Development of a bedside tool to predict the probability of drug-resistant pathogens among hospitalized adult patients with gram-negative infections

Abstract

Background We developed a clinical bedside tool to simultaneously estimate the probabilities of third-generation cephalosporin-resistant Enterobacteriaceae (3GC-R), carbapenem-resistant Enterobacteriaceae (CRE), and multidrug-resistant Pseudomonas aeruginosa (MDRP) among hospitalized adult patients with Gram-negative infections. Methods Data were obtained from a retrospective observational study of the Premier Hospital that included hospitalized adult patients with a complicated urinary tract infection (cUTI), complicated intra-abdominal infection (cIAI), hospital-acquired/ventilator-associated pneumonia (HAP/VAP), or bloodstream infection (BSI) due to Gram-negative bacteria between 2011 and 2015. Risk factors for 3GC-R, CRE, and MDRP were ascertained by multivariate logistic regression, and separate models were developed for patients with community-acquired versus hospital-acquired infections for each resistance phenotype ( N = 6). Models were converted to a singular user-friendly interface to estimate the probabilities of a patient having an infection due to 3GC-R, CRE, or MDRP when ≥ 1 risk factor was present. Results Overall, 124,068 patients contributed to the dataset. Percentages of patients admitted for cUTI, cIAI, HAP/VAP, and BSI were 61.6, 4.6, 16.5, and 26.4%, respectively (some patients contributed > 1 infection type). Resistant infection rates were 1.90% for CRE, 12.09% for 3GC-R, and 3.91% for MDRP. A greater percentage of the resistant infections were community-acquired relative to hospital-acquired (CRE, 1.30% vs 0.62% of 1.90%; 3GC-R, 9.27% vs 3.42% of 12.09%; MDRP, 2.39% vs 1.59% of 3.91%). The most important predictors of having an 3GC-R, CRE or MDRP infection were prior number of antibiotics; infection site; infection during the previous 3 months; and hospital prevalence of 3GC-R, CRE, or MDRP. To enable application of the six predictive multivariate logistic regression models to real-world clinical practice, we developed a user-friendly interface that estimates the risk of 3GC-R, CRE, and MDRP simultaneously in a given patient with a Gram-negative infection based on their risk (Additional file 1). Conclusions We developed a clinical prediction tool to estimate the probabilities of 3GC-R, CRE, and MDRP among hospitalized adult patients with confirmed community- and hospital-acquired Gram-negative infections. Our predictive model has been implemented as a user-friendly bedside tool for use by clinicians/healthcare professionals to predict the probability of resistant infections in individual patients, to guide early appropriate therapy. Electronic supplementary material The online version of this article (10.1186/s12879-019-4363-y) contains supplementary material, which is available to authorized users.

Approach to the Treatment of Patients with Serious Multidrug‐Resistant Pseudomonas aeruginosa Infections

“…In a large database study that evaluated risk factors for development of MDR-PSA infection, patients from hospitals with an MDR-PSA prevalence ≥ 4% were at greatest risk. 37 Critical illness requiring prolonged intensive care and prolonged mechanical ventilation appear to be particularly important health care-associated risk factors for colonization and infection. 37,38 Previous exposure to antipseudomonal antibiotics, particularly carbapenems and fluoroquinolones, also contribute to increased risk for infection with an MDR-PSA.…”

“…37 Critical illness requiring prolonged intensive care and prolonged mechanical ventilation appear to be particularly important health care-associated risk factors for colonization and infection. 37,38 Previous exposure to antipseudomonal antibiotics, particularly carbapenems and fluoroquinolones, also contribute to increased risk for infection with an MDR-PSA. [37][38][39][40] In addition to specific agent exposures, data suggest that cumulative antibiotic exposure increases the risk of infection with an MDR-PSA.…”

“…37,38 Previous exposure to antipseudomonal antibiotics, particularly carbapenems and fluoroquinolones, also contribute to increased risk for infection with an MDR-PSA. [37][38][39][40] In addition to specific agent exposures, data suggest that cumulative antibiotic exposure increases the risk of infection with an MDR-PSA. 37 Empiric Therapy Options for Patients with Suspected or Documented MDR-PSA Infections Across published guidelines, the most commonly recommended agents for the empiric treatment of patients with suspected or documented MDR-PSA include the use of one of the commercially available conventional antipseudomonal blactams (i.e., meropenem, imipenem, piperacillintazobactam, cefepime, and ceftazidime), potentially in combination with a second agent such as an aminoglycoside, fluoroquinolone, or polymyxin.…”

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

“…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.…”

Early Prediction of the Carbapenem Resistance Gram-negative Bacteria Carriage in Intensive Care Unit using Machine-Learning

“…the previous three months 25 . The researchers included a total of 120,000 samples, but there were a few variables and the models could only vaguely indicate which patients were likely to develop into the infections.…”