Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials

Background: After breast-conserving surgery, radiotherapy reduces recurrence and breast cancer death, but it may do so more for some groups of women than for others. We describe the absolute magnitude of these reductions according to various prognostic and other patient characteristics, and relate the absolute reduction in 15-year risk of breast cancer death to the absolute reduction in 10-year recurrence risk.
Methods: We undertook a meta-analysis of individual patient data for 10?801 women in 17 randomised trials of radiotherapy versus no radiotherapy after breast-conserving surgery, 8337 of whom had pathologically confirmed node-negative (pN0) or node-positive (pN+) disease.
Findings: Overall, radiotherapy reduced the 10-year risk of any (ie, locoregional or distant) first recurrence from 35·0% to 19·3% (absolute reduction 15·7%, 95% CI 13·7–17·7, 2p<0·00001) and reduced the 15-year risk of breast cancer death from 25·2% to 21·4% (absolute reduction 3·8%, 1·6–6·0, 2p=0·00005). In women with pN0 disease (n=7287), radiotherapy reduced these risks from 31·0% to 15·6% (absolute recurrence reduction 15·4%, 13·2–17·6, 2p<0·00001) and from 20·5% to 17·2% (absolute mortality reduction 3·3%, 0·8–5·8, 2p=0·005), respectively. In these women with pN0 disease, the absolute recurrence reduction varied according to age, grade, oestrogen-receptor status, tamoxifen use, and extent of surgery, and these characteristics were used to predict large (=20%), intermediate (10–19%), or lower (<10%) absolute reductions in the 10-year recurrence risk. Absolute reductions in 15-year risk of breast cancer death in these three prediction categories were 7·8% (95% CI 3·1–12·5), 1·1% (–2·0 to 4·2), and 0·1% (–7·5 to 7·7) respectively (trend in absolute mortality reduction 2p=0·03). In the few women with pN+ disease (n=1050), radiotherapy reduced the 10-year recurrence risk from 63·7% to 42·5% (absolute reduction 21·2%, 95% CI 14·5–27·9, 2p<0·00001) and the 15-year risk of breast cancer death from 51·3% to 42·8% (absolute reduction 8·5%, 1·8–15·2, 2p=0·01). Overall, about one breast cancer death was avoided by year 15 for every four recurrences avoided by year 10, and the mortality reduction did not differ significantly from this overall relationship in any of the three prediction categories for pN0 disease or for pN+ disease.
Interpretation: After breast-conserving surgery, radiotherapy to the conserved breast halves the rate at which the disease recurs and reduces the breast cancer death rate by about a sixth. These proportional benefits vary little between different groups of women. By contrast, the absolute benefits from radiotherapy vary substantially according to the characteristics of the patient and they can be predicted at the time when treatment decisions need to be made.
Funding: Cancer Research UK, British Heart Foundation, and UK Medical Research Council.

Keeping it real! Enhancing realism in standardised patient OSCE stations

Background: Objective structured clinical examinations (OSCEs) are a
commonly used method of assessing clinical competency in healthcare education. They can providean opportunity to observe candidates interacting with patients.
There are many challenges in using real patients in OSCEs, and increasingly standardised patients are being used as a preference. However, by using standardised patients there is a risk of making the encounter arti?cial and removed from actual clinical practice.
Context: Efforts made in terms of cognitive, auditory, visual, tactile, psychological and emotional cues can minimise the differences between a simulated
and real clinical scenario. However, a number of factors, including feasibility, cost and usability, need to be considered if such techniques are to be practicable
within an OSCE framework.
Innovation: This article describes a series of techniques that have been used in our institution to enhance the realism of a standardised patient encounter in an
OSCE. Efforts in preparing standardised patient roles, and how they portray these roles, will be considered. A wide variety of equipment can also be used in
combination with a patient and the surrounding environment, which can further enhance the authenticity of the simulated scenario.
Implications: By enhancing the realism in simulated patient OSCE encounters, there is potential to trigger more authentic conscious responses from candidates and implicit reactions that the candidates themselves may be less
aware of. Furthermore, using such techniques may allow faculty members to select scenarios that were previously not thought possible in an OSCE

A systematic review of quantitative studies reporting selected patient experienced outcomes, with a specific focus on gender differences in people with colorectal cancer

Men with colorectal cancer have a higher mortality rate than their female counterparts. Despite this, there is a limited understanding of the impact gender has on the experience of colorectal cancer.