Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

Emergency Medicine Palliative Care Access (EMPallA): Protocol for a multicentre randomised controlled trial comparing the effectiveness of specialty outpatient versus nurse-led telephonic palliative care of older adults with advanced illness

Introduction Emergency department (ED)-initiated palliative care has been shown to improve patient-centred outcomes in older adults with serious, life-limiting illnesses. However, the optimal modality for providing such interventions is unknown. This study aims to compare nurse-led telephonic case management to specialty outpatient palliative care for older adults with serious, life-limiting illness on: (1) quality of life in patients; (2) healthcare utilisation; (3) loneliness and symptom burden and (4) caregiver strain, caregiver quality of life and bereavement. Methods and analysis This is a protocol for a pragmatic, multicentre, parallel, two-arm randomised controlled trial in ED patients comparing two established models of palliative care: nurse-led telephonic case management and specialty, outpatient palliative care. We will enrol 1350 patients aged 50+ years and 675 of their caregivers across nine EDs. Eligible patients: (1) have advanced cancer (metastatic solid tumour) or end-stage organ failure (New York Heart Association class III or IV heart failure, end-stage renal disease with glomerular filtration rate /min/m2, or global initiative for chronic obstructive lung disease stage III, IV or oxygen-dependent chronic obstructive pulmonary disease); (2) speak English; (3) are scheduled for ED discharge or observation status; (4) reside locally; (5) have a working telephone and (6) are insured. Patients will be excluded if they: (1) have dementia; (2) have received hospice care or two or more palliative care visits in the last 6 months or (3) reside in a long-term care facility. We will use patient-level block randomisation, stratified by ED site and disease. Effectiveness will be compared by measuring the impact of each intervention on the specified outcomes. The primary outcome will measure change in patient quality of life. Ethics and dissemination Institutional Review Board approval was obtained at all study sites. Trial results will be submitted for publication in a peer-reviewed journal

Evaluation of vaccine effectiveness in older adults using routinely collected data: a quasi-experimental approach

Vaccination of older adults is a key component of public health policy, but further evidence is required to understand its effectiveness in practice. Electronic health records (EHRs) present a potential alternative to the gold-standard evidence of clinical trials, particularly for populations, such as older adults, who may be under-represented in trials due to ethical and practical constraints in recruitment. Importantly, EHRs also allow the real-world study of an intervention as it is delivered in practice, and its effect in clinically important sub-groups. However, EHRs are not purposed to collect informaton on confounders, which may bias results from the analyisis of routinely-collected data. This motivated my review of quasi-experimental (QE) methods as a means of indirectly adjusting for confounding. My published methodological review found that the longitudinal information available in EHRs offer many opportunities for mitigating against confounding bias, but many methods may be under-utilised. The prior event rate ratio (PERR) and its alternative formulation, described under the Pairwise framework, is a recently developed method that utilises longitudinal information. This before-and-after approach can be applied to rate and survival data, allowing an easy comparison to many trial results. The data on vaccination in UK older adults was also the basis for further study of the performance and limitations of the method beyond exisiting simulation studies. Through comparison to weighted regression, I demonstrated how the source of confounding and robustness of the results could be explored. In a novel application of the PERR and Pairwise methods to interactions, I investigated the effectiveness of the pneumococcal vaccine in older patients, and found evidence for an increase in effectiveness with age across the years of policy implementation, 2003-2005. In my investigation of the influenza vaccine in annual cohorts from 1997 to 2011, I found consistent evidence of a moderately protective effect against myocardial infarction, but that this may decrease with age. The evidence also indicated a protective effect against influenza itself, but no age trend in its effectiveness was detected