Development of a risk prediction model of carbapenem-resistant Enterobacteriaceae colonization among patients in intensive care units

2018 ◽  
Vol 46 (11) ◽  
pp. 1240-1244 ◽  
Author(s):  
Ju Yeon Song ◽  
Ihn Sook Jeong
Keyword(s):  
Intensive Care ◽  
Prediction Model ◽  
Intensive Care Units ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Carbapenem Resistant ◽  
Carbapenem Resistant Enterobacteriaceae

Development of a Risk Prediction Model for Carbapenem-Resistant Enterobacteriaceae Infection after Liver Transplantation: A Multinational Cohort Study

2021 ◽  
Author(s):  
Maddalena Giannella ◽  
Maristela Freire ◽  
Matteo Rinaldi ◽  
Edson Abdala ◽  
Arianna Rubin ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Univariate Analysis ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
High Morbidity ◽  
Carbapenem Resistant ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Background Patients colonized with carbapenem resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT) with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies. Methods Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. Results 840 LT recipients found to be colonized with CRE before (n=203) or after (n=637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention were retained in the prediction model. Median 30 and 60-day predicted risk was 15% (IQR 11-24%) and 21% (IQR 15-33%), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (AUC 74.6, Brier index 16.3) and bootstrapped validation dataset (AUC 73.9, Brier index 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/. Conclusions Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.

scholarly journals Construction of a risk prediction model for subsequent bloodstream infection in intestinal carriers of carbapenem-resistant Enterobacteriaceae: a retrospective study in hematology department and intensive care unit

2020 ◽  
Author(s):  
Yue Wang ◽  
Qun Lin ◽  
Ju Zhong Chen ◽  
Yan Hong Hou ◽  
Na Shen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Prediction Model ◽  
Risk Prediction ◽  
Carbapenem Resistance ◽  
Risk Prediction Model ◽  
Gastrointestinal Injury ◽  
Logistic Analysis ◽  
Carbapenem Resistant ◽  
Resistance Score ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Background To establish a risk prediction model for carbapenem-resistant Enterobacteriaceae (CRE) bloodstream infection (BSI) in intestinal carriers. Methods CRE screenings were performed every two weeks in hematology department and intensive care unit (ICU). Patients with positive CRE rectal swab screening were identified using electronic healthcare records from 15 May 2018 to 31 December 2019. All CRE strains were collected and identified. Carriers who developed CRE BSI were compared with those who did not develop CRE infection. The control group 1:1 stratified randomly matched the case group. Univariate logistic analysis, multivariate logistic analysis and stepwise regression analysis were carried out. Results A total of 42 cases were included. Multivariate analysis showed that gastrointestinal injury (OR 86.82, 95%CI 2.58-2916.59, P = 0.013), tigecycline exposure (OR 14.99, 95%CI 1.82-123.74 P = 0.012) and carbapenem resistance score (OR 11.24, 95% CI 1.81–69.70, P = 0.009) were independent risk factors for CRE BSI in intestinal carriers (P < 0.05). They were included in the Logistic regression model to predict BSI. According to receiver operating characteristic (ROC) curve analysis, the cut-off value of the model was 0.72, and the sensitivity, specificity and area under the curve (AUC) were 90.5%, 85.7% and 0.92, respectively. Conclusions The risk prediction model based on gastrointestinal injury, tigecycline exposure and carbapenem resistance score of colonizing strain can effectively predict CRE BSI in patients with CRE colonization. Early CRE screening and detection for inpatients in key departments may early warning and reduce the risk of nosocomial infection of CRE.

scholarly journals A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Risk Prediction Model ◽  
Risk Patients

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573

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