Rapid identification of hospitalized patients at high risk for MRSA carriage

Clinical prediction tools for identifying antimicrobial-resistant organism (ARO) carriage on hospital admissions: a systematic review

BACKGROUND

Increasing prevalence of antimicrobial-resistant organisms (AROs) is a growing economic and healthcare challenge. Increasing utilization of electronic medical record (EMR) systems and improvements in computation and analytical techniques afford an opportunity to reduce the spread of AROs through the development of clinical prediction tools to identify ARO carriers on admission to hospital.

AIM

To identify existing clinical prediction tools for meticillin-resistant Staphylococcus aureus (MRSA) and carbapenemase-producing organisms (CPOs), their predictive performance, and risk factors utilized in these tools.

METHODS

The CHARMS checklist was followed. Medline, EMBASE, Cochrane SR, CRD databases (DARE, NHS EED), CINAHL and Web of Science were searched from database inception to 26^th July 2021. Full-text articles were assessed independently, and quality assessment was conducted using the Prediction Model Risk of Bias Assessment Tool.

FINDINGS

In total, 3809 abstracts were identified and 22 studies were included. Among these studies, risk score models were the most common prediction tool (N=16). Previous admission, recent antibiotic exposure, age and sex were the most common risk factors for ARO carriage. Prediction tools were commonly evaluated on sensitivity and specificity with ranges of 15-100% and 46-98.6%, respectively, for MRSA, and 30-81.3% and 79.8-99.9%, respectively, for CPOs.

CONCLUSION

There is no gold standard ARO prediction tool. However, high-performance clinical prediction tools and identification of key risk factors for the early detection of AROs exist. Risk score models are easier to use and interpret; however, with recent improvements in machine learning techniques, highly robust models can be developed with data stored in an EMR.

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based "white-box" classification algorithm for medical data

BACKGROUND

Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay.

METHODS

Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection. A risk score was then computed from each stay according to the triggered rules.Recall and precision curves were plotted.

RESULTS

The classification can be focused on specifically detecting high risk of having a positive test, or identifying large numbers of at-risk patients by modulating the risk score cut-off level. For a risk score above 0.85,recall (sensitivity) is 62 % with 69 % precision (confidence) for MDR bacteria, recall is 58 % with 88 % precision for MRSA. In addition, MOCA-I identifies 38 and 21 cases of previously unknown MDR and MRSA respectively.

CONCLUSIONS

MOCA-I generates medically pertinent alert rules. This classification algorithm can be used to detect patients with high risk of testing positive to MDR bacteria (including MRSA). Classification can be modulated by appropriately setting the risk score cut-off level to favor specific detection of small numbers of patients at very high risk or identification of large numbers of patients at risk. MOCA-I can thus contribute to more adapted treatments and preventive measures from admission, depending on the clinical setting or management strategy.

Effects of the proportion of high-risk patients and control strategies on the prevalence of methicillin-resistant Staphylococcus aureus in an intensive care unit

Background

The presence of nosocomial pathogens in many intensive care units poses a threat to patients and public health worldwide. Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen endemic in many hospital settings. Patients who are colonized with MRSA may develop an infection that can complicate their prior illness.

Methods

A mathematical model to describe transmission dynamics of MRSA among high-risk and low-risk patients in an intensive care unit (ICU) via hands of health care workers is developed. We aim to explore the effects of the proportion of high-risk patients, the admission proportions of colonized and infected patients, the probability of developing an MRSA infection, and control strategies on MRSA prevalence among patients.

Results

The increasing proportion of colonized and infected patients at admission, along with the higher proportion of high-risk patients in an ICU, may significantly increase MRSA prevalence. In addition, the prevalence becomes higher if patients in the high-risk group are more likely to develop an MRSA infection. Our results also suggest that additional infection prevention and control measures targeting high-risk patients may considerably help reduce MRSA prevalence as compared to those targeting low-risk patients.

Conclusions

The proportion of high-risk patients and the proportion of colonized and infected patients in the high-risk group at admission may play an important role on MRSA prevalence. Control strategies targeting high-risk patients may help reduce MRSA prevalence.

MiBAlert-a new information tool to fight multidrug-resistant bacteria in the hospital setting

BACKGROUND

Although the timely isolation of patients is an essential intervention to limit spread of drug-resistant bacteria, information about the colonization status is often unavailable or lost when patients are readmitted or transferred between hospitals. Therefore, carriers of drug resistant bacteria are not recognized sufficiently early, and proper and timely isolation precautions are not taken. Consequently, resistant bacteria of public health concerns including vancomycin resistant enterococci (VRE) and methicillin resistant Staphylococcus aureus (MRSA) can spread epidemically. To ensure timely identification and proper isolation of such patients we developed an automatic real-time alert of carriers of drug resistant bacteria.

OBJECTIVES

The aim of this paper is to describe the system, called MiBAlert, and share the initial experiences in connection with an outbreak of VRE in the greater Copenhagen area (the Capital region), Denmark.

METHODS

We obtained data on cases of VRE from hospitals in Copenhagen during the period when the first version of MiBAlert was implemented and log-data on the use of MiBAlert. Furthermore, a survey was conducted among 88 staff members to investigate their experiences of MiBAlert.

RESULTS

The alert is a tool directed toward healthcare personnel accessing the electronic health record (EHR) and those further involved in the care and treatment of the patient. It is based on a web service using data from the national microbiological database, MiBa. MiBAlert is a real-time electronic non-intrusive alert generated automatically in the header of the EHR each time record is accessed. On February 15, 2015 a pilot version of MiBAlert was launched. All positive tests for VRE throughout 1year were shown with alert status by MiBAlert visible to all medical staff with access to EHR. The alert system was automatically updated directly in the EHR across the five hospitals in the Capital region. We found that the system performed satisfactorily, being operational 24/7 all 135 trial days, apart from 72min, for all the hospitals. Of the staff who responded to the survey, 82% considered that MiBAlert overall improved compliance with isolation precautions regarding VRE-positive patients. We found a marked decline of new patients infected or colonized with VRE concomitant with the implementation of MiBAlert and the survey results.

CONCLUSION

We found that MiBAlert was a valuable tool in a bundle approach to counter a multiple hospital outbreak of VRE, and that it has a great potential to improve the control of other drug-resistant bacteria.

Clinical decision support systems and infection prevention: to know is not enough

Clinical decision support (CDS) systems are an increasingly used form of technology designed to guide health care providers toward established protocols and best practices with the intent of improving patient care. Utilization of CDS for infection prevention is not widespread and is particularly focused on antimicrobial stewardship. This article provides an overview of CDS systems and summarizes key attributes of successfully executed tools. A selection of published reports of CDS for infection prevention and antimicrobial stewardship are described. Finally, an individual organization describes its CDS infrastructure, process of prioritization, design, and development, with selected highlights of CDS tools specifically targeting common infection prevention quality improvement initiatives.

Electronic Prediction Rules for Methicillin-ResistantStaphylococcus aureusColonization

Background.

Considerable hospital resources are dedicated to minimizing the number of methicillin-resistantStaphylococcus aureus(MRSA) infections. One tool that is commonly used to achieve this goal is surveillance for MRSA colonization. This process is costly, and false-positive test results lead to isolation of individuals who do not carry MRSA. The performance of this technique would improve if patients who are at high risk of colonization could be readily targeted.

Methods.

Five MRSA colonization prediction rules of varying complexity were derived in a population of 23,314 patients who were consecutively admitted to a US hospital and tested for colonization. Rules incorporated only prospectively collected, structured electronic data found in a patient's record within 1 day of hospital admission. These rules were tested in a validation cohort of 26,650 patients who were admitted to 2 other hospitals.

Results.

The prevalence of MRSA at hospital admission was 2.2% and 4.0% in the derivation and validation cohorts, respectively. Multivariable modeling identified predictors of MRSA colonization among demographic, admission-related, pharmacologic, laboratory, physiologic, and historical variables. Five prediction rules varied in their performance, but each could be used to identify the 30% of patients who accounted for greater than 60% of all cases of MRSA colonization and approximately 70% of all MRSA-associated patient-days. Most rules could also identify the 20% of patients with a greater than 8% chance of colonization and the 40% of patients among whom colonization prevalence was 2% or less.

Conclusions.

We report electronic prediction rules that can fully automate triage of patients for MRSA-related hospital admission testing and that offer significant improvements on previously reported rules. The efficiencies introduced may result in savings to infection control programs with little sacrifice in effectiveness.

Infection preventionists' awareness of and engagement in health information exchange to improve public health surveillance

BACKGROUND

Advances in electronic health record (EHR) systems and health information exchange (HIE) are shifting efforts in public health toward greater use of information systems to automate notifiable disease surveillance. Little is known about infection preventionists' (IPs) awareness, adoption, and use of these technologies to report information to public health.

METHODS

To measure awareness and engagement in EHR and HIE activities, an online survey of IPs was conducted in states with HIE networks. A total of 63 IPs was invited to participate; 44 IPs (69%) responded. The survey asked about the adoption and use of EHR systems, participation in regional HIE initiatives, and IP needs with respect to EHR systems and public health reporting.

RESULTS

Over 70% of responding IPs reported access to an EHR system, but less than 20% of IPs with access to an EHR reported being involved in the design, selection, or implementation of the system. Just 10% of IPs reported that their organizations were formally engaged in HIE activities, and 49% were unaware of organizational involvement in HIE. IPs expressed a desire for better decision support, paperless reporting methods, and situational awareness of community outbreaks.

CONCLUSION

Many IPs lack awareness and engagement in EHR and HIE activities, which may limit IPs ability to influence or utilize key information technologies as they are implemented in health care organizations.

The screening of methicillin-resistant staphylococcus aureus in vascular surgery patients: a comparison of molecular testing and broth-enriched culture

BACKGROUND

Methicillin-resistant Staphylococcus aureus (MRSA) is a major global health care-associated pathogen. This study sought to examine the prevalence of MRSA in patients who were admitted to a vascular surgery ward during a 3-month period.

METHODS

MRSA screening was accomplished through the acquisition of nasal, throat and perineal swabs. These swabs were placed in tryptic soy broth that had been supplemented with 6.5% NaCl and incubated for 24 h. The resulting isolates were subcultured on agar plates containing 5% sheep blood. The BD GeneOhm MRSA assay for screening swabs was performed in accordance with the manufacturer's instructions.

RESULTS

A total of 58 patients were included in the study and swabs from 232 sites were obtained during the sampling period. MRSA was detected in 33 samples of 12 patients during the study period; thus, there was a 20.6% prevalence of patients who were recognized as MRSA carriers. There were discrepancies between the results of classical bacteriological screening and molecular MRSA detection methods in 8 of the patients.

CONCLUSIONS

Nasal, throat and perineal MRSA screening can detect the carriage of this pathogen and allow for the timely use of appropriate infection control measures. The choice of screening techniques poses a challenge; it has been demonstrated that molecular detection methods should be performed with great sensitivity, specificity and, most importantly, speed. The cost of the PCR screening method is the only disadvantage of this approach.

Clinical use of an enterprise data warehouse

The enormous amount of data being collected by electronic medical records (EMR) has found additional value when integrated and stored in data warehouses. The enterprise data warehouse (EDW) allows all data from an organization with numerous inpatient and outpatient facilities to be integrated and analyzed. We have found the EDW at Intermountain Healthcare to not only be an essential tool for management and strategic decision making, but also for patient specific clinical decision support. This paper presents the structure and two case studies of a framework that has provided us the ability to create a number of decision support applications that are dependent on the integration of previous enterprise-wide data in addition to a patient's current information in the EMR.

Effectiveness of automated notification and customer service call centers for timely and accurate reporting of critical values: a laboratory medicine best practices systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review of the evidence available in support of automated notification methods and call centers and to acknowledge other considerations in making evidence-based recommendations for best practices in improving the timeliness and accuracy of critical value reporting.

DESIGN AND METHODS

This review followed the Laboratory Medicine Best Practices (LMBP) review methods (Christenson, et al. 2011). A broad literature search and call for unpublished submissions returned 196 bibliographic records which were screened for eligibility. 41 studies were retrieved. Of these, 4 contained credible evidence for the timeliness and accuracy of automatic notification systems and 5 provided credible evidence for call centers for communicating critical value information in in-patient care settings.

RESULTS

Studies reporting improvement from implementing automated notification findings report mean differences and were standardized using the standard difference in means (d=0.42; 95% CI=0.2-0.62) while studies reporting improvement from implementing call centers generally reported criterion referenced findings and were standardized using odds ratios (OR=22.1; 95% CI=17.1-28.6).

CONCLUSIONS

The evidence, although suggestive, is not sufficient to make an LMBP recommendation for or against using automated notification systems as a best practice to improve the timeliness of critical value reporting in an in-patient care setting. Call centers, however, are effective in improving the timeliness of critical value reporting in an in-patient care setting, and meet LMBP criteria to be recommended as an "evidence-based best practice."

Data linkage between existing healthcare databases to support hospital epidemiology

Enhancing the use of existing datasets within acute hospitals will greatly facilitate hospital epidemiology, surveillance, the monitoring of a variety of processes, outcomes and risk factors, and the provision of alert systems. Multiple overlapping data systems exist within National Health Service (NHS) hospitals in the UK, and many duplicate data recordings take place because of the lack of linkage and interfaces. This results in hospital-collected data not being used efficiently. The objective was to create an inventory of all existing systems, including administrative, management, human resources, microbiology, patient care and other platforms, to describe the data architecture that could contribute valuable information for a hospital epidemiology unit. These datasets were investigated as to how they could be used to generate surveillance data, key performance indicators and risk information that could be shared at board, clinical programme group, specialty and ward level. An example of an output of this integrated data platform and its application in influenza resilience planning and responsiveness is described. The development of metrics for staff absence and staffing levels may also be used as key indicators for risk-monitoring for infection prevention. This work demonstrates the value of such a data inventory and linkage and the importance of more sophisticated uses of existing NHS data, and innovative collaborative approaches to support clinical care, quality improvement, surveillance, emergency planning and research.

Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

BACKGROUND

Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems.

OBJECTIVE

To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs.

STUDY DESIGN AND METHODS

We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4).

RESULTS

Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common.

CONCLUSION

We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.

Data-driven approach for creating synthetic electronic medical records

BACKGROUND

New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.

METHODS

This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.

RESULTS

We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.

CONCLUSIONS

A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.

Occurrence of skin and environmental contamination with methicillin-resistant Staphylococcus aureus before results of polymerase chain reaction at hospital admission become available

BACKGROUND

Active surveillance to detect patients colonized with methicillin-resistant Staphylococcus aureus (MRSA) is increasingly practiced in healthcare settings. However, inpatients may already become sources of transmission before appropriate precautions are implemented.

OBJECTIVE

To examine the frequency of MRSA contamination of commonly touched skin and environmental surfaces before patient carriage status became known.

METHODS

We conducted a 6-week prospective study of patients who were identified by use of polymerase chain reaction (PCR) at hospital admission as having nasal MRSA colonization. Skin and environmental contamination was assessed within hours of completion of PCR screening.

RESULTS

There were 116 patients identified by PCR screening as having nasal MRSA colonization during the period from mid-April to May 2008, of whom 83 (72%) were enrolled in our study. Overall, MRSA was detected on the skin of 38 (51%) of 74 patients and in the environment of 37 (45%) of 83 patients. Of 83 environmental culture samples, 63 (76%) were obtained within 7 hours after PCR results became available, and 73 (88%) were obtained before wards were notified of PCR results. Of the 83 MRSA-colonized patients, 15 (18%) had contaminated their environment 25 hours after admission, and 29 (35%) had contaminated their environment 33 hours after admission. Thirty-two (39%) of the 83 patients had roommates, 13 (41%) of whom contaminated their environment. The median interval from admission to PCR result was 20 hours, and the median interval from PCR result to notification was 23 hours. An increased quantity of MRSA cultured from a nasal sample was significantly associated with contamination.

CONCLUSIONS

Before any contact precautions can be implemented, newly identified MRSA carriers frequently have contaminated their environment with MRSA and have contamination of commonly examined skin sites. In hospitals that perform active surveillance, strategies are needed to minimize delays in screening or to preemptively identify patients at high risk for disseminating MRSA.

WTW--an algorithm for identifying "who transmits to whom" in outbreaks of interhuman transmitted infectious agents

The authors developed a computerized algorithm that estimates 'who transmits to whom'--that is, the likeliest transmission paths during an outbreak of person-to-person transmitted illness. This algorithm uses basic information about natural history of the disease, population structure, and chronology of observed symptoms. To assess the algorithm efficacy, the authors built a simulator with parameters describing the disease and the population to simulate random outbreaks of influenza. The algorithm's performance was compared with three reference methods that simulated how human operators would handle such situations. For any size of outbreak, the algorithm outperformed the reference methods and provided a higher proportion of cases for whom the source subject who transmitted infection was identified. The authors also illustrated applicability of the algorithm for describing outbreaks of influenza in nursing homes. The use of this algorithm to draw transmission maps in investigations of outbreaks with person-to-person transmitted agents could potentially guide public health measures regarding the control of such outbreaks.

Construction and validation of synthetic electronic medical records

There is a current and pressing need for a test bed of electronic medical records (EMRs) to insure consistent development, validation and verification of public health related algorithms that operate on EMRs. However, access to full EMRs is limited and not generally available to the academic algorithm developers who support the public health community. This paper describes a set of algorithms that produce synthetic EMRs using real EMRs as a model. The algorithms were used to generate a pilot set of over 3000 synthetic EMRs that are currently available on CDC’s Public Health grid. The properties of the synthetic EMRs were validated, both in the entire aggregate data set and for individual (synthetic) patients. We describe how the algorithms can be extended to produce records beyond the initial pilot data set.

A comparison of automated methicillin-resistant Staphylococcus aureus identification with current infection control practice

Infections with Methicillin-Resistant Staphylococcus aureus (MRSA) account for almost 20,000 deaths per year. Early identification of patients with MRSA infection or colonization aids in stopping spread. We compared automated identification of MRSA using HL7 lab result messages to current manual infection control practices at a local hospital during July-September 2008. We used data from infection control providers (ICPs), the microbiology lab, and a Regional Healthcare Information Exchange to assess the accuracy of manual and automated methods. Three hundred seventy MRSA cases were identified from July-September 2008. Manual identification recognized 314 (sensitivity 84.9%, positive predictive value 99.4%) MRSA cases and automated detection from HL7 messages identified 341 (sensitivity 92.2%, positive predictive value 98.8%). Automated processing of HL7 lab report messages is a more sensitive method of capturing MRSA cases than current standard infection control practice, with minimal loss of specificity.

Implementing the MRSA recommendations made by the Commission for Hospital Hygiene and Infection Prevention (KRINKO) of 1999 - current considerations by the DGKH Management Board

In Germany, recommendations on dealing with patients who are colonised with methicillin-resistant S. aureus (MRSA) for the inpatient sector have been published in 1999 by the Commission for Hospital Hygiene and Infection Prevention (KRINKO). Some challenges arise with regard to the practical implementation of the KRINKO recommendations. These challenges do not principally question the benefit of the recommendations but have come into criticism from users. In this commentary the German Society for Hospital Hygiene (DGKH) discusses some controversial issues and adds suggestions for unresolved problems regarding the infection control management of MRSA in healthcare settings.