria, renal tubular epithelial cells, white blood cells and red blood cells were affected and potassium and sodium may have been affected. CONCLUSIONS: Though pneumatic tube transportation did affect some of the investigated components, in most cases the changes induced were clinically acceptable, and hence samples could be safely transported by the Tempus600 pneumatic tube system. For bacteria, white blood cells and red blood cells local quality demands will determine if pneumatic tube transportation is appropriate.