Modelling future bone mineral density: Simplicity or complexity?

BACKGROUND: Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS: We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS: 2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS: Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.

Virtual rehabilitation for patients with osteoporosis or other musculoskeletal disorders: a systematic review.

This study aims to identify effective ways to design virtual rehabilitation to obtain physical improvement (e.g. balance and gait) and support engagement (i.e. motivation) for people with osteoporosis or other musculoskeletal disorders. Osteoporosis is a systemic skeletal disorder and is among the most prevalent diseases globally, affecting 0.5 billion adults. Despite the fact that the number of people with osteoporosis is similar to, or greater than those diagnosed with cardiovascular disease and dementia, osteoporosis does not receive the same recognition. Worldwide, osteoporosis causes 8.9 million fractures annually; it is associated with substantial pain, suffering, disability and increased mortality. The importance of physical therapy as a rehabilitation strategy to avoid osteoporosis fracture cannot be over-emphasised. However, the main rehabilitation challenges relate to engagement and participation. The use of virtual rehabilitation to address such challenges in the delivery of physical improvement is gaining in popularity. As there currently is a paucity of literature applying virtual rehabilitation to patients with osteoporosis, the authors broadened the search parameters to include articles relating to the virtual rehabilitation of other skeletal disorders (e.g. Ankylosing spondylitis, spinal cord injury, motor rehabilitation, etc.). This systematic review initially identified 130 titles, from which 23 articles (involving 539 participants) met all eligibility and selection criteria. Four groups of devices supporting virtual rehabilitation were identified: a head-mounted display, a balance board, a camera and more specific devices. Each device supported physical improvement (i.e. balance, muscle strength and gait) post-training. This review has shown that: (a) each device allowed improvement with different degrees of immersion, (b) the technology choice is dependent on the care need and (c) virtual rehabilitation can be equivalent to and enhance conventional therapy and potentially increase the patient's engagement with physical therapy.

Bone mineral density and fractures in patients with rheumatoid arthritis: the DXA-HIP project.

Objectives: RA is a chronic disabling disease affecting 0.5-1% of adults worldwide. People with RA have a greater prevalence of multimorbidity, particularly osteoporosis and associated fractures. Recent studies suggest that fracture risk is related to both non-RA and RA factors, whose importance is heterogeneous across studies. This study seeks to compare baseline demographic and DXA data across three cohorts: healthy controls, RA patients and a non-RA cohort with major risk factors and/or prior major osteoporotic fracture (MOF). Methods: This is a cross-sectional study using data collected from three DXA centres in the west of Ireland from January 2000 to November 2018. Results: Data were available for 30 503 subjects who met our inclusion criteria: 9539 (31.3%) healthy controls, 1797 (5.9%) with RA and 19 167 (62.8%) others. Although age, BMI and BMD were similar between healthy controls, the RA cohort and the other cohort, 289 (16.1%) RA patients and 5419 (28.3%) of the non-RA cohort had prior MOF. In the RA and non-RA cohorts, patients with previous MOF were significantly older and had significantly lower BMD at the femoral neck, total hip and spine. Conclusion: Although age, BMI and BMD were similar between a healthy control cohort and RA patients and others with major fracture risk factors, those with a previous MOF were older and had significantly lower BMD at all three measured skeletal sites. Further studies are needed to address the importance of these and other factors for identifying those RA patients most likely to experience fractures.

Prevalence of Low Bone Mass and Osteoporosis in Ireland: the Dual-Energy X-Ray Absorptiometry (DXA) Health Informatics Prediction (HIP) Project.

Osteoporosis is a common disease that has a significant impact on patients, healthcare systems, and society. World Health Organization (WHO) diagnostic criteria for postmenopausal women were established in 1994 to diagnose low bone mass (osteopenia) and osteoporosis using dual-energy X-ray absorptiometry (DXA)-measured bone mineral density (BMD) to help understand the epidemiology of osteoporosis, and identify those at risk for fracture. These criteria may also apply to men ≥50 years, perimenopausal women, and people of different ethnicity. The DXA Health Informatics Prediction (HIP) project is an established convenience cohort of more than 36,000 patients who had a DXA scan to explore the epidemiology of osteoporosis and its management in the Republic of Ireland where the prevalence of osteoporosis remains unknown. In this article we compare the prevalence of a DXA classification low bone mass (T-score < -1.0) and of osteoporosis (T-score ≤ -2.5) among adults aged ≥40 years without major risk factors or fractures, with one or more major risk factors, and with one or more major osteoporotic fractures. A total of 33,344 subjects met our study inclusion criteria, including 28,933 (86.8%) women; 9362 had no fractures or major risk factors, 14,932 had one or more major clinical risk factors, and 9050 had one or more major osteoporotic fractures. The prevalence of low bone mass and osteoporosis increased significantly with age overall. The prevalence of low bone mass and osteoporosis was significantly greater among men and women with major osteoporotic fractures than healthy controls or those with clinical risk factors. Applying our results to the national population census figure of 5,123,536 in 2022 we estimate between 1,039,348 and 1,240,807 men and women aged ≥50 years have low bone mass, whereas between 308,474 and 498,104 have osteoporosis. These data are important for the diagnosis of osteoporosis in clinical practice, and national policy to reduce the illness burden of osteoporosis. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Ireland DXA-FRAX may differ significantly and substantially to Web-FRAX.

Appropriate use of FRAX reduces the number of people requiring DXA scans, while contemporaneously determining those most at risk. We compared the results of FRAX with and without inclusion of BMD. It suggests clinicians to carefully consider the importance of BMD inclusion in fracture risk estimation or interpretation in individual patients. PURPOSE: FRAX is a widely accepted tool to estimate the 10-year risk of hip and major osteoporotic fracture in adults. Prior calibration studies suggest this works similarly with or without the inclusion of bone mineral density (BMD). The purpose of the study is to compare within-subject differences between FRAX estimations derived using DXA and Web software with and without the inclusion of BMD. METHOD: A convenience cohort was used for this cross-sectional study, consisting of 1254 men and women aged between 40 and 90 years who had a DXA scan and complete validated data available for analysis. FRAX 10-year estimations for hip and major osteoporotic fracture were calculated using DXA software (DXA-FRAX) and the Web tool (Web-FRAX), with and without BMD. Agreements between estimates within each individual subject were examined using Bland-Altman plots. We performed exploratory analyses of the characteristics of those with very discordant results. RESULTS: Overall median DXA-FRAX and Web-FRAX 10-year hip and major osteoporotic fracture risk estimations which include BMD are very similar: 2.9% vs. 2.8% and 11.0% vs. 11% respectively. However, both are significantly lower than those obtained without BMD: 4.9% and 14% respectively, P < 0.001. Within-subject differences between hip fracture estimates with and without BMD were < 3% in 57% of cases, between 3 and 6% in 19% of cases, and > 6% in 24% of cases, while for major osteoporotic fractures such differences are < 10% in 82% of cases, between 10 and 20% in 15% of cases, and > 20% in 3% of cases. CONCLUSIONS: Although there is excellent agreement between the Web-FRAX and DXA-FRAX tools when BMD is incorporated, sometimes there are very large differences for individuals between results obtained with and without BMD. Clinicians should carefully consider the importance of BMD inclusion in FRAX estimations when assessing individual patients.

Long term survival in patients with human papillomavirus-positive oropharyngeal cancer and equivocal response on 12-week PET-CT is not compromised by the omission of neck dissection.

BACKGROUND AND AIM: The aim of this study was to evaluate the long-term safety of the omission of immediate neck dissections (IND) in patients with human papillomavirus (HPV)-positive head and neck squamous cell carcinoma (HNSCC) achieving a less than complete nodal response on 12-week FDG PET-CT. MATERIAL AND METHODS: Patients with HPV-positive, node-positive HNSCC that were treated with radical (chemo) radiotherapy (RT) between January 2013 and September 2019 were identified. PET-CT responses were classified as complete (CR), incomplete (ICR) or equivocal (EQR) nodal responses. Clinical outcomes were obtained. RESULTS: 347 patients were identified. Median follow-up was 43.9 (IQR, 30.8-61.2) months. 62.8% (218/347) achieved a CR, 23.4% (81/347) EQR and 13.8% (48/347) ICR nodal response. 70 of 81 (86.4%) patients with an EQR and 25 of 48 (52.1%) with an ICR had no residual disease during follow up (a pathologically negative ND if surgery undertaken or no subsequent neck or distant relapse clinically/radiologically). Median survival of the EQR and CR groups were not reached, and despite the omission of IND in 95% of the EQR group there was no statistically significant differences in overall survival (OS) between the groups, p = 1.0. Median survival of ICR was not reached. However, OS for ICR group was significantly worse than that of CR, and EQR, both p < 0.001. CONCLUSION: The omission of IND in those achieving an EQR nodal response does not compromise long-term survival. This supports the safety of extended surveillance in patients with HPV-positive disease and an EQR on 12-week FDG PET-CT.

Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

How does proximal femur BMD of healthy Irish adults compare to NHANES III? Results of the DXA-HIP Project.

This study examines the distribution of proximal femur bone mineral density in a cohort of healthy Irish adults. These values are similar to those of the NHANES III Caucasian cohorts, supporting international recommendations to use this reference group for calculating DXA T-scores and Z-scores in Irish adults. INTRODUCTION: Bone mineral density (BMD) is widely used in the assessment and monitoring of osteoporosis. International guidelines recommend referencing proximal femur BMD measurements to NHANES III values to calculate T-scores and Z-scores, but their validity for the Irish population has not been established. In this study, we compare BMD values of healthy Irish Caucasian adults to those of Caucasian men and women in the NHANES III cohort study. METHODS: Men and women without bone disease and/or major risk factors for fracture, and/or not taking osteoporosis medication who had a screening DXA scan (GE Lunar, Madison, USA) at one of 3 centres in the West of Ireland were selected for this study. We calculated the mean and standard deviation (SD) used by GE for calculating white female NHANES III T-scores at the femoral neck and total hip sites, and used these values to calculate white female T-scores for men and women across each decade in our study sample. We calculated mean white female T-scores for each decade for both Caucasian men and women in the NHANES III cohort using the published data. Finally, we plotted these results against those of our study population. RESULTS: In total, 6729 (18.5%) of 36,321 adults were included in our analyses, including 5923 (88%) women. The majority of the study population were aged between 40 and 89 years. Our results show that the proximal femur BMD of healthy Irish men and women is broadly similar to that of the NHANES III reference population, especially middle-aged adults. Results differ for very young and very old adults, likely reflecting the small sample size and a referral bias. Further studies of these populations and other manufacturers could help clarify these uncertainties. CONCLUSIONS: Our results support using the NHANES III reference population to calculate proximal femur adult T-scores and Z-scores to establish the presence or prevalence of osteoporosis in Ireland.

Vertebral Fractures in Ireland: A Sub-analysis of the DXA HIP Project.

Osteoporosis is an important global health problem resulting in fragility fractures. The vertebrae are the commonest site of fracture resulting in extreme illness burden, and having the highest associated mortality. International studies show that vertebral fractures (VF) increase in prevalence with age, similarly in men and women, but differ across different regions of the world. Ireland has one of the highest rates of hip fracture in the world but data on vertebral fractures are limited. In this study we examined the prevalence of VF and associated major risk factors, using a sample of subjects who underwent vertebral fracture assessment (VFA) performed on 2 dual-energy X-ray absorptiometry (DXA) machines. A total of 1296 subjects aged 40 years and older had a valid VFA report and DXA information available, including 254 men and 1042 women. Subjects had a mean age of 70 years, 805 (62%) had prior fractures, mean spine T-score was - 1.4 and mean total hip T-scores was - 1.2, while mean FRAX scores were 15.4% and 4.8% for major osteoporotic fracture and hip fracture, respectively. Although 95 (7%) had a known VF prior to scanning, 283 (22%) patients had at least 1 VF on their scan: 161 had 1, 61 had 2, and 61 had 3 or more. The prevalence of VF increased with age from 11.5% in those aged 40-49 years to > 33% among those aged ≥ 80 years. Both men and women with VF had significantly lower BMD at each measured site, and significantly higher FRAX scores, P < 0.01. These data suggest VF are common in high risk populations, particularly older men and women with low BMD, previous fractures, and at high risk of fracture. Urgent attention is needed to examine effective ways to identify those at risk and to reduce the burden of VF.

Utility of Osteoporosis Self-Assessment Tool as a Screening Tool for Osteoporosis in Irish Men and Women: Results of the DXA-HIP Project.

Many algorithms have been developed and publicised over the past 2 decades for identifying those most likely to have osteoporosis or low BMD, or at increased risk of fragility fracture. The Osteoporosis Self-assessment Tool index (OSTi) is one of the oldest, simplest, and widely used for identifying men and women with low BMD or osteoporosis. OSTi has been validated in many cohorts worldwide but large studies with robust analyses evaluating this or other algorithms in adult populations residing in the Republic of Ireland are lacking, where waiting times for public DXA facilities are long. In this study we evaluated the validity of OSTi in men and women drawn from a sampling frame of more than 36,000 patients scanned at one of 3 centres in the West of Ireland. 18,670 men and women aged 40 years and older had a baseline scan of the lumbar spine femoral neck and total hip available for analysis. 15,964 (86%) were female, 5,343 (29%) had no major clinical risk factors other than age, while 5,093 (27%) had a prior fracture. Approximately 2/3 had a T-score ≤-1.0 at one or more skeletal sites and 1/3 had a T-score ≤-1.0 at all 3 skeletal sites, while 1 in 5 had a DXA T-score ≤-2.5 at one or more skeletal sites and 5% had a T-score ≤-2.5 at all 3 sites. OSTi generally performed well in our population with area under the curve (AUC) values ranging from 0.581 to 0.881 in men and 0.701 to 0.911 in women. The performance of OSTi appeared robust across multiple sub-group analyses. AUC values were greater for women, proximal femur sites, those without prior fractures and those not taking osteoporosis medication. Optimal OSTi cut-points were '2' for men and '0' for women in our study population. OSTi is a simple and effective tool to aid identification of Irish men and women with low BMD or osteoporosis. Use of OSTi could improve the effectiveness of DXA screening programmes for older adults in Ireland.

Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study.

BACKGROUND: Identification of those at high risk before a fracture occurs is an essential part of osteoporosis management. This topic remains a significant challenge for researchers in the field, and clinicians worldwide. Although many algorithms have been developed to either identify those with a diagnosis of osteoporosis or predict their risk of fracture, concern remains regarding their accuracy and application. Scientific advances including machine learning methods are rapidly gaining appreciation as alternative techniques to develop or enhance risk assessment and current practice. Recent evidence suggests that these methods could play an important role in the assessment of osteoporosis and fracture risk. METHODS: Data used for this study included Dual-energy X-ray Absorptiometry (DXA) bone mineral density and T-scores, and multiple clinical variables drawn from a convenience cohort of adult patients scanned on one of 4 DXA machines across three hospitals in the West of Ireland between January 2000 and November 2018 (the DXA-Heath Informatics Prediction Cohort). The dataset was cleaned, validated and anonymized, and then split into an exploratory group (80%) and a development group (20%) using the stratified sampling method. We first established the validity of a simple tool, the Osteoporosis Self-assessment Tool Index (OSTi) to identify those classified as osteoporotic by the modified International Society for Clinical Densitometry DXA criteria. We then compared these results to seven machine learning techniques (MLTs): CatBoost, eXtreme Gradient Boosting, Neural network, Bagged flexible discriminant analysis, Random forest, Logistic regression and Support vector machine to enhance the discrimination of those classified as osteoporotic or not. The performance of each prediction model was measured by calculating the area under the curve (AUC) with 95% confidence interval (CI), and was compared against the OSTi. RESULTS: A cohort of 13,577 adults aged ≥40 yr at the age of their first scan was identified including 11,594 women and 1983 men. 2102 (18.13%) females and 356 (17.95%) males were identified with osteoporosis based on their lowest T-score. The OSTi performed well in our cohort in both men (AUC 0.723, 95% CI 0.659-0.788) and women (AUC 0.810, 95% CI 0.787-0.833). Four MLTs improved discrimination in both men and women, though the incremental benefit was small. eXtreme Gradient Boosting showed the most promising results: +4.5% (AUC 0.768, 95% CI 0.706-0.829) for men and +2.3% (AUC 0.833, 95% CI 0.812-0.853) for women. Similarly MLTs outperformed OSTi in sensitivity analyses-which excluded those subjects taking osteoporosis medications-though the absolute improvements differed. CONCLUSION: The OSTi retains an important role in identifying older men and women most likely to have osteoporosis by bone mineral density classification. MLTs could improve DXA detection of osteoporosis classification in older men and women. Further exploration of MLTs is warranted in other populations, and with additional data.

The Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project.

PURPOSE: The purpose of the Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project is to create a large retrospective cohort of adults in Ireland to examine the validity of DXA diagnostic classification, risk assessment tools and management strategies for osteoporosis and osteoporotic fractures for our population. PARTICIPANTS: The cohort includes 36 590 men and women aged 4-104 years who had a DXA scan between January 2000 and November 2018 at one of 3 centres in the West of Ireland. FINDINGS TO DATE: 36 590 patients had at least 1 DXA scan, 6868 (18.77%) had 2 scans and 3823 (10.45%) had 3 or more scans. There are 364 unique medical disorders, 186 unique medications and 46 DXA variables identified and available for analysis. The cohort includes 10 349 (28.3%) individuals who underwent a screening DXA scan without a clear fracture risk factor (other than age), and 9947 (27.2%) with prevalent fractures at 1 of 44 skeletal sites. FUTURE PLANS: The Irish DXA HIP Project plans to assess current diagnostic classification and risk prediction algorithms for osteoporosis and fractures, identify the risk predictors for osteoporosis and develop novel, accurate and personalised risk prediction tools, by using the large multicentre longitudinal follow-up cohort. Furthermore, the dataset may be used to assess, and possibly support, multimorbidity management due to the large number of variables collected in this project.

An exploratory study into the use of Lean Six Sigma to reduce medication errors in the Norwegian public healthcare context.

PURPOSE: Medication errors are a significant cause of injury in Norwegian hospitals. The purpose of this study is to explore how Lean Six Sigma (LSS) has been used in the Norwegian public health-care context to reduce medication errors. DESIGN/METHODOLOGY/APPROACH: A mixed method approach was used to gather data from participants working in the four regions served by the Norway health authorities. A survey questionnaire was distributed to 38 health-care practitioners and semi-structured interviews were conducted with 12 health-care practitioners. FINDINGS: The study finds that the implementation of LSS in the Norwegian public health-care context is still in its infancy. This is amidst several challenges faced by Norwegian hospitals such as the lack of top-management support, lack of LSS training and coaching and a lack of awareness around the benefits of LSS in health care. RESEARCH LIMITATIONS/IMPLICATIONS: Because of the large geographical area, it was difficult to reach participants from all health regions in Norway. However, the study managed to assess the current status of LSS implementation through the participants' perspectives. This is a fruitful area for future research whereby an action research methodology could be used. ORIGINALITY/VALUE: To the best of the authors' knowledge, this is the first empirical study into the use of LSS methodology in reducing medication errors. In addition, this study is valuable for health-care practitioners and professionals as a guideline to achieve the optimal benefit of LSS implementation to reduce medication errors.

12 week PET-CT has low positive predictive value for nodal residual disease in human papillomavirus-positive oropharyngeal cancers.

OBJECTIVES: Surveillance PET-CT scans at 12 weeks post-radiotherapy for head and neck cancer can be used to omit neck dissections with no detriment in overall survival. Human Papillomavirus (HPV) driven tumours behave differently on conventional imaging after radiotherapy but it is unknown if this effect is seen on PET-CT and if HPV status affects the accuracy of PET-CT. We aimed to determine the negative and positive predictive values (NPV and PPV) of 12 week surveillance PET-CT in HPV positive and negative tumours, and investigate predictors of relapse in equivocal responders. MATERIALS AND METHODS: A retrospective cohort study in a UK tertiary level oncology hospital, between 2013 and 2016 included adults with oropharyngeal squamous cell carcinoma, or HPV positive head and neck squamous cell cancers of unknown primary, treated with radiotherapy. RESULTS: The PPVs of 12 week PET-CT in HPV positive and negative disease are 30% and 81.8% respectively (p < 0.01). The NPVs of 12 week PET-CT in HPV positive and negative disease are 92.9% and 55.6% respectively (p < 0.01). 67% of HPV positive patients with equivocal responses on 12 week PET-CT achieved complete response by 24 weeks. Equivocal responses in HPV positive disease had statistically similar survival to patients with complete responses. Comparing disease and imaging characteristics, there were no predictors of residual tumour. CONCLUSIONS: HPV positive tumours have a poor PPV of 30% on 12 week surveillance PET-CTs and take longer to achieve complete response. A period of further surveillance can be considered instead of an immediate neck dissection in this group of patients.

Using Telehealth to Reduce All-Cause 30-Day Hospital Readmissions among Heart Failure Patients Receiving Skilled Home Health Services.

BACKGROUND: The reduction of all-cause hospital readmission among heart failure (HF) patients is a national priority. Telehealth is one strategy employed to impact this sought-after patient outcome. Prior research indicates varied results on all-cause hospital readmission highlighting the need to understand telehealth processes and optimal strategies in improving patient outcomes. OBJECTIVES: The purpose of this paper is to describe how one Medicare-certified home health agency launched and maintains a telehealth program intended to reduce all-cause 30-day hospital readmissions among HF patients receiving skilled home health and report its impact on patient outcomes. METHODS: Using the Transitional Care Model as a guide, the telehealth program employs a 4G wireless tablet-based system that collects patient vital signs (weight, heart rate, blood pressure and blood oxygenation) via wireless peripherals, and is preloaded with subjective questions related to HF and symptoms and instructional videos. RESULTS: Year one all-cause 30-day readmission rate was 19.3%. Fiscal year 2015 ended with an all-cause 30-day readmission rate of 5.2%, a reduction by 14 percentage points (a 73% relative reduction) in three years. Telehealth is now an integral part of the University of Pennsylvania Health System's readmission reduction program. CONCLUSIONS: Telehealth was associated with a reduction in all-cause 30-day readmission for one mid-sized Medicare-certified home health agency. A description of the program is presented as well as lessons learned that have significantly contributed to this program's success. Future expansion of the program is planned. Telehealth is a promising approach to caring for a chronically ill population while improving a patient's ability for self-care.

Influence of slice overlap on positron emission tomography image quality.

PET scans use overlapping acquisition beds to correct for reduced sensitivity at bed edges. The optimum overlap size for the General Electric (GE) Discovery 690 has not been established. This study assesses how image quality is affected by slice overlap. Efficacy of 23% overlaps (recommended by GE) and 49% overlaps (maximum possible overlap) were specifically assessed. European Association of Nuclear Medicine (EANM) guidelines for calculating minimum injected activities based on overlap size were also reviewed. A uniform flood phantom was used to assess noise (coefficient of variation, (COV)) and voxel accuracy (activity concentrations, Bq ml(-1)). A NEMA (National Electrical Manufacturers Association) body phantom with hot/cold spheres in a background activity was used to assess contrast recovery coefficients (CRCs) and signal to noise ratios (SNR). Different overlap sizes and sphere-to-background ratios were assessed. COVs for 49% and 23% overlaps were 9% and 13% respectively. This increased noise was difficult to visualise on the 23% overlap images. Mean voxel activity concentrations were not affected by overlap size. No clinically significant differences in CRCs were observed. However, visibility and SNR of small, low contrast spheres (⩽13 mm diameter, 2:1 sphere to background ratio) may be affected by overlap size in low count studies if they are located in the overlap area. There was minimal detectable influence on image quality in terms of noise, mean activity concentrations or mean CRCs when comparing 23% overlap with 49% overlap. Detectability of small, low contrast lesions may be affected in low count studies-however, this is a worst-case scenario. The marginal benefits of increasing overlap from 23% to 49% are likely to be offset by increased patient scan times. A 23% overlap is therefore appropriate for clinical use. An amendment to EANM guidelines for calculating injected activities is also proposed which better reflects the effect overlap size has on image noise.

PET-CT evaluation of solitary pulmonary nodules: correlation with maximum standardized uptake value and pathology.

PURPOSE: 18-fluorine fluorodeoxyglucose ((18)F-FDG) positron emission tomography-computed tomography (PET-CT) has an established role for the characterization of solitary pulmonary nodules (SPN). Visual assessment of nodule morphology, together with maximum standardized uptake value (SUVmax), is used to estimate likelihood of malignancy. We correlated SUVmax value with pathology of SPN and assessed diagnostic accuracy in differentiating malignant from benign nodule, using 2.5 as threshold SUVmax. METHODS: Retrospective review of PET-CT scans for SPN characterization between April 2008 and June 2011 was performed. Only cases with pathological verification were included. RESULTS: A total of 641 PET-CTs were performed for SPN characterization and staging; 186 patients (77 males, 109 females) with pathological confirmation were included, and 158 (85 %) nodules were malignant: adenocarcinomas (n = 66), squamous cell carcinomas (n = 40), and metastases (n = 20) were the commonest. 28 lesions (15 %) were benign, including granuloma/chronic inflammation (n = 8), infection (n = 7), and hamartomas (n = 5). Using cutoff SUVmax of 2.5, the accuracy of PET-CT in diagnosing malignant SPN is 81.2 %, with sensitivity 86.7 %, specificity 50 %, PPV 90.7 %, and NPV 40 %. The likelihood of malignancy increases with SUVmax. Nevertheless, even with SUVmax <2.5, there is a 62 % chance that a nodule is malignant. CONCLUSIONS: Although PET-CT is useful in diagnostic workup of SPN, it cannot replace "gold standard" tissue diagnosis.

Quantitative analysis shows that contrast medium in positron emission tomography/computed tomography may cause significant artefacts.

OBJECTIVES: Attenuation correction algorithms are required for accurate quantification of PET data and for mapping of radioactive tracers. Modern PET systems incorporate computed tomography (CT) systems to perform attenuation correction. However, high-density media, such as contrast agents, may introduce potentially clinically significant artefacts in PET images when CT-based attenuation correction algorithms are used. Although various groups have investigated this issue, no study has quantitatively assessed the clinical significance of these artefacts by comparing artefact and lesion standardized uptake values (SUVs) in controlled phantom experiments. Furthermore, previous studies have focussed on the effects of increasing the concentration of contrast medium, without investigating the effects of increasing its transaxial area. This study quantifies the clinical significance of increasing the concentration and transaxial area of contrast agents and evaluates a commercially available contrast agent correction algorithm. METHODS: Images of a phantom containing background activity, a volume of contrast agent and varying sizes of hot lesions were acquired using clinical acquisition protocols. Quantitative analysis was performed on transaxial image slices of PET data. RESULTS: The densest medium caused a 125% SUV(mean) increase in the area containing, and immediately adjacent to, contrast medium when compared with a reference water phantom. As the transaxial area of the contrast medium increased, artefacts appeared as a ring of activity around the periphery of the contrast medium. The contrast correction algorithm reduced these artefacts to within ± 39% of the reference results. CONCLUSION: Oral and IV contrast agents can cause clinically significant artefacts in CT-based attenuation-corrected PET images and should be used with caution.

Aquatic insects of New York salt marsh associated with mosquito larval habitat and their potential utility as bioindicators.

The aquatic insect fauna of salt marshes is poorly characterized, with the possible exception of biting Diptera. Aquatic insects play a vital role in salt marsh ecology, and have great potential importance as biological indicators for assessing marsh health. In addition, they may be impacted by measures to control mosquitoes such as changes to the marsh habitat, altered hydrology, or the application of pesticides. Given these concerns, the goals of this study were to conduct the first taxonomic survey of salt marsh aquatic insects on Long Island, New York, USA and to evaluate their utility for non-target pesticide impacts and environmental biomonitoring. A total of 18 species from 11 families and five orders were collected repeatedly during the five month study period. Diptera was the most diverse order with nine species from four families, followed by Coleoptera with four species from two families, Heteroptera with three species from three families, then Odonata and the hexapod Collembola with one species each. Water boatmen, Trichocorixa verticalis Fieber (Heteroptera: Corixidae) and a shore fly, Ephydra subopaca Loew (Diptera: Ephydridae), were the two most commonly encountered species. An additional six species; Anurida maritima Guérin-Méneville (Collembola: Neanuridae), Mesovelia mulsanti White (Heteroptera: Mesovelidae), Enochrus hamiltoni Horn (Coleoptera: Hydrophilidae), Tropisternus quadristriatus Horn (Coleoptera: Hydrophilidae), Dasyhelea pseudocincta Waugh and Wirth (Diptera: Ceratopogonidae), and Brachydeutera argentata Walker (Diptera: Ephydridae), were found regularly. Together with the less common Erythrodiplax berenice Drury (Odonata: Libellulidae), these nine species were identified as the most suitable candidates for pesticide and environmental impact monitoring due to abundance, position in the food chain, and extended seasonal occurrence. This study represents a first step towards developing an insect-based index of biological integrity for salt marsh health assessment.