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

2021 ◽  
Vol Volume 14 ◽  
pp. 815-824
Author(s):  
Yue Wang ◽  
Qun Lin ◽  
Zhongju Chen ◽  
Hongyan Hou ◽  
Na Shen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Prediction Model ◽  
Bloodstream Infection ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Carbapenem Resistant ◽  
Carbapenem Resistant Enterobacteriaceae

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 Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

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.

PROPER: Development of an Early Pediatric Intensive Care Unit Readmission Risk Prediction Tool

2016 ◽  
Vol 33 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Harsheen Kaur ◽  
James M. Naessens ◽  
Andrew C. Hanson ◽  
Karen Fryer ◽  
Michael E. Nemergut ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Pediatric Intensive Care Unit ◽  
Risk Prediction ◽  
Pediatric Population ◽  
Cerebral Performance Category ◽  
Pediatric Intensive Care ◽  
Risk Prediction Model ◽  
Retrospective Case

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

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