Fabiola Fernandez-Gutierrez

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

Objectives 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. Methods This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. Results Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. Conclusion Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

Framework for detailed workflow analysis and modelling for simulation of multi‐modal image‐guided interventions

Purpose – The purpose of this paper is to provide a framework for analysing and modelling detailed workflow of image‐guided interventions to facilitate simulation and the re‐engineering process for the development of new procedures in multi‐modal imaging environments.Design/methodology/approach – The methodology presented includes a literature review on workflow simulation in surgery, focussing on radiology environments, an assessment of simulation tools, a data gathering and management framework and research on methods for conceptual modelling of the processes.Findings – The literature review reveals that few authors attempted to analyse the phases within image‐guided interventions, and those that did, only did so partially. The framework developed for this work intends to fill the gap found in the survey. It allows the maintenance and management of large amounts of data, one of the most critical factors when modelling detailed workflow. In addition, selecting the appropriate simulation software plays an...

MR-Guided Interventions and Surgery

This chapter covers the use of magnetic resonance imaging (MRI) for guidance of interventional and operative procedures, including the basics on MRI systems design, integration, workflow principles, device visualization, and instrument positioning and tracking. MRI provides soft tissue contrast superior to x-ray technology and ultrasound. The contrast can be weighted towards water or fat, and images acquired in arbitrary, multiplanar orientations and three-dimensional (3-D) volumes. MRI’s flow and temperature sensitivity alongside its lack of ionizing radiation and nephrotoxic iodinated contrast agents renders it a suitable imaging technique for vascular and percutaneous interventions. Intraoperative MR imaging allows detection of hidden structures of the tissue volume in the surgical field. Although MRI has been applied since the beginning of the 1990s during operation and intervention, the lack of approved MRI-compatible tools and the technical hurdles in integrating MRI systems into clinical applications have hampered its wider distribution. These problems can be overcome through appropriate technical solutions and the use of suitable nonmagnetic and nonconductive materials.

Comparative ergonomic workflow and user experience analysis of MRI versus fluoroscopy-guided vascular interventions: an iliac angioplasty exemplar case study

PurposeA methodological framework is introduced to assess and compare a conventional fluoroscopy protocol for peripheral angioplasty with a new magnetic resonant imaging (MRI)-guided protocol. Different scenarios were considered during interventions on a perfused arterial phantom with regard to time-based and cognitive task analysis, user experience and ergonomics.MethodsThree clinicians with different expertise performed a total of 43 simulated common iliac angioplasties (9 fluoroscopic, 34 MRI-guided) in two blocks of sessions. Six different configurations for MRI guidance were tested in the first block. Four of them were evaluated in the second block and compared to the fluoroscopy protocol. Relevant stages’ durations were collected, and interventions were audio-visually recorded from different perspectives. A cued retrospective protocol analysis (CRPA) was undertaken, including personal interviews. In addition, ergonomic constraints in the MRI suite were evaluated.ResultsSignificant differences were found when comparing the performance between MRI configurations versus fluoroscopy. Two configurations [with times of 8.56 (0.64) and 9.48 (1.13) min] led to reduce procedure time for MRI guidance, comparable to fluoroscopy [8.49 (0.75) min]. The CRPA pointed out the main influential factors for clinical procedure performance. The ergonomic analysis quantified musculoskeletal risks for interventional radiologists when utilising MRI. Several alternatives were suggested to prevent potential low-back injuries.ConclusionsThis work presents a step towards the implementation of efficient operational protocols for MRI-guided procedures based on an integral and multidisciplinary framework, applicable to the assessment of current vascular protocols. The use of first-user perspective raises the possibility of establishing new forms of clinical training and education.

Characteristics of Children Prescribed Antipsychotics: Analysis of Routinely Collected Data

Abstract Objective: Antipsychotics are licensed for psychosis and are also prescribed for behavior control. This study aims to examine characteristics and outcomes of children prescribed antipsychotics. Methods: A cohort study using general practice and hospital records linked with education records for 1,488,936 children living in Wales between 1999 and 2015. The characteristics of the children who were prescribed antipsychotics are presented using descriptive statistics and outcomes such as respiratory illness, diabetes, and injury were analyzed using multilevel logistic regression and the prior event rate ratio (PERR). Results: Children with intellectual difficulty/autism were more likely to be prescribed antipsychotics (2.8% have been prescribed an antipsychotic [75% with autism] compared with 0.15% of children without intellectual difficulty). Those with intellectual disabilities/autism were prescribed antipsychotics at a younger age and for a longer period. Antipsychotic use was associated with a higher rate of respiratory illness for all (PERR of hospital admission: 1.55 [95% CI: 1.51–1.598] or increase in rate of 2 per 100 per year in those treated), and for those with intellectual difficulty/autism, there was a higher rate of injury and hospitalized depression. However, among those without intellectual difficulty/autism, there were lower rates of depression (PERR: 0.55 [95% CI: 0.51–0.59]). Conclusions: This work shows real-world use of antipsychotics and provides information on the rate of possible adverse events in children treated. Antipsychotics are predominantly used for those with intellectual difficulty/autism rather than those with a psychotic diagnosis. There is evidence that rates of respiratory disease, epilepsy, and diabetes are also higher postantipsychotic use for all. In those with intellectual difficulty/autism, hospital-admitted depression and injury are higher postantipsychotic use. The use of antipsychotics for behavioral management is likely to have increased cost implications to the healthcare system.

Workflow optimisation for multimodal imaging procedures: a case of combined X-ray and MRI-guided TACE.

Abstract This study presents a framework for workflow optimisation of multimodal image-guided procedures (MIGP) based on discrete event simulation (DES). A case of a combined X-Ray and magnetic resonance image-guided transarterial chemoembolisation (TACE) is presented to illustrate the application of this method. We used a ranking and selection optimisation algorithm to measure the performance of a number of proposed alternatives to improve a current scenario. A DES model was implemented with detail data collected from 59 TACE procedures and durations of magnetic resonance imaging (MRI) diagnostic procedures usually performed in a common MRI suite. Fourteen alternatives were proposed and assessed to minimise the waiting times and improve workflow. Data analysis observed an average of 20.68 (7.68) min of waiting between angiography and MRI for TACE patients in 71.19% of the cases. Following the optimisation analysis, an alternative was identified to reduce waiting times in angiography suite up to 48.74%. The model helped to understand and detect ‘bottlenecks’ during multimodal TACE procedures, identifying a better alternative to the current workflow and reducing waiting times. Simulation-based workflow analysis provides a cost-effective way to face some of the challenges of introducing MIGP in clinical radiology, highligthed in this study.

Building Bridges Between User and Designer: Co-creation, Immersion and Perspective Taking

Designing for users rather than with users is still a common practice in technology design and innovation as opposed to taking them on board in the process. Design for inclusion aims to define and understand end-users, their needs, context of use, and, by doing so, ensure that end-users are catered for and included, while the results are geared towards universality of use. We describe the central role of end-user and designer participation, immersion and perspective to build user-driven solutions. These approaches provided a critical understanding of the counterpart role. Designer(s) could understand what the user’s needs were, experience physical impairments, and see from other’s perspective the interaction with the environment. Users could understand challenges of designing for physical impairments, build a sense of ownership with technology and explore it from a creative perspective. The understanding of the peer’s role (user and designer), needs and perspective enhanced user participation and inclusion.

Preclinical feasibility of a technology framework for MRI-guided iliac angioplasty

PurposeInterventional MRI has significant potential for image guidance of iliac angioplasty and related vascular procedures. A technology framework with in-room image display, control, communication and MRI-guided intervention techniques was designed and tested for its potential to provide safe, fast and efficient MRI-guided angioplasty of the iliac arteries.MethodsA 1.5-T MRI scanner was adapted for interactive imaging during endovascular procedures using new or modified interventional devices such as guidewires and catheters. A perfused vascular phantom was used for testing. Pre-, intra- and post-procedural visualization and measurement of vascular morphology and flow was implemented. A detailed analysis of X-ray fluoroscopic angiography workflow was conducted and applied. Two interventional radiologists and one physician in training performed 39 procedures. All procedures were timed and analyzed.ResultsMRI-guided iliac angioplasty procedures were successfully performed with progressive adaptation of techniques and workflow. The workflow, setup and protocol enabled a reduction in table time for a dedicated MRI-guided procedure to 6 min 33 s with a mean procedure time of 9 min 2 s, comparable to the mean procedure time of 8 min 42 s for the standard X-ray-guided procedure.ConclusionsMRI-guided iliac vascular interventions were found to be feasible and practical using this framework and optimized workflow. In particular, the real-time flow analysis was found to be helpful for pre- and post-interventional assessments. Design optimization of the catheters and in vivo experiments are required before clinical evaluation.

Workflow Analysis, Design, Modeling and Simulation for the Multimodality Imaging Therapy Operating System (MITOS)

Image-guided therapy has the great benefit for patients by reducing invasiveness of procedures to avoid complications and improve effectiveness and efficacy and carries the potential to improve the outcome for the patients. However, it is utilizing very costly and complex technologies such as imaging, navigation, and robotics. Although there should be no price tag of any of the mentioned improvements, we must face an increasing pressure on the healthcare systems to be more economic. It is therefore mandatory to utilize expensive equipment as efficient as possible and to reduce unnecessary staff involvement handling complex equipment instead of serving the patients personal needs. This chapter describes the attempt to apply state-of-the-art industrial modeling and simulation tools to optimized workflow, equipment, and staff allocation for more efficient, effective, and economic image-guided therapy.

Workflow and intervention times of MR-guided focused ultrasound - Predicting the impact of new techniques

Magnetic resonance guided focused ultrasound surgery (MRgFUS) has become an attractive, non-invasive treatment for benign and malignant tumours, and offers specific benefits for poorly accessible locations in the liver. However, the presence of the ribcage and the occurrence of liver motion due to respiration limit the applicability MRgFUS. Several techniques are being developed to address these issues or to decrease treatment times in other ways. However, the potential benefit of such improvements has not been quantified. In this research, the detailed workflow of current MRgFUS procedures was determined qualitatively and quantitatively by using observation studies on uterine MRgFUS interventions, and the bottlenecks in MRgFUS were identified. A validated simulation model based on discrete events simulation was developed to quantitatively predict the effect of new technological developments on the intervention duration of MRgFUS on the liver. During the observation studies, the duration and occurrence frequencies of all actions and decisions in the MRgFUS workflow were registered, as were the occurrence frequencies of motion detections and intervention halts. The observation results show that current MRgFUS uterine interventions take on average 213min. Organ motion was detected on average 2.9 times per intervention, of which on average 1.0 actually caused a need for rework. Nevertheless, these motion occurrences and the actions required to continue after their detection consumed on average 11% and up to 29% of the total intervention duration. The simulation results suggest that, depending on the motion occurrence frequency, the addition of new technology to automate currently manual MRgFUS tasks and motion compensation could potentially reduce the intervention durations by 98.4% (from 256h 5min to 4h 4min) in the case of 90% motion occurrence, and with 24% (from 5h 19min to 4h 2min) in the case of no motion. In conclusion, new tools were developed to predict how intervention durations will be affected by future workflow changes and by the introduction of new technology.