Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission

Abstract

Background:Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.Objective:Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.Methods:We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.Results:Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.Conclusions:In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

The Likelihood of Developing a Carbapenem-Resistant Enterobacteriaceae Infection during a Hospital Stay

“…Ertapenem and meropenem disks were added manually to plates and incubated overnight at 37°C (11). Colonies growing within Յ27 mm of ertapenem and Յ32 mm of meropenem were further identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry (Bruker Daltonics, Inc., Billerica, MA), as previously described (11)(12)(13). For Enterobacteriaceae isolates growing within the aforementioned zone diameters (including both discrete colonies and confluent growth), carbapenem (ertapenem, meropenem, and imipenem) antimicrobial susceptibility testing was performed to identify CRE by the disk diffusion method (14).…”

Independent, external validation of clinical prediction rules for the identification of extended-spectrum β-lactamase-producing Enterobacterales, University Hospital Basel, Switzerland, January 2010 to December 2016

“…Future prediction models need to address differences in local epidemiology, possibly by allowing for adaption of specific variables according to the geographic setting. Adapting variables to specific settings may, however, prove to be challenging, as a recent study [32] aiming to predict the probability of colonisation with carbapenem-resistant organisms by including 125 variables and machine learning methods at a single institution, representing a constricted geographic setting, failed to derive a clinically useful prediction model. This points to intrinsic difficulties in generating such models, even when considering a large amount of variables deriving from one single setting and thus not subjected to differences in local epidemiology [32].…”

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

“…Recently, artificial intelligence has been used to predict the incidence of hospitalization and colonization of CRO 17 . Although there is no research on machine learning to predict the mortality rate of CRKP bloodstream infection, this study partially proves the superiority of the machine learning algorithm 18,19 .…”

“…There are few kinds of researches on the high-risk prediction model of CRKP infection with previous researches using the multiple logistic regression methods 6,[12][13][14] . However, some studies have proved the effect of the machine learning is better than the multiple logical regression in the prediction of gram-negative bacteria and CRE colonization [13][14][15] . Therefore, this study plans to use machine learning to build a risk predictive model of the mortality of CRKP bloodstream infection.…”