Developing a risk prediction model for multidrug-resistant bacterial infection in patients with biliary tract infection

Abstract Background Although Escherichia coli is the most frequently isolated microorganism in acute biliary tract infections with bacteremia, data regarding its virulence are limited. Results Information on cases of bacteremia in acute biliary tract infection in a retrospective study was collected from 2013 to 2015 at a tertiary care hospital in Japan. Factors related to the severity of infection were investigated, including patient background, phylogenetic typing, and virulence factors of E. coli, such as adhesion, invasion, toxins, and iron acquisition. In total, 72 E. coli strains were identified in 71 cases, most of which primarily belonged to the B2 phylogroup (68.1%). The presence of the iutA gene (77.3% in the non-severe group, 46.4% in the severe group, P = 0.011) and the ibeA gene (9.1% in the non-severe group, and 35.7% in the severe group, P = 0.012) was significantly associated with the severity of infection. Among the patient characteristics, diabetes mellitus with organ involvement and alkaline phosphatase were different in the severe and non-severe groups. Conclusions We showed that bacteremic E. coli strains from acute biliary tract infections belonged to the virulent (B2) phylogroup. The prevalence of the iutA and ibeA genes between the two groups of bacteremia severity was significantly different.

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Wound dehiscence after radical cystectomy: A novel risk-prediction model

European Urology ◽

10.1016/s0302-2838(21)01180-5 ◽

2021 ◽

Vol 79 ◽

pp. S1112-S1113

Author(s):

A.A. Nasrallah ◽

M. Mansour ◽

C.H. Ayoub ◽

N. Abou Heidar ◽

J.A. Najdi ◽

...

Keyword(s):

Prediction Model ◽

Radical Cystectomy ◽

Risk Prediction ◽

Wound Dehiscence ◽

Risk Prediction Model

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

Diagnostic and Prognostic Research ◽

10.1186/s41512-020-00089-w ◽

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.

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