Establishment of the Risk Prediction Model for Significant Bacteriuria in Adult Patients with Automated Urine Analysis

2021 ◽  
pp. 1-6
Author(s):  
Hualin He ◽  
Zheng Wang ◽  
Li Zuo ◽  
Ling Zhang ◽  
Cheng Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Urine Analysis ◽  
Roc Curves ◽  
Bacterial Count ◽  
Analysis Data ◽  
Automatic Analysis ◽  
Good Prediction ◽  
Predictive Values ◽  
Significant Bacteriuria ◽  
Tract Infections

<b><i>Introduction:</i></b> Urinary tract infections (UTIs) have been proven to be the most encountered bacterial infection in humans. We hope to establish a prediction model for significant bacteriuria by comprehensively analyzing the relevant parameters of age, gender, and urine automatic analysis data. <b><i>Methods:</i></b> A retrospective study was performed at Tai’an Central Hospital. All samples included in the study were tested for urine culture and urine automatic analysis. Data analysis was conducted with the SPSS. <b><i>Results:</i></b> The binary logistic regression module is used to establish the forecast formula, which gender, age, leukocyte count, bacterial count, leukocyte esterase, and nitrite were included. Receiver operating characteristic (ROC) curves showed that the area under ROC curve (AUC) of the prediction model was 0.878, bigger than the AUCs of the other 6 independent variables. The sensitivity and specificity of prediction model were 61.68 and 95.98%, respectively. The positive and the negative predictive values of the predictive model are 87.13 and 85.02%, respectively. <b><i>Conclusions:</i></b> The prediction formula obtained in our study can achieve good prediction effect for significant bacteriuria, which can effectively avoid the treatment delay or antibiotic abuse caused by the subjective judgment of doctors.

scholarly journals A new point-of-care test for the rapid detection of urinary tract infections

2019 ◽  
Vol 39 (2) ◽  
pp. 325-332
Author(s):  
Alyexandra Arienzo ◽  
Valentina Cellitti ◽  
Valeria Ferrante ◽  
Francesca Losito ◽  
Ottavia Stalio ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Urine Analysis ◽  
Point Of Care ◽  
Age Groups ◽  
Bacterial Count ◽  
Open Label ◽  
Point Of Care Test ◽  
Biological Survey ◽  
Tract Infections

Abstract Urinary tract infections (UTIs) are among the most common infections in all age groups. Fast and accurate diagnosis is essential to ensure a timely and effective therapy. Alongside with reference culture-based methods, several point-of-care tests (POCTs) for early detection of UTIs have been developed, but they have not been significantly implemented in current clinical practice. The Micro Biological Survey (MBS) POCT is a simple test developed by MBS Diagnostics Ltd. (London, UK) for the detection and management of UTIs. The present study has been undertaken to investigate the potentials and limits of the MBS POCT. A total of 349 patients were enrolled in two open-label, monocentric, non-interventional clinical trials in collaboration with an Emergency Medicine department and the outpatient clinic of two hospitals in Rome. Results of urine analysis using the MBS POCT were compared with those of the routine culture-based tests for UTI diagnosis performed by the hospital laboratory. The MBS POCT provided fast results revealing high bacterial count UTIs (≥ 105 CFU/ml) with 97% accuracy, 92% sensitivity, 100% specificity, 99% PPV, and 96% NPV within a 5-h analytical time threshold.

Accuracy of urinary symptoms and urine microscopy in diagnosing urinary tract infection in women

2018 ◽  
Vol 36 (4) ◽  
pp. 417-424 ◽  
Author(s):  
Ngiap Chuan Tan ◽  
Agnes Ying Leng Koong ◽  
Lok Pui Ng ◽  
Pei Lin Hu ◽  
Eileen Yi Ling Koh ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Demographic Data ◽  
Bacterial Count ◽  
Urinary Symptoms ◽  
Adult Women ◽  
Cross Sectional ◽  
Predictive Values ◽  
Tract Infections ◽  
Urine Microscopy ◽  
Culture Positive

Abstract Background Women with urinary tract infections (UTIs) often present with urinary complaints such as frequency of micturition, dysuria, foul-smelling urine and other non-specific symptoms like fever. Physicians may order urine microscopy to guide empirical antibiotic prescription. However, the performance of this approach has not been assessed. Objectives This study aimed to determine the accuracy of UTI symptoms and urine microscopy associated with culture-positive UTI in Asian women. Methods A cross-sectional study of adult women who presented with UTI-related symptoms was conducted at three public primary care clinics in Singapore. Demographic data and information on their symptoms were collected, followed by urine microscopy and culture to diagnose UTI. The sensitivity, specificity, positive (PPV), negative predictive values (NPV), accuracy (ACC) and area under curve (AUC) of combinations of symptom and urine investigations were analysed in association with culture-positive UTI, which was regarded as a benchmark. Results Data on 564 women (73.9% Chinese, 11.5% Malay, 8.2% Indian) were analysed, of which 259 (45.9%) had culture-positive UTI. Frequency and foul-smelling urine, pyuria (WBC ≥10/hpf) and semi-quantitative bacterial count (≥2+) were significantly associated with positive urine culture. The ACC and AUC for single or multiple urinary and/or general symptoms were low. Urine pyuria (minimally >10/hpf) alone or in combination with symptoms and/or semi-quantitative bacterial count achieved high sensitivity (>85%) and PPV, NPV, ACC and AUC of >70%. Conclusion Urinary symptoms have limited accuracy in diagnosing culture-positive UTI. Concurrent urine microscopy showing presence of pyuria and/or bacterial count increased the diagnostic accuracy of culture-positive UTI.

scholarly journals Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2869
Author(s):  
Indah Jamtani ◽  
Kwang-Woong Lee ◽  
Yun-Hee Choi ◽  
Young-Rok Choi ◽  
Jeong-Moo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Prediction Model ◽  
Risk Group ◽  
Significant Risk ◽  
Risk Scores ◽  
Accurate Information ◽  
Survival Prediction ◽  
Good Prediction ◽  
Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

scholarly journals Catheter-Associated Urinary Tract Infections (CAUTIs) Reduction: A Multidisciplinary Approach

2020 ◽  
Vol 41 (S1) ◽  
pp. s154-s154
Author(s):  
Hanish Jain ◽  
Elizabeth Hartigan ◽  
Joseph Tschopp ◽  
Paul Suits ◽  
Kristopher Paolino
Keyword(s):  
Best Practice ◽  
Registered Nurse ◽  
Urine Analysis ◽  
Urine Culture ◽  
Care Center ◽  
Foley Catheter ◽  
Tertiary Care ◽  
Culture Collection ◽  
Institutional Cost ◽  
Tract Infections

Background: CAUTIs remain one of the most common hospital-acquired infections (HAIs) accounting for prolonged hospital stay and increased healthcare costs. According to the NHSN, the standardized infection ratio (SIR) at our institution was 1.6 compared to national average of 0.84 in 2018. We highlight the interventions implemented in our institution to prevent CAUTIs. These interventions have shown a reduction in the rate of CAUTIs, the SIR, Foley catheter days, and institutional cost. Methods: In addition to standard CAUTI prevention practices, we hypothesized that we could decrease CAUTIs through the daily implementation of specific practices. We developed a comprehensive interdisciplinary team which included the staff or charge registered nurse (RN), the unit manager, an infection preventionist, an advanced practice registered nurse (APRN), a pharmacist with an antimicrobial focus, and a physician from the infectious disease department who would conduct daily rounds on different units in the institution for education and assessment of catheter indications. A detailed review and analysis of the urine culture orders for patients with a Foley catheter was performed. A nurse-driven Foley catheter removal protocol before urine culture collection was initiated. We implemented a Foley catheter bundle that has guidelines for Foley insertion, best practice competency, and urinary catheter best practice algorithm and advocated alternative use of male or female external catheter. We educated physicians about ordering a reflexive urine analysis test followed by urine culture instead of testing either individually after removal of a Foley catheter. Lastly, we performed a root-cause analysis on all reported CAUTIs. These policies were implemented in a 435-bed tertiary-care center in November 2018, and we present data from 1-year before and after the interventions. Results: At our institution, we had 71 CAUTIs, with an SIR of 1.6, a standardized utilization ratio (SUR) of 0.92, 27,621 Foley days, and institutional cost of $979,303 compared to 40 CAUTIs with an SIR of 1, an SUR of 0.88, 24,193 Foley days, and institutional cost $537,927 after implementing our interventions. Conclusions: CAUTIs can be reduced by implementing specific measures that include infection control team rounds, nurse-driven protocol, and the use of Foley catheter bundles. Measures should be undertaken to prioritize these practices as part of a protocol. We advocate further studies to evaluate these measures. Education programs for healthcare professionals concerning CAUTIs and its complications can be implemented to carry out the prevention methods efficiently.Funding: NoneDisclosures: None

Predictive value of time-variant color-coded multiphase CT angiography (mCTA) regarding clinical outcome of acute ischemic stroke: in comparison with conventional mCTA and CT perfusion

2020 ◽  
pp. 028418512098177
Author(s):  
Yu Lin ◽  
Nannan Kang ◽  
Jianghe Kang ◽  
Shaomao Lv ◽  
Jinan Wang
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Predictive Value ◽  
Ct Perfusion ◽  
Roc Curves ◽  
Multivariate Logistic Regression Model ◽  
Infarct Core ◽  
Predictive Values ◽  
Significant Difference ◽  
Core Volume

Background Color-coded multiphase computed tomography angiography (mCTA) can provide time-variant blood flow information of collateral circulation for acute ischemic stroke (AIS). Purpose To compare the predictive values of color-coded mCTA, conventional mCTA, and CT perfusion (CTP) for the clinical outcomes of patients with AIS. Material and Methods Consecutive patients with anterior circulation AIS were retrospectively reviewed at our center. Baseline collateral scores of color-coded mCTA and conventional mCTA were assessed by a 6-point scale. The reliabilities between junior and senior observers were assessed by weighted Kappa coefficients. Receiver operating characteristic (ROC) curves and multivariate logistic regression model were applied to evaluate the predictive capabilities of color-coded mCTA and conventional mCTA scores, and CTP parameters (hypoperfusion and infarct core volume) for a favorable outcome of AIS. Results A total of 138 patients (including 70 cases of good outcomes) were included in our study. Patients with favorable prognoses were correlated with better collateral circulations on both color-coded and conventional mCTA, and smaller hypoperfusion and infarct core volume (all P < 0.05) on CTP. ROC curves revealed no significant difference between the predictive capability of color-coded and conventional mCTA ( P = 0.427). The predictive value of CTP parameters tended to be inferior to that of color-coded mCTA score (all P < 0.001). Both junior and senior observers had consistently excellent performances (κ = 0.89) when analyzing color-coded mCTA maps. Conclusion Color-coded mCTA provides prognostic information of patients with AIS equivalent to or better than that of conventional mCTA and CTP. Junior radiologists can reach high diagnostic accuracy when interpreting color-coded mCTA images.

scholarly journals Nomogram based on homogeneous and heterogeneous associated factors for predicting distant metastases in patients with colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tianwen Luo ◽  
Yutong Wang ◽  
Xuefeng Shan ◽  
Ye Bai ◽  
Chun Huang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lung Metastases ◽  
Roc Curves ◽  
Distant Metastases ◽  
Good Prediction ◽  
Operating Characteristics ◽  
Good Prediction Performance ◽  
Different Types ◽  
Associated Risk Factors

Abstract Background The identification of the homogeneous and heterogeneous risk factors for different types of metastases in colorectal cancer (CRC) may shed light on the aetiology and help individualize prophylactic treatment. The present study characterized the incidence differences and identified the homogeneous and heterogeneous risk factors associated with distant metastases in CRC. Methods CRC patients registered in the SEER database between 2010 and 2016 were included in this study. Logistic regression was used to analyse homogeneous and heterogeneous risk factors for the occurrence of different types of metastases. Nomograms were constructed to predict the risk for developing metastases, and the performance was quantitatively assessed using the receiver operating characteristics (ROC) curve and calibration curve. Results A total of 204,595 eligible CRC patients were included in our study, and 17.07% of them had distant metastases. The overall incidences of liver metastases, lung metastases, bone metastases, and brain metastases were 15.34%, 5.22%, 1.26%, and 0.29%, respectively. The incidence of distant metastases differed by age, gender, and the original CRC sites. Poorly differentiated grade, more lymphatic metastasis, higher carcinoembryonic antigen (CEA), and different metastatic organs were all positively associated with four patterns of metastases. In contrast, age, sex, race, insurance status, position, and T stage were heterogeneously associated with metastases. The calibration and ROC curves exhibited good performance for predicting distant metastases. Conclusions The incidence of distant metastases in CRC exhibited distinct differences, and the patients had homogeneous and heterogeneous associated risk factors. Although limited risk factors were included in the present study, the established nomogram showed good prediction performance.

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

scholarly journals Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Miyoshi ◽  
Tsubasa Maeda ◽  
Katsuyoshi Matsuoka ◽  
Daisuke Saito ◽  
Sawako Miyoshi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ulcerative Colitis ◽  
Prediction Model ◽  
Clinical Features ◽  
Unmet Need ◽  
Patient Treatment ◽  
Clinical Activity ◽  
Prediction Tool ◽  
Real World Data ◽  
Predictive Values

AbstractPredicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.

scholarly journals A multicenter evaluation of viral bloodstream detections in children presenting to the Emergency Department with suspected systemic infection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christina A. Rostad ◽  
Neena Kanwar ◽  
Jumi Yi ◽  
Claudia R. Morris ◽  
Jennifer Dien Bard ◽  
...  
Keyword(s):  
Emergency Department ◽  
Whole Blood ◽  
Bacterial Infections ◽  
Viral Infections ◽  
Systemic Infection ◽  
Common Symptom ◽  
Predictive Values ◽  
Healthcare Facilities ◽  
Tract Infections ◽  
Systemic Infections

Abstract Background Fever is a common symptom in children presenting to the Emergency Department (ED). We aimed to describe the epidemiology of systemic viral infections and their predictive values for excluding serious bacterial infections (SBIs), including bacteremia, meningitis and urinary tract infections (UTIs) in children presenting to the ED with suspected systemic infections. Methods We enrolled children who presented to the ED with suspected systemic infections who had blood cultures obtained at seven healthcare facilities. Whole blood specimens were analyzed by an experimental multiplexed PCR test for 7 viruses. Demographic and laboratory results were abstracted. Results Of the 1114 subjects enrolled, 245 viruses were detected in 224 (20.1%) subjects. Bacteremia, meningitis and UTI frequency in viral bloodstream-positive patients was 1.3, 0 and 10.1% compared to 2.9, 1.3 and 9.7% in viral bloodstream-negative patients respectively. Although viral bloodstream detections had a high negative predictive value for bacteremia or meningitis (NPV = 98.7%), the frequency of UTIs among these subjects remained appreciable (9/89, 10.1%) (NPV = 89.9%). Screening urinalyses were positive for leukocyte esterase in 8/9 (88.9%) of these subjects, improving the ability to distinguish UTI. Conclusions Viral bloodstream detections were common in children presenting to the ED with suspected systemic infections. Although overall frequencies of SBIs among subjects with and without viral bloodstream detections did not differ significantly, combining whole blood viral testing with urinalysis provided high NPV for excluding SBI.

A New Screening Approach For Fast And Accurate Prediction Of Positive And Negative Urine Cultures By SediMAX Compared With The Standard Urine Culture

2021 ◽  
Vol 1 (1) ◽  
pp. 46-55
Author(s):  
Massimo Pieri ◽  
Flaminia Tomassetti ◽  
Paola Cerini ◽  
Roberta Felicetti ◽  
Lucia Ceccaroni ◽  
...  
Keyword(s):  
Blood Cell ◽  
Bacterial Infections ◽  
Urine Culture ◽  
Screening Method ◽  
Significant Proportion ◽  
Bacterial Count ◽  
Urine Samples ◽  
Screening Methods ◽  
Urine Sediment ◽  
Tract Infections

Urinary tract infections (UTI) are the most frequent bacterial infections, and the detection of infection in urine samples is expensive and time-consuming. Also, in laboratories a significant proportion of samples processed yield negative results. For this, screening methods represent an important improvement towards the final UTI diagnosis. SediMAX is an automated microscopy, easier to use in laboratories due to its basic procedure and it is widely used for urine sediment analysis. In our study, we evaluated the performance of SediMAX, applying some screening parameters, compared with the gold standard methods, urine culture, to identify all the positive cases for UTI. We analysed 1185 urine samples from our daily laboratory routine. The basis of our screening model was to establish a cut-off for bacterial count (BACT), as 300 bacteria/µL in order to avoid missing positive cases. However, the sensitivity and the specificity achieved were not enough to identify all UTI infection in urine samples. So, in addition to BACT we have considered other parameters, such as White Blood Cell (WBC), Red Blood Cell (RBC), Yeasts (YEST), Age and Nitrates (NIT). The second screening method reached a sensitivity of 100%, that could be reliably employed in detect of UTIs.

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