Hasil untuk "Computer applications to medicine. Medical informatics"

Aron B. Syversen, Alexios Dosis, Zhiqiang Zhang et al.

Abstract Background Wearable sensors offer a scalable alternative to cardiopulmonary exercise testing for assessing cardiorespiratory fitness, and there is growing evidence to support their use for remote VO2max estimation. This study investigated whether heart rate variability (HRV) measures derived from wearable ECG sensors improve VO2max estimations in a preoperative cohort and compared the relative contributions of short- and long-term HRV features. ECG and accelerometer data from 198 participants scheduled for major abdominal surgery (REMOTES study, ClinicalTrials.gov: ID NCT06042023) were collected over 72 h. Measures including physical activity, steps, heart rate, and HRV were extracted. Short-term (5-minutes) and long-term (24-hour) heart rate variability features were extracted from free-living ECG data. Two LASSO regression models with five-fold cross-validation were developed: a baseline model (excluding HRV) and a HRV model. Results After exclusions, 163 participants were included in analyses. The HRV model outperformed the baseline across all metrics, achieving a higher R2 (0.47 ± 0.12 vs. 0.42 ± 0.13) and lower mean absolute error (2.63 ± 0.34 vs. 2.77 ± 0.38 ml/kg/min), root mean square error (3.38 ± 0.53 vs. 3.54 ± 0.57 ml/kg/min) and absolute percentage error (15.55 ± 2.19% vs. 16.22 ± 2.45%). Analysis of feature contributions identified long-term HRV (SDANNHR24), age, gender, and step-counts as key contributors to model performance. Conclusion HRV features from wearable data, especially long-term measures, can improve remote VO2max predictions in a clinical cohort. While performance gains were small, these findings support the integration of HRV features into remote monitoring systems in real-world settings. Long-term HRV measures derived from heart rate signals offer a practical option for cardiorespiratory fitness assessment, requiring minimal additional processing. Trail registration This study was registered at ClinicalTrials.gov (Clinical trial number: NCT06042023) and was registered retrospectively on 11/09/2023.

Javier Gómez Sánchez-Seco, Mary Luz Mouronte-López, Rosa M. Benito

Abstract Using network theory and data analysis, we study the messages on Twitter (X) about ecological sustainability over the period 2007-2022. With a global view of 70,311,541 messages we examined the sentiment, keywords and hashtags utilised, as well as the correlations between sentiment and both socioeconomic and environmental variables. In addition to the above, we carried out an in-depth analysis of the global interactions network (retweets, replies and quotes), with a special focus on the study of the community network (CNET) (with 4576 supernodes, and 9855 links). The sentiment shown in the text of the tweets was positive over the years in all analysed locations, although close to neutral. Keyword analysis detected terms present in tweets posted from various regions, showing global thinking in the world. The relationships between sentiment and variables examined were continent- and country-specific, identifying a stronger correlation with socioeconomic attributes. Regarding CNET, according to the study performed using adjacency and laplacian embeddings, as well as Chebyshev, Euclidean, Minkowski, and Manhattan distances, pairs of unconnected supernodes appeared to have more similarity in their connection patterns than pairs of connected supernodes, due to the topological structure of CNET which has a large number of peripheral nodes that are not connected to each other, but are connected to nodes with higher centrality. In agreement with the Jaccard coefficient, resource allocation index, Adamic Adar index, and preferential attachment score, there is little possibility of link formation between supernodes. Statistically the supernodes also exhibited high topological similarity. A few specific supernodes host most of the users, showing the highest centralities among those analysed. The basic structure of CNET, which maintained its key properties, was also examined. Strategies that promote communication between supernodes to achieve greater participation and diversity in discussions need to be further investigated.

Sharmala Thuraisingam, D Himasara Marasinghe, Kendra Barrick et al.

BackgroundGeneral practice electronic health records (EHRs) contain a wealth of patient information. However, these data are collected for clinical purposes. Hence, questions remain around the suitability of using these data for other purposes, including epidemiological research, developing and validating clinical prediction models, conducting audits, and informing policy. ObjectiveThis study aimed to compare the quality of osteoarthritis-related data in Australian and Canadian general practice EHRs for externally validating a clinical prediction model for total knee replacement surgery. MethodsA data quality assessment was conducted on 201,462 patient general practice EHRs from Australia provided by National Prescribing Service MedicineWise, and 92,425 from Canada provided by the Canadian Primary Care Sentinel Surveillance Network. Completeness, plausibility, and external validity of data elements relevant to osteoarthritis were assessed. Completeness and plausibility were evaluated using counts and proportions. For external validity, prevalence was estimated using proportions, and knee replacement summarized as a rate per 100,000 population. ResultsThere were minimal incomplete and implausible data fields for age and sex (<1%), geographic location (<5%), and commonly cooccurring comorbidities (<10%) in both datasets. However, weight, height, BMI, and Canadian Index of Multiple Deprivation contained >50% missing data. The recording of osteoarthritis by age and sex in both datasets were similar to national estimates, except for patients aged >80 years (Australia: 16.6%, 95% CI 16%-17.3% vs 13.1%, 95% CI 11.2%-15.4%; Canada: 36.7%, 95% CI 36.1%-37.2% vs 50.8%, 95% CI 50.7%-50.9%). Total knee replacement rates were substantially lower in both EHR datasets compared with national estimates (Australia: 72 vs 218 per 100,000; Canada: 0.84 vs 200 per 100,000). ConclusionsAge, sex, geographic location, commonly cooccurring comorbidities, and prescribing of osteoarthritis medications in Australian and Canadian general practice EHRs are suitable for use in clinical prediction model validation studies. However, BMI and the Canadian Index of Multiple Deprivation are unfit for such use due to large proportions of missing data. Rates of total knee replacement surgery were substantially underreported and should not be used for prediction model validation. Better harmonization of patient data across primary and tertiary care is required to improve the suitability of these data. In the meantime, data linkage with national registries and other health datasets may overcome some of the data quality challenges in general practice EHRs.

Ruyi Ma, Weifeng Yu, Jian Tian et al.

Abstract BackgroundWith the increasing use of machine learning (ML)–based risk prediction models for venous thromboembolism (VTE) in patients, the quality and applicability of these models in practice and future research remain unknown. The prediction mechanism of ML and the number of selected factors have been research hotspots in VTE prediction. ObjectiveThis study aimed to systematically review the literature on the predictive value of ML for VTE. MethodsPubMed, Web of Science, MEDLINE, Embase, CINAHL, and Cochrane Library databases were searched for studies published up to March 26, 2025. Studies that developed and validated an ML model for VTE prediction in the patient population and were published in English were eligible, and studies with duplicate data were excluded. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias in the included studies. Meta-analyses were performed to evaluate the C-index, sensitivity, and specificity. ResultsA total of 27 studies with 596,092 patients reported the assessment value of ML models for predicting VTE. The risk of bias assessment yielded 18 (67%) studies with a high risk of bias, 8 (30%) with an unclear risk of bias, and 1 (4%) with a low risk of bias. The pooled sensitivity and specificity were 0.79 (95% CI 0.78-0.80) and 0.82 (95% CI 0.81-0.82), respectively. The positive likelihood ratio was 5.02 (95% CI 3.81-6.60), the negative likelihood ratio was 0.27 (95% CI 0.22-0.33), and the diagnostic odds ratio was 20.14 (95% CI 13.69-29.63; P ConclusionsML has been shown to effectively predict VTE in patients. However, a high risk of bias was identified in most of the included studies (18/27, 67%), primarily due to shortcomings in handling missing data and reporting the study design. Consequently, future research must prioritize external validation and address methodological rigor to facilitate the translation of these models into routine clinical practice.

Sabit Ahamed Preanto, Tapon Paul, Abid Khan et al.

Moringa Oleifera, which has outstanding nutritional and health benefits, is prized around the world because its leaves are rich in essential vitamins, antioxidants, and minerals that support digestion, help the immune system, and fight inflammation. Still, growing Moringa can be difficult because diseases such as Yellow Leaf, Bacterial Leaf Spot, and Cercospora Leaf Spot are hard to detect early and spread fast, leading to a lot of damage. These illnesses cause plants to make less yield, so farmers depend on pesticides and spend more, which also damages the environment and their crops. Here, we make available the MoringaLeafNet dataset, including high-quality images of leaves from the Moringa tree affected by different diseases. The images in the dataset, gathered from March to April and August to September 2025, are divided into four classes: Healthy Leaf, Yellow Leaf, Bacterial Leaf Spot, and Cercospora Leaf Spot. We collected images from Sumi Nursery in Madhupur, Tangail, Bangladesh, and Rafin Nursery in Birulia, Savar, Bangladesh, under various weather conditions. To facilitate better use in deep learning, random rotation, flipping, and brightness/contrast adjustments were performed on the data. The dataset will help develop new disease detection systems in agriculture that allow the early recognition of Moringa leaf diseases. It can also support the development of real-time diagnostic systems that provide farmers with timely insights for decision-making.

Dandan Ge, Yong Xia, Zhonghua Zhang

Abstract Background The medical record homepage represents the core and quintessential distillation of the entire medical record. This study aims to investigate the problems with the medical record homepages data quality after the upgrade of the electronic medical record system, while simultaneously proposing practical and feasible measures to catalyze substantive improvements in data quality standards. Methods A retrospective analysis of data extracted from the medical record homepage system was conducted at a Chinese tertiary hospital affiliated with a medical university between January and December 2021. Analysis of Moment Structures (AMOS) was used to construct a structural equation model, with the aim of elucidating the influence of individual variables on dependent variables. Furthermore, a fish bone diagram analysis was utilized to systematically analyze the underlying causes of quality defects. Results Among the 2,731 medical record homepages subjected to scrutiny, a substantial proportion of 1,531 records (56.1%) exhibited quality issues. The structural equation model revealed that patient demographic information exerted the most profound influence on data quality, as evidenced by the greatest value of the standardized total effects (β = -0.729), followed by surgery (β = -0.606) and diagnosis information (β = -0.363). Moreover, the fish-bone diagram analysis was employed to systematically dissect the underlying causes of quality defects in the medical record homepages, encompassing human factors, surroundings, regulatory system, and machinery. Conclusions The predominant factor contributing to the poor data on the medical record homepage was inaccuracies in demographic information, closely followed by errors in surgical and diagnosis information. It is helpful to improve the data quality of the medical record homepages by establishing a coder qualification certification system, strengthening the construction of medical informatization, and adding data validation and prompt functions.

Hao Zhu, Keith Tsz-Suen Tung, Hung-kwan So et al.

Abstract BackgroundThe significant association between blood pressure (BP) in children and young adulthood and risks of cardiovascular diseases in adulthood highlights the critical need for early BP control. While lifestyle modifications such as increased physical exercise have proven effective, traditional exercise forms always suffer from low motivation and adherence. Active video games (AVGs), combining exercise with engaging gameplay, may present a promising alternative for managing BP in children and young adults. ObjectiveThis study aims to evaluate the effectiveness of AVGs in managing BP among the population aged 6 to 25 years. MethodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, this study retrieved and screened publications archived in the 4 databases (Web of Science, Cochrane Library, PubMed, and Embase) and the registration (ClinicalTrials.gov) up to December 30, 2024. Eligible studies were defined as interventional trials involving participants aged 6 to 25 years, using AVGs as one of the intervention protocols, and reporting BP outcomes. Studies were excluded if they involved participants with heart diseases, combined AVGs protocol with other intervention components, limited outcomes to immediate postgame BP, or included only control groups that received additional physical activity interventions. Depending on the heterogeneity among included trials, random-effects or fixed-effects models were selected to pool the effect sizes of individual trials, with 95% CIs. The risk of bias was assessed using the Cochrane Risk of Bias tool for controlled trials and the Methodological Index for Non-Randomized Studies for prepost design. Sensitivity analyses were performed to evaluate result robustness, while Egger tests investigated publication bias. ResultsA total of 17 trials from 16 studies, involving 503 participants who are normotensive, were included in this study. The analysis showed that AVGs significantly reduced systolic blood pressure (standardized mean difference=−0.50, PP ConclusionsThese findings shed light on the cardiovascular benefits of AVGs in children younger than 18 years, underscoring their potential to improve vascular elasticity while maintaining organ perfusion. However, considering the limitations arising from small sample sizes, as well as inadequate allocation concealment and blinding in the included studies, these findings should be interpreted with caution.

Shaher Momani, Iqbal M. Batiha, Issam Bendib et al.

This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.

Richard Fechner, Jens Dörpinghaus, Robert Rockenfeller et al.

<b>Background:</b> Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. <b>Methods:</b> We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. <b>Results:</b> We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. <b>Conclusions:</b> We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Matthias Weemaes, Jeroen Appermont, Joris Welkenhuysen et al.

Background and aims: This is a narrative report on the development of specifications and of the stepwise implementation of a computerized physician order entry system (CPOE) in the University Hospitals Leuven, based on order sets integrated in care pathways. Integration of the CPOE into the electronic health record optimally supports those care pathways. Materials and methods: We investigated adherence to consensus-based use of laboratory tests. We analysed data post- versus pre-implementation test requests for departments selected to cover various patient populations, using the Mann-Whitney U test corrected for the False Discovery Rate. Results: We investigated the effect on the number of tests post- versus pre-implementation. In the emergency and inpatient wards, the CPOE implementation resulted in decreases in requests for uric acid, total protein, direct bilirubin, reticulocyte count, and increases in requests for albumin, glucose and calcium. In the outpatient clinics, where the medical specialties already used non-computerized consensus-based test requesting strategies, effects on the number of requested tests were marginal. In all settings, operational improvements could be inferred: the centrally managed CPOE system resulted in fast and well-controlled management of test strategies, digital traceability of the laboratory test requesting process, automatic archiving of requests, assurance of availability of obligatory information. Conclusions: Overall, test requesting strategies improved towards more consensus-based and operational benefits resulted in time gains and improved efficiency of time spend.

Vimla L. Patel, David R. Kaufman

Nazanin Alavi, Callum Stephenson, Shadé Miller et al.

BackgroundThe demand for mental health care, particularly for depression and anxiety, is 3-fold greater among patients receiving oncologic and palliative care than for the general population. This population faces unique barriers, making them more susceptible to mental health challenges. Various forms of psychotherapy have been deemed effective in addressing mental health challenges in this population, including supportive psychotherapy, cognitive behavioral therapy, problem-based therapy, and mindfulness; however, their access to traditional face-to-face psychotherapy resources is limited owing to their immunocompromised status, making frequent hospital visits dangerous. Additionally, patients can face hospital fatigue from numerous appointments and investigations or may live in remote areas, which makes commutes both physically and financially challenging. Web-based psychotherapy is a promising solution to address these accessibility barriers. Moreover, web-based psychotherapy has been proven effective in addressing depression and anxiety in other populations and may be implementable among patients receiving oncologic and palliative care. ObjectiveThe study will investigate the feasibility and effectiveness of web-based psychotherapy among patients receiving oncologic and palliative care, who have comorbid depression or anxiety. We hypothesized that this program will be a viable and efficacious treatment modality compared to current treatment modalities in addressing depression and anxiety symptoms in this population. MethodsParticipants (n=60) with depression or anxiety will be recruited from oncology and palliative care settings in Kingston (Ontario, Canada). Participants will be randomly allocated to receive either 8 weeks of web-based psychotherapy plus treatment as usual (treatment arm) or treatment as usual exclusively (control arm). The web-based psychotherapy program will incorporate cognitive behavioral therapy, mindfulness, and problem-solving skills, and homework assignments with personalized feedback from a therapist. All web-based programs will be delivered through a secure platform specifically designed for web-based psychotherapy delivery. To evaluate treatment efficacy, all participants will complete standardized symptomology questionnaires at baseline, midpoint (week 4), and posttreatment. ResultsThe study received ethics approval in February 2021 and began recruiting participants in April 2021. Participant recruitment has been conducted through social media advertisements, physical advertisements, and physician referrals. To date, 11 participants (treatment, n=5; control, n=4; dropout, n=2) have been recruited. Data collection and analysis are expected to conclude by December 2021 and January 2022, respectively. Linear regression (for continuous outcomes) will be conducted with interpretive qualitative methods. ConclusionsOur findings can be incorporated into clinical policy and help develop more accessible mental health treatment options for patients receiving oncologic and palliative care. Asynchronous and web-based psychotherapy delivery is a more accessible, scalable, and financially feasible treatment that could have major implications on the health care system. Trial RegistrationClinicalTrials.gov NCT04664270; https://clinicaltrials.gov/ct2/show/NCT04664270 International Registered Report Identifier (IRRID)DERR1-10.2196/30735

Marta Vázquez, Raquel Nieto, Margarida L.R. Liberato et al.

Most of the moisture transported in the globe has its origin in the well-known main moisture sources defined by Gimeno et al. [1]. They provide moisture for precipitation over continental areas in the world in different proportions. This paper presents the daily moisture contribution over each 0.5 × 0.5 continental gridded point from the three preferred moisture sources (primary, secondary, and tertiary) for continental extreme precipitation during the Peak Precipitation Month. The data consist of the moisture contribution (|E−P<0|) field by month from the three preferred sources obtained using an approach based on the Lagrangian particle dispersion model FLEXPART. The data here presented is directly related to the results presented in Vazquez et al. [2].

Walter Costa, Maria Beatriz, Wernsdorfer, Mark, Kehrer, Alexander et al.

BackgroundLaboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. ObjectiveWith this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. MethodsTechnical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. ResultsWe present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. ConclusionsAMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

Stefan Nickels, Alexander K. Schuster, Heike Elflein et al.

Abstract Purpose Most definitions of visual impairment focus on the status of the better-seeing eye only, but this approach might underestimate the influence of the worse-seeing eye on the vision-related quality of life (VRQoL). Methods We assessed distance-corrected visual acuity in both eyes and VRQoL using the “National Eye Institute 25-Item Visual Function Questionnaire” (NEI VFQ-25) in the German population-based Gutenberg Health Study. We calculated the Rasch-based visual functioning scale (VFS) and socioemotional scale (SES). We categorized the visual acuity of the better-seeing eye (BE) and worse-seeing eye (WE) as follows: (1) no visual impairment (VI) (< 0.32 logMAR)), (2) mild VI (0.32–0.5 logMAR), and (3) moderate to severe VI (> 0.5 logMAR). Next, the subjects were categorized as follows: both eyes with no VI (no/no), the better-seeing eye with no VI and the worse-seeing eye with mild VI (no/mild), no VI/severe VI (no/severe), both eyes with mild VI (mild/mild), light VI/severe VI (mild/severe), and both eyes with severe VI (severe/severe). We calculated the median scores for VFS and SES. We used linear regression to estimate the combined influence of BE/WE on VFS and SES. Results We included 11,941 participants (49.9% female, age range: 35–74 years) with information on VRQoL and visual acuity. The median VFS/SES scores were 90/100 (no/no VI group), 84/97 (no/mild group), 81/94 (no/severe group), 70/90 (mild/mild group), 67/74 (mild/severe group), and 63/76 (severe/severe group). These differences were supported by the regression analysis results. Conclusion Relying on the function of the better-seeing eye considerably underestimates the impact of visual impairment on VRQoL.

Sofie Goethals, Annemieke De Wilde, Katrien Lesage et al.

<p class="p1">Tumorbank@UZA is an academic hospital integrated biobank that collects tissue, blood and urine samples from oncology patients. We work according to a quality management system and have established SOPs for all work procedures in the biobank. Tumorbank@UZA is funded by the National Cancer Plan, an initiative from the Belgian government since 2009. Samples from our biobank are available for both academic as well as commercial researchers, through a well-established access procedure. Currently the collection consists of more than 85.000 samples of more than 8000 patients.</p><p class="p1"> </p><p class="p2"><strong>Funding statement: </strong>Tumorbank@UZA is funded by the National Cancer Plan (initiative 27) from the Ministry of Health of the Belgian Federal Government.</p>

Tina Lien Barken, Elin Thygesen, Ulrika Söderhamn

Abstract Background Telemedicine is changing traditional nursing care, and entails nurses performing advanced and complex care within a new clinical environment, and monitoring patients at a distance. Telemedicine practice requires complex disease management, advocating that the nurses’ reasoning and decision-making processes are supported. Computerised decision support systems are being used increasingly to assist reasoning and decision-making in different situations. However, little research has focused on the clinical reasoning of nurses using a computerised decision support system in a telemedicine setting. Therefore, the objective of the study is to explore the process of telemedicine nurses’ clinical reasoning when using a computerised decision support system for the management of patients with chronic obstructive pulmonary disease. The factors influencing the reasoning and decision-making processes were investigated. Methods In this ethnographic study, a combination of data collection methods, including participatory observations, the think-aloud technique, and a focus group interview was employed. Collected data were analysed using qualitative content analysis. Results When telemedicine nurses used a computerised decision support system for the management of patients with complex, unstable chronic obstructive pulmonary disease, two categories emerged: “the process of telemedicine nurses’ reasoning to assess health change” and “the influence of the telemedicine setting on nurses’ reasoning and decision-making processes”. An overall theme, termed “advancing beyond the system”, represented the connection between the reasoning processes and the telemedicine work and setting, where being familiar with the patient functioned as a foundation for the nurses’ clinical reasoning process. Conclusion In the telemedicine setting, when supported by a computerised decision support system, nurses’ reasoning was enabled by the continuous flow of digital clinical data, regular video-mediated contact and shared decision-making with the patient. These factors fostered an in-depth knowledge of the patients and acted as a foundation for the nurses’ reasoning process. Nurses’ reasoning frequently advanced beyond the computerised decision support system recommendations. Future studies are warranted to develop more accurate algorithms, increase system maturity, and improve the integration of the digital clinical information with clinical experiences, to support telemedicine nurses’ reasoning process.

Stefano Zanigni, Giovanna Calandra-Buonaura, David Neil Manners et al.

Background: Advanced brain MR techniques are useful tools for differentiating Progressive Supranuclear Palsy from Parkinson's disease, although time-consuming and unlikely to be used all together in routine clinical work. We aimed to compare the diagnostic accuracy of quantitative morphometric, volumetric and DTI metrics for differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease. Methods: 23 Progressive Supranuclear Palsy-Richardson's Syndrome and 42 Parkinson's disease patients underwent a standardized 1.5T brain MR protocol comprising high-resolution T1W1 and DTI sequences. Brainstem and cerebellar peduncles morphometry, automated volumetric analysis of brain deep gray matter and DTI metric analyses of specific brain structures were carried out. We determined diagnostic accuracy, sensitivity and specificity of MR-markers with respect to the clinical diagnosis by using univariate receiver operating characteristics curve analyses. Age-adjusted multivariate receiver operating characteristics analyses were then conducted including only MR-markers with a sensitivity and specificity exceeding 80%. Results: Morphometric markers (midbrain area, pons to midbrain area ratio and MR Parkinsonism Index), DTI parameters (infratentorial structures) and volumetric analysis (thalamus, putamen and pallidus nuclei) presented moderate to high diagnostic accuracy in discriminating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, with midbrain area showing the highest diagnostic accuracy (99%) (mean ± standard deviation: 75.87 ± 16.95 mm2 vs 132.45 ± 20.94 mm2, respectively; p < 0.001). Conclusion: Although several quantitative brain MR markers provided high diagnostic accuracy in differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, the morphometric assessment of midbrain area is the best single diagnostic marker and should be routinely included in the neuroradiological work-up of parkinsonian patients.

Jana Cason, Ellen R. Cohn

The spring 2014 issue of the International Journal of Telerehabilitation (IJT) contains four informative and timely policy articles: (1) an invited commentary describing the exploratory process underway within physical therapy to create licensure portability for physical therapists, (2) an analysis of state telehealth laws and regulations for occupational therapy and physical therapy, (3) an overview of telehealth evidence and key telehealth policy issues in occupational therapy, and (4) the World Federation of Occupational Therapists’ (WFOT) Position Statement on Telehealth. This issue also contains original research evaluating the feasibility of providing pediatric dysphagia treatment via telepractice, a clinical report of student learning outcomes associated with an innovative experiential learning assignment involving (international) teleconsultation in a Master of Science in Occupational Therapy (MSOT) curriculum, a book review, and announcements from the American Telemedicine Association.

Kevin B. Johnson, George R. Kim