CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

OBJECTIVE

To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.

METHODS

A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.

RESULTS

Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.

CONCLUSIONS

Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage.

CRITICAL RELEVANCE STATEMENT

An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage.

KEY POINTS

• 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI

OBJECTIVES

Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients.

MATERIALS AND METHODS

Vertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC).

RESULTS

The ICCs between the readers ranged from 0.90 (95% CI 0.83-0.94) to 0.93 (95% CI 0.88-0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71-0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87-0.96) to 0.97 (95% CI 0.94-0.98).

CONCLUSION

The Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured.

CLINICAL RELEVANCE STATEMENT

Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care.

KEY POINTS

• While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Background Multiple commercial artificial intelligence (AI) products exist for assessing radiographs; however, comparable performance data for these algorithms are limited. Purpose To perform an independent, stand-alone validation of commercially available AI products for bone age prediction based on hand radiographs and lung nodule detection on chest radiographs. Materials and Methods This retrospective study was carried out as part of Project AIR. Nine of 17 eligible AI products were validated on data from seven Dutch hospitals. For bone age prediction, the root mean square error (RMSE) and Pearson correlation coefficient were computed. The reference standard was set by three to five expert readers. For lung nodule detection, the area under the receiver operating characteristic curve (AUC) was computed. The reference standard was set by a chest radiologist based on CT. Randomized subsets of hand (n = 95) and chest (n = 140) radiographs were read by 14 and 17 human readers, respectively, with varying experience. Results Two bone age prediction algorithms were tested on hand radiographs (from January 2017 to January 2022) in 326 patients (mean age, 10 years ± 4 [SD]; 173 female patients) and correlated strongly with the reference standard (r = 0.99; P < .001 for both). No difference in RMSE was observed between algorithms (0.63 years [95% CI: 0.58, 0.69] and 0.57 years [95% CI: 0.52, 0.61]) and readers (0.68 years [95% CI: 0.64, 0.73]). Seven lung nodule detection algorithms were validated on chest radiographs (from January 2012 to May 2022) in 386 patients (mean age, 64 years ± 11; 223 male patients). Compared with readers (mean AUC, 0.81 [95% CI: 0.77, 0.85]), four algorithms performed better (AUC range, 0.86-0.93; P value range, <.001 to .04). Conclusions Compared with human readers, four AI algorithms for detecting lung nodules on chest radiographs showed improved performance, whereas the remaining algorithms tested showed no evidence of a difference in performance. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Omoumi and Richiardi in this issue.

Modifiable Risk Factors for Accelerated Decline in Processing Speed: Results from Three Dutch Population Cohorts

BACKGROUND

Several lifestyle, cardiovascular and psychosocial factors are associated with risk of cognitive decline and dementia. We studied the independent associations of a broad set of modifiable risk factors with decline in processing speed in three large population-based cohorts with up to 23 years of follow-up.

METHODS

We used data of 9,666 participants from the Doetinchem Cohort Study, the Longitudinal Aging Study Amsterdam, and the Maastricht Aging Study. Decline in processing speed was measured with the letter digit substitution task or the alphabet coding task and modeled using quadratic latent growth curves. Associations of modifiable risk factors with level and rate of decline in processing speed were investigated by estimating associations with level of processing speed at different centering ages.

RESULTS

Latent growth curves showed that decline in processing speed accelerated with age. Smoking, not drinking alcohol and depressive symptoms were associated with a lower level of processing speed in all cohorts. In two of the cohorts, more physical activity, drinking more than two glasses of alcohol per day, higher BMI and diabetes were associated with a lower level of processing speed. Depressive symptoms and diabetes were also associated with faster decline in processing speed.

CONCLUSION

Several modifiable risk factors are associated with the level of processing speed in older age, while few are also related to the rate of decline.

Previous fracture and subsequent fracture risk: a meta-analysis to update FRAX

A large international meta-analysis using primary data from 64 cohorts has quantified the increased risk of fracture associated with a previous history of fracture for future use in FRAX.

INTRODUCTION

The aim of this study was to quantify the fracture risk associated with a prior fracture on an international basis and to explore the relationship of this risk with age, sex, time since baseline and bone mineral density (BMD).

METHODS

We studied 665,971 men and 1,438,535 women from 64 cohorts in 32 countries followed for a total of 19.5 million person-years. The effect of a prior history of fracture on the risk of any clinical fracture, any osteoporotic fracture, major osteoporotic fracture, and hip fracture alone was examined using an extended Poisson model in each cohort. Covariates examined were age, sex, BMD, and duration of follow-up. The results of the different studies were merged by using the weighted β-coefficients.

RESULTS

A previous fracture history, compared with individuals without a prior fracture, was associated with a significantly increased risk of any clinical fracture (hazard ratio, HR = 1.88; 95% CI = 1.72-2.07). The risk ratio was similar for the outcome of osteoporotic fracture (HR = 1.87; 95% CI = 1.69-2.07), major osteoporotic fracture (HR = 1.83; 95% CI = 1.63-2.06), or for hip fracture (HR = 1.82; 95% CI = 1.62-2.06). There was no significant difference in risk ratio between men and women. Subsequent fracture risk was marginally downward adjusted when account was taken of BMD. Low BMD explained a minority of the risk for any clinical fracture (14%), osteoporotic fracture (17%), and for hip fracture (33%). The risk ratio for all fracture outcomes related to prior fracture decreased significantly with adjustment for age and time since baseline examination.

CONCLUSION

A previous history of fracture confers an increased risk of fracture of substantial importance beyond that explained by BMD. The effect is similar in men and women. Its quantitation on an international basis permits the more accurate use of this risk factor in case finding strategies.

Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

BACKGROUND

Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES

To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.

METHODS

The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.

RESULTS

A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.

CONCLUSION

We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Update of the fracture risk prediction tool FRAX: a systematic review of potential cohorts and analysis plan

We describe the collection of cohorts together with the analysis plan for an update of the fracture risk prediction tool FRAX with respect to current and novel risk factors. The resource comprises 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures.

INTRODUCTION

The availability of the fracture risk assessment tool FRAX® has substantially enhanced the targeting of treatment to those at high risk of fracture with FRAX now incorporated into more than 100 clinical osteoporosis guidelines worldwide. The aim of this study is to determine whether the current algorithms can be further optimised with respect to current and novel risk factors.

METHODS

A computerised literature search was performed in PubMed from inception until May 17, 2019, to identify eligible cohorts for updating the FRAX coefficients. Additionally, we searched the abstracts of conference proceedings of the American Society for Bone and Mineral Research, European Calcified Tissue Society and World Congress of Osteoporosis. Prospective cohort studies with data on baseline clinical risk factors and incident fractures were eligible.

RESULTS

Of the 836 records retrieved, 53 were selected for full-text assessment after screening on title and abstract. Twelve cohorts were deemed eligible and of these, 4 novel cohorts were identified. These cohorts, together with 60 previously identified cohorts, will provide the resource for constructing an updated version of FRAX comprising 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures. For each known and candidate risk factor, multivariate hazard functions for hip fracture, major osteoporotic fracture and death will be tested using extended Poisson regression. Sex- and/or ethnicity-specific differences in the weights of the risk factors will be investigated. After meta-analyses of the cohort-specific beta coefficients for each risk factor, models comprising 10-year probability of hip and major osteoporotic fracture, with or without femoral neck bone mineral density, will be computed.

CONCLUSIONS

These assembled cohorts and described models will provide the framework for an updated FRAX tool enabling enhanced assessment of fracture risk (PROSPERO (CRD42021227266)).

Exploring the conceptual framework and measurement model of intrinsic capacity defined by the World Health Organization: A scoping review

WHO has defined intrinsic capacity (IC) as the composite of all physical and mental capacities of an individual covering five subdomains: cognition, locomotion, sensory, vitality, and psychological. Despite this well accepted definition, the conceptual and measurement model of IC remains unclear, which hampers a standardized operationalization of the construct. We performed a scoping review to give a comprehensive overview of the extent to which the current literature of IC addresses and assumes the conceptual framework and measurement model of IC as reflective or formative. For inclusion, we considered all types of articles that were published in peer-reviewed journals except for protocol articles. A systematic search of 6 databases from different disciplines led to the inclusion of 31 papers. We found inconsistency and gaps in the descriptions of IC. Most of the papers did not define the measurement model. In the conceptual background and validation articles, we identified descriptions of both reflective and formative measurement models while in empirical studies applying IC measurements the underlying assumptions remained mainly unclear. Defining a measurement model is not merely a theoretical matter but influences the operationalization and validation processes of the construct. This study raised questions about the most fundamental features of the IC construct and discusses whether IC should be considered as an underlying latent trait of all capacities (reflective construct) or an aggregate summary measure of the subdomain capacities (formative construct).

Aversive response to uncertainty as a mediator for the effect of a mindfulness intervention on symptoms of anxiety

THEORETICAL BACKGROUND

Intolerance of uncertainty plays a central role in anxiety and research suggests that it's an important treatment target. Investigating response to uncertainty using other dimensions than self-report, such as physiological responses, can further the effort to understand the role of uncertainty in anxiety more fully. Mindfulness interventions have become increasingly interesting in their application to anxiety, as they foster acceptance of unpleasant aspects of experience. The aims of the study were to examine whether a mindfulness intervention reduced response to uncertainty and anxiety symptoms, and to examine the associations between intolerance of uncertainty, physiological response to uncertainty, mindfulness and anxiety.

METHODS

Participants were 117 students who completed a two-week mindfulness or audiobook control intervention. At pre- and post-intervention assessments, measures of anxiety, mindfulness, and intolerance of uncertainty were completed and a threat-of-shock task assessing startle responding to unpredictable shock was administered.

RESULTS

Findings showed a significant effect of the intervention for social anxiety symptoms. Furthermore, intolerance of uncertainty mediated the effect of the intervention on symptoms for social anxiety and worry. No such effects were found for physiological response to uncertainty.

CONCLUSION

The study adds to the understanding of the role of response to uncertainty in anxiety as well as to its mechanistic role in the context of mindfulness practice. Implications and possible explanations for the non-significant main effects of the intervention on anxiety symptoms and physiological response to uncertainty are discussed.

Impact of the COVID-19-pandemic on patients with gynecological malignancies undergoing surgery: A Dutch population-based study using data from the 'Dutch Gynecological Oncology Audit'

OBJECTIVE

The COVID-19-pandemic caused drastic healthcare changes worldwide. To date, the impact of these changes on gynecological cancer healthcare is relatively unknown. This study aimed to assess the impact of the COVID-19-pandemic on surgical gynecological-oncology healthcare.

METHODS

This population-based cohort study included all surgical procedures with curative intent for gynecological malignancies, registered in the Dutch Gynecological Oncology Audit, in 2018-2020. Four periods were identified based on COVID-19 hospital admission rates: 'Pre-COVID-19', 'First wave', 'Interim period', and 'Second wave'. Surgical volume, perioperative care processes, and postoperative outcomes from 2020 were compared with 2018-2019.

RESULTS

A total of 11,488 surgical procedures were analyzed. For cervical cancer, surgical volume decreased by 17.2% in 2020 compared to 2018-2019 (mean 2018-2019: n = 542.5, 2020: n = 449). At nadir (interim period), only 51% of the expected cervical cancer procedures were performed. For ovarian, vulvar, and endometrial cancer, volumes remained stable. Patients with advanced-stage ovarian cancer more frequently received neoadjuvant chemotherapy in 2020 compared to 2018-2019 (67.7% (n = 432) vs. 61.8% (n = 783), p = 0.011). Median time to first treatment was significantly shorter in all four malignancies in 2020. For vulvar and endometrial cancer, the length of hospital stay was significantly shorter in 2020. No significant differences in complicated course and 30-day-mortality were observed.

CONCLUSIONS

The COVID-19-pandemic impacted surgical gynecological-oncology healthcare: in 2020, surgical volume for cervical cancer dropped considerably, waiting time was significantly shorter for all malignancies, while neoadjuvant chemotherapy administration for advanced-stage ovarian cancer increased. The safety of perioperative healthcare was not negatively impacted by the pandemic, as complications and 30-day-mortality remained stable.

How COVID-19 kick-started online learning in medical education-The DigiMed study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students' attitudes to online learning to provide a broad scientific basis to guide future development of medical education.

METHODS

Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education.

RESULTS

The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching.

CONCLUSION

This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new "online era" in medical education.

'Improving Health through Reducing Stress': Parents' Priorities in the Participatory Development of a Multilevel Family Health Programme in a Low-Income Neighbourhood in The Netherlands

In order to reduce health inequities, a socio-ecological approach and community engagement are needed to develop sustained interventions with a positive effect on the health of disadvantaged groups. This qualitative study was part of the development phase of a community health promotion programme. The study aimed to provide insight into the perceptions of parents in a disadvantaged neighbourhood about health, and their priorities for the community health programme. It also described the process of integrating these perceptions in the development of a multilevel plan for this programme. Participatory methods were applied to enable the engagement of all groups involved. Ten parents from a low-income neighbourhood in the Netherlands participated in five panel sessions. Parents’ priorities for improving family health were reducing chronic stress and not so much healthy eating and physical activity. They prioritised solutions to reduce their financial stress, to provide a safe place for their children to meet and play and to establish good quality communication with authorities. The programme development process resulted in objectives in which both parents and professionals were willing to invest, such as a safe playground for children. This study shows that target population engagement in health programme development is possible and valuable.

Land use mix and physical activity in middle-aged and older adults: a longitudinal study examining changes in land use mix in two Dutch cohorts

Background

With urbanization and aging increasing in coming decades, societies face the challenge of keeping aging populations active. Land use mix (LUM) has been associated with cycling and walking, but whether changes in LUM relate to changes in cycling/walking is less known.

Objectives

Our objective was to study the effect of LUM on cycling/walking in two Dutch aging cohorts using data with 10 years of follow-up.

Methods

Data from 1183 respondents from the Health and Living Conditions of the Population of Eindhoven and Surroundings (GLOBE) study and 918 respondents from the Longitudinal Aging Study Amsterdam (LASA) were linked to LUM in 1000-m sausage network buffers at three time-points. Cycling/walking outcomes were harmonized to include average minutes spent cycling/walking per week. Data was pooled and limited to respondents that did not relocate between follow-up waves. Associations between LUM and cycling/walking were estimated using a Random Effects Within-Between (REWB) model that allows for the estimation of both within and between effects. Sensitivity analyses were performed on smaller (500-m) and larger (1600-m) buffers.

Results

We found evidence of between-individual associations of LUM in 1000-m buffers and walking (β: 11.10, 95% CI: 0.08; 21.12), but no evidence of within-associations in 1000-m buffers. Sensitivity analyses using 500-m buffers showed similar between-associations, but negative within-associations (β: -35.67, 95% CI: − 68.85; − 2.49). We did not find evidence of between-individual associations of LUM in any buffer size and cycling, but did find evidence of negative within-associations between LUM in 1600-m buffers and cycling (β: -7.49, 95% CI: − 14.31; − 0.66).

Discussion

Our study found evidence of positive associations between LUM and average walking time, but also some evidence of negative associations between a change in LUM and cycling/walking. LUM appears to be related to cycling/walking, but the effect of changes in LUM on cycling/walking is unclear.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-021-01083-1.

Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (ie, death and/or intensive care unit admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, chest radiographic, and laboratory findings. Distribution and severity of lung involvement were visually assessed by using an eight-point scale (chest radiography score). Internal validation was performed by using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve. Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (mean age, 69 years ± 12 [standard deviation]; 237 men) of whom 168 (47%) developed critical illness. The final risk model's variables included sex, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease, and chest radiography score at hospital presentation. The area under the receiver operating characteristic curve of the model was 0.77 (95% CI: 0.72, 0.81; P < .001). A risk calculator was derived for individual risk assessment: Dutch COVID-19 risk model. At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.

The Application of the Concept of Resilience in Aging Research and Older Adult Care: A Focus Group Study

Introduction: Research incorporating resilience, a concept featuring a positive outcome despite some type of stressor, has the potential to identify possibilities for promotion of the well-being of older people. This study aims to gain insight into the value and potential applications of resilience in both research and care practice from the perspective of researchers and care professionals. Specifically, the value of two scientific approaches, the a priori (i.e., based on a priori definition of a stressor and outcome) and dynamical systems approaches (i.e., based on mathematically modeled patterns in the real-time response to perturbations), was explored. Methods: Focus groups were performed to explore the thoughts of academic researchers from different disciplines in the fields of aging and care and care professionals on the application of the concept of resilience, including the a priori and dynamical systems approaches. Analysis of these focus groups was based on the framework method. Results: Five focus groups were held with a total of nine researchers from different disciplines (e.g., epidemiology, sociology) and 15 older adult care professionals from different professions (e.g., elderly care physician, physiotherapist). The participants described resilience as a concept with value for both aging research and care through its positive connotation and comprehensiveness. Continued research was thought to play an important role in clearing up some of the existing ambiguity surrounding resilience. The importance of resilience in the context of both high- and low-intensity stressors was underscored. The a priori and dynamical systems approaches were considered to have their specific advantages and disadvantages on both conceptual and feasibility levels. Therefore, the use of both approaches, side by side and in combination, was suggested. Conclusion: This qualitative exploration among researchers and care professionals confirms that the concept of resilience, including the a priori and dynamical systems approaches, is valuable. However, more work is necessary before can be delivered on the potential of resilience in aging research and older adult care practice. Greater conceptual and operational clarity can be achieved through more qualitative studies on the concept that take the perspective of older people into account and through empirical studies that work with both approaches simultaneously and/or in combination.

Metabolic syndrome detection with biomarkers in childhood cancer survivors

PURPOSE

Augmented survival of childhood nephroblastoma and neuroblastoma has increased long-term side effects such as metabolic syndrome (MetS). Risk stratification is difficult after abdominal radiation because waist circumference underestimates adiposity. We aimed to develop a strategy for determining MetS in irradiated survivors using an integrated biomarker profile and vascular ultrasonography.

METHODS

The NCEP-ATPIII MetS-components, 14 additional serum biomarkers and 9 vascular measurements were assessed in a single-centre cohort of childhood nephroblastoma (n = 67) and neuroblastoma (n = 36) survivors and controls (n = 61). Multivariable regression models were used to study treatment effects. Principal component analysis (PCA) was used to study all biomarkers in a combined analysis, to identify patterns and correlations.

RESULTS

After 27.5 years of follow-up, MetS occurred more often in survivors (14%) than controls (3%). Abdominal radiotherapy and nephrectomy, to a lesser extent, were associated with MetS and separate components and with several biomarker abnormalities. PCA of biomarkers revealed a pattern on PC1 from favourable lipid markers (HDL-cholesterol, adiponectin) towards unfavourable markers (triglycerides, LDL-cholesterol, apoB, uric acid). Abdominal radiotherapy was associated with the unfavourable biomarker profile (β = 1.45, P = 0.001). Vascular measurements were not of added diagnostic value.

CONCLUSIONS

Long-term childhood nephro- and neuroblastoma survivors frequently develop MetS. Additional assessment of biomarkers identified in PCA - adiponectin, LDL, apoB, and uric acid - may be used especially in abdominally irradiated survivors, to classify MetS as alternative for waist circumference. Vascular ultrasonography was not of added value.

Comparison of Imaging Changes and Pain Responses in Patients with Intra- or Extraosseous Bone Metastases Treated Palliatively with Magnetic Resonance-Guided High-Intensity-Focused Ultrasound

PURPOSE

This study compared changes in imaging and in pain relief between patients with intraosseous, as opposed to extraosseous bone metastases. Both groups were treated palliatively with magnetic resonance-guided high-intensity-focused ultrasound (MRgHIFU).

MATERIALS AND METHODS

A total of 21 patients were treated prospectively with MRgHIFU at 3 centers. Intraprocedural thermal changes measured using proton resonance frequency shift (PRFS) thermometry and gadolinium-enhanced T1-weighted (Gd-T1W) image appearances after treatment were compared for intra- and extraosseous metastases. Pain scores and use of analgesic therapy documented before and up to 90 days after treatment were used to classify responses and were compared between the intra- and extraosseous groups. Gd-T1W changes were compared between responders and nonresponders in each group.

RESULTS

Thermal dose volumes were significantly larger in the extraosseous group (P = 0.039). Tumor diameter did not change after treatment in either group. At day 30, Gd-T1W images showed focal nonenhancement in 7 of 9 patients with intraosseous tumors; in patients with extraosseous tumors, changes were heterogeneous. Cohort reductions in worst-pain scores were seen for both groups, but differences from baseline at days 14, 30, 60, and 90 were only significant for the intraosseous group (P = 0.027, P = 0.013, P = 0.012, and P = 0.027, respectively). By day 30, 67% of patients (6 of 9) with intraosseous tumors were classified as responders, and the rate was 33% (4 of 12) for patients with extraosseous tumors. In neither group was pain response indicated by nonenhancement on Gd-T1W.

CONCLUSIONS

Intraosseous tumors showed focal nonenhancement by day 30, and patients had better pain response to MRgHIFU than those with extraosseous tumors. In this small cohort, post-treatment imaging was not informative of treatment efficacy.

Associations of neighbourhood sociodemographic characteristics with depressive and anxiety symptoms in older age: Results from a 5-wave study over 15 years

We examined the long-term association between objective neighbourhood sociodemographic characteristics (index of socioeconomic position (SEP), average income, percent low-income earners, average house price, percent immigrants and urban density) with depressive and anxiety symptoms, covering five 3-year waves of the Longitudinal Aging Study Amsterdam (n = 3,772). Multi-level regression models assessed each neighbourhood-level characteristic separately, adjusting for individual-level covariates. A higher percentage of immigrants and higher urban density, but not other neighbourhood characteristics, were significantly associated with depressive and anxiety symptoms over time in models adjusted for individual SEP. Results of time interaction models indicated that the associations were stable over the 15-year period.

Do older individuals who are diagnosed with cancer have worse physical performance prior to diagnosis compared to matched controls? A longitudinal cohort study

Background

Impaired physical performance is highly prevalent in older cancer patients and is associated with cancer-related outcomes such as mortality and chemotherapy-related toxicity. Physical performance might already decline prior to the cancer diagnosis due to undiagnosed disease. This study aimed to assess whether the physical performance of community-dwelling individuals prior to cancer diagnosis is worse compared to matched controls who are not diagnosed with cancer.

Methods

The study sample was selected from the Longitudinal Aging Study Amsterdam, a longitudinal study on a nationally representative sample of the Dutch older population. Physical performance of initially cancer-free individuals aged 55–84 years who were diagnosed with cancer during 10 or 20 years of follow-up was compared to the physical performance of controls who were not diagnosed with cancer. For controls, the physical performance measurements of the cycle with a median age closest to the cancer group were used. The time interval between physical performance measurements and the report of cancer was 2 to 4 years. Groups were compared using logistic and linear regression analysis.

Results

The study sample included 1735 individuals with a median age of 68.7 [interquartile range 63.3–76.4] years. During follow-up, 414 (23.9%) individuals were diagnosed with cancer. Handgrip strength, gait speed, chair stand ability, chair stand test time and ability to put on and take off a cardigan did not differ between groups. Individuals prior to cancer diagnosis were more likely to complete the tandem balance test.

Conclusions

Physical performance of individuals 2 to 4 years prior to report of cancer diagnosis is not lower compared to controls. This suggests that physical performance may not be influenced by cancer before diagnosis.

Combination of Pulmonary Embolism Rule-out Criteria and YEARS Algorithm in a European Cohort of Patients with Suspected Pulmonary Embolism

Background Both the YEARS algorithm and the pulmonary embolism (PE) rule-out criteria (PERC) were created to exclude PE with limited diagnostic tests. A diagnostic strategy combining both scores might save additional computed tomography pulmonary angiography (CTPA) scans, but they have never been evaluated in conjunction.

Aim The aim of this study was to determine the safety and efficiency of combining YEARS and PERC in a single diagnostic strategy for suspected PE.

Methods The PERC rule was assessed in 1,316 consecutive patients with suspected PE who were managed according to YEARS. We calculated the absolute difference (with 95% confidence interval [CI]) in failure rate and the number of ‘saved’ CTPAs for the scenario that PE would have been ruled out without CTPA in the absence of all PERC items.

Results Using the YEARS algorithm, PE was diagnosed in 189 patients (14%), 680 patients (52%) were managed without CTPA and the 3-month rate of venous thromboembolism in patients in whom PE was ruled out was 0.44% (95% CI: 0.19–1.0). Only 6 of 154 patients (3.9%; 95% CI: 1.4–8.2) with no YEARS items who were referred for CTPA would have been PERC negative, of whom none were diagnosed with PE at baseline or during follow-up (0%; 95% CI: 0–64). Applying PERC before YEARS in all patients would have led to a failure rate of 1.42% (95% CI: 0.87–2.3%), 0.98% (95% CI: 0.17–1.9) more than shown in patients managed by YEARS.

Conclusion Combining YEARS with PERC would have yielded only a modest improvement of efficiency in patients without a YEARS item and an unacceptable failure rate in patients with ≥ 1 YEARS item.

How to determine an impaired health status in COPD: Results from a population-based study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is associated with a significantly impaired health status and lost work productivity across all degrees of airflow limitation. The current study investigated whether an impaired health status is better represented by the recommended COPD Assessment Test (CAT) cut-point of 10 points, or the 95th percentile of the CAT score in a non-COPD population. Additionally, the impact of COPD on health status in a Dutch population, after stratification for work status, was measured.

METHODS

Demographics, clinical characteristics, post-bronchodilator spirometry, and CAT were assessed in subjects from the Longitudinal Aging Study Amsterdam (LASA), a large Dutch population-based study. Normative values for the CAT score were described by percentiles using the mean, standard deviation, median and range.

RESULTS

In total, 810 COPD and non-COPD subjects (50.4% male, mean age 60.5 ±; 2.9 years) were analysed. Significant differences were observed in CAT scores between non-COPD and COPD subjects (6.7 ±; 5.2 vs. 9.5 ±; 5.9, p < 0.001 respectively). The proportion of COPD subjects with an impaired health status differed between applying the CAT ≥ 10 cut-point (50.0%) and applying the 95th percentile of CAT in non-COPD subjects (> 18 cut-point; 7.6%). Higher CAT scores were seen in working COPD patients compared with working non-COPD subjects (9.3 ±; 5.2 vs. 6.0 ±; 4.6, p < 0.001).

CONCLUSION

We suggest a CAT cut-point of > 18 points to indicate an impaired health status in COPD. This would imply an adaptation of the current GOLD classification of the disease.

Longitudinal associations between late-life depression dimensions and cognitive functioning: a cross-domain latent growth curve analysis

BACKGROUND

Cognitive impairment and depression often co-occur in older adults, but it is not clear whether depression is a risk factor for cognitive decline, a psychological reaction to cognitive decline, or whether changes in depressive symptoms correlate with changes in cognitive performance over time. The co-morbid manifestation of depression and cognitive impairment may reflect either a causal effect or a common cause, depending on the specific symptoms experienced and the cognitive functions affected.

METHOD

The study sample comprised 1506 community-dwelling older adults aged ⩾65 years from the Longitudinal Aging Study Amsterdam (LASA). We conducted cross-domain latent growth curve analyses to examine longitudinal associations between late-life depression dimensions (i.e. depressed affect, positive affect, and somatic symptoms) and specific domains of cognitive functioning (i.e. processing speed, inductive reasoning, immediate recall, and delayed recall).

RESULTS

Poorer delayed recall performance at baseline predicted a steeper increase in depressed affect over time. Steeper decline in processing speed correlated with a steeper increase in somatic symptoms of depression over time.

CONCLUSIONS

Our findings suggest a prospective association between memory function and depressed affect, whereby older adults may experience an increase in depressed affect in reaction to poor memory function. Somatic symptoms of depression increased concurrently with declining processing speed, which may reflect common neurodegenerative processes. Our findings do not support the hypothesis that depression symptoms may be a risk factor for cognitive decline in the general population. These findings have potential implications for the treatment of late-life depression and for the prognosis of cognitive outcomes.

The long-term outcome of subthreshold depression in later life

BACKGROUND

Subthreshold depression (SUBD) in later life is common and important as prodromal state and prominent risk factor in the development of major depressive disorder (MDD). Indicated prevention can reduce the incidence of MDD among people with SUBD substantially, but needs to be targeted to those that are truly at risk of developing MDD.

METHOD

N = 341 eligible participants with SUBD were included from the first (1992/1993), second (1995/1996) and third (1998/1999) cycle from the Longitudinal Aging Study Amsterdam (LASA) by using a two-stage screening design. LASA is an ongoing prospective cohort study in The Netherlands among the older population (55-85 years). At baseline (1992/1993) N = 3107 participants were interviewed and follow-up cycles were conducted every 3 years until 2008/2009, resulting in maximal 17 years of observational period. The proportion of people that developed MDD, remained SUBD, or recovered from SUBD was measured and Cox proportional regression analyses were performed to investigate 29 putative predictors of MDD and recovery from SUBD.

RESULTS

N = 153 (44.9%) recovered from SUBD, N = 138 (40.5%) remained chronically SUBD, and N = 50 (14.7%) developed MDD (incidence rate 15.1/1000 person-years). Women, high neuroticism, more chronic diseases, high body mass index, smoking and less social support predicted conversion to MDD. Men, low neuroticism and absence of pain predicted recovery from SUBD.

CONCLUSIONS

Although older people with SUBD are clearly at risk of developing MDD, the majority did not, even after a long and thorough follow-up. Given the risk factors that were uncovered, targeting and prevention of MDD in those at very high risk is feasible.