Comparison of two computer algorithms to identify surgical site infections

Establishment and application of TSDPSO-SVM model combined with multi-dimensional feature fusion method in the identification of fracture-related infection

Fracture-related infection (FRI) is one of the most common and intractable complications in orthopedic trauma surgery. This complication can impose severe psychological burdens and socio-economic impacts on patients. Although the definition of FRI has been proposed recently by an expert group, the diagnostic criteria for FRI are not yet standardized. A total of 4761 FRI patients and 4761 fracture patients (Non-FRI) were included in the study. The feature set of patients included imaging characteristics, demographic information, clinical symptoms, microbiological findings, and serum inflammatory markers, which were reduced by the Principal Component Analysis. To optimize the Support Vector Machine (SVM) model, the Traction Switching Delay Particle Swarm Optimization (TSDPSO) algorithm, a recognition method was proposed. Moreover, five machine learning models, including TSDPSO-SVM, were employed to distinguish FRI from Non-FRI. The Area under the Curve of TSDPSO-SVM was 0.91, at least 5% higher than that of other models. Compared with the Random Forest, Backpropagation Neural Network (BP), SVM and eXtreme Gradient Boosting (XGBoost), TSDPSO-SVM demonstrated remarkable accuracy in the test set ([Formula: see text]). The recall of TSDPSO-SVM was 98.32%, indicating a significant improvement ([Formula: see text]). Compared with BP and SVM, TSDPSO-SVM exhibited significantly superior specificity, false positive rate and precision ([Formula: see text]. The five models yielded consistent results in the training and testing of FRI patients across different age groups. TSDPSO-SVM is validated to have the maximum overall prediction ability and can effectively distinguish between FRI and Non-FRI. For the early diagnosis of FRI, TSDPSO-SVM may provide a reference basis for clinicians, especially those with insufficient experience. These results also lay a foundation for the intelligent diagnosis of FRI. Furthermore, these findings exhibit the application potential of this model in the diagnosis and classification of other diseases.

Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization

BACKGROUND

As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce.

AIM

We aimed to describe key aspects of the design of AS systems and implementation in European institutions and hospitals.

METHODS

An online survey was distributed via email in February/March 2019 among 1) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; 2) corresponding authors of peer-reviewed European publications on existing AS systems; and 3) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice.

FINDINGS

Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure.

CONCLUSION

AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods and the state of implementation.

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Background

Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.

Objectives

To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.

Methods

In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.

Results

A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.

Conclusions

Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

Surgical Antimicrobial Prophylaxis and Incidence of Surgical Site Infections at Ethiopian Tertiary-Care Teaching Hospital

BACKGROUND

Surgical site infections (SSIs) are infections that develop within 30 days after an operation or surveillance of surgical wound infection implementation within 90 days after surgery when an implant is placed. The objective of this study was to assess preoperative and postoperative antimicrobial use in St. Paul's Hospital Millennium Medical College (SPHMMC), Addis Ababa, Ethiopia.

METHODS

A hospital-based cross-sectional study was undertaken in surgery wards of SPHMMC for 4 months by reviewing 413 patients' charts. All patients 13 years and older who were admitted and underwent different types of surgical procedures were included in the study. Epi info 7 was used for data entry, and then data were exported to Statistical Package for Social Sciences (SPSS) version 20.0 software for analysis. Descriptive analyses were computed and rate of SSI was calculated in this study. Moreover, bivariate analysis was done to examine the relationship between the outcome variable and predictor variables with a value of P < .2 retained for subsequent multivariate analyses using multiple logistic regressions. P value of <.05 was considered as statistically significant.

RESULTS

Out of 413 patients, 152 (36.8%) were operated for general surgery, and the remaining were for other types of surgeries. Most of the patients, 196 (79.7%), were managed by a single surgical antibiotic agent, followed by 2 agents (20.3%) for surgical prophylaxis indication. Surgical site infections occurred in 46 (11.1%) patients before discharge from the hospital. In those patients who need treatment for SSIs, almost half of them (49.5%) received combination therapy of ceftriaxone and metronidazole. Emergency surgical cases were 2.647 times more likely to develop SSIs than the elective surgical cases (adjusted odds ratio [AOR] = 2.647; 95% confidence interval [CI] = 1.406-4.983; P = .003). Patients who did not receive antibiotic prophylaxis were 2.572 times more likely to develop SSIs compared to those who received antibiotic prophylaxis (AOR = 2.572; 95% CI = 1.02-6.485; P = .045). Clean-contaminated and contaminated types of wound were a protective factor against SSI in our study.

CONCLUSIONS

This study indicated that most of the patients (72.1%) received surgical antimicrobial prophylaxis. The overall incidence rate of SSIs was 11.1% in the studied hospital. Ceftriaxone was the most commonly used drug. Being not receiving prophylaxis, wound class, and surgery types were significantly associated with the development of SSI.

Infectious complications of endoarterial interventional radiology: protocol for an observational study of a longitudinal national cohort of patients assessed in the French hospital discharge database (MOEVA study)

Around seven million patients undergo endoarterial interventional radiology procedures (EAIRP) annually worldwide. These procedures have become part of the standard repertoire of vascular surgery. However, the healthcare-associated infections related to these procedures are relatively unknown. Prevalence and adverse outcome of infectious complication (IC) post-EAIRP may be underestimated. We aim to provide national trend estimation of EAIRP incidence and outcome in France. METHODS AND ANALYSIS: From the French Common Classification of Medical Acts, we will define four categories of EAIRP. We will collect procedures via the French nationwide hospital discharge database, called Programme de Médicalisation des Systèmes d'Information (PMSI) and derived from the Diagnosis Related Group system. Various combinations to identify the numerator will be employed according to a pre-established algorithm. Technical data wrangling tools facilitating the use of PMSI will be developed to obtain a clean and well-structured database ready for statistical analysis. This protocol will require competences in medicine, epidemiology, statistics, data processing and techniques through various stages of the study. The cohort will contain the denominator (the first act of the first stay of each patient) and the corresponding numerator (the IC which will occur during the first stay). ETHICS AND DISSEMINATION: No nominative, sensitive or personal data on patients have been collected. The study of the MOrtality and infectious complications of therapeutic EndoVAscular interventional radiology (MOEVA) study does not involve humans, and falls within the scope of the French Reference Methodology MR-004 according to 2016-41 law dated 26 January 2016 on the modernisation of the French health system. Our study involves the reuse of already recorded data, which require neither information or non-opposition of the included individuals. Access to linked ANOnymous (ANO) file in the PMSI databases was approved by the French National Commission for Data Protection and Liberties (CNIL number 1564135). The results will be disseminated through a peer-reviewed publication.

Risk of subsequent health care-associated infection among patients with a bloodstream infection present on hospital admission

BACKGROUND

The risks and outcomes of acquiring a health care-associated infection (HAI) among patients with a bloodstream infection present on hospital admission (BSI-POA) have not been well described. The objective of this study was to examine the incidence of and risk factors for developing a subsequent HAI and to compare length of stay and mortality between patients with a BSI-POA who develop an HAI and those who do not.

METHODS

This was a retrospective cohort study of patients aged ≥18 years discharged with a BSI-POA from 3 hospitals in New York City between 2006 and 2014.

RESULTS

There were 761 HAIs among the 11,436 patients with a BSI-POA. Incidence rates were: catheter-associated urinary tract infections, 5.03 infections per 1,000 catheter days; pneumonia, 2.7% among BSI-POA patients; surgical site infections, 9.2% among BSI-POA patients. Length of stay was longer among patients who developed an HAI (mean ± SD, 35.0 ± 29.8 vs 12.4 ± 11.9, P < .001). Mortality was higher in patients who developed an HAI (23.9% vs 11.6%, P < .001).

CONCLUSIONS

Risk factors for those who developed an HAI differed by infection type. Overall, HAI was associated with longer hospitalization, and pneumonia was associated with increased mortality.

Changes in the incidence and antimicrobial susceptibility of healthcare-associated infections in a New York hospital system, 2006-2012

Introduction

National efforts to curtail healthcare-associated infections (HAI) proliferated recently, though data detailing progress over time are limited. This retrospective cohort study aims to describe changes in incidence and antimicrobial susceptibility of HAI in four New York City hospitals over seven years.

Methods

Electronic data were collected retrospectively for all patients discharged from 2006 through 2012. Previously validated computerized algorithms based on National Healthcare Safety Network criteria detected bloodstream infections, pneumonia, surgical site infections, and urinary tract infections with Enterococcus spp., Staphylococcus aureus, Streptococcus pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae. Antimicrobial susceptibilities were obtained from electronic laboratory records. Logistic regression was used to assess changes in odds of acquiring an HAI and odds of antimicrobial resistance over time, controlling for age, gender, severity of illness, previous hospitalizations, and admission source.

Results

In total, 19,052 HAI were identified among 761,426 discharges. HAI rates fell for all organisms, all infection types, and within all hospitals. Odds of acquiring an HAI decreased significantly over time for all organisms. Resistance levels were stable for Enterococcus spp., S. aureus, A. baumannii, and S. pneumoniae. Multidrug resistance increased for P. aeruginosa and decreased for K. pneumoniae, though imipenem resistance among K. pneumoniae climbed sharply in 2011.

Conclusions

This study suggests that HAI incidence rates are falling, possibly due to increased federal, state and local attention to healthcare quality and patient safety. Though we found no substantial reductions in resistance, recent national attention towards antimicrobial stewardship may precipitate a change in coming years.

Electronic Surveillance of Surgical Site Infections

BACKGROUND

Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic classification algorithm for conservative case finding and identify alterations that would adapt the method for other purposes.

METHODS

Two computer algorithms were created to identify SSI. One model used a strict National Healthcare Safety Network (NHSN) based SSI algorithm, which was applied to all discharges from 443,284 all discharges from four hospitals in Manhattan, NY, 2009 through 2012. The second model used discharges that only had NHSN-defined SSI procedures during the same period.

RESULTS

The strict SSI algorithm was able to classify SSI status for 27.3% of discharges; there was a high number of indeterminate cases. In contrast, the modified, less strict model, classified 97.2% of discharges with NHSN-approved SSI procedures.

CONCLUSION

Electronic records provide several options for aiding with the identification of infections in healthcare settings and can be tailored to suit specific uses. While algorithms for SSI classification should reflect the NHSN definition, our research emphasizes how variations of model building can affect the number of indeterminate cases that may necessitate manual review.

Trends in mortality, length of stay, and hospital charges associated with health care-associated infections, 2006-2012

BACKGROUND

Many factors associated with hospital-acquired infections (HAIs), including reimbursement policies, drug prices, practice patterns, and the distribution of organisms causing infections, change over time. We examined whether outcomes, including mortality, length of stay (LOS), daily charges, and total charges associated with HAIs, changed during 2006-2012.

METHODS

Electronic data on adults discharged from 2 tertiary-quaternary hospitals and 1 community hospital during 2006-2012 were collected retrospectively. Computerized algorithms identified infections using laboratory and administrative codes. Propensity scores were used to match cases with uninfected controls. Differences in mortality, LOS, daily charges, and total charges were modeled against infection status and time period (2006-2008 vs 2009-2012), including interaction for infection status by time period.

RESULTS

Among 352,077 discharges, 24,466 HAIs were detected. There was no significant change in mortality. LOS declined only for bloodstream infections (3-day reduction; P < 0.01). Daily charges rose 4% for urinary tract infections but did not change significantly for other HAIs. Total charges declined by 11% for bloodstream infections and 13% for pneumonia.

CONCLUSIONS

We found no appreciable or consistent improvement in HAI mortality or LOS during 2006-2012. Costs of bloodstream infections and pneumonia have declined, with most of the change occurring before 2008.

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation

Background

Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent.

Objective

To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system.

Methods

We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs.

Results

In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days.

Conclusions

This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

OBJECTIVE

Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.

METHODS

Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995-2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.

RESULTS

57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.

CONCLUSIONS

Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

Data elements and validation methods used for electronic surveillance of health care-associated infections: a systematic review

BACKGROUND

We describe the primary data sources, data elements, and validation methods currently used in electronic surveillance systems (ESS) for identification and surveillance of health care-associated infections (HAIs), and compares these data elements and validation methods with recommended standards.

METHODS

Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a PubMed and manual search was conducted to identify research articles describing ESS for identification and surveillance of HAIs published January 1, 2009-August 31, 2014. Selected articles were evaluated to determine what data elements and validation methods were included.

RESULTS

Among the 509 articles identified in the original literature search, 30 met the inclusion criteria. Whereas the majority of studies (83%) used recommended data sources and validated the numerator (80%), only 10% of studies performed external and internal validation. In addition, there was variation in the ESS data formats used.

CONCLUSIONS

Our findings suggest that the majority of ESS for HAI surveillance use standard definitions, but the lack of widespread internal data, denominator, and external validation in these systems reduces the reliability of their findings. Additionally, advanced programming skills are required to create, implement, and maintain these systems and to reduce the variability in data formats.

Studies on nurse staffing and health care-associated infection: methodologic challenges and potential solutions

BACKGROUND

Researchers have been studying hospital nurse staffing in relation to health care-associated infections (HAIs) for >2 decades, and the results have been mixed. We summarized published research examining these issues, critically analyzed the commonly used approaches, identified methodologic challenges, proposed potential solutions, and suggested the possible benefits of applying an electronic health record (EHR) system.

METHODS

A scoping review was conducted using MEDLINE and CINAHL from 1990 onward. Original research studies examining relationships between nurse staffing and HAIs in the hospital setting and published in peer-reviewed English-language journals were selected.

RESULTS

A total of 125 articles and abstracts were identified, and 45 met inclusion criteria. Findings from these studies were mixed. The methodologic challenges identified included database selection, variable measurement, methods to link the nurse staffing and HAI data, and temporality. Administrative staffing data were often not precise or specific. The most common method to link staffing and HAI data did not assess the temporal relationship. We proposed using daily staffing information 2-4 days prior to HAI onset linked to individual patient HAI data.

CONCLUSION

To assess the relationships between nurse staffing and HAIs, methodologic decisions are necessary based on what data are available and feasible to obtain. National efforts to promote an EHR may offer solutions for future studies by providing more comprehensive data on HAIs and nurse staffing.

Matching bacteriological and medico-administrative databases is efficient for a computer-enhanced surveillance of surgical site infections: retrospective analysis of 4,400 surgical procedures in a French university hospital

OBJECTIVE

Our goal was to estimate the performance statistics of an electronic surveillance system for surgical site infections (SSIs), generally applicable in French hospitals.

METHODS

Three detection algorithms using 2 different data sources were tested retrospectively on 9 types of surgical procedures performed between January 2010 and December 2011 in the University Hospital of Nantes. The first algorithm was based on administrative codes, the second was based on bacteriological data, and the third used both data sources. For each algorithm, sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated. The reference method was the hospital's routine surveillance: a comprehensive review of the computerized medical charts of the patients who underwent one of the targeted procedures during the study period.

SETTING

A 3,000-bed teaching hospital in western France.

POPULATION

We analyzed 4,400 targeted surgical procedures.

RESULTS

Sensitivity results varied significantly between the three algorithms, from 25% (95% confidence interval, 17-33) when using only administrative codes to 87% (80%-93%) with the bacteriological data and 90% (85%-96%) with the combined algorithm. Fewer variations were observed for specificity (91%-98%), PPV (21%-25%), and NPV (98% to nearly 100%). Overall, performance statistics were higher for deep SSIs than for superficial infections.

CONCLUSIONS

A reliable computer-enhanced SSI surveillance can easily be implemented in French hospitals using common data sources. This should allow infection control professionals to spend more time on prevention and education duties. However, a multicenter study should be conducted to assess the generalizability of this method.

Utility of Electronic Medical Records to Assess the Relationship Between Parenteral Nutrition and Central Line-Associated Bloodstream Infections in Adult Hospitalized Patients

BACKGROUND

Parenteral nutrition is associated with increased central line-associated bloodstream infections (CLABSIs). Electronic databases are important for identifying independent risk factors for prevention strategies. Our aims were to evaluate the utility of using electronic data sources to identify risk factors for CLABSIs, including parenteral nutrition (PN), and to assess the association between CLABSI and PN administration.

METHODS

Data were obtained for all discharges of adult patients in whom a central line was inserted between September 1, 2007, and December 31, 2008, in a large, academically affiliated hospital in New York City. CLABSI was defined electronically using a modified definition from the Centers for Disease Control and Prevention. A manual chart review was also undertaken to assess validity/reliability of the electronic database and gather additional information. Risk factors for CLABSI were examined using logistic regression.

RESULTS

Among 4840 patients, there were 220 CLABSIs, an incidence of 5.4 CLABSIs per 1000 central line days. Risk factors included PN (odds ratio [OR], 4.33; 95% confidence interval [CI], 2.50-7.48), intensive care unit stay (OR, 2.26; 95% CI, 1.58-3.23), renal disease (OR, 2.79; 95% CI, 2.00-3.88), and immunodeficiency (OR, 2.26; 95% CI, 1.70-3.00). Diabetes mellitus was associated with reduced CLABSI rates (OR, 0.63; 95% CI, 0.45-0.88).

CONCLUSIONS

The utility of electronic medical records for determining risk factors is limited by such things as free-text data entry. Using a hybrid between fully electronic and manual chart review, reliable data were obtained. PN is associated with a high risk for CLABSI in a population highly selected for indications for PN.

Incidence and risk factors for and the effect of a program to reduce the incidence of surgical site infection after cardiac surgery

BACKGROUND

Surgical site infection (SSI) after cardiac surgery (CS) is a serious complication that increases hospital length of stay (LOS), has a substantial financial impact, and increases mortality. The study described here was done to evaluate the effect of a program to reduce SSI after CS.

METHODS

In January 2007, a multi-disciplinary CS infection-prevention team developed guidelines and implemented bundled tactics for reducing SSI. Data for all patients who underwent CS from 2006-2008 were used to determine whether there was: 1) A difference in the incidence of SSI in white patients and those belonging to minority groups; 2) a reduction in SSI after intervention; and 3) a statistically significant difference in the incidence of SSI in the third quarter of each year as compared with the other quarters of the year.

RESULTS

Of 3,418 patients who underwent CS; 1,125 (32.9%) were members of minority groups and 2,293 (67.1%) were white. Eighty (2.3%) patients developed SSI. There was no significant difference in the incidence of SSI in non-Hispanic white patients and all others (2.1% vs. 2.8%, p=0. 42). The incidence of SSI decreased significantly from 2006 (3.0%) to 2007 (2.5%) and 2008 (1.4%), (p=0.03). Surgical site infection occurred more often in the third quarter of each of the years of the study than in other quarters of each year (3.3 vs. 2.0%, p=0.038).

CONCLUSIONS

Implementation of a program to reduce SSI after CS was associated with a lower incidence of SSI across all racial and ethnic groups and over time, but was not associated with a lower incidence of SSI in the third quarter of each year than in the other quarters.

Prevalence and risk factors for antibiotic-resistant community-associated bloodstream infections

BACKGROUND

Antibiotic resistance is increasing in many community settings. The purpose of this study was to determine the proportion of antibiotic resistant community-associated bloodstream infections (CA-BSIs) present on hospital admissions to identify risk factors for acquiring resistant versus susceptible CA-BSIs and to describe the incidence of concurrent infections with CA-BSIs.

METHODS

We conducted a retrospective cohort study of patients discharged from one community, one pediatric, and two tertiary/quaternary care hospitals within an academically affiliated network in the borough of Manhattan in New York, NY, from 2006 to 2008. The CA-BSIs present at hospital admission were defined as BSIs occurring within the first 48h of hospitalization. Infections and patient characteristics were identified using data available from patients' electronic medical records and discharge records.

RESULTS

In total, 1677 CA-BSIs were identified. Staphylococcus aureus had the largest proportion of resistance (41.2%), followed by enterococcal species (24.3%), Pseudomonas aeruginosa (20.2%), Streptococcus pneumoniae (16.6%), Acinetobacter baumannii (10.0%), and Klebsiella pneumoniae (9.9%). Significant predictors of resistance were prior residence in a skilled nursing facility (OR, 2.55; 95% CI, 1.39-4.70), advanced age (1.01; 1.002-1.02), presence of malignancy (0.58; 0.37-0.91), prior hospitalization (1.62; 1.17-2.23), a weighted Charlson score (1.09; 1.02-1.17) for S. aureus, presence of malignancy (1.82; 1.004-3.30), prior hospitalizations (2.03; 1.12-3.38) for enterococcal species, and younger age for S. pneumoniae (p=0.02). Urinary tract infections were the most common concurrent infection (n=45/87, 51.7%).

CONCLUSION

Over 27% of the CA-BSIs present on admission were antibiotic resistant. Understanding the prevalence and risk factors for CA-BSIs may help improve empiric antibiotic therapy and outcomes for patients with community-onset infections.

Gender differences in risk of bloodstream and surgical site infections

BACKGROUND

Identifying patients most at risk for hospital- and community-associated infections is one essential strategy for preventing infections.

OBJECTIVE

To investigate whether rates of community- and healthcare-associated bloodstream and surgical site infections varied by patient gender in a large cohort after controlling for a wide variety of possible confounders.

DESIGN

Retrospective cohort study.

PARTICIPANTS

All patients discharged from January 1, 2006 through December 31, 2008 (133,756 adult discharges and 66,592 pediatric discharges) from a 650-bed tertiary care hospital, a 220-bed community hospital, and a 280-bed pediatric acute care hospital within a large, academic medical center in New York, NY.

MAIN MEASURES

Data were collected retrospectively from various electronic sources shared by the hospitals and linked using patients' unique medical record numbers. Infections were identified using previously validated computerized algorithms.

KEY RESULTS

Odds of community-associated bloodstream infections, healthcare-associated bloodstream infections, and surgical site infections were significantly lower for women than for men after controlling for present-on-admission patient characteristics and events during the hospital stay [odds ratios (95 % confidence intervals) were 0.85 (0.77-0.93), 0.82 (0.74-0.91), and 0.78 (0.68-0.91), respectively]. Gender differences were greatest for older adolescents (12-17 years) and adults 18-49 years and least for young children (<12 years) and older adults (≥ 70 years).

CONCLUSIONS

In this cohort, men were at higher risk for bloodstream and surgical site infections, possibly due to differences in propensity for skin colonization or other anatomical differences.

Time-saving impact of an algorithm to identify potential surgical site infections

OBJECTIVE

To develop and validate a partially automated algorithm to identify surgical site infections (SSIs) using commonly available electronic data to reduce manual chart review.

DESIGN

Retrospective cohort study of patients undergoing specific surgical procedures over a 4-year period from 2007 through 2010 (algorithm development cohort) or over a 3-month period from January 2011 through March 2011 (algorithm validation cohort).

SETTING

A single academic safety-net hospital in a major metropolitan area.

PATIENTS

Patients undergoing at least 1 included surgical procedure during the study period.

METHODS

Procedures were identified in the National Healthcare Safety Network; SSIs were identified by manual chart review. Commonly available electronic data, including microbiologic, laboratory, and administrative data, were identified via a clinical data warehouse. Algorithms using combinations of these electronic variables were constructed and assessed for their ability to identify SSIs and reduce chart review.

RESULTS

The most efficient algorithm identified in the development cohort combined microbiologic data with postoperative procedure and diagnosis codes. This algorithm resulted in 100% sensitivity and 85% specificity. Time savings from the algorithm was almost 600 person-hours of chart review. The algorithm demonstrated similar sensitivity on application to the validation cohort.

CONCLUSIONS

A partially automated algorithm to identify potential SSIs was highly sensitive and dramatically reduced the amount of manual chart review required of infection control personnel during SSI surveillance.

Advances in electronic surveillance for healthcare-associated infections in the 21st Century: a systematic review

BACKGROUND

Traditional methodologies for healthcare-associated infection (HCAI) surveillance can be resource intensive and time consuming. As a consequence, surveillance is often limited to specific organisms or conditions. Various electronic databases exist within the healthcare setting and may be utilized to perform HCAI surveillance.

AIM

To assess the utility of electronic surveillance systems for monitoring and detecting HCAI.

METHODS

A systematic review of published literature on surveillance of HCAI was performed. Databases were searched for studies published between January 2000 and December 2011. Search terms were divided into infection, surveillance and data management terms, and combined using Boolean operators. Studies were included for review if they demonstrated or proposed the use of electronic systems for HCAI surveillance.

FINDINGS

In total, 44 studies met the inclusion criteria. For the majority of studies, emphasis was on the linkage of electronic databases to provide automated methods for monitoring infections in specific clinical settings. Twenty-one studies assessed the performance of their method with traditional surveillance methodologies or a manual reference method. Where sensitivity and specificity were calculated, these varied depending on the organism or condition being surveyed and the data sources employed.

CONCLUSIONS

The implementation of electronic surveillance was found to be feasible in many settings, with several systems fully integrated into hospital information systems and routine surveillance practices. The results of this review suggest that electronic surveillance systems should be developed to maximize the efficacy of abundant electronic data sources existing within hospitals.

The role of pre-operative and post-operative glucose control in surgical-site infections and mortality

Background and Objective

The impact of glucose control on surgical-site infection (SSI) and death remains unclear. We examined how pre- and post-operative glucose levels and their variability are associated with the risk of SSI or in-hospital death.

Methods

This retrospective cohort study employed data on 13,800 hospitalized patients who underwent a surgical procedure at a large referral hospital in New York between 2006 and 2008. Over 20 different sources of electronic data were used to analyze how thirty-day risk of SSI and in-hospital death varies by glucose levels and variability. Maximum pre- and post-operative glucose levels were determined for 72 hours before and after the operation and glucose variability was defined as the coefficient of variation of the glucose measurements. We employed logistic regression to model the risk of SSI or death against glucose variables and the following potential confounders: age, sex, body mass index, duration of operation, diabetes status, procedure classification, physical status, emergency status, and blood transfusion.

Results

While association of pre- and post-operative hyperglycemia with SSI were apparent in the crude analysis, multivariate results showed that SSI risk did not vary significantly with glucose levels. On the other hand, in-hospital deaths were associated with pre-operative hypoglycemia (OR = 5.09, 95% CI (1.80, 14.4)) and glucose variability (OR = 1.14, 95% CI (1.03, 1.27) for 10% increase in coefficient of variation).

Conclusion

In-hospital deaths occurred more often among those with pre-operative hypoglycemia and higher glucose variability. These findings warrant further investigation to determine whether stabilization of glucose and prevention of hypoglycemia could reduce post-operative deaths.