Undiagnosed dementia in primary care: A record linkage study

BackgroundThe number of people living with dementia is greater than the number with a diagnosis of dementia recorded in primary care. This suggests that a significant number are living with dementia that is undiagnosed. Little is known about this group and there is little quantitative evidence regarding the consequences of diagnosis for people with dementia.ObjectivesThe aims of this study were to (1) describe the population meeting the criteria for dementia but without diagnosis, (2) identify predictors of being diagnosed and (3) estimate the effect of diagnosis on mortality, move to residential care, social participation and well-being.DesignA record linkage study of a subsample of participants (n = 598) from the Cognitive Function and Ageing Study II (CFAS II) (n = 7796), an existing cohort study of the population of England aged ≥ 65 years, with standardised validated assessment of dementia and consent to access medical records.Data sourcesData on dementia diagnoses from each participant’s primary care record and covariate and outcome data from CFAS II.SettingA population-representative cohort of people aged ≥ 65 years from three regions of England between 2008 and 2011.ParticipantsA total of 598 CFAS II participants, which included all those with dementia who consented to medical record linkage (n = 449) and a stratified sample without dementia (n = 149).Main outcome measuresThe main outcome was presence of a diagnosis of dementia in each participant’s primary care record at the time of their CFAS II assessment(s). Other outcomes were date of death, cognitive performance scores, move to residential care, hospital stays and social participation.ResultsAmong people with dementia, the proportion with a diagnosis in primary care was 34% in 2008–11 and 44% in 2011–13. In both periods, a further 21% had a record of a concern or a referral but no diagnosis. The likelihood of having a recorded diagnosis increased with severity of impairment in memory and orientation, but not with other cognitive impairment. In multivariable analysis, those aged ≥ 90 years and those age

Machine Learning for Diabetes and Mortality Risk Prediction From Electronic Health Records

Data science can provide invaluable tools to better exploit healthcare data to improve patient outcomes and increase cost-effectiveness. Today, electronic health records (EHR) systems provide a fascinating array of data that data science applications can use to revolutionise the healthcare industry. Utilising EHR data to improve the early diagnosis of a variety of medical conditions/events is a rapidly developing area that, if successful, can help to improve healthcare services across the board. Specifically, as Type-2 Diabetes Mellitus (T2DM) represents one of the most serious threats to health across the globe, analysing the huge volumes of data provided by EHR systems to investigate approaches for early accurately predicting the onset of T2DM, and medical events such as in-hospital mortality, are two of the most important challenges data science currently faces. The present thesis addresses these challenges by examining the research gaps in the existing literature, pinpointing the un-investigated areas, and proposing a novel machine learning modelling given the difficulties inherent in EHR data. To achieve these aims, the present thesis firstly introduces a unique and large EHR dataset collected from Saudi Arabia. Then we investigate the use of a state-of-the-art machine learning predictive models that exploits this dataset for diabetes diagnosis and the early identification of patients with pre-diabetes by predicting the blood levels of one of the main indicators of diabetes and pre-diabetes: elevated Glycated Haemoglobin (HbA1c) levels. A novel collaborative denoising autoencoder (Col-DAE) framework is adopted to predict the diabetes (high) HbA1c levels. We also employ several machine learning approaches (random forest, logistic regression, support vector machine, and multilayer perceptron) for the identification of patients with pre-diabetes (elevated HbA1c levels). The models employed demonstrate that a patient's risk of diabetes/pre-diabetes can be reliably predicted from EHR records. We then extend this work to include pioneering adoption of recent technologies to investigate the outcomes of the predictive models employed by using recent explainable methods. This work also investigates the effect of using longitudinal data and more of the features available in the EHR systems on the performance and features ranking of the employed machine learning models for predicting elevated HbA1c levels in non-diabetic patients. This work demonstrates that longitudinal data and available EHR features can improve the performance of the machine learning models and can affect the relative order of importance of the features. Secondly, we develop a machine learning model for the early and accurate prediction all in-hospital mortality events for such patients utilising EHR data. This work investigates a novel application of the Stacked Denoising Autoencoder (SDA) to predict in-hospital patient mortality risk. In doing so, we demonstrate how our approach uniquely overcomes the issues associated with imbalanced datasets to which existing solutions are subject. The proposed model –– using clinical patient data on a variety of health conditions and without intensive feature engineering –– is demonstrated to achieve robust and promising results using EHR patient data recorded during the first 24 hours after admission

Prediction of outcome after abdominal aortic aneurysm rupture

This thesis aims to examine the validity of existing tools recommended for outcome prediction after abdominal aortic aneurysm (AAA) rupture and to design and validate a novel risk scoring instrument. It also aims to examine the utility of novel predictive variables. Finally, it examines the functional outcomes achieved by survivors of aneurysm rupture.Existing risk models and predictive variables for outcome were validated on a retrospective cohort of consecutive patients with ruptured AAA. These data were also used to design a novel prognostic index for outcome prediction. A prospective cohort of consecutive patients was used to further validate these scoring systems, examine novel prognostic variables and determine functional outcome.Existing risk scoring instruments for patients with ruptured AAA lack validity. Analysis of preoperative variables in patients with ruptured AAA shows that absolute surgical futility cannot be predicted. However, in-hospital hypotension (<90mmHg), reduced Glasgow Coma Scale (<15) and anaemia (<9g/dL) are associated with perioperative death. When these risk factors are equally weighted and combined to create a novel risk scoring instrument (Edinburgh Ruptured Aneurysm Score-ERAS), three discriminatory tiers of risk are demonstrable. The validity of this risk instrument is confirmed on prospective data. Examination of novel perioperative prognostic variables shows that elevated cardiac troponin I, with or without clinically apparent cardiac dysfunction, is predictive of death after ruptured AAA repair. However, although ruptured AAA are associated with an early elevation in inflammatory biomarkers, these do not appear to confer additional prognostic value. Furthermore, for the first time, prospective study shows that patients who survive ruptured AAA repair achieve a good recovery in terms of functional outcome within six months of operation.Surgical futility cannot be predicted prior to operation in patients with AAA. However, the ERAS shows potential as a preoperative prognostic index in patients with ruptured AAA