Survival mixture modelling of recurrent infections

Recurrent infections data are commonly encountered in biomedical applications, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings, are presented as a flexible method of analysing such data. To account for the inherent dependency of the recurrent observations, random effects are incorporated within the conditional hazard function. Assuming a Weibull or log-logistic baseline hazard in both mixture components of the survival mixture model, an EM algorithm is developed for the residual maximum quasi-likelihood estimation of fixed effect and variance components parameters. The methodology is implemented as a graphical user interface coded using Microsoft visual C++. Application to model recurrent urinary tract infections for elderly women is illustrated, where significant individual variations are evident at both acute and stable phases. The survival mixture methodology developed enable practitioners to identify pertinent risk factors affecting the recurrent times and to draw valid conclusions inferred from these correlated and heterogeneous survival data

Predictive Models for the Management of Vesicoureteral Reflux from the View of Statisticians

Abstract: The management of vesicoureteral reflux (VUR) is one of the most challenging issues not only for pediatric urologists but also for pediatric nephrologists and all other related subspecialties. Urinary tract infections (UTI), pyelonephritis and renal scarring which may lead to deterioration in renal function are the common complications in a child presenting with VUR. Due to the patient heterogeneity and varying management options, patient selection for each treatment modality remains as a controversial issue. The different bio-statistical models have been used in order to disclose the factors affecting success of different management modalities and represent the incidence of possible complications. Bio-statistical models are useful to define variables which may help predict the outcome of disease during the different managements. Artificial neural networks (ANN) and regression models are popular methods employed to predict the outcome of urological abnormalities. Statistical models and ANNs provide an estimation of the probability of outcome that is of utmost importance in clinical decision. This study addresses both bio-statistical methods and ANNs employed to predict the outcome of VUR management and their clinical applications. To reach the best fit model that predicts the VUR outcome in a child, widespread knowledge regarding available bio-statistical methods is needed

Primary Care Antibiotic Prescribing and Infection-Related Hospitalisation

Inappropriate prescribing of antibiotics has been widely recognised as a leading cause of antimicrobial resistance, which in turn has become one of the most significant threats to global health. Given that most antibiotic prescriptions are issued in primary care settings, investigating the associations between primary care prescribing of antibiotics and subsequent infection-related hospitalisations affords a valuable opportunity to understand the long-term health implications of primary care antibiotic intervention. A narrative review of the scientific literature studying associations between primary care antibiotic prescribing and subsequent infection-related hospitalisation was conducted. The Web of Science database was used to retrieve 252 potentially relevant studies, with 23 of these studies included in this review (stratified by patient age and infection type). The majority of studies (n = 18) were published in the United Kingdom, while the remainder were conducted in Germany, Spain, Denmark, New Zealand, and the United States. While some of the reviewed studies demonstrated that appropriate and timely antibiotic prescribing in primary care could help reduce the need for hospitalisation, excessive antibiotic prescribing can lead to antimicrobial resistance, subsequently increasing the risk of infection-related hospitalisation. Few studies reported no association between primary care antibiotic prescriptions and subsequent infection-related hospitalisation. Overall, the disparate results in the extant literature attest to the conflicting factors influencing the decision-making regarding antibiotic prescribing and highlight the necessity of adopting a more patient-focussed perspective in stewardship programmes and the need for increased use of rapid diagnostic testing in primary care

Bayesian Autoregressive Frailty Models for Inference in Recurrent Events

We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections

The feasibility of using of electronic health records to inform clinical decision making for community-onset urinary tract infection in England

Urinary tract infections (UTIs) are a major source of morbidity, yet differentiating UTI from other conditions and choosing the right treatment remains challenging. Using case studies from English primary and secondary care, this thesis investigates the potential use of electronic health records (EHR) - i.e., data recorded as part of routine care - to aid the diagnosis and management of community-onset UTI. I start by introducing sources of uncertainty in diagnosing UTI (Chapter 1) and review how EHRs have previously been used to study UTIs (Chapter 2). In Chapter 3, I discuss EHR sources available to study UTIs in England. In Chapter 4, I explore how EHRs from primary care can be used to guide antibiotic prescribing for UTI, by evaluating harms of delaying treatment in key patient groups. In Chapters 5 and 6, I explore the use of EHR data as a diagnostic tool to guide antibiotic de-escalation in patients with suspected UTI in the emergency department (ED). Cases of community-onset UTI could be identified in both primary and secondary care data but case definitions relied heavily on coarse diagnostic codes. A lack of information on patients' acute health status, clinical observations (e.g., urine dipstick tests), and reasons for antibiotic prescribing resulted in heterogeneous study cohorts, which likely confounded estimated effects of antibiotic treatment in primary care. In secondary care, early prediction of bacteriuria to guide antibiotic prescribing decisions in the ED proved promising, but model performance varied greatly by patient mix and variable definitions. Better recording of clinical information and a combination of retrospective EHR analysis with prospective cohorts and qualitative approaches will be required to derive actionable insights on UTI. Results based solely on currently available EHR data need to be interpreted carefully

Sub-Inhibitory Antibiotics Enhance Virulence, Persistence, and Pathogenesis of Uropathogens

In addition to their bactericidal effects, antibiotics are potent signal mediators at sub-inhibitory levels in the environment. The ability to modulate community structure in this niche raises concerns over their capacity to influence pathogenesis in patients during antibiotic therapy. This concept forms the basis of this thesis, and is explored using models of prophylactic therapy for recurrent urinary tract infection (UTI) management. Sub-inhibitory ciprofloxacin, ampicillin, and gentamicin were found to augment virulence in vitro, increasing adherence and urothelial cell invasion in uropathogenic Escherichia coli (UPEC) and Staphylococcus saprophyticus. In addition, biofilm formation was increased, and swarming motility decreased. In UPEC, the effect of antibiotics on these processes was abolished in SOS-deficient strains. Trans-urethral inoculation of mice with ciprofloxacin-primed S. saprophyticus or UPEC significantly increased bacterial burden in both bladders and kidneys at one and 14 days post-inoculation (dpi). Sub-therapeutic ciprofloxacin supplemented in the drinking water of chronically infected mice significantly increased bacterial urine load. In addition, mice previously infected but clinically resolved suffered recurrences. These mice had impaired urinary polymorphonuclear leukocyte infiltrates, in part due to antibiotic-dependent cytokine suppression during initial infection. Prophylactic intervention had no significant effect on UPEC clearance, but did significantly increase bacterial intracellular bladder reservoirs, raising concerns over the clinical efficacy of this management strategy and risks of promoting persistent infection. The inability of antibiotics to clear infection in prophylaxis models was attributed to the presence of MDT persister cells. Sub-inhibitory antibiotic pre-treatments were found to increase persister fractions, but this effect was abolished in SOS-deficient strains. Conducting these assays with UPEC isolated from recurrent UTI patients revealed an enriched persister fraction compared to organisms cleared with standard antibiotic therapy, suggesting persister traits are either selected for during prolonged antibiotic treatment or initially contribute to therapy failure. This work represents the first attempt to illustrate that observed sub-inhibitory, pathogen associated antibiotic-dependent changes in vitro have significant in vivo consequences. It is hoped that this research will lead to a re-examination of how antibiotics are administered for management of patients suffering from recurrent UTI and other chronic diseases

Improving antibiotic prescribing for older people with urinary tract infection in primary care

Urinary tract infection (UTI) is a common cause of morbidity, NHS use, and antibiotic prescribing in older people. However, few randomised trials or observational studies have explored the impact of different antibiotic prescribing strategies on UTI-related outcomes in older people. Routinely collected healthcare data provides an opportunity to investigate associations between different treatment approaches and outcomes efficiently and cost-effectively. The aim of this thesis was to carry out epidemiological analyses of linked general practice, hospital, and mortality data from the Clinical Practice Research Datalink, to understand the impact of different antibiotic prescribing strategies on outcomes in older people with acute and recurrent UTI. In chapter 4, we investigate the burden of clinically diagnosed UTI in older people in UK primary care and found that in a sample of adults aged ≥65, 21% present with at least one UTI over a 10-year period. We also found that choice and duration of antibiotic therapy improved over time. For example, between 2004 and 2014, nitrofurantoin prescribing increased, broad-spectrum antibiotic prescribing decreased, and there was an increase in the proportion of patients prescribed antibiotics for durations recommended by clinical guidelines. In chapters 5 and 7, we investigate associations between antibiotic choice and risk of treatment failure, hospitalisation and death. We found that broad-spectrum antibiotics offer little benefit over nitrofurantoin, and nitrofurantoin is associated with better outcomes than trimethoprim in patients with renal impairment. Chapter 6 investigates the impact of short versus long course antibiotic treatment on UTI outcomes in older men and found that shorter durations of treatment are associated with higher rates of treatment failure but lower rates of acute kidney injury. Chapter 8 reports a systematic review and meta-analysis of randomised trials and found that the evidence for prophylactic antibiotics for recurrent UTI in older people is based on three studies of postmenopausal women. In chapter 9, we provide the only currently available data on outcomes in older men with recurrent UTI prescribed long-term antibiotic prophylaxis. This thesis reports new evidence to support more prudent antibiotic prescribing for UTI in older people and highlights the need for more robust evidence to address challenges in diagnosis and treatment of UTI