Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model

Implications of deduplication on the detection rates of multidrug-resistant organism (MDRO) in various specimens: insights from the hospital infection surveillance program

BACKGROUND

Currently, different guidelines recommend using different methods to determine whether deduplication is necessary when determining the detection rates of multidrug-resistant organisms (MDROs). However, few studies have investigated the effect of deduplication on MDRO monitoring data. In this study, we aimed to investigate the influence of deduplication on the detection rates of MDROs in different specimens to assess its impact on infection surveillance outcomes.

METHODS

Samples were collected from hospitalized patients admitted between January 2022 and December 2022; four types of specimens were collected from key monitored MDROs, including sputum samples, urine samples, blood samples, and bronchoalveolar lavage fluid (BALF) samples. In this study, we compared and analysed the detection rates of carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Escherichia coli (CRECO), carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Pseudomonas aeruginosa (CRPA), and methicillin-resistant Staphylococcus aureus (MRSA) under two conditions: with and without deduplication.

RESULTS

When all specimens were included, the detection rates of CRKP, CRAB, CRPA, and MRSA without deduplication (33.52%, 77.24%, 44.56%, and 56.58%, respectively) were significantly greater than those with deduplication (24.78%, 66.25%, 36.24%, and 50.83%, respectively) (all P < 0.05). The detection rates in sputum samples were significantly different between samples without duplication (28.39%, 76.19%, 46.95%, and 70.43%) and those with deduplication (19.99%, 63.00%, 38.05%, and 64.50%) (all P < 0.05). When deduplication was not performed, the rate of detection of CRKP in urine samples reached 30.05%, surpassing the rate observed with deduplication (21.56%) (P < 0.05). In BALF specimens, the detection rates of CRKP and CRPA without deduplication (39.78% and 53.23%, respectively) were greater than those with deduplication (31.62% and 42.20%, respectively) (P < 0.05). In blood samples, deduplication did not have a significant impact on the detection rates of MDROs.

CONCLUSION

Deduplication had a significant effect on the detection rates of MDROs in sputum, urine, and BALF samples. Based on these data, we call for the Infection Prevention and Control Organization to align its analysis rules with those of the Bacterial Resistance Surveillance Organization when monitoring MDRO detection rates.

Efficacy of Random Forest Models in Predicting Multidrug-Resistant Gram-Negative Bacterial Nosocomial Infections Compared to Traditional Logistic Regression Models

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application.

Development and application of a nomogram model for the prediction of carbapenem-resistant Klebsiella pneumoniae infection in neuro-ICU patients

IMPORTANCE

Patients in neuro-ICU are at a high risk of developing nosocomial CRKP infection owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of CRKP infection in neurocritically ill patients are lacking at present. Therefore, this study aims to establish a simple-to-use nomogram for predicting the risk of CRKP infection in patients admitted to the neuro-ICU. Three easily accessed variables were included in the model, including the number of antibiotics used, surgery, and the length of neuro-ICU stay. This nomogram might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk of neurocritically ill patients.

A nomogram to predict nosocomial infection in patients on venoarterial extracorporeal membrane oxygenation after cardiac surgery

INTRODUCTION

After cardiac surgery, patients on venoarterial extracorporeal membrane oxygenation (VA-ECMO) have a higher risk of nosocomial infection in the intensive care unit (ICU). We aimed to establish an intuitive nomogram to predict the probability of nosocomial infection in patients on VA-ECMO after cardiac surgery.

METHODS

We included patients on VA-ECMO after cardiac surgery between January 2011 and December 2020 at a single center. We developed a nomogram based on independent predictors identified using univariate and multivariate logistic regression analyses. We selected the optimal model and assessed its performance through internal validation and decision-curve analyses.

RESULTS

Overall, 503 patients were included; 363 and 140 patients were randomly divided into development and validation sets, respectively. Independent predictors derived from the development set to predict nosocomial infection included older age, white blood cell (WBC) count abnormality, ECMO environment in the ICU, and mechanical ventilation (MV) duration, which were entered into the model to create the nomogram. The model showed good discrimination, with areas under the curve (95% confidence interval) of 0.743 (0.692-0.794) in the development set and 0.732 (0.643-0.820) in the validation set. The optimal cutoff probability of the model was 0.457 in the development set (sensitivity, 0.683; specificity, 0.719). The model showed qualified calibration in both the development and validation sets (Hosmer-Lemeshow test, p > .05). The threshold probabilities ranged from 0.20 to 0.70.

CONCLUSIONS

For adult patients receiving VA-ECMO treatment after cardiac surgery, a nomogram-monitoring tool could be used in clinical practice to identify patients with high-risk nosocomial infections and provide an early warning.

A deep learning model for predicting multidrug-resistant organism infection in critically ill patients

BACKGROUND

This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients.

METHODS

This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively.

CONCLUSIONS

This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.

The ICU-CARB score: a novel clinical scoring system to predict carbapenem-resistant gram-negative bacteria carriage in critically ill patients upon ICU admission

BACKGROUND

With the widespread spread of carbapenem-resistant gram-negative bacteria (CR-GNB) in medical facilities, the carriage of CR-GNB among critically ill patients has become a significant concern in intensive care units (ICU). This study aimed to develop a scoring system to identify CR-GNB carriers upon ICU admission.

METHODS

Consecutive critically ill patients admitted to the ICU of Shanghai Ruijin Hospital between January 2017 and December 2020 were included. The patients were then divided into training and testing datasets at a 7:3 ratio. Parameters associated with CR-GNB carriage were identified using least absolute shrinkage and selection operator regression analysis. Each parameter was assigned a numerical score ranging from 0 to 100 using logistic regression analysis. Subsequently, a four-tier risk-level system was developed based on the cumulative scores, and assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

Of the 1736 patients included in this study, the prevalence of CR-GNB carriage was 10.60%. The clinical scoring system including seven variables (neurological disease, high-risk department history, length of stay ≥ 14 days, ICU history, invasive mechanical ventilation, gastrointestinal tube placement, and carbapenem usage) exhibited promising predictive capabilities. Patients were then stratified using the scoring system, resulting in CR-GNB carriage rates of 2.4%, 12.0%, 36.1%, and 57.9% at the respective risk levels (P < 0.001). Furthermore, the AUC of the developed model in the training set was calculated to be 0.82 (95% CI, 0.78-0.86), while internal validation yielded an AUC of 0.83 (95% CI, 0.77-0.89).

CONCLUSIONS

The ICU-CARB Score serves as a straightforward and precise tool that enables prompt evaluation of the risk of CR-GNB carriage at the time of ICU admission, thereby facilitating the timely implementation of targeted pre-emptive isolation.

Development and Validation of a Nomogram Prediction Model for Multidrug-Resistant Organisms Infection in a Neurosurgical Intensive Care Unit

Objective

To develop a predictive model for assessing the risk of multidrug-resistant organisms (MDROs) infection and validate its effectiveness.

We conducted a study on a total of 2516 patients admitted to the neurosurgery intensive care unit (NICU) of a Grade-III hospital in Nantong City, Jiangsu Province, China, between January 2014 and February 2022. Patients meeting the inclusion criteria were selected using convenience sampling. The patients were randomly divided into modeling and validation groups in a 7:3 ratio. To address the category imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to adjust the MDROs infection ratio from 203:1558 to 812:609 in the training set. Univariate analysis and logistic regression analysis were performed to identify risk factors associated with MDROs infection in the NICU. A risk prediction model was developed, and a nomogram was created. Receiver operating characteristic (ROC) analysis was used to assess the predictive performance of the model.

Patients and Methods

Results

Logistic regression analysis revealed that sex, hospitalization time, febrile time, invasive operations, postoperative prophylactic use of antibiotics, mechanical ventilator time, central venous catheter indwelling time, urethral catheter indwelling time, ALB, PLT, WBC, and L% were independent predictors of MDROs infection in the NICU. The area under the ROC curve for the training set and validation set were 0.880 (95% CI: 0.857-0.904) and 0.831 (95% CI: 0.786-0.876), respectively. The model's prediction curve closely matched the ideal curve, indicating excellent predictive performance.

Conclusion

The prediction model developed in this study demonstrates good accuracy in assessing the risk of MDROs infection. It serves as a valuable tool for neurosurgical intensive care practitioners, providing an objective means to effectively evaluate and target the risk of MDROs infection.

Tracking Epidemiological Characteristics and Risk Factors of Multi-Drug Resistant Bacteria in Intensive Care Units

Objectives

Multi-drug resistance (MDR) emerged as a serious threat in intensive care unit (ICU) settings. Our study aimed to investigate the major pathogens in ICU and identify the risk factors for MDR infection.

Methods

We performed a retrospective analysis of patients admitted to the ICU. Multivariate logistic regression was applied to identify the independent predictors, and then a nomogram to predict the probability of MDR infection.

Results

A total of 278 patients with 483 positive cultures were included. 249 (51.55%) had at least one MDR pathogen, including extensively drug-resistant (XDR) 77 (30.92%) and pan drug-resistant (PDR) 39 (15.66%), respectively. Klebsiella pneumonia was the most frequently isolated pathogen. We identified the number of bacteria (OR 2.91, 95% CI 1.97-4.29, P < 0.001), multiple invasive procedures (OR 2.23, 95% CI 1.37-3.63, P = 0.001), length of stay (LOS) (OR 1.01, 95% CI 1.00-1.02, P = 0.007), Hemoglobin (Hb) (OR 0.99, 95% CI 0.98-1.00, P = 0.01) were independent risk factors for MDR infection. Our nomogram displayed good discrimination with curve AUC was 0.75 (95% CI: 0.70-0.81). The decision curves also indicate the clinical utility of our nomogram. Additionally, the in-hospital mortality with MDR pathogens was independently associated with XDR (HR, 2.60; 95% CI: 1.08-6.25; P = 0.03) and total protein (TP) (HR, 0.95; 95% CI: 0.91-0.99; P = 0.03).

Conclusion

The number of bacteria, multiple invasive procedures, LOS, and Hb were the independent predictors associated with MDR pathogens. Our nomogram is potentially useful for predicting the occurrence of MDR infection. Besides, we also identify XDR and TP as the independent risk factors for in-hospital mortality with MDR infection. The current prevalence of MDR strains was also described. The results will contribute to the identification and preventive management of patients at increased risk of infection.

Lumbar Cistern Drainage and Gentamicin Intrathecal Injection in the Treatment of Carbapenem-Resistant Klebsiella Pneumoniae Intracranial Infection After Intracerebral Hemorrhage craniotomy: A Case Report

Background

Intracranial infection is a common complication caused by craniotomy. In particular, patients in Intensive Care Units (ICU) are prone to intracranial infection with multiple drug-resistant bacteria. Due to the lack of sensitive antibiotics for the treatment of multiple drug-resistant bacteria, there are few literatures focusing on the treatment of intracranial infection, and patients often fail to receive unified and standardized treatment. Consequently, patients with Carbapenem-resistant bacteria intracranial infection report poor prognosis and high mortality. It is very important to discuss how to treat patients with intracranial infection caused by multidrug resistant bacteria.

Case Presentation

We reported a case of intracranial infection of Carbapenem-resistant Klebsiella pneumoniae(CRKp) due to high flap tension, poor wound healing and CSF leakage caused by subcutaneous fluid accumulation after intracerebral hemorrhage craniotomy. Since the patient was exposed to intracranial infection resulted from subcutaneous fluid accumulation, we adopted the method of continuous drainage with subcutaneous tube. When subcutaneous effusion disappeared, the subcutaneous drainage tube was pull out, while patients exhibited high fever again, the waist big pool drainage catheter and continuous drainage were carried out. According to the result of Subcutaneous effusion and CSF culture indicated multiple drug resistant Klebsiella pneumoniae intracranial infection and drug susceptibility, The treatment of gentamicin intrathecal injection, intravenous use amikacin and oral Paediatric Compound Sulfamethoxazole Tablets was adopted, the condition of intracranial infection was eventually controlled, with the consciousness restored. This patient was characterized by intracranial infection with Carbapenem-resistant Klebsiella pneumoniae(CRKp).

Conclusions

Subcutaneous effusion is a high-risk factor for poor wound healing and interventions are required to be conducted to promote healing as early as possible to contribute to decreasing the menace of CSF leakage. In this case, Continuous drainage and intrathecal injection of sensitive antibiotics serve as critical process to determine the best strategy for clinical treatment of intracranial infection.

Study on the Detection and Infection Distribution of Multidrug-Resistant Organisms in Different Specimens

Objective

To analyze the infection and distribution of multidrug-resistant organisms (MDRO) in different clinical specimens, thereby providing a reference for clinical diagnosis and treatment and prevention and control.

Patient and Methods

2314 strains of MDRO isolated from clinical specimens in the First Affiliated Hospital of Nanjing Medical University from January to December 2020. MDRO were collected by Information System. The detection rate of MDRO, infection rate, the proportion of infection, and detection rate of MDRO infection in different specimens were analyzed.

Results

The top three specimens in the detection rate of MDRO were BALF (60.71%), sputum (33.68%), and blood (28.79%). The top three specimens in the proportion of MDRO infection were blood (97.74), other sterile body fluids (90.35%), and BALF (90.20%). The top three specimens in the MDRO infection rate were BALF (9.75%), sputum (3.07%), and secretions (2.90%). The top three specimens in the detection rate of MDRO infection were sputum (0.63‰), other sterile body fluids (0.13‰), and secretions (0.11‰).

Conclusion

The detection and infection distribution of MDRO vary greatly in different specimens. The submission of sterile body fluids for examination should be strengthened and the standard of sample collection should be highlighted.

Clinical prediction models for multidrug-resistant organism colonisation or infection in critically ill patients: a systematic review protocol

INTRODUCTION

Multidrug-resistant organisms (MDROs) are pathogenic bacteria that are the leading cause of hospital-acquired infection which is associated with high morbidity and mortality rates in intensive care units, increasing hospitalisation duration and cost. Predicting the risk of MDRO colonisation or infection for critically ill patients supports clinical decision-making. Several models predicting MDRO colonisation or infection have been developed; however, owing to different disease scenarios, bacterial species and few externally validated cohorts in different prediction models; the stability and applicability of these models for MDRO colonisation or infection in critically ill patients are controversial. In addition, there are currently no standardised risk scoring systems to predict MDRO colonisation or infection in critically ill patients. The aim of this systematic review is to summarise and assess models predicting MDRO colonisation or infection in critically ill patients and to compare their predictive performance.

METHODS AND ANALYSIS

We will perform a systematic search of PubMed, Cochrane Library, CINAHL, Embase, Web of science, China National Knowledge Infrastructure and Wanfang databases to identify all studies describing the development and/or external validation of models predicting MDRO colonisation or infection in critically ill patients. Two reviewers will independently extract and review the data using the Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist; they will also assess the risk of bias using the Prediction Model Risk of Bias Assessment Tool. Quantitative data on model predictive performance will be synthesised in meta-analyses, as applicable.

ETHICS AND DISSEMINATION

Ethical permissions will not be required because all data will be extracted from published studies. We intend to publish our results in peer-reviewed scientific journals and to present them at international conferences on critical care.

PROSPERO REGISTRATION NUMBER

CRD42022274175.

Development and Validation of a Tool for the Prediction of Vancomycin-Resistant Enterococci Colonization Persistence-the PREVENT Score

Vancomycin-resistant enterococci (VRE) are nosocomial pathogens with increasing prevalence worldwide. Extensive hygiene measures have been established to prevent infection transmission in hospitals. Here, we developed a predictive score system (the predictive vancomycin-resistant enterococci [PREVENT] score) to identify the clearance or persistence in patients with a history of VRE carrier status at readmission. Over a cumulative 3-year period, patients with a positive VRE carrier status were included. The study population was recruited in two successive time periods and separated into training data for predictive score development and validation data for evaluation of the predictive power. The risk factors for persistent VRE colonization were analyzed in a univariable analysis before development of a logistic regression model based on the potential risk factors. The score points were determined proportionally to the beta coefficients of the logistic regression model. The data from 448 (79%) patients were used as the training data, and those from 119 (21%) as the validation data. Multivariable analysis revealed the following variables as independent risk factors: age of ≥60 years, hemato-oncological disease, cumulative antibiotic treatment for >4 weeks, and a VRE infection. The resulting logistic regression model exhibited an acceptable area under the curve (AUC) of 0.81 (95% confidence interval [CI], 0.72 to 0.91). The predictive score system had a sensitivity of 82% (95% CI, 65 to 93%) and a specificity of 77% (95% CI, 66 to 85%). The developed predictive score system is a useful tool to assess the VRE carrier status of patients with a history of VRE colonization. On the basis of this risk assessment, more focused and cost-effective infection control measures can be implemented. IMPORTANCE Given the increasing relevance of VRE as nosocomial pathogens worldwide, infection prevention and control measures, including patient isolation and contact precautions, are indispensable to avoid their spread in the hospital setting. In this study, we developed and validated the PREVENT score, a tool for rapid risk assessment of VRE persistence in patients with a history of previous VRE colonization. The score is designed to be easily performed, employing clinical information available in a regular admission setting and immediately providing information to inform the decision of whether to adopt patient isolation and contact precautions during the hospital stay. After validation, the score was shown to accurately identify patients with persistent VRE colonization upon admission, representing a suitable option as (i) a complementary method yielding preliminary results significantly more quickly than culture-based VRE detection techniques and (ii) an alternative strategy for VRE detection in settings in which microbiological VRE screening is not routinely performed due to limited resources.

Profiles of multidrug-resistant organisms among patients with bacteremia in intensive care units: an international ID-IRI survey

Evaluating trends in antibiotic resistance is a requisite. The study aimed to analyze the profile of multidrug-resistant organisms (MDROs) among hospitalized patients with bacteremia in intensive care units (ICUs) in a large geographical area. This is a 1-month cross-sectional survey for blood-borne pathogens in 57 ICUs from 24 countries with different income levels: lower-middle-income (LMI), upper-middle-income (UMI), and high-income (HI) countries. Multidrug-resistant (MDR), extensively drug-resistant (XDR), or pan-drug-resistant isolates were searched. Logistic regression analysis determined resistance predictors among MDROs. Community-acquired infections were comparable to hospital-acquired infections particularly in LMI (94/202; 46.5% vs 108/202; 53.5%). Although MDR (65.1%; 502/771) and XDR (4.9%; 38/771) were common, no pan-drug-resistant isolate was recovered. In total, 32.1% of MDR were Klebsiella pneumoniae, and 55.3% of XDR were Acinetobacter baumannii. The highest MDR and XDR rates were in UMI and LMI, respectively, with no XDR revealed from HI. Predictors of MDR acquisition were male gender (OR, 12.11; 95% CI, 3.025-15.585) and the hospital-acquired origin of bacteremia (OR, 2.643; 95%CI, 1.462-3.894), and XDR acquisition was due to bacteremia in UMI (OR, 3.344; 95%CI, 1.189-5.626) and admission to medical-surgical ICUs (OR, 1.481; 95% CI, 1.076-2.037). We confirm the urgent need to expand stewardship activities to community settings especially in LMI, with more paid attention to the drugs with a higher potential for resistance. Empowering microbiology laboratories and reports to direct prescribing decisions should be prioritized. Supporting stewardship in ICUs, the mixed medical-surgical ones in particular, is warranted.