Developing a breast cancer screening decision aid in Spanish for average-risk women: a mixed methods study

Introduction

We aimed to develop a decision aid to support shared-decision making between physicians and women with average breast cancer risk when deciding whether to participate in breast cancer screening.

Methods

We included women at average risk of breast cancer and physicians involved in supporting the decision of breast cancer screening from an Academic Hospital in Buenos Aires, Argentina. We followed the International Patient Decision Aid Standards to develop our decision aid. Guided by a steering group and a multidisciplinary consultancy group including a patient advocate, we reviewed the evidence about breast cancer screening and previous decision aids, explored the patients' information needs on this topic from the patients' and physicians' perspective using semi-structured interviews, and we alpha-tested the prototype to determine its usability, comprehensibility and applicability.

Results

We developed the first prototype of a web-based decision aid to use during the clinical encounter with women aged 40 to 69 with average breast cancer risk. After a meeting with our consultancy group, we developed a second prototype that underwent alpha-testing. Physicians and patients agreed that the tool was clear, useful and applicable during a clinical encounter. We refined our final prototype according to their feedback.

Conclusion

We developed the first decision aid in our region and language on this topic, developed with end-users' input and informed by the best available evidence. We expect this decision aid to help women and physicians make shared decisions during the clinical encounter when talking about breast cancer screening.

Patient values in patient-provider communication about participation in early phase clinical cancer trials: a qualitative analysis before and after implementation of an online value clarification tool intervention

BACKGROUND

Patients with advanced cancer who no longer have standard treatment options available may decide to participate in early phase clinical trials (i.e. experimental treatments with uncertain outcomes). Shared decision-making (SDM) models help to understand considerations that influence patients' decision. Discussion of patient values is essential to SDM, but such communication is often limited in this context and may require new interventions. The OnVaCT intervention, consisting of a preparatory online value clarification tool (OnVaCT) for patients and communication training for oncologists, was previously developed to support SDM. This study aimed to qualitatively explore associations between patient values that are discussed between patients and oncologists during consultations about potential participation in early phase clinical trials before and after implementation of the OnVaCT intervention.

METHODS

This study is part of a prospective multicentre nonrandomized controlled clinical trial and had a between-subjects design: pre-intervention patients received usual care, while post-intervention patients additionally received the OnVaCT. Oncologists participated in the communication training between study phases. Patients' initial consultation on potential early phase clinical trial participation was recorded and transcribed verbatim. Applying a directed approach, two independent coders analysed the transcripts using an initial codebook based on previous studies. Steps of continuous evaluation and revision were repeated until data saturation was reached.

RESULTS

Data saturation was reached after 32 patient-oncologist consultations (i.e. 17 pre-intervention and 15 post-intervention). The analysis revealed the values: hope, perseverance, quality or quantity of life, risk tolerance, trust in the healthcare system/professionals, autonomy, social adherence, altruism, corporeality, acceptance of one's fate, and humanity. Patients in the pre-intervention phase tended to express values briefly and spontaneously. Oncologists acknowledged the importance of patients' values, but generally only gave 'contrasting' examples of why some accept and others refuse to participate in trials. In the post-intervention phase, many oncologists referred to the OnVaCT and/or asked follow-up questions, while patients used longer phrases that combined multiple values, sometimes clearly indicating their weighing.

CONCLUSIONS

While all values were recognized in both study phases, our results have highlighted the different communication patterns around patient values in SDM for potential early phase clinical trial participation before and after implementation of the OnVaCT intervention. This study therefore provides a first (qualitative) indication that the OnVaCT intervention may support patients and oncologists in discussing their values.

TRIAL REGISTRATION

Netherlands Trial Registry: NL7335, registered on July 17, 2018.

Factors influencing the engagement of older adults with neurocognitive disorders in the design of decision aids: A qualitative descriptive study

OBJECTIVE

To identify factors influencing the engagement of older adults with neurocognitive disorders (NCDs) in the design of decision aids (DAs).

METHODS

We conducted a qualitative descriptive study using semi-structured interviews with 23 older adults with NCDs who were accompanied by 27 caregivers. This is a secondary analysis of a published study to identify the features of DAs designed for this population and their caregivers.

RESULTS

Several behaviours and attitudes of caregivers and researchers hindered the older adults' engagement in the DA design process. Specific communication strategies can be employed to support their engagement and overcome the communication challenges inherent to this population, such as memory, attention, hearing, or visual impairments. Adopting the appropriate attitude, taking the time, and providing guidance to the older person can help them focus on the topic, while developing trust between participants is a facilitator to obtain their feedback.

CONCLUSION

Findings from this project could serve to inform the communication and co-design of DAs with older people with NCDs and their caregivers.

PRACTICE IMPLICATIONS

Caregivers and researchers have key roles to play in facilitating communication with older persons with NCDs so they are empowered to help in co-designing DAs.

A nomogram-based clinical tool for acute ischemic stroke screening in prehospital setting

Background: We believe that designing a new tool which is comparable in terms of both sensitivity and specificity may play an important role in rapid and more accurate diagnosis of acute ischemic stroke (AIS) in prehospital stage. Therefore, we intended to develop a new clinical tool for the diagnosis of AIS in the prehospital stage. Methods: This was a cross-sectional diagnostic accuracy study. All patients transferred to the emergency department (ED) who underwent brain magnetic resonance imaging (MRI) with impression of AIS were evaluated by 9 clinical tools for stroke diagnosis in the pre-hospital phase including Rapid Arterial Occlusion Evaluation (RACE), Cincinnati Prehospital Stroke Scale (CPSS), Los Angeles Prehospital Stroke Screen (LAPSS), Melbourne Ambulance Stroke Screen (MASS), Medic Prehospital Assessment for Code Stroke (Med PACS), Ontario Prehospital Stroke Screening Tool (OPSS), PreHospital Ambulance Stroke Test (PreHAST), Recognition of Stroke in the Emergency Room (ROSIER), and Face Arm Speech Test (FAST), and totally 19 items were reviewed and recorded. The new clinical tool was developed based on backward method of multivariable logistic regression analysis. The discrimination power of the new clinical tool for diagnosis of AIS was assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: Data from 806 patients were analyzed; of them, 57.4% were men. The mean age of the study patients was 66.9 years [standard deviation (SD) = 13.9]. In the multivariable model, 8 items remained. The AUC-ROC of the new clinical tool was 0.893 [95% confidence interval (CI): 0.869-0.917], and its best cut-off point was score ≥ 3 for positive AIS. At this cut-off point, sensitivity and specificity were 84.42% and 79.72%, respectively. Conclusion: We introduced a new nomogram-based clinical tool for the diagnosis of AIS in the prehospital stage, which has acceptable specificity and sensitivity; moreover, it is comparable with previous tools.

Usability, acceptability and feasibility of a novel technology with visual guidance with video and audio recording during newborn resuscitation: a pilot study

OBJECTIVE

Inadequate adherence to resuscitation for non-crying infants will have poor outcome and thus rationalise a need for real-time guidance and quality improvement technology. This study assessed the usability, feasibility and acceptability of a novel technology of real-time visual guidance, with sound and video recording during resuscitation.

SETTING

A public hospital in Nepal.

DESIGN

A cross-sectional design.

INTERVENTION

The technology has an infant warmer with light, equipped with a tablet monitor, NeoBeat and upright bag and mask. The tablet records resuscitation activities, ventilation sound, heart rate and display time since birth. Healthcare providers (HCPs) were trained on the technology before piloting.

DATA COLLECTION AND ANALYSIS

HCPs who had at least 8 weeks of experience using the technology completed a questionnaire on usability, feasibility and acceptability (ranged 1-5 scale). Overall usability score was calculated (ranged 1-100 scale).

RESULTS

Among the 30 HCPs, 25 consented to the study. The usability score was good with the mean score (SD) of 68.4% (10.4). In terms of feasibility, the participants perceived that they did not receive adequate support from the hospital administration for use of the technology, mean score (SD) of 2.44 (1.56). In terms of acceptability, the information provided in the monitor, that is, time elapsed from birth was easy to understand with mean score (SD) of 4.60 (0.76).

CONCLUSION

The study demonstrates reasonable usability, feasibility and acceptability of a technological solution that records audio visual events during resuscitation and provides visual guidance to improve care.

An Ethical Framework for Incorporating Digital Technology into Advance Directives: Promoting Informed Advance Decision Making in Healthcare

Despite the presumed value of advance directives, research to demonstrate impact has shown mixed results. For advance directives to serve their role promoting patient autonomy, it is important that patients be informed decision makers. The capacity to make decisions depends upon understanding, appreciation, reasoning, and communication. Advance directives are in part faulty because these elements are often limited. The present paper explores how the application of digital technology could be organized around a framework promoting these four elements. Given the state of digital advancements, there is great potential for advance directives to be meaningfully enhanced. The beneficial effects of incorporating digital technology would be maximized if they were organized around the aim of making advance directives not only documents for declaring preferences but also ethics-driven tools with decision aid functionality. Such advance directives would aid users in making decisions that involve complex factors with potentially far-reaching impact and would also elucidate the users' thought processes to aid those tasked with interpreting and implementing decisions based on an advance directive. Such advance directives might have embedded interactive features for learning; access to content that furthers one's ability to project oneself into possible, future scenarios; review of the logical consistency of stated preferences; and modes for effective electronic sharing. Important considerations include mitigating the introduction of bias depending on the presentation of information; optimizing interfacing with surrogate decision makers and treating clinicians; and prioritizing essential components to respect time constraints.

EsteR - A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities

In Germany, the current COVID-19 cases are managed and reported by the local health authorities. The workload of their employees during the pandemic is high, especially in periods of high infection numbers. In this work a decision support toolkit for local health authorities is introduced. A demonstrator web application was developed with the R Shiny framework and is publicly accessible online. It contains five separate tools based on statistical models for specific use cases and corresponding questions of COVID-19 cases and their contacts. The underlying statistical methods have been implemented in a new open-source R package. The toolkit has the potential to support local health authorities' employees in their daily work. A simulated-based validation of the statistical models and a usability evaluation of the demonstrator application in a user study will be carried out in the future.

Consistency of Feature Importance Algorithms for Interpretable EEG Abnormality Detection

Recent advances in machine learning show great potential for automatic detection of abnormalities in electroencephalography (EEG). While simple and interpretable models combined with expert-comprehensible input features offer full control of the decision making process, these methods commonly lag behind complex deep learning and feature extraction methods in terms of performance. Here we study a feasibility of a bridging solution, where deep learning is combined with interpretable input and an algorithm computing the importance of particular EEG features in the decision process. We built a convolutional neural network with multi-channel EEG frequency bands as input and investigated four different methods for feature importance attribution: Layer-wise Relevance Propagation (LRP), DeepLIFT, Integrated Gradients (IG) and Guided GradCAM. Our analysis showed consistency between the first three methods, and deviating attributions of the fourth method, suggesting the importance of using a package of methods together to ensure the robustness of medical interpretation.

Supporting Doctor's Decisions Based on Electronic Medical Documentation in Polish

We describe new functionalities that have been added to an existing and widely used software solution in Polish public healthcare. The system automatically evaluates a number of medical scores. It provides epidemiologic monitoring concerning infectious diseases; classifies a particular patient to a specific risk group; and detects anomalies. Moreover, an analysis of a prescribed drug is performed.

Letter to the editor, "Validation and clinical value of the MANAGE-PD tool: A clinician-reported tool to identify Parkinson's disease patients inadequately controlled on oral medications"

The MANAGE-PD tool may help general neurologists in deciding whether a patient with advanced Parkinson's disease should be referred for an advanced therapy. Although the development and clinical validation of MANAGE-PD would appear to serve an important need, we urge the reader to be aware of several methodological concerns.

Factors Associated with Decision Aid Use in Localized Prostate Cancer

Purpose

Decision aids have been found to improve patients' knowledge of treatments and decrease decisional regrets. Despite these benefits, there is not widespread use of decision aids for newly diagnosed prostate cancer (PCa). This analysis investigates factors that impact men's choice to use a decision aid for newly diagnosed prostate cancer.

Materials and Methods

This is a retrospective analysis of a PCa registry from the Michigan Urological Surgery Improvement Collaborative (MUSIC). We included data from men with newly diagnosed, clinically localized PCa seen from 2018-21 at practices offering a PCa decision aid (Personal Patient Profile-Prostate; P3P). The primary outcome was men's registration to use P3P. We fit a multilevel logistic regression model with patient-level factors and included urologist specific random intercepts. We estimated the intra-class correlation (ICC) and predicted the probability of P3P registration among urologists.

Results

A total of 2629 men were seen at practices that participated in P3P and 1174 (45%) registered to use P3P. Forty-one percent of the total variance of P3P registration was attributed to clustering of men under a specific urologist's care. In contrast, only 1.5% of the variance of P3P registration was explained by patient factors. Our model did not include data on socioeconomic, literacy or psychosocial factors, which limits the interpretation of the results.

Conclusions

These results suggest that urologists' effect far outweighs patient factors in a man's decision to enroll in P3P. Strategies that encourage providers to increase decision aid adoption in their practices are warranted.

Decision aid for women with newly diagnosed breast cancer seeking breast reconstruction surgery: A prospective, randomized, controlled, single-blinded, pilot study

BACKGROUND

Women undergoing immediate breast reconstruction for breast cancer often experience anxiety when faced with reconstruction and the type of reconstruction to choose. The purpose of this study is to analyze the impact of a decision aid on decisional conflict in women with newly diagnosed breast cancer seeking breast reconstruction.

METHODS

Newly diagnosed breast cancer patients seeking breast reconstruction at a single center were prospectively randomized into two groups. Comparisons were made between two groups using the decisional conflict scale (DCS): women who reviewed the standard educational materials prior to initial consultation (control) and women who reviewed standard materials prior to initial consultation and then reviewed a decision aid brochure at initial consultation and two-week post-consultation (intervention). Pre-to-post-consultation DCS scores were compared within and between the control groups and intervention groups to assess which group had lower DCS scores.

RESULTS

A total of 20 patients, mean age 53±9 years were included. Median differences between pre-to-post-consultation DCS total scores in the control and intervention groups lowered from 32 to 22 and 28 to 16, respectively. Significant differences in subscores were control group: uncertainty: 54-21 (p = 0.030), and intervention group: uncertainty: 46-29 (p = 0.036) and values clarity: 29-25(p = 0.042). Pre-to-post-consultation differences between DCS scores and subscores did not demonstrate any statistical significance.

CONCLUSION

Decision aids did not significantly reduce pre-operative DCS total scores compared to current educational materials. More educational materials may not always be helpful for patients.

[Computerized method for calculating cervical vertebral maturation]

For Helm, "the rhythm of facial growth often governs the course of orthodontic treatment". The moment of treatment is an important dimension for our therapy to last a minimum of time with a greatest chance of success and stability. This notion of processing time is a daily requirement in our practices. The radiographs of the wrists will gradually disappear according to the ALARA principle, since we can in a single irradiation, via the profile radiography, have sufficient information to situate the patient on its growth curve. The vertebral stages are good biological indicators of individual skeletal maturity but their interpretation remains difficult. In this work, a computerized method was used to determine the stage of vertebral maturation in a reliable and reproducible manner. In this study, 15 young boys and 15 young girls (total 30 patients) were included, 12,2 years old on average with a standard deviation of 2,6 years. To determine the skeletal age of these patients, the practitioner made an hand-wrist x-ray and, for diagnostic reasons, he also made a profile radiography the same day. The patients who didn't made an hand-wrist x-ray were excluded. The vertebral computerized method seems to be a reliable method to be used in orthodontic practices. Other studies would allow to use this method for average ages, gender-appropriate.

Accuracy of prehospital ambulance stroke test in terms of diagnosis of patients with acute ischemic stroke: A multi-center study

Background: Andsberg et al. have recently introduced a novel scoring system entitled "PreHospital Ambulance Stroke Test (PreHAST)", which helps to early identification of patients with acute ischemic stroke (AIS) even in prehospital setting. Its validity has not been assessed in a study yet, and the purpose of this study was to assess this scoring system on a larger scale to provide further evidence in this regard. Methods: This was a cross-sectional multi-center accuracy study, in which, sampling was performed prospectively. All patients over 18 years of age admitted to the emergency department (ED) and suspected as AIS cases were included. All required data were recorded in a form consisting of 3 parts: baseline characteristics, neurological examination findings required for calculating PreHAST score, and the ultimate diagnosis made from interpretation of their brain magnetic resonance imaging (MRI). Results: Data from 805 patients (57.5% men) with the mean age of 67.1 ± 13.6 years were analyzed. Of all the patients presenting with suspected AIS, 562 (69.8%) had AIS based on their MRI findings. At the suggested cut-off point (score ≥ 1), PreHAST had a specificity of 46.5% [95% confidence interval (CI): 40.1%-53.0%) and a sensitivity of 93.2% (95% CI: 90.8%-95.2%). Conclusion: According to the findings of our study, at the suggested cut-off point (score ≥ 1), PreHAST had 93.2% sensitivity and 46.5% specificity in detection of patients with AIS, which were somewhat different from those reported in the original study, where 100% sensitivity and 40% specificity were reported for this scoring system.

Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football

The aim of this study was to assess if tactical and technical performance indicators (PIs) could be used in combination to model match outcomes in Australian Football (AF). A database of 101 technical PIs and 14 tactical PIs from every match in the 2009-2016 Australian Football League (AFL) seasons was merged. Two outcome measures Win-loss and Score margin were used as dependent variables. The top 45 ranked technical and tactical PIs from a feature selection process were used to model match outcome using decision tree and Generalised Linear Models (GLMs). Of the top 45 selected features, this included seven tactical PIs. The Win-loss-based Decision tree model achieved a classification accuracy of 89.0% and GLM 93.2%. A Score margin-based GLM achieved a root mean squared error (RMSE) of 6.9 points. A combined approach to the classification of match outcomes provided no improvement in model accuracy compared with previous literature. However, this study has established the relative importance of technical and tactical measures of performance in relation to successful team performance in AF.

Designing and Evaluating a Prediabetes Shared Decision Aid

BACKGROUND

Prediabetes is increasing in prevalence and is associated with risk of developing diabetes, heart disease, stroke, and retinopathy. Clinicians have limited tools to facilitate prediabetes discussions within primary care visits.

PURPOSE

1) Develop a Patient and Stakeholder Advisory Committee (PASAC) to design, evaluate, and revise a prediabetes shared decision aid, and 2) evaluate the feasibility and experience of implementing the tool within primary care practice.

METHODS

A prediabetes decision aid (double-sided infographic with decision questions) was created by a PASAC that included patients, primary care clinicians, diabetes educators, endocrinologists, and pharmacists. Five clinicians within 3 primary care practices tested the prediabetes tool with 50 adult patients with prediabetes. Patients completed 2 surveys immediately after the office visit and 6 weeks later. Clinicians and PASAC members completed a postintervention survey.

RESULTS

The prediabetes shared decision aid was created through a deliberative process over 3 PASAC meetings. Ninety-six percent of patients felt the tool prepared them to decide on a diabetes prevention plan, and 100% of clinicians would use the tool again and felt the tool did not extend visit length.

DISCUSSION

It was feasible to cocreate a prediabetes shared decision aid within a PASAC and implement the tool within a primary care setting. Patients and clinicians reported a prediabetes discussion, which may mitigate rates of progression to diabetes and associated complications. Future research should evaluate which of the intervention components most effectively promotes discussion of prediabetes within a primary care setting.

The LEAD trial-The effectiveness of a decision aid on decision making among citizens with lower educational attainment who have not participated in FIT-based colorectal cancer screening in Denmark: A randomised controlled trial

OBJECTIVES

This trial tested the effectiveness of a self-administered web-based decision aid, targeted at citizens with lower educational attainment, on informed choice about colorectal cancer screening participation as assessed by group levels of knowledge, attitudes and uptake.

METHODS

The randomised controlled trial was conducted among 2702 screening-naïve Danish citizens, 53-74 years old, with lower educational attainment. Baseline questionnaire respondents (62%) were allocated to intervention and control groups. Intervention group citizens received the decision aid. Outcomes were informed choice, worries and decisional conflict.

RESULTS

Analyses were conducted among 339 eligible citizens. The mean difference in knowledge score change between intervention and control group was 0.00 (95% confidence interval (CI): -0.38;0.38). Trends towards more positive screening attitudes (mean difference in score change: 0.72, 95% CI: -0.38;1.81) and higher screening uptake (7.6%, 95% CI:-2.2;17.4%) were observed. Worries (-0.33, 95% CI: -0.97;0.32) and decisional conflict (mean difference: -3.5, 95%CI: -7.0;-0.1) were slightly reduced.

CONCLUSIONS

The decision aid did not affect informed choice or knowledge. However, there were trends towards increased screening uptake and more positive screening attitudes.

PRACTICE IMPLICATIONS

Being a simple intervention and easily administered, the decision aid could represent a cost-effective way of enhancing screening uptake, and some elements of informed decision-making.

[Current modeling of suicidal behavior : crisis, transition, and vulnerability]

Current modeling of suicidal behavior: crisis, transition, and vulnerability. Suicidal behavior is multifactorial and polymorphic. Modeling - although simplifying - is necessary. This article presents three interactive processes: the suicidal crisis, the transition from ideas to action, and the suicidal vulnerability. The suicidal crisis is a state of rupture of the psychic balance, often occurring in response to an event of social and interpersonal character, and leading the individual to the significant thoughts that only suicide is the solution to one's own problems. This crisis is particularly marked by an unbearable psychic pain whose cessation is the goal of most suicidal gestures. The acting out occurs in less than a quarter of people with suicidal ideas. The mechanisms responsible for the emergence of suicidal ideation appear partly different from those allowing the transition to a suicidal act. Suicidal risk in a context of psychosocial stress is increased by the existence of vulnerability factors, whose complex nature is developmental, genetic, cognitive, and temperamental, among others.

Development of a Clinical Decision Aid for Chiropractic Management of Common Conditions Causing Low Back Pain in Veterans: Results of a Consensus Process

OBJECTIVE

The purpose of this study was to develop a clinical decision aid for chiropractic management of common conditions causing low back pain (LBP) in veterans receiving treatment in US Veterans Affairs (VA) health care facilities.

METHODS

A consensus study using an online, modified Delphi technique and Research Electronic Data Capture web application was conducted among VA doctors of chiropractic. Investigators reviewed the scientific literature pertaining to diagnosis and treatment of nonsurgical, neuromusculoskeletal LBP. Thirty seed statements summarizing evidence for chiropractic management, a graphical stepped management tool outlining diagnosis-informed treatment approaches, and support materials were then reviewed by an expert advisory committee. Email notifications invited 113 VA chiropractic clinicians to participate as Delphi panelists. Panelists rated the appropriateness of the seed statements and the stepped process on a 1-to-9 scale using the RAND/University of California, Los Angeles methodology. Statements were accepted when both the median rating and 80% of all ratings occurred within the highly appropriate range.

RESULTS

Thirty-nine panelists (74% male) with a mean (standard deviation) age of 46 (11) years and clinical experience of 17 (11) years participated in the study. Accepted statements addressed included (1) essential components of chiropractic care, (2) treatments for conditions causing or contributing to LBP, (3) spinal manipulation mechanisms, (4) descriptions and mechanisms of commonly used chiropractic interventions, and (5) a graphical stepped clinical management tool.

CONCLUSION

This study group produced a chiropractic clinical decision aid for LBP management, which can be used to support evidence-based care decisions for veterans with LBP.

ICT-Based Comprehensive Health and Social-Needs Assessment System for Supporting Person-Centered Community Care

Objectives

This study developed an information and communication technology (ICT)-based comprehensive health and social-needs assessment (CHSNA) system based on the International Classification of Functioning, Disability, and Health (ICF) with the aim of enhancing person-centered community care for community residents and supporting healthcare professionals and social workers who provide healthcare and social services in the community.

Methods

Items related to a CHSNA tool were developed and mapped with ICF codes. Experts validated the CHSNA system design and process using the Delphi method, and a pilot test of the initial version of the system was conducted.

Results

The following three steps of CHSNA were embedded in the system, which had a user-friendly screen and images: basic health assessment, life and activity assessment, and in-depth health assessment. The assessment results for the community residents were presented with visualized health profiles, including images, graphs, and an ICF model.

Conclusions

The developed CHSNA system can be used by healthcare professionals, social workers, and community residents to evaluate the reasoning underlying health and social needs, to facilitate the identification of more appropriate healthcare plans, and to guide community residents to receive the best healthcare services. A CHSNA system can improve the implementation of standardized terminology utilizing the ICF and the accuracy of needs assessments of community residents.

Using Simulation Modeling to Inform Policy Makers for Planning Physician Workforce in Healthcare System in Croatia

The objective of this paper is to show how a simple but powerful simulation model can be build up using standard spreadsheet program and used to simulate future, needs and supply of physicians in order to inform policy makers at national level when deciding on enrollment to medical schools and immigration quotas for physicians. The Republic of Croatia is facing a serious shortage of physicians in the healthcare system and simulation results have shown that the gap between needs and supply will even increase if current enrollment qoutas to medical schools would persist. Increasing enrollment quotas, adjusting immigration policy, re-directing physicians from other professions to the healthcare system, task shift and skill mix options are just some of the measures needed to be taken promptly in order to prevent a huge deficit of physicians in the future. Simulation modeling is certainly a method for predicting changes within healthcare systems with a possibility to examine multiple different scenarios and suggest interventions.

Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do

Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit.

Predicting Psychiatric Hospitalizations among Elderly Veterans with a History of Mental Health Disease

Introduction:

Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. However, few resources exist that use real-time information on patient risk to prioritize coordinating appropriate care amongst a complex aging population.

Objective:

To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease.

Methods:

This study applied a cohort design to historical data available in the Veterans Affairs (VA) Corporate Data Warehouse (CDW). The Least Absolute Shrinkage and Selection Operator (LASSO) regularization regression technique was used for model development and variable selection. Individual predicted probabilities were estimated using logistic regression. A split-sample approach was used in performing external validation of the fitted model. The concordance statistic (C-statistic) was calculated to assess model performance.

Results:

Prior to modeling, 61 potential candidate predictors were identified and 27 variables remained after applying the LASSO method. The final model’s predictive accuracy is represented by a C-statistic of 0.903. The model’s predictive accuracy during external validation is represented by a C-statistic of 0.935. Having a previous psychiatric hospitalization, psychosis, bipolar disorder, and the number of mental-health related social work encounters were strong predictors of a geriatric psychiatric hospitalization.

Conclusion:

This predictive model is capable of quantifying the risk of a geriatric psychiatric hospitalization with acceptable performance and allows for the development of interventions that could potentially reduce such risk.

[Application of 3D virtual reality technology with multi-modality fusion in resection of glioma located in central sulcus region]

Objective: To explore the clinical and teaching application value of virtual reality technology in preoperative planning and intraoperative guide of glioma located in central sulcus region. Method: Ten patients with glioma in the central sulcus region were proposed to surgical treatment. The neuro-imaging data, including CT, CTA, DSA, MRI, fMRI were input to 3dgo sczhry workstation for image fusion and 3D reconstruction. Spatial relationships between the lesions and the surrounding structures on the virtual reality image were obtained. These images were applied to the operative approach design, operation process simulation, intraoperative auxiliary decision and the training of specialist physician. Results: Intraoperative founding of 10 patients were highly consistent with preoperative simulation with virtual reality technology. Preoperative 3D reconstruction virtual reality images improved the feasibility of operation planning and operation accuracy. This technology had not only shown the advantages for neurological function protection and lesion resection during surgery, but also improved the training efficiency and effectiveness of dedicated physician by turning the abstract comprehension to virtual reality. Conclusion: Image fusion and 3D reconstruction based virtual reality technology in glioma resection is helpful for formulating the operation plan, improving the operation safety, increasing the total resection rate, and facilitating the teaching and training of the specialist physician.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System

Objectives

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery.

Methods

A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated.

Results

The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60).

Conclusions

The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

A Markov Analysis of Screening for Late-Onset Cytomegalovirus Disease in Cytomegalovirus High-Risk Kidney Transplant Recipients

BACKGROUND AND OBJECTIVES

Management strategies are unclear for late-onset cytomegalovirus infection occurring beyond 6 months of antiviral prophylaxis in cytomegalovirus high-risk (cytomegalovirus IgG positive to cytomegalovirus IgG negative) kidney transplant recipients. Hybrid strategies (prophylaxis followed by screening) have been investigated but with inconclusive results. There are clinical and potential cost benefits of preventing cytomegalovirus-related hospitalizations and associated increased risks of patient and graft failure. We used decision analysis to evaluate the utility of postprophylaxis screening for late-onset cytomegalovirus infection.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used the Markov decision analysis model incorporating costs and utilities for various cytomegalovirus clinical states (asymptomatic cytomegalovirus, mild cytomegalovirus infection, and cytomegalovirus infection necessitating hospitalization) to estimate cost-effectiveness of postprophylaxis cytomegalovirus screening strategies. Five strategies were compared: no screening and screening at 1-, 2-, 3-, or 4-week intervals. Progression to severe cytomegalovirus infection was modeled on cytomegalovirus replication kinetics. Incremental cost-effectiveness ratios were calculated as a ratio of cost difference between two strategies to difference in quality-adjusted life-years starting with the low-cost strategy. One-way and probabilistic sensitivity analyses were performed to test model's robustness.

RESULTS

There was an incremental gain in quality-adjusted life-years with increasing screening frequency. Incremental cost-effectiveness ratios were $783 per quality-adjusted life-year (every 4 weeks over no screening), $1861 per quality-adjusted life-year (every 3 weeks over every 4 weeks), $10,947 per quality-adjusted life-year (every 2 weeks over every 3 weeks), and $197,086 per quality-adjusted life-year (weekly over every 2 weeks). Findings were sensitive to screening cost, cost of hospitalization, postprophylaxis cytomegalovirus incidence, and graft loss after cytomegalovirus infection. No screening was favored when willingness to pay threshold was <$14,000 per quality-adjusted life-year, whereas screening weekly was favored when willingness to pay threshold was >$185,000 per quality-adjusted life-year. Screening every 2 weeks was the dominant strategy between willingness to pay range of $14,000-$185,000 per quality-adjusted life-year.

CONCLUSIONS

In cytomegalovirus high-risk kidney transplant recipients, compared with no screening, screening for postprophylactic cytomegalovirus viremia is associated with gains in quality-adjusted life-years and seems to be cost effective. A strategy of screening every 2 weeks was the most cost-effective strategy across a wide range of willingness to pay thresholds.

A Review of Decision Support Systems for Smart Homes in the Health Care System

The use of decision support systems for smart homes can provide attractive solutions for challenges that have arisen in the Health Care System due to ageing of society. In order to provide an overview of current research projects in this field, a systematic literature review was performed according to the PRISMA approach. The aims of this work are to provide an overview of current research projects and to update a similar study from 2012. The literature search engines IEEE Xplore and PubMed were used. 23 papers were included. Most of the systems presented are developed for monitoring the patient regardless of their illness. For decision support, mainly rule-based approaches are used.

Value of Manchester Acute Coronary Syndromes Decision Rule in the Detection of Acute Coronary Syndrome; a Systematic Review and Meta-Analysis

INTRODUCTION

There is still no consensus on the value of Manchester Acute Coronary Syndromes (MACS) decision rule in detecting acute coronary syndrome (ACS). Therefore, the purpose of the present systematic review and meta-analyzes is to summarize the clinical evidence in the evaluation of the value of MACS in the diagnosis of ACS.

METHODS

A literature search was performed on the Medline, Embase, Scopus, and Web of Science databases. Outcomes included acute myocardial infarction (AMI) and major adverse cardiac event (MACE). Data were analyzed in the STATA 14.0 statistical program and the results were reported as summary receiver operating characteristics (SROC), sensitivity, specificity, positive and negative likelihood ratio, and diagnostic odds ratio with 95% confidence interval (95% CI).

RESULTS

Finally, 8 articles included in the meta-analysis. The area under the SROC of MACS was excellent in rule out of AMI (AUC = 0.99, 95% CI: 0.97 to 0.99) and MACE (AUC = 0.97, 95% CI: 0.95 to 0.98). The sensitivity and specificity of the troponin-only MACS/history electrocardiogram alone MACS (HE-MACS) in the rule out of AMI were0.99 (95% CI: 0.98-0.99) and 0.22 (95% CI: 0.11-0.37), respectively, and for the original MACS were in order 0.99 (95% CI: 0.98-0.99) and 0.26 (95% CI: 0.20-0.34),. The sensitivity and specificity of the troponin-only MACS / HE-MACS in the rule out of MACE were 0.94 (95% CI: 0.92-0.96) and 0.22 (95% CI: 0.12-0.39) compared to the 0.99 (95% CI: 0.98-0.99) and 0.27 (95% CI: 0.22-0.33) for the original MACS.

CONCLUSION

The findings of this study showed that original MACS, troponin-only MACS, and HE-MACS are able to rule out AMI and MACE. However, further studies are needed in developing countries to confirm its external validity.

Use of System Dynamics Modeling in Medical Education and Research Projects

The paper reviews experiences and accomplishments in application of system dynamics modeling in education, training and research projects at the Andrija Stampar School of Public Health, a branch of the Zagreb University School of Medicine, Croatia. A number of simulation models developed over the past 40 years are briefly described with regard to real problems concerned, objectives and modeling methods and techniques used. Many of them have been developed as the individual students' projects as a part of their graduation, MSc or PhD theses and subsequently published in journals or conference proceedings. Some of them were later used in teaching and simulation training. System dynamics modeling proved to be not only powerful method for research and decision making but also a useful tool in medical and nursing education enabling better understanding of dynamic systems' behavior.

Network meta-analysis: a technique to gather evidence from direct and indirect comparisons

Systematic reviews and pairwise meta-analyses of randomized controlled trials, at the intersection of clinical medicine, epidemiology and statistics, are positioned at the top of evidence-based practice hierarchy. These are important tools to base drugs approval, clinical protocols and guidelines formulation and for decision-making. However, this traditional technique only partially yield information that clinicians, patients and policy-makers need to make informed decisions, since it usually compares only two interventions at the time. In the market, regardless the clinical condition under evaluation, usually many interventions are available and few of them have been studied in head-to-head studies. This scenario precludes conclusions to be drawn from comparisons of all interventions profile (e.g. efficacy and safety). The recent development and introduction of a new technique – usually referred as network meta-analysis, indirect meta-analysis, multiple or mixed treatment comparisons – has allowed the estimation of metrics for all possible comparisons in the same model, simultaneously gathering direct and indirect evidence. Over the last years this statistical tool has matured as technique with models available for all types of raw data, producing different pooled effect measures, using both Frequentist and Bayesian frameworks, with different software packages. However, the conduction, report and interpretation of network meta-analysis still poses multiple challenges that should be carefully considered, especially because this technique inherits all assumptions from pairwise meta-analysis but with increased complexity. Thus, we aim to provide a basic explanation of network meta-analysis conduction, highlighting its risks and benefits for evidence-based practice, including information on statistical methods evolution, assumptions and steps for performing the analysis.

Effect of a decision aid with patient narratives in reducing decisional conflict in choice for surgery among early-stage breast cancer patients: A three-arm randomized controlled trial

OBJECTIVE

We aimed to evaluate the effect of a decision aid (DA) with patient narratives on decisional conflict in surgery choice for Japanese women with early-stage breast cancer.

METHODS

Two hundred ten women with early-stage breast cancer were randomly assigned to an intervention or control group. Groups 1 and 2 received standard information and a DA, with or without patient narratives, and Group 3 received standard information (control) before surgery choice. At baseline, post-intervention (Time 2), and 1 month after surgery (Time 3), we evaluated decisional conflict as the primary outcome using a decisional conflict scale (DCS). Sidak corrections for multiple comparisons in analysis of covariate were used to compare Time 2 and Time 3 DCS mean scores between each pair of groups.

RESULTS

At Time 3, decisional conflict was significantly reduced for Group 1 vs control (P=0.021, Cohen's d =0.26) and Group 2 vs control (P=0.008, Cohen's d=0.40).

CONCLUSION

The DAs with and without patient narratives are equivalently effective at reducing postoperative decisional conflict in Japanese women with early-stage breast cancer.

PRACTICE IMPLICATIONS

The DAs with and without patient narratives can be used in clinical practice for women with early-stage breast cancer.

Exploiting Temporal Constraints of Clinical Guidelines by Applying OpenEHR Archetypes

Studies describing Computer-Interpretable Clinical Guidelines (CIG) with temporal constrains (TC) generally have not addressed issues related to their integration into Electronic Health Record (EHR) systems. This study aimed to represent TCs contained in clinical guidelines by applying archetypes and Guideline Definition Language (GDL) to incorporate decision support into EHRs. An example of each TC class in the clinical guideline for management of Atrial Fibrillation was represented using archetypes and GDL.

A Mobile System for Music Anamnesis and Receptive Music Therapy in the Personal Home

Receptive music therapy is active hearing of music that is specifically selected to cause a certain effect on a person, such as pain reduction, mental opening, confrontation etc. This active, guided hearing could be helpful as a supporting ritual for patients at home and could extend traditional therapy. However, patients are often unable to select the music pieces that might be helpful for them in a current situation. We are suggesting a self-learning decision support system that allows a patient to answer questions on music anamnesis, is ready for inclusion into an electronic health record, and which enables a therapist to compile a therapeutic music program for the patient at home. Beyond this, the system also suggests appropriate music and duration of listening based on the patient's reported current mental state. In this paper, a concept for such a mobile system for receptive music therapy will be proposed.

Processual Reasoning over Sequences of Situations in Endoscopic Surgery

Minimally invasive surgery is a highly complex and technically demanding alternative to open surgery. Surgical procedures based on this method are characterized by small incisions and allow for a fast recovery of the patient. Such techniques are challenging for surgeons since they do not have a direct view of the surgical area. Systems that provide surgical navigation are well established in clinical practice but depend on external markers allowing a mapping between a surgeon's tools and a patient's medical images. As of today, these systems are prone to inaccuracies, the reasons of which lie in their extensive technical requirements. The BIOPASS project aims to develop an alternative that works without external markers and indirect computation of locations. An ontology has been used to provide an adequate vocabulary describing situations and their temporal relationship. This ontology is expected to relate real time multimodal sensor data and static surgical process models in order to infer movement directions, subsequent actions and hidden anatomical structures that inhere risk for surgical interventions. However, the Web Ontology Language is not capable of modelling temporal conditions, which are necessary to provide such exhaustive situational descriptions as expected by a surgeon. This paper concerns an ontology design pattern developed to overcome this issue by the integration of dynamic ontological classes that are assigned according to the temporal relations between situations.

Bringing Knowledge to Users in One Click: Infobuttons in the Problem List of an EHR

The infobuttons allows the solving of information needs. In our study, the use of Infobuttons is described, analyzing the number of queries to UpToDate® from the problem list of an Electronic Health Record. There were 26419 requests in 8 months. The highest average use occurred in June. The links to knowledge bases can help to solve information needs, even before they occur.

Supportive Care: Communication Strategies to Improve Cultural Competence in Shared Decision Making

Historic migration and the ever-increasing current migration into Western countries have greatly changed the ethnic and cultural patterns of patient populations. Because health care beliefs of minority groups may follow their religion and country of origin, inevitable conflict can arise with decision making at the end of life. The principles of truth telling and patient autonomy are embedded in the framework of Anglo-American medical ethics. In contrast, in many parts of the world, the cultural norm is protection of the patient from the truth, decision making by the family, and a tradition of familial piety, where it is dishonorable not to do as much as possible for parents. The challenge for health care professionals is to understand how culture has enormous potential to influence patients' responses to medical issues, such as healing and suffering, as well as the physician-patient relationship. Our paper provides a framework of communication strategies that enhance crosscultural competency within nephrology teams. Shared decision making also enables clinicians to be culturally competent communicators by providing a model where clinicians and patients jointly consider best clinical evidence in light of a patient's specific health characteristics and values when choosing health care. The development of decision aids to include cultural awareness could avoid conflict proactively, more productively address it when it occurs, and enable decision making within the framework of the patient and family cultural beliefs.

Quantitative assessment of washout in hepatocellular carcinoma using MRI

Background

Arterial hyperenhancement and washout on computed tomography and magnetic resonance imaging (MRI) are described by all major guidelines as specific criteria for non-invasive diagnosis of hepatocellular carcinoma (HCC). However, publications on the quantitative assessment of washout in MRI are lacking. Therefore, we evaluated a method for quantitatively measuring and defining washout in MRI in order to determine a cutoff value that allows objective HCC diagnosis.

Methods

We analyzed all patients who underwent liver transplantation for cirrhosis or liver resection for HCC at our institution between 2003 and 2014. Washout was quantitatively investigated by placing a 25-mm² region of interest (ROI) over each nodule and two 25-mm² ROIs over adjacent liver parenchyma. The percentage signal ratio (PSR = 100 × ratio of signal intensity of adjacent liver to that of the lesion) was calculated for each series in both groups. Accordingly, this quantitative measurement was compared to a qualitative approach.

Results

A total of 16 hypervascularized non-HCC nodules and 69 HCC nodules were identified. Interobserver reliability was reasonably good for the measurement of PSRs and readers showed a substantial agreement for the qualitative assessment. In the HCC group, the median PSR was 116.2 at equilibrium and 112.9 in the delayed phase. In the non-HCC group, the median PSR was 93.8 at equilibrium and 96.0 in the delayed phase. Receiver operating characteristic analysis indicated areas under the curve of 0.902 (p < 0.001) and 0.873 (p < 0.001) at equilibrium and in the delayed phase. PSR values of 102 at equilibrium and 101.5 in the delayed phase led to the highest Youden’s index of 0.82 and 0.77, respectively. These PSR cutoffs yielded sensitivities of 82 and 77 %, respectively, with specificities of 100 %. The sensitivity for the qualitative assessment of washout was 88 and 93 % and the specificity was 48 and 56 %. For the classification of HCC, sensitivity yielded 95 and 97 % and specificity was 68 and 56 %, respectively.

Conclusion

Quantitatively measuring HCC washout in MRI is easy and reproducible. It can objectify and support diagnosis of HCC. However, the quantitative measurement of washout can only serve as one of several components of HCC assessment.

Implementation of Technology-based Patient Engagement Strategies within Practice-based Research Networks

BACKGROUND

Technology-based patient engagement strategies (such as patient portals) are increasingly available, yet little is known about current use and barriers within practice-based research networks (PBRNs). PBRN directors have unique opportunities to inform the implementation of patient-facing technology and to translate these findings into practice.

METHODS

PBRN directors were queried regarding technology-based patient engagement strategies as part of the 2015 CAFM Educational Research Alliance (CERA) survey of PBRN directors. A total of 102 PBRN directors were identified via the Agency for Healthcare Research and Quality's registry; 54 of 96 eligible PBRN directors completed the survey, for a response rate of 56%.

RESULTS

Use of technology-based patient engagement strategies within PBRNs was limited, with less than half of respondents reporting experience with the most frequently named tools (risk assessments/decision aids). Information technology (IT) support was the top barrier, followed by low rates of portal enrollment. For engaging participant practices, workload and practice leadership were cited as most important, with fewer respondents noting concerns about patient privacy.

DISCUSSION

Given limited use of patient-facing technologies, PBRNs have an opportunity to clarify the optimal use of these strategies. Providing IT support and addressing clinician concerns regarding workload may facilitate the inclusion of innovative technologies in PBRNs.

Comparison of Predictive Models for the Early Diagnosis of Diabetes

OBJECTIVES

This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts.

METHODS

We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC).

RESULTS

The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively.

CONCLUSIONS

The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer

OBJECTIVES

Breast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world. The principal objective of this study was to develop a nomogram based on a naïve Bayesian model for the prediction of breast cancer recurrence within 5 years after breast cancer surgery.

METHODS

The nomogram can provide a visual explanation of the predicted probabilities on a sheet of paper. We used a data set from a Korean tertiary teaching hospital of 679 patients who had undergone breast cancer surgery between 1994 and 2002. Seven prognostic factors were selected as independent variables for the model.

RESULTS

The accuracy was 80%, and the area under the receiver operating characteristics curve (AUC) of the model was 0.81.

CONCLUSIONS

The nomogram can be easily used in daily practice to aid physicians and patients in making appropriate treatment decisions after breast cancer surgery.

Factors influencing nonabsolute indications for surgery in patients with lower urinary tract symptoms suggestive of benign prostatic hyperplasia: analysis using causal bayesian networks

Purpose

To identify the factors affecting the surgical decisions of experienced physicians when treating patients with lower urinary tract symptoms that are suggestive of benign prostatic hyperplasia (LUTS/BPH).

Methods

Patients with LUTS/BPH treated by two physicians between October 2004 and August 2013 were included in this study. The causal Bayesian network (CBN) model was used to analyze factors influencing the surgical decisions of physicians and the actual performance of surgery. The accuracies of the established CBN models were verified using linear regression (LR) analysis.

Results

A total of 1,108 patients with LUTS/BPH were analyzed. The mean age and total prostate volume (TPV) were 66.2 (±7.3, standard deviation) years and 47.3 (±25.4) mL, respectively. Of the total 1,108 patients, 603 (54.4%) were treated by physician A and 505 (45.6%) were treated by physician B. Although surgery was recommended to 699 patients (63.1%), 589 (53.2%) actually underwent surgery. Our CBN model showed that the TPV (R=0.432), treating physician (R=0.370), bladder outlet obstruction (BOO) on urodynamic study (UDS) (R=0.324), and International Prostate Symptom Score (IPSS) question 3 (intermittency; R=0.141) were the factors directly influencing the surgical decision. The transition zone volume (R=0.396), treating physician (R=0.340), and BOO (R=0.300) directly affected the performance of surgery. Compared to the LR model, the area under the receiver operating characteristic curve of the CBN surgical decision model was slightly compromised (0.803 vs. 0.847, P<0.001), whereas that of the actual performance of surgery model was similar (0.801 vs. 0.820, P=0.063) to the LR model.

Conclusions

The TPV, treating physician, BOO on UDS, and the IPSS item of intermittency were factors that directly influenced decision-making in physicians treating patients with LUTS/BPH.

Quantitative measurement method for possible rib fractures in chest radiographs

Objectives

This paper proposes a measurement method to quantify the abnormal characteristics of the broken parts of ribs using local texture and shape features in chest radiographs.

Methods

Our measurement method comprises two steps: a measurement area assignment and sampling step using a spline curve and sampling lines orthogonal to the spline curve, and a fracture-ness measurement step with three measures, asymmetry and gray-level co-occurrence matrix based measures (contrast and homogeneity). They were designed to quantify the regional shape and texture features of ribs along the centerline. The discriminating ability of our method was evaluated through region of interest (ROI) analysis and rib fracture classification test using support vector machine.

Results

The statistically significant difference was found between the measured values from fracture and normal ROIs; asymmetry (p < 0.0001), contrast (p < 0.001), and homogeneity (p = 0.022). The rib fracture classifier, trained with the measured values in ROI analysis, detected every rib fracture from chest radiographs used for ROI analysis, but it also classified some unbroken parts of ribs as abnormal parts (8 to 17 line sets; length of each line set, 2.998 ± 2.652 mm; length of centerlines, 131.067 ± 29.460 mm).

Conclusions

Our measurement method, which includes a flexible measurement technique for the curved shape of ribs and the proposed shape and texture measures, could discriminate the suspicious regions of ribs for possible rib fractures in chest radiographs.

Cost-effectiveness analysis of adding low dose ribavirin to peginterferon alfa-2a for treatment of chronic hepatitis C infected thalassemia major patients in iran

BACKGROUND

The prevalence of hepatitis C in Iran is 1% and 18% in general population and thalassemia patients respectively. The cost effectiveness analysis of adding Ribavirin to Peginterferon alfa-2a (PEG IFN alfa-2a) as a combination treatment strategy of chronic hepatitis C in thalassemia patients in comparison with monotherapy could help clinicians and policy makers to provide the best treatment for the patients.

OBJECTIVES

In this study we aimed to assess whether adding Ribavirin to PEG IFN alfa-2a is a cost effective strategy in different genotypes and different subgroups of 280 patients with chronic hepatitis C infection from the perspective of society in Iranian setting.

PATIENTS AND METHODS

A cost effectiveness analysis including all costs and outcomes of treatments for chronic hepatitis C infected thalassemia major patients was conducted. We constructed a decision tree of treatment course in which a hypothetical cohort of 100 patients received "PEG IFN alfa-2a" or "Peg IFN alfa-2a plus Ribavirin." The cost analysis was based on cost data for 2008 and we used 9300 Iranian Rials (IR Rial) as exchange rate declared by the Iranian Central Bank on that time to calculating costs by US Dollar (USD). To evaluate whether a strategy is cost effective, one time and three times of GDP per capita were used as threshold based on recommendation of the World Health Organization.

RESULTS

The Incremental Cost Effectiveness Ratio (ICER) for combination therapy in genotype-1 and genotypes non-1 subgroups was 2,673 and 19,211 US dollars (USD) per one Sustain Virological Response (SVR), respectively. In low viral load and high viral load subgroups, the ICER was 5,233 and 14,976 USD per SVR, respectively. The calculated ICER for combination therapy in subgroup of patients with previously resistant to monotherapy was 13,006 USD per SVR. Combination therapy in previously resistant patients to combination therapy was a dominant strategy.

CONCLUSIONS

Adding low dose of Ribavirin to PEG IFN alfa-2a for treatment of chronic hepatitis C patients with genotype-1 was "highly cost effective" and in patients with low viral load and in previous monotherapy resistant patients was "cost effective."

Are there any differences between features of proteins expressed in malignant and benign breast cancers?

BACKGROUND

The most common cancer among women is breast cancer and it has been blamed as the second leading cause of cancer death in women; so far many approaches have been used to analyze and detect benign and malignant forms of cancer and understanding the features involved in proteins expressed by various types of breast cancers is crucial.

METHODS

Herein features of proteins expressed in malignant, benign and both cancers were compared using different screening techniques, clustering methods, decision tree models and generalized rule induction (GRI) algorithms to look for patterns of similarity in two benign and malignant breast cancer groups.

RESULTS

The findings showed that the N-terminal amino acid was Met and 57 out of 838 proteins' features ranked as important (p > 0.05). The depth of the trees induced by tree induction models varied from 5 (in the Quest model) to 2 (in the C5.0 model) branches. The best performance evaluation found when C&RT model applied and the worst evaluation found when CHAID model applied. No significant difference in the percentage of correctness, performance evaluation, and mean correctness in tree induction algorithms was found when feature selection applied on datasets, but the number of peer groups reduced significantly (p < 0.05) when feature selection model applied.

CONCLUSIONS

The frequency of Ile-Ile was the most important protein attributes in all tree and rule induction models. The importance of sequence-based classification and the frequency of Ile-Ile in prediction of malignant and benign breast cancer have been discussed here.