Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Background: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. Methods: In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. Results: The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. Conclusion: The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan

Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm.

BACKGROUND: A third of deaths in the UK from ruptured abdominal aortic aneurysm (AAA) are in women. In men, national screening programmes reduce deaths from AAA and are cost-effective. The benefits, harms, and cost-effectiveness in offering a similar programme to women have not been formally assessed, and this was the aim of this study. METHODS: We developed a decision model to assess predefined outcomes of death caused by AAA, life years, quality-adjusted life years, costs, and the incremental cost-effectiveness ratio for a population of women invited to AAA screening versus a population who were not invited to screening. A discrete event simulation model was set up for AAA screening, surveillance, and intervention. Relevant women-specific parameters were obtained from sources including systematic literature reviews, national registry or administrative databases, major AAA surgery trials, and UK National Health Service reference costs. FINDINGS: AAA screening for women, as currently offered to UK men (at age 65 years, with an AAA diagnosis at an aortic diameter of ≥3·0 cm, and elective repair considered at ≥5·5cm) gave, over 30 years, an estimated incremental cost-effectiveness ratio of £30 000 (95% CI 12 000-87 000) per quality-adjusted life year gained, with 3900 invitations to screening required to prevent one AAA-related death and an overdiagnosis rate of 33%. A modified option for women (screening at age 70 years, diagnosis at 2·5 cm and repair at 5·0 cm) was estimated to have an incremental cost-effectiveness ratio of £23 000 (9500-71 000) per quality-adjusted life year and 1800 invitations to screening required to prevent one AAA-death, but an overdiagnosis rate of 55%. There was considerable uncertainty in the cost-effectiveness ratio, largely driven by uncertainty about AAA prevalence, the distribution of aortic sizes for women at different ages, and the effect of screening on quality of life. INTERPRETATION: By UK standards, an AAA screening programme for women, designed to be similar to that used to screen men, is unlikely to be cost-effective. Further research on the aortic diameter distribution in women and potential quality of life decrements associated with screening are needed to assess the full benefits and harms of modified options. FUNDING: UK National Institute for Health Research Health Technology Assessment programme

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Safety Study of Photodynamic Therapy Using Talaporfin Sodium in the Pancreas and Surrounding Tissues in the Syrian Golden Hamster

Aim. To assess the safety of photodynamic therapy (PDT) using talaporfin sodium on the pancreas and surrounding organs in normal hamsters. Methods. Fluorescence microscopy documented talaporfin levels in liver, duodenum, and pancreas up to 24 hours after photosensitisation. Lesion size in liver 3 days after PDT (50 J, 5 mg/kg, variable drug-light interval (DLI)) was documented to optimise the DLI. Using optimum DLI, pancreas and surrounding organs were treated with laser fibre touching the surface and animals were killed at 3 or 21 days. Results. Peak fluorescence was seen in duodenum and pancreas at 15 mins (second lower peak at 2 hours). Liver fluorescence was consistently high (peak 1 hour) until after 4 hours. Optimum DLI was seen at 15 minutes. The pancreas was relatively resistant to direct PDT injury (small lesions at high doses) but surrounding stomach, duodenum, and liver were more susceptible with evidence of adhesions and full thickness damage (localised peritonitis and duodenal perforation at highest doses). Conclusion. The safety profile is similar to PDT with longer acting photosensitisers. The pancreas appears safe to treat, but care is required to avoid high light doses to the intestinal tract, particularly the duodenum

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Plasma Desmosine and Abdominal Aortic Aneurysm Disease

Background It is recognized that factors beyond aortic size are important in predicting outcome in abdominal aortic aneurysm (AAA) disease. AAA is characterized by the breakdown of elastin within the aortic tunica media, leading to aortic dilatation and rupture. The aim of this study was to investigate the association of plasma desmosine (pDES), an elastin-specific degradation product, with disease severity and clinical outcome in patients with AAA. Methods and Results We measured pDES and serum biomarker concentrations in 507 patients with AAAs (94% men; mean age, 72.4±6.1 years; mean AAA diameter, 48±8 mm) and 162 control subjects (100% men; mean age, 71.5±4.4 years) from 2 observational cohort studies. In the longitudinal cohort study (n=239), we explored the incremental prognostic value of pDES on AAA events. pDES was higher in patients with AAA compared with control subjects (mean±SD: 0.46±0.22 versus 0.33±0.16 ng/mL; P<0.001) and had the strongest correlation with AAA diameter (r=0.39; P<0.0001) of any serum biomarker. After adjustment for baseline AAA diameter, pDES was associated with an AAA event (hazard ratio, 2.03 per SD increase [95% CI, 1.02-4.02]; P=0.044). In addition to AAA diameter, pDES provided incremental improvement in risk stratification (continuous net reclassification improvement, 34.4% [95% CI, -10.8% to 57.5%; P=0.09]; integrated discrimination improvement, 0.04 [95% CI, 0.00-0.15; P=0.050]). Conclusions pDES concentrations predict disease severity and clinical outcomes in patients with AAA. Clinical Trial Registration http://www.isrctn.com. Unique identifier: ISRCTN76413758

Current prevalence of screen-detected abdominal aortic aneurysm in women: a systematic review with meta-analysis

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Wiley.Background: Although women represent an increasing proportion of those now presenting with abdominal aortic aneurysm (AAA) rupture, the current prevalence of AAA in women is unknown. The contemporary population prevalence of screen-detected AAA in women by both age and smoking status was investigated. Methods: Systematic review of studies screening for AAA, including >1000 women, aged ≥60 years, undertaken since the year 2000, searching MEDLINE, EMBASE and CENTRAL databases until 13th January 2016. Study quality was assessed using the Newcastle-Ottawa scoring system. Results: Eight studies were identified, including only 3 based on population registers. The largest studies were based on self-purchase of screening. Altogether 1,537,633 women were screened. Overall AAA prevalence rates were very heterogeneous, ranging from 0.37 to 1.53%: pooled prevalence 0.74% [95%CI 0.53, 1.03]. The pooled prevalence increased with both age (>1% for women >70 years) and smoking (>1% for ever smokers and >2% in current smokers). Conclusions: The current population prevalence of screen-detected AAA in older women is subject to wide demographic variation. However, in ever smokers and those over 70 years, the prevalence is >1%. Registration: PROSPERO database of systematic reviews CRD42015020444This project was funded by the UK National Institute for Health Research (NIHR) Health Technology Appraisal (HTA) programme (project number 14/179/01)

Screening women aged 65 years or over for abdominal aortic aneurysm: a modelling study and health economic evaluation.

BACKGROUND: Abdominal aortic aneurysm (AAA) screening programmes have been established for men in the UK to reduce deaths from AAA rupture. Whether or not screening should be extended to women is uncertain. OBJECTIVE: To evaluate the cost-effectiveness of population screening for AAAs in women and compare a range of screening options. DESIGN: A discrete event simulation (DES) model was developed to provide a clinically realistic model of screening, surveillance, and elective and emergency AAA repair operations. Input parameters specifically for women were employed. The model was run for 10 million women, with parameter uncertainty addressed by probabilistic and deterministic sensitivity analyses. SETTING: Population screening in the UK. PARTICIPANTS: Women aged ≥ 65 years, followed up to the age of 95 years. INTERVENTIONS: Invitation to ultrasound screening, followed by surveillance for small AAAs and elective surgical repair for large AAAs. MAIN OUTCOME MEASURES: Number of operations undertaken, AAA-related mortality, quality-adjusted life-years (QALYs), NHS costs and cost-effectiveness with annual discounting. DATA SOURCES: AAA surveillance data, National Vascular Registry, Hospital Episode Statistics, trials of elective and emergency AAA surgery, and the NHS Abdominal Aortic Aneurysm Screening Programme (NAAASP). REVIEW METHODS: Systematic reviews of AAA prevalence and, for elective operations, suitability for endovascular aneurysm repair, non-intervention rates, operative mortality and literature reviews for other parameters. RESULTS: The prevalence of AAAs (aortic diameter of ≥ 3.0 cm) was estimated as 0.43% in women aged 65 years and 1.15% at age 75 years. The corresponding attendance rates following invitation to screening were estimated as 73% and 62%, respectively. The base-case model adopted the same age at screening (65 years), definition of an AAA (diameter of ≥ 3.0 cm), surveillance intervals (1 year for AAAs with diameter of 3.0-4.4 cm, 3 months for AAAs with diameter of 4.5-5.4 cm) and AAA diameter for consideration of surgery (5.5 cm) as in NAAASP for men. Per woman invited to screening, the estimated gain in QALYs was 0.00110, and the incremental cost was £33.99. This gave an incremental cost-effectiveness ratio (ICER) of £31,000 per QALY gained. The corresponding incremental net monetary benefit at a threshold of £20,000 per QALY gained was -£12.03 (95% uncertainty interval -£27.88 to £22.12). Almost no sensitivity analyses brought the ICER below £20,000 per QALY gained; an exception was doubling the AAA prevalence to 0.86%, which resulted in an ICER of £13,000. Alternative screening options (increasing the screening age to 70 years, lowering the threshold for considering surgery to diameters of 5.0 cm or 4.5 cm, lowering the diameter defining an AAA in women to 2.5 cm and lengthening the surveillance intervals for the smallest AAAs) did not bring the ICER below £20,000 per QALY gained when considered either singly or in combination. LIMITATIONS: The model for women was not directly validated against empirical data. Some parameters were poorly estimated, potentially lacking relevance or unavailable for women. CONCLUSION: The accepted criteria for a population-based AAA screening programme in women are not currently met. FUTURE WORK: A large-scale study is needed of the exact aortic size distribution for women screened at relevant ages. The DES model can be adapted to evaluate screening options in men. STUDY REGISTRATION: This study is registered as PROSPERO CRD42015020444 and CRD42016043227. FUNDING: The National Institute for Health Research Health Technology Assessment programme