Can routinely collected electronic health data be used to develop novel healthcare associated infection surveillance tools?

Background: Healthcare associated infections (HCAI) pose a significant burden to health systems both within the UK and internationally. Surveillance is an essential component to any infection control programme, however traditional surveillance systems are time consuming and costly. Large amounts of electronic routine data are collected within the English NHS, yet these are not currently exploited for HCAI surveillance. Aim: To investigate whether routinely collected electronic hospital data can be exploited for HCAI surveillance within the NHS. Methods: This thesis made use of local linked electronic health data from Imperial College Healthcare NHS Trust, including information on patient admissions, discharges, diagnoses, procedures, laboratory tests, diagnostic imaging requests and traditional infection surveillance data. To establish the evidence base on surveillance and risks of HCAI, two literature reviews were carried out. Based on these, three types of innovative surveillance tools were generated and assessed for their utility and applicability. Results: The key findings were firstly the emerging importance of automated and syndromic surveillance in infection surveillance, but the lack of investigation and application of these tools within the NHS. Syndromic surveillance of surgical site infections was successful in coronary artery bypass graft patients; however it was an inappropriate methodology for caesarean section patients. Automated case detection of healthcare associated urinary tract infections, based on electronic microbiology data, demonstrated similar rates of infection to those recorded during a point prevalence survey. Routine administrative data demonstrated mixed utility in the creation of simplified risk scores or infection, with poorly performing risk models of surgical site infections but reasonable model fit for HCA UTI. Conclusion: Whilst in principle routine administrative data can be used to generate novel surveillance tools for healthcare associated infections; in reality it is not yet practical within the IT infrastructure of the NHS

From Hospital-Level to Patient-Level Antibiotic Consumption Data: How Can We Improve Surveillance of Antibiotic Use in the Frame of Antibiotic Stewardship Programmes?

Infections with antimicrobial-resistant bacteria caused approximately 4.95 million deaths worldwide in 2019 and are, thus, one of the major threats to public health [1, 2]. Antimicrobial resistance is the ability of a microorganism to withstand antimicrobial treatment and occurs naturally [3]. However, its spread has been driven by the extensive use of antibiotics in agriculture, and human and veterinary medicine during recent decades [4]. Improving the adequate use of antibiotics in order to slow antimicrobial resistance, treat patients effectively and enhance patient safety is referred to as antibiotic stewardship [5]. Surveillance of antibiotic consumption is a crucial element in antibiotic stewardship programmes in defining interventions to optimise antibiotic use [6, 7]. The quantity of antibiotic consumption is analysed routinely in Switzerland. The Swiss Centre for Antibiotic Resistance (ANRESIS) has collected and analysed antimicrobial resistance data and antibiotic consumption data from an increasing number of microbiology laboratories and hospital pharmacies throughout Switzerland since 2006 [8]. Antimicrobial resistance data are provided at the patient-level while antibiotic consumption data are aggregated at the department or hospital-level per year or month. The results of the analyses are sent back to individual hospitals in the form of feedback and benchmark reports. The purpose of these reports is to support local antibiotic stewardship teams when defining interventions. The quality of antibiotic use has been analysed only sporadically in Switzerland [9, 10]. Recently, a consensus on quality indicators for antibiotic use was published [11]. This consensus includes antibiotic stewardship indicators that assess antibiotic treatment decisions. In recent years, most of the larger Swiss hospitals have implemented electronic medical record systems. Hence, patient-level antibiotic prescription data are increasingly available that could improve the monitoring of antibiotic use and provide better support for antibiotic stewardship programmes. Aims: The overall aims of this PhD thesis were, first, to evaluate the benefits of extracting patient-level antibiotic prescription data compared to hospital-level data and, second, to propose a method for incorporating these data into future surveillance of antibiotic use. The purpose of the first part was to assess whether associations between AMR and antibiotic consumption can be investigated using hospital-level data generated for routine surveillance. Three epidemiological projects aimed to investigate the temporal trends including explanatory variables of 1) consumption of antibiotics active against methicillin-resistant Staphylococcus aureus (MRSA), 2) the incidence of Staphylococcus aureus bloodstream infections and 3) extended-spectrum cephalosporin-resistant Klebsiella pneumoniae (ESCR-KP). The objective of the fourth project was to develop an interactive dashboard to improve data visualisation for routine surveillance. In the second part, we aimed to assess 5) the feasibility of converting patient-level antibiotic prescription data of the electronic medical record into antibiotic stewardship indicators. The last project (6) aimed to identify risk factors for the occurrence of extended-spectrum cephalosporin resistance in Escherichia coli and Klebsiella pneumoniae. Methods: Data from the ANRESIS database were used to analyse trends and risk factors for 1) consumption of anti-MRSA antibiotics (glycopeptides, daptomycin, linezolid) and 2) incidence of ESCR-KP in 21 hospitals between 2009 and 2019. The same data source was used for analysing 3) the incidence of Staphylococcus aureus bloodstream infections in 70 hospitals over time (2008-2021). Trends and risk factors were analysed by applying multiple linear regression models. 4) A dashboard visualising antibiotic consumption of hospitals participating in the ANRESIS surveillance system was developed using the R software environment and packages such as Shiny and Plotly. For projects 5 and 6, patients hospitalised between 1 October 2019 and 30 September 2021 at Lucerne cantonal hospital and who received at least one dose of a systemic antibiotic were included. Antibiotic prescription data were obtained from the electronic medical record Epic software® and linked with microbiological data from the ANRESIS database. Antibiotic stewardship indicators proposed by the literature were collected and, if needed, rephrased or specified to be calculable (project 5). Algorithms were programmed in R to convert electronic medical record data into antibiotic stewardship indicators. These were calculated, and the validity of each output value was assessed and categorised as either good quality data, missing data due to incomplete documentation or data processing issues or not computable. For the resistance models with patient-level data, the dataset was restricted to patients with possibly nosocomial Escherichia coli and Klebsiella pneumoniae (project 6). A multiple logistic regression model was applied to investigate risk factors for the occurrence of extend-spectrum cephalosporin resistance in Escherichia coli and Klebsiella pneumoniae. Results: Analysis of hospital-level antibiotic consumption data revealed an increase in the consumption of anti-MRSA antibiotics in Switzerland between 2009 and 2019 (project 1). Hospitals with lower levels of consumption of anti-MRSA antibiotics were associated with having an antibiotic stewardship group and restrictions for prescriptions of anti-MRSA antibiotics. The MRSA incidence decreased significantly in the French-speaking region while increasing significantly in the German-speaking region, although at a low incidence level (project 2). The incidence of Staphylococcus aureus bloodstream infections increased in Switzerland between 2008 and 2021, mainly due to the increasing incidence of methicillin-susceptible Staphylococcus aureus bloodstream infections in elderly males. The increase was more pronounced in the German-speaking than in the French-speaking region. Project 3 described a significant increase in the incidence of invasive ESCR-KP infections in Switzerland between 2009 and 2019. The incidence was higher in university than in non-university hospitals and in the French-speaking compared to the German-speaking region. However, the incidence was not associated with antibiotic consumption. Analysing the overall ESCR-KP incidence (all sample sites) revealed high variability between university hospitals, mainly due to a high proportion of patients with screening isolates at Geneva University Hospital (50% of patients with ESCR-KP). A dashboard was developed that visualised antibiotic consumption of the user's hospital (project 4). The hospital-specific login provides free access to interactive graphics and interactive tables for the 71 hospitals that are part of the ANRESIS surveillance system. Antibiotic consumption is depicted graphically over ten years and the graphics can be adjusted according to selection criteria. A benchmark boxplot enables users to compare antibiotic consumption of their hospital with other hospitals of comparable size or in the same linguistic region. Project 5 demonstrated the feasibility of converting electronic medical records data into antibiotic stewardship indicators. In total, data from 25,338 hospitalisations from 20,723 individual patients were analysed and visualised in an interactive dashboard. Data extraction allowed us to program algorithms for 89% (25/28) of the indicators assessing treatment decisions, and data quality was classified as good in 46% (13/28). According to the data quality observed, the most important issues were A) missing (58% of hospitalisations) or meaningless (37% of hospitalisations) information on indication (e.g. general indication, infection) and B) data processing issues such as insufficiently categorised metadata. The result of the resistance model with patient-level data was not meaningful since the number of patients with ESCR isolates was too low (project 6). Conclusion: Our studies revealed that several national trends in antibiotic consumption and resistance were mainly caused by subpopulations. This demonstrates the need for stratifying surveillance analyses to formulate appropriate target measures at the right intervention level. Higher resolution data on antibiotic use are essential to provide better decision support to policy makers in hospitals and on regional and national committees. To improve surveillance analysis for hospitals, we developed a procedure that converts electronic medical record data into antibiotic stewardship indicators. The routine monitoring of these indicators would be very useful for local antibiotic stewardship teams when defining and measuring the effectiveness of interventions. This PhD project has demonstrated the benefit of patient-level antibiotic data and is therefore the first step towards integrating patient-level antibiotic prescription data into routine surveillance

PREDICTION MODELS FOR CARBAPENEM-RESISTANT ENTEROBACTERIACEAE (CRE) AND OTHER MULTIDRUG-RESISTANT GRAM-NEGATIVE (MDRGN) BACTERIA IN HEALTHCARE SETTINGS

Background. Carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) pose urgent challenges to patient care. These bacteria are highly drug-resistant and are associated with significant attributable mortality. Current prevention strategies in United States (U.S.) healthcare facilities aim to reduce selective pressure from antibiotic exposure and to reduce patient-to-patient spread. These efforts are hampered by a lack of rapid and cost-effective diagnostics to identify these organisms. These diagnostic challenges leave basic epidemiological questions unanswered, including how many and which types of U.S. inpatients are asymptomatic carriers. Objectives. We aimed to measure the prevalence of, and risk factors for, CRO colonization among high-risk U.S. hospitalized patients and to develop statistical and machine learning prediction models that could help to address existing diagnostic limitations. Methods. To achieve these aims, we developed two study cohorts. The first, a one-year prospective cohort of Johns Hopkins Hospital (JHH) intensive care unit patients, screened patients for CRO carriage at unit admission. Isolates were speciated and molecularly characterized, and pre-admission exposure data were used to evaluate colonization risk factors and to develop predictive models of colonization with machine learning methodologies (Aim 1). The second, a retrospective cohort of JHH Gram-negative bacteremic patients, generated a clinical decision tree (Aim 2) and a risk score (Aim 3) to predict whether infections were extended-spectrum B-lactamase (ESBL)-producing. ESBLs confer resistance to most antibiotics except carbapenems, and rapid identification can reduce unnecessary carbapenem administration. Through the lens of this real-world example, we methodologically compared these two prediction approaches (Aim 3). Results. Aim 1 included 3,327 unit visits and 2,878 (87%) admission swabs. Our study found that 7.5% of patients were perirectally colonized with CROs and identified high organism and resistance mechanism diversity. Many variables were significantly associated with carriage, but resulting models were not highly predictive. Aims 2 and 3 analyzed 1,288 bacteremic patients and yielded higher performing prediction models for ESBL infection. We found that decision trees and risk scores performed similarly in our case study, but they offered different strengths and limitations. Conclusions. Statistical and machine learning prediction models offer an important complement to microbiological diagnostics. They can circumvent existing resource and practical constraints, but high biological heterogeneity can compromise their performance. Increasing familiarity with these methods, as well as refining distinctions between causal inference and prediction, may improve statistical tools for identifying colonization or infection with CROs and other multidrug-resistant bacteria

Impact of Continuity in Nursing Care on Patient Outcomes in the Pediatric Intensive Care Unit

Background: Nursing care is known to improve patient outcomes during hospitalization, but the mechanisms by which outcomes are improved have not been fully explicated. Continuity in nursing care (CINC) may be an important characteristic of nursing care delivery that impacts patient outcomes. However, evidence linking CINC to patient outcomes is limited. Purpose: The first aim of this study was to examine the relationship between CINC and patient outcomes - length of intensive care unit (ICU) stay, duration of mechanical ventilation, adverse events, and ICU-acquired infections - in a pediatric ICU. The second aim was to examine whether the match of nursing expertise to mortality risk enhances the relationship between CINC and patient outcomes. Methods: This cross-sectional study was a secondary data analysis of prospectively collected data that were merged from multiple databases from one pediatric ICU. The analytical database was a combination of four databases: the Nightingale Metrics database, the Virtual Pediatric Intensive Care Unit Performance System database, the Medical/Surgical Intensive Care Unit-Acquired Infection database, and the Safety Errors Reporting System database. The relationships between CINC and patient outcomes were assessed using a proportional hazard regression model and a logistic regression model. The final sample included 332 pediatric ICU subjects. Results: In multivariable regression analyses, more CINC was associated with a longer ICU stay and a longer duration of mechanical ventilation. CINC was not significantly associated with adverse events and ICU-acquired infections. A match of nursing expertise and mortality risk did not have a significant effect on the relationship between CINC and any of the four patient outcomes. However, the moderating effect of the match variable on the negative association between CINC and nurse-sensitive adverse event was significantly less for the matched group; specifically fewer different experienced nurses created a safer environment, than the mismatched group. Conclusion: This study provides preliminary data evaluating the relationship between CINC and pediatric ICU patient outcomes. Additional studies in other settings are needed to better understand these findings. Future research should focus on refining the measurement of CINC and exploring links between CINC and other outcomes such as patient/family satisfaction and being well-cared-for

Modelling Hospital Acquired Clostridium difficile Infections And Its Transmission In Acute Hospital Settings

The thesis explored a number of fundamental issues regarding the development of predictive models for hospital acquired Clostridium difficile infection (HA CDI) and its outbreaks. As predictive modeling for hospital acquired infection is still an emerging field and the ability to analyse HA CDI and potential outbreaks are in a developmental stage, the research documented in this thesis is exploratory and preliminary. Predictive modeling for the outbreak of hospital acquired infections can be considered at two levels: population and individual. We provide a comprehensive review regarding modeling methodology in this field at both population level and individual level. The transmission of HA CDI is not well understood. An agent based simulation model was built to evaluate the relative importance of the potential sources of Clostridium difficile (C. difficile) infection in a non-outbreak ward setting in an acute care hospital. The model was calibrated through a two stage procedure which utilized Latin Hypercube Sampling methodology and Genetic Algorithm optimization to capture five different patterns reported in the literature. A number of aspects of the model including housekeeping, hand hygiene compliance, patient turnover, and antibiotic pressure were explored. Based on the modeling results, several prevention policies are recommended. One widely used tool to better understand the dynamics of infectious disease outbreaks is network epidemiology. We explored the potential of using network statistics for the prediction of the transmission of HA CDIs in the hospital. Two types of dynamic networks were studied: ward level contacts and hospital transfers. An innovative method that combines time series data mining and predictive classification models was introduced for the analysis of these dynamic networks and for the prediction of HA CDI transmission. The results suggest that the network statistics extracted from the dynamic networks are potential predictors for the transmission of HA CDIs. We explored the potential of using the “multiple modeling methods approach” to predict HA CDI patient at risk by using the data from the information systems in the hospital. A range of machine learning predictive models were utilized to analyse collected data from a hospital. Our results suggest that the multiple modeling methods approach is able to improve prediction performance and to reveal new insights in the data set. We recommend that this approach might be considered for future studies on the predictive model construction and risk factor analysis

Pharmacokinetics of Extended vs Intermittent Intravenous Infusion of Meropenem in Critically Ill Patients Receiving Continuous Veno-Venous Haemofiltration

peer reviewe

The incidence and economic burden of hospital acquired infections occurring in surgical patients

Background: Approximately 9% of patients in hospital have a hospital acquired infection (HAl). These infections place a burden on the health sector, patients and carers. Objectives: To assess the incidence of, and independent risk factors for HAls occurring in adult surgical patients; to assess the impact of these infections on the hospital sector; and to show how this information may be used to assess the potential benefits of prevention. Design: A prospective survey of the incidence of HAl was conducted. Resources used by both infected and uninfected patients were recorded and costed. Generalised linear modelling techniques were used to estimate the impact of HAl on the observed variation in costs. Logistic regression analysis was used to determine independent risk factors for HAI. Setting: A district general hospital in England SubJects: 2469 adult patients admitted to five surgical specialties between April 1994 and May 1995. Results: 7.5% (95% Cl: 6.4, 8.6) acquired one or more HAls that presented during the in-patient period. The incidence, economic impact and independent risk factors varied with site of infection. On average HAls increased hospital costs by a factor of 2.3 (95% Cl: 2.0, 3.0), equivalent to an additional £2,254 (95% Cl: £1,738, £2,770) per case and increased length of stay by a factor of 2.1 (95% Cl: 1.8, 2.5), equivalent to an extra 7.8 days (95% Cl: 5.7, 10.0) per case. The estimates represent the average gross benefits of prevention. Net benefits depend on the cost and effectiveness of prevention activities. Estimates of the gross benefits of a 15% reduction in infection rates and a framework for assessing the net benefits of prevention are presented. Conclusion: The study provides an estimate of HAl by specialty and site for surgical patients. It calculates the burden on the hospital sector and shows the benefits that might accrue if HAl rates were reduced

Fatores preditores para o desenvolvimento de pneumonia hospitalar não associada à ventilação mecânica : revisão sistemática e metanálise

Introdução: A pneumonia hospitalar não associada à ventilação mecânica (PNVM) é uma infecção importante associada a alta morbidade e mortalidade e que, por ser distinta da pneumonia que se desenvolve em pacientes em ventilação mecânica, precisa ter seus fatores preditores estabelecidos. Objetivo: Identificar, quantificar e sumarizar a evidência existente na literatura sobre os fatores preditores para PNVM em pacientes adultos admitidos em unidades de cuidados não intensivos. Métodos: Uma busca sistemática da literatura foi realizada no PubMed, Embase, Scopus e LILACS. Estudos caso-controle e de coorte avaliando os fatores de risco para PNVM em pacientes adultos foram selecionados de acordo com os critérios de inclusão pré-definidos. Metanálise foi realizada para os fatores de risco para os quais os dados estavam disponíveis em mais de um estudo. A ferramenta de avaliação do National Institute of Health para estudos de coorte e caso-controle foi aplicada para avaliar a qualidade metodológica dos estudos incluídos. Foi atribuída uma avaliação com classificação de qualidade boa, razoável ou ruim para cada estudo. Resultados: Foram encontrados 11.380 estudos, 35 dos quais atendiam aos critérios de inclusão e fizeram parte desta revisão sistemática. A revisão encontrou 269 fatores de risco distintos, sendo que 58 estavam presentes em mais de um estudo e foram incluídos na metanálise com 33 significativamente associados à PNVM. A avaliação da qualidade foi realizada e 14 estudos foram classificados como ruins e 15 como qualidade razoável. A análise de sensibilidade foi realizada removendo os estudos classificados como ruins e 22 fatores de risco permaneceram significativamente associados à PNVM. Conclusão: A literatura mostra que existem 22 fatores de risco associados estatisticamente a PNVM. Mais estudos são necessários para estabelecer a associação dos fatores que não puderam ser associados a PNVM devido à baixa classificação da qualidade epidemiológica.Background: Non-ventilated hospital acquired pneumonia (NVHAP) is an important infection associated to a high morbidity and mortality and, because it is distinct from pneumonia developing in patients undergoing mechanical ventilation, it must have its predictor factor established. Aim: To identify, quantify and summarize the existing evidence in the literature on the predictor factors for NVHAP in adult patients admitted to nonintensive care units. Methods: A systematic literature search was undertaken on PubMed, Embase, Scopus and LILACS. Case-control and cohort studies evaluating NVHAP predictor factors in adult patients were selected according to the inclusion criteria previously defined. Metanalysis was performed for those risk factors available from more than one study. The National Institute of Health assessment tool for cohort and case-control studies was applied to assess the quality of the included studies. An assessment of good, fair or poor quality rating was assigned for each study. Findings: A total of 11,380 studies were found, 35 of which met our inclusion criteria for this systematic review. The review found 269 distinct risk factors, of which 58 were present in more than one study and were included in the metanalysis with 33 being significantly associated to NVHAP. Quality assessment was performed and 14 studies were rated as poor and 15 as fair quality. Sensitivity analysis was performed without studies rated as poor in quality assessment and 22 risk factors remained significantly associated to NVHAP. Conclusion: Literature shows that there are 22 risk factors statistically associated to NVHAP. More studies are needed to establish the association of those factors that could not be associated to NVHAP due to poor quality rating