Feasibility of EPID Based In Vivo Dosimetry for On-Couch Adaptive Radiotherapy

PURPOSE/OBJECTIVE(S)

CBCT-based online adaptive radiotherapy allows treatment plans to be tailored to the anatomy of the day. For this purpose, dose optimization and computation are performed on a synthetic CT (sCT), i.e., a density map of the planning CT deformably registered onto the acquired CBCT. Plan-specific quality assurance of adaptive treatment sessions is currently limited to the vendors own secondary dose calculation on the sCT. The use of EPID recordings promises to detect deviations in beam delivery or patient position, incorrect sCT data and anatomical changes. The purpose of this research was to evaluate the feasibility of using a new commercial technology available for in-vivo dose reconstruction based on EPID exit beam measurements.

MATERIALS/METHODS

EPID images were recorded from on-couch adapted, hypofractionated treatment plans created for a prostate patient. Secondary dose calculation of the adapted plan and 3D reconstructions from EPID images of the absolute dose delivered to the patient were performed with the in-vivo dosimetry system. Comparison of reconstructed and planned delivery was conducted by means of gamma analysis and DVH metrics.

RESULTS

Comparisons of the intended plan to a second volumetric check (collapsed cone - CC algorithm), and to the EPID in-vivo calculation, produced gamma passing rates averaged over the 7 fractions of 98.3% and 88.8% at TG-218/219 recommended criteria (Table 1). DVH metrics showed average deviations of -2.9% / -3.3% for PTV D95% and -1.2% / -0.2% for PTV D50% between intended dose and secondary / EPID in-vivo dose. The pronounced discrepancies for fraction 3 in terms of both reduced gamma passing rates and increased deviations for the EPID reconstructed dose coincide with a large air cavity in the rectum showing up in the patient's CBCT prior to treatment. Secondary dose calculation based on the density information of the CBCT as an additional option for QA confirmed origin and magnitude of this effect.

CONCLUSION

In-vivo EPID dosimetry of adaptive clinical plans is feasible. It was shown exemplary that it reveals fractions with noticeable anatomical changes between the sCT and the patient anatomy during treatment. As new recommendations were published on in-vivo dosimetry through TG 307, the development of new clinical tools and evaluation of existing commercial solutions is essential, also in the context of the growing use of on-couch adaptive technology.

Endovascular therapy outcome in isolated posterior cerebral artery occlusion strokes: A multicenter analysis of the Swiss Stroke Registry

PURPOSE

There is little data on the safety and efficacy of endovascular treatment (EVT) in comparison with intravenous thrombolysis (IVT) in acute ischemic stroke due to isolated posterior cerebral artery occlusion (IPCAO). We aimed to investigate the functional and safety outcomes of stroke patients with acute IPCAO treated with EVT (with or without prior bridging IVT) compared to IVT alone.

METHODS

We did a multicenter retrospective analysis of data from the Swiss Stroke Registry. The primary endpoint was overall functional outcome at 3 months in patients undergoing EVT alone or as part of bridging, compared with IVT alone (shift analysis). Safety endpoints were mortality and symptomatic intracranial hemorrhage. EVT and IVT patients were matched 1:1 using propensity scores. Differences in outcomes were examined using ordinal and logistic regression models.

FINDINGS

Out of 17,968 patients, 268 met the inclusion criteria and 136 were matched by propensity scores. The overall functional outcome at 3 months was comparable between the two groups (EVT vs IVT as reference category: OR = 1.42 for higher mRS, 95% CI = 0.78-2.57, p = 0.254). The proportion of patients independent at 3 months was 63.2% in EVT and 72.1% in IVT (OR = 0.67, 95% CI = 0.32-1.37, p = 0.272). Symptomatic intracranial hemorrhages were overall rare and present only in the IVT group (IVT = 5.9% vs EVT = 0%). Mortality at 3 months was also similar between the two groups (IVT = 0% vs EVT = 1.5%).

CONCLUSION

In this multicenter nested analysis, EVT and IVT in patients with acute ischemic stroke due to IPCAO were associated with similar overall good functional outcome and safety. Randomized studies are warranted.

The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease

Objective

Clinical implementation of remote monitoring of human function requires an understanding of its feasibility. We evaluated adherence and the resources required to monitor physical, cognitive, and psychosocial function in individuals with either chronic obstructive pulmonary disease or stroke during a three-month period.

Methods

Seventy-three individuals agreed to wear a Fitbit to monitor physical function and to complete monthly online assessments of cognitive and psychosocial function. During a three-month period, we measured adherence to monitoring (1) physical function using average daily wear time, and (2) cognition and psychosocial function using the percentage of assessments completed. We measured the resources needed to promote adherence as (1) the number of participants requiring at least one reminder to synchronize their Fitbit, and (2) the number of reminders needed for each completed cognitive and psychosocial assessment.

Results

After accounting for withdrawals, the average daily wear time was 77.5 ± 19.9% of the day and did not differ significantly between months 1, 2, and 3 (p = 0.30). To achieve this level of adherence, 64.9% of participants required at least one reminder to synchronize their device. Participants completed 61.0% of the cognitive and psychosocial assessments; the portion of assessments completed each month didnot significantly differ (p = 0.44). Participants required 1.13 ± 0.57 reminders for each completed assessment. Results did not differ by disease diagnosis.

Conclusions

Remote monitoring of human function in individuals with either chronic obstructive pulmonary disease or stroke is feasible as demonstrated by high adherence. However, the number of reminders required indicates that careful consideration must be given to the resources available to obtain high adherence.

Understanding child disability: Factors associated with child disability at the Iganga-Mayuge Health and Demographic Surveillance Site in Uganda

Introduction

There is scarcity of data on children with disabilities living in low-and-middle-income countries, including Uganda. This study describes disability prevalence and explores factors associated with different disability categories. It highlights the value of using a standardized, easy-to-use tool to determine disability in children and contextualizing disability in children in light of their developmental needs.

Methods

A cross-sectional study was conducted between September 2018–January 2019 at the Iganga-Mayuge Health and Demographic Surveillance Site in Uganda. Respondents were caregivers of children between 5–17 years and were administered an in-depth Child Functioning Module (CFM). The outcome variable, disability, was defined as an ordered categorical variable with three categories–mild, moderate, and severe. Generalized ordered logit model was applied to explore factors associated with disability categories.

Results

Out of 1,842 caregivers approached for the study, 1,439 (response: 78.1%) agreed to participate in the study. Out of these 1,439, some level of disability was reported by 67.89% (n = 977) of caregivers. Of these 977 children with disability, 48.01% (n = 692) had mild disability and 15.84% (n = 228) had moderate disability, while 3.96% (n = 57) had severe disability. The mean (SD) score for mild disability was 2.22±1.17, with a median of 2. The mean and median for moderate disability was 5.26±3.28 and 4 (IQR:3–6), and for severe disability was 14.23±9.51 and 12 (IQR:6–22). The most common disabilities reported were depression (54.83%) and anxiety (50.87%). Statistically significant association was found for completion of immunization status and school enrollment when controlled for a child’s age, sex, having a primary caregiver, age of mother at child’s birth, family system, family size and household wealth quintile.

Conclusion

This study suggests association between incomplete immunization status and school enrollment for children with disability. These are areas for further exploration to ensure inclusive health and inclusive education of children with disabilities in Uganda.

Precision rehabilitation: optimizing function, adding value to health care

Precision medicine efforts are underway in many medical disciplines; however, the power of precision rehabilitation has not yet been explored. Precision medicine aims to deliver the right intervention, at the right time, in the right setting, for the right person, ultimately, bolstering the value of the care that we provide. To date precision medicine efforts have rarely focused on function at the level of a person, but precision rehabilitation is poised to change this and bring the focus on function to the broader precision medicine enterprise. To do this, subgroups of individuals must be identified based on their level of function via precise measurement of their abilities in the physical, cognitive, and psychosocial domains. Adoption of electronic health records, advances in data storage and analytics, and improved measurement technology make this shift possible. Here we detail critical components of the precision rehabilitation framework, including 1) the synergistic use of various study designs, 2) the need for standardized functional measurements, 3) the importance of precise and longitudinal measures of function, 4) the utility of comprehensive databases, 5) the importance of predictive analyses, and 6) the need for system and team science. Precision rehabilitation has the potential to revolutionize clinical care, optimize function for all individuals, and magnify the value of rehabilitation in healthcare; however, to reap the benefits of precision rehabilitation, the rehabilitation community must actively pursue this shift.

Outcomes after reperfusion therapies in patients with ACA stroke: A multicenter cohort study from the EVATRISP collaboration

BACKGROUND

Patients with stroke secondary to occlusions of the anterior cerebral artery (ACA) often have poor outcomes. The optimal acute therapeutic intervention for these patients remains unknown.

METHODS

Patients with isolated ACA-stroke were identified from 10 centers participating in the EndoVascular treatment And ThRombolysis in Ischemic Stroke Patients (EVATRISP) prospective registry. Patients treated with endovascular thrombectomy (EVT) were compared to those treated with intravenous thrombolysis (IVT). Odds ratios with 95% confidence intervals (OR; 95%CI) were calculated using multivariate regression analysis.

RESULTS

Included were 92 patients with ACA-stroke. Of the 92 ACA patients, 55 (60%) were treated with IVT only and 37 (40%) with EVT (±bridging IVT). ACA patients treated with EVT had more often wake-up stroke (24% vs. 6%, p = 0.044) and proximal ACA occlusions (43% vs. 24%, p = 0.047) and tended to have higher stroke severity on admission [NIHSS: 10.0 vs 7.0, p = 0.054). However, odds for favorable outcome, mortality or symptomatic intracranial hemorrhage did not differ significantly between both groups. Exploration of the effect of clot location inside the ACA showed that in patients with A1 or A2/A3 ACA occlusions the chances of favorable outcome were not influenced by treatment allocation to IVT or EVT.

DISCUSSION

Treatment with either IVT or EVT could be safe with similar effect in patients with ACA-strokes and these effects may be independent of clot location within the occluded ACA.

A data-pipeline processing electrocardiogram recordings for use in artificial intelligence algorithms

Introduction

Artificial intelligence (AI) can be used for various tasks in medicine and specifically in cardiology. Medical data such as electrocardiogram recordings (ECGs) are widely used and universally accepted as diagnostic and prognostic tools. It has been shown that deep learning methods using ECGs yield excellent results detecting cardiac pathologies. A significant amount of reliable data is required for supervised learning algorithms such as deep learning models. However, only a small fraction of ECG data generated in daily practice is available in a fully digital and machine-readable format, such as XML. Frequently, used ECG devices produce PDF files or even paper-based print outs, which need to be digitised later for inclusion in clinical information systems. Such ECGs cannot be used without further effort for training or application of deep learning models. Therefore, aim of the present project was to develop a data-pipeline that generates machine-readable ECG data for AI use data irrespective of the initial ECG format.

Methods

We propose an end-to-end pipeline that can not only process data from modern digital ECG devices but is also capable of extracting all necessary information from PDF files (both scanned hard copies and digitally generated PDFs) (see Figure 1). By using different techniques including adaption of open source libraries for vectorisation of image data, and modern computer vision technologies, such as optical character recognition (OCR), our pipeline is able to flexibly process data from different recording devices and read both data in PDF format and data from native digital devices delivered in XML. The processed files from various sources are either saved as a common and easily accessible CSV file format, or are processed directly with deep learning models (see Figure 2).

Results

The developed data-pipeline was validated using data from a set of 113 12-lead ECGs for which data was available in multiple formats. Each format dataset was separately processed by our pipeline and then used for training and validation of a deep learning architecture for myocardial scar detection based on raw ECG signals. The quality of the extraction process by our pipeline was assessed by the respective deep learning models with their prediction capability depicted by receiver operator characteristic analyses (ROC). Comparing the benchmark model that was generated from XML data against a model that was purely trained on PDF data processed by the pipeline shows that both models produced comparable results, reaching area under the curve (AUC) values of 0:79±0:10 (XML) and 0:83±0:07 (PDF).

Conclusion

The data pipeline facilitates acceleration of ECG-based AI research and application of AI algorithms by providing access to ECG data irrespective of the format of the stored ECG. Future work will focus on independent validation as well as expanding this pipeline to include additional ECG types.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Flexi Funds by Forschungscampus Mittelhessen

On the importance of representative datasets in ECG-based artificial intelligence

Background/Introduction

ECG-based artificial intelligence (AI) is an emerging field in digital cardiology. Training on diseased records vs. healthy controls is common practice. We aimed to evaluate if such an approach can lead to unwanted behaviour in real-world settings and thus unnecessarily reduce diagnostic precision of the developed AI model.

Purpose

Several studies have shown that deep neural networks are able to exceed performance of medical experts. However, when these models are applied to different cohorts, results vary strongly. We hypothesise that this is because the datasets used for training were not representative for the target population.

Methods

Based on the public ECG database PTB-XL we sampled three distinct subsets of n=150 records representing ECG groups labelled for diagnoses 'old myocardial infarction' (M), 'normal ECG' (N), or 'other cardiac abnormality' (O). These groups were combined to three datasets ([M, N] (n=300), [M, O] (n=300), [M, N, O] (n=450)), representing different approaches to data sampling. On each dataset, we trained a separate but equally structured deep neural network using 100-fold bootstrapping. The diagnostic performance of each model was validated on unseen data from all datasets with sensitivity, specificity and area under the receiver operator characteristic curve.

Results

Evaluation of the three differently trained models shows best diagnostic performance on the M vs. N records and worst on the M vs. O records. However, in the out-of-dataset setting, the best-performing model (trained on [M, N]) shows weaker performance on the [M, N, O] and [M, O] datasets. Sensitivity for the same model remained equal, as identical M records were used throughout corresponding bootstrapping folds. Detailed results are presented in Table 1.

Conclusions

Our results suggest that the model trained on a dataset including only diseased records vs. healthy controls [M, N] learned to recognise healthy (N) instead of diseased (M) records, which explains why it performed poorly on datasets including records showing other cardiac abnormalities (O). Such behaviour is a common problem in AI and requires special attention in dataset sampling. For small cohorts, it is tempting to increase the amount of training data by using healthy controls. However, we have shown that this can be a poor option, since classifiers can more easily rely on features that are not actually related to the target disease. Training and validation of classifiers should therefore be performed on representative datasets that are as close as possible to the target population.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Forschungscampus Mittelhessen, Flexi Funds Table 1

Predicting mortality in cardiovascular patients using electrocardiogram data and artificial intelligence

Background/Introduction

The electrocardiogram (ECG) is an ubiquitously used non-invasive tool for diagnosis and risk prediction in cardiology, granting deep extensive insights into the heart. Artificial intelligence (AI) is a modern resource allowing the processing of vast complex datasets in a way that is comparable to humans. Risk stratification in cardiovascular patients is mainly based on scoring systems, such as the ESC-SCORE, relying on traditional risk variables like cholesterol levels or arterial hypertension, rather than actual cardiac structure and function. Goal of this project was to predict mortality using AI in patients with cardiovascular risk based on the current cardiac situation represented by a standard 12-lead ECG recording.

Methods

The study population is based on an ongoing registry that started in 2010 and enrolled patients scheduled for an invasive coronary angiography due to suspected chronic coronary syndrome. Data of the following study patients were analysed: enrolment within the first two study years with available long-term follow-up data on the outcome measure overall mortality, availability of an ECG at admission without pacemaker stimulation and availability of all variables needed to calculate the ESC-SCORE (in the version weighed for a German population) as comparison. This led to a cohort of 720 patients, of whom 70 died within the follow-up period. Information on presence of a relevant coronary artery disease (CAD) was available for all patients, to differentiate between primary and secondary prevention. A deep learning architecture that was previously developed to detect myocardial scar in raw ECG time-series data was used. This model was trained with 1400 ECG recordings, from the publicly available PTB-XL dataset with 700 of those ECGs labelled for acute, recent or old myocardial infarction while 700 were labelled as healthy. This pre-trained model was then applied to our study cohort to predict long-term mortality based on a single 12-lead ECG obtained at admission.

Results

For mortality prediction in patients without CAD (primary prevention) the AI model compares to the ESC-SCORE with an AUROC of 0.606 vs 0.584. For CAD patients (secondary prevention) the AI model compares with an AUROC of 0.612 vs 0.658. Detailed results are presented in Table 1.

Conclusion(s)

Our data underlines the potential of an AI based approach, predicting mortality in cardiovascular patients using only single 12-lead ECG recordings. Additionally, our model achieved similar predictive information to established risk classification systems, such as the ESC-SCORE. Since data acquisition is still ongoing, we will continue to improve our model. In future work training AI to specifically predict mortality while also exploring explainable AI could lead to breakthrough findings in ECG interpretation.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): FlexiFunds by Forschungscampus Mittelhessen

Detecting a broader spectrum of cardiac pathologies in electrocardiogram data by applying a deep neural network designed to detect a specific cardiac disease

Background/Introduction

The electrocardiogram (ECG) is a widely used and inexpensive tool that provides extensive insights into the cardiac structure and function. Artificial intelligence (AI) algorithms, especially deep learning (DL) models, are efficient computer based instruments with which large and complex datasets can be processed for identification of e.g. specific diseases. PhysioNet is a NIH research resource for complex signals including a large amount of labelled ECG time-series data. Our aim was to evaluate the diagnostic performance of an AI architecture developed to detect a specific cardiac pathology in a large ECG data set including a broad range of cardiac abnormalities.

Methods

The PhysioNet ECG dataset provided as part of the PhysioNet Challenge 2020 consists of five distinct databases with a total of 43100 12-Lead ECG recordings of varying length stemming from patients from China, Russia, Europe and the United States. Each ECG recording is annotated with diagnoses based on a set of 111 possible labels, which express either a cardiac pathology, e.g. atrial flutter or anterior wall ischemia, or unspecific changes in the ECG, e.g. a prolonged qt interval or low qrs voltages. Based on these labels we defined 10 groups merging PhysioNet labels describing related cardiac abnormalities (see Table 1). We adapted a recently published DL model which used raw ECG time-series data of all 12-leads rather than extracted features as model input. This DL model was adapted to the larger number of output variables and then trained on 80% (n=34480 ECGs) of the PhysioNet dataset. The remaining 20% (n=8620 ECGs) of the PhysioNet dataset were used to evaluate the diagnostic performance of the AI model. Sensitivities, specificities and the areas under the receiver operator characteristic curves (AUROC) were used as performance metrices.

Results

The AI model, that was initially designed to detect a specific cardiac pathology, performed well in the large PhysioNet dataset providing AUROCs ranging from 0.78 to 0.95 to detect the defined 10 cardiac abnormality groups. Interestingly, the AI model was able to detect disease groups with changes in the chronological sequence of the ECG, e.g. arrhythmia, with comparable precision as disease groups associated primarily with changes in the ECG amplitude like e.g. ischemia. Detailed results are presented in Table 2.

Conclusion(s)

Our evaluation shows that an AI model that uses raw ECG time-series data rather than extracted features as model input can be easily transferred to other large datasets with different prediction variables. This might also serve as a proof of concept that raw data instead of pre-selected features should be used as model input if developing AI applications for medical use cases.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): FlexiFunds by Forschungscampus Mittelhessen

Investigating the Association of Wallerian Degeneration and Diaschisis After Ischemic Stroke With BOLD Cerebrovascular Reactivity

Introduction

Wallerian degeneration and diaschisis are considered separate remote entities following ischemic stroke. They may, however, share common neurophysiological denominators, since they are both related to disruption of fiber tracts and brain atrophy over time. Therefore, with advanced multimodal neuroimaging, we investigate Wallerian degeneration and its association with diaschisis.

Methods

In order to determine different characteristics of Wallerian degeneration, we conducted examinations on seventeen patients with chronic unilateral ischemic stroke and persisting large vessel occlusion, conducting high-resolution anatomical magnetic resonance imaging (MRI) and blood oxygenation-level dependent cerebrovascular reactivity (BOLD-CVR) tests, as well as Diamox ¹⁵(O)–H₂O–PET hemodynamic examinations. Wallerian degeneration was determined using a cerebral peduncle asymmetry index (% difference of volume of ipsilateral and contralateral cerebral peduncle) of more than two standard deviations away from the average of age-matched, healthy subjects (Here a cerebral peduncle asymmetry index > 11%). Diaschisis was derived from BOLD-CVR to assess the presence of ipsilateral thalamus diaschisis and/or crossed cerebellar diaschisis.

Results

Wallerian degeneration, found in 8 (47%) subjects, had a strong association with ipsilateral thalamic volume reduction (r² = 0.60) and corticospinal-tract involvement of stroke (p < 0.001). It was also associated with ipsilateral thalamic diaschisis (p = 0.021), No cerebral peduncular hemodynamic differences were found in patients with Wallerian degeneration. In particular, no CBF decrease or BOLD-CVR impairment was found.

Conclusion

We show a strong association between Wallerian degeneration and ipsilateral thalamic diaschisis, indicating a structural pathophysiological relationship.

Predictors for affected stroke territory and outcome of acute stroke treatments are different for posterior versus anterior circulation stroke

Distinct patient characteristics have been proposed for ischaemic stroke in the anterior versus posterior circulation. However, data on functional outcome according to stroke territory in patients with acute stroke treatment are conflicting and information on outcome predictors is scarce. In this retrospective study, we analysed functional outcome in 517 patients with stroke and thrombolysis and/or thrombectomy treated at the University Hospital Zurich. We compared clinical factors and performed multivariate logistic regression analyses investigating the effect of outcome predictors according to stroke territory. Of the 517 patients included, 80 (15.5%) suffered a posterior circulation stroke (PCS). PCS patients were less often female (32.5% vs. 45.5%, p = 0.031), received thrombectomy less often (28.7% vs. 48.3%, p = 0.001), and had lower median admission NIHSS scores (5 vs. 10, p < 0.001) as well as a better median three months functional outcome (mRS 1 vs. 2, p = 0.010). Predictors for functional outcome were admission NIHSS (OR 0.864, 95% CI 0.790–0.944, p = 0.001) in PCS and age (OR 0.952, 95% CI 0.935–0.970, p < 0.001), known symptom onset (OR 1.869, 95% CI 1.111–3.144, p = 0.018) and admission NIHSS (OR 0.840, 95% CI 0.806–0.876, p < 0.001) in ACS. Acutely treated PCS and ACS patients differed in their baseline and treatment characteristics. We identified specific functional outcome predictors of thrombolysis and/or thrombectomy success for each stroke territory.

Measuring child functioning: Assessing correlation and agreement between caregiver and child responses at the Iganga-Mayuge health and demographic surveillance site in Uganda

INTRODUCTION

Empirical data is scare on assessment of concordance between caregiver-child responses on child functioning.

OBJECTIVE

To assess correlation and agreement between children (11-17 years old) and their caregivers' responses to the UNICEF/Washington Group Child Functioning Module (CFM) at the Iganga-Mayuge Health and Demographic Surveillance Site (IM-HDSS) in Uganda.

METHODS

CFM with 24-questions corresponding to 13 domains of functioning was administered to children between 11 and 17 years of age and their caregivers. Descriptive analyses of the child/caregiver responses were conducted. Correlation and agreement between caregiver and child responses were assessed.

RESULTS

Of the 217 caregiver/child pairs eligible for this study, 181 pairs agreed to participate (83.4%). The mean age of children was 13.9 ± 1.9 years, and 56.4% were males. Cronbach's alpha was 0.892 and 0.886 for the caregiver and child versions of CFM respectively, showing good internal consistency in both. There was a significant overall agreement between mean score of caregiver (5.36 ± 5.63 out of 39) and child (5.45 ± 5.34) pairs. Spearman's rank correlation between the pairs was 0.806 (strong positive correlation). Bland-Altman plots for CFM scores showed greater agreement between caregiver and child at lower scores. Percentage agreement between the pairs for overall disability was greater for mild (83.53%) and moderate (79.37%) categories as compared to the severe (66.67%) category. There was substantial agreement (kappa 0.623) for overall disability between the pairs.

CONCLUSION

This study indicates that there is significant correlation and agreement between self-reported caregiver-child pair responses, opening the way for considering children as CFM respondents, when possible.

Embolization of tumor cells is rare in patients with systemic cancer and cerebral large vessel occlusion

BACKGROUND AND PURPOSE

Stroke is a dreaded complication in patients with cancer. Besides paraneoplastic coagulopathy, chemotherapy, radiotherapy and tumor-directed invasive procedures, circulating cancer cells may contribute to thrombus formation and embolic stroke. However, the incidence of tumor cells within the blood clots of cancer patients with stroke is unknown and the role of circulating tumor cells in the formation of cerebrovascular thrombi remains unclear.

METHODS

All patients who had undergone cerebrovascular thrombectomy at the University Hospital Zurich between 2014 and 2017 were screened for history of cancer. Clinical information was retrieved from the local stroke registry and the electronic charts and thrombi underwent a thorough histopathological re-review.

RESULTS

Thirty-two of 182 patients (18%) with thrombectomy had a history of cancer. The majority of patients had advanced stage cancer. However, even after extensive histopathological re-review, only one specimen revealed tumor cells in the thrombus: a 75-year-old patient with acute occlusion of the middle cerebral artery who had been diagnosed with non-small-cell lung cancer 8.1 months prior to stroke.

CONCLUSIONS

The presence of cancer cells in clots from cerebrovascular thrombectomy, indicative of a direct involvement of circulating tumor cells in the causation of stroke, is rare.

Which orthopaedic trauma patients are likely to refuse to participate in a clinical trial? A latent class analysis

OBJECTIVE

The study aimed to assess systematic differences in the characteristics of patients that consented for the trial compared with the broader pool of eligible patients in a large, pragmatic orthopaedic trauma trial.

DESIGN

A retrospective observational study performed from April 2017 to March 2018.

SETTING

Academic trauma centre in Baltimore, USA.

PARTICIPANTS

There were 642 eligible adult trial participants with an operative fracture to the appendicular skeleton and were indicated for blood clot prophylaxis. The median age of the sample was 50 years (IQR: 31-63), and 60% were male.

PRIMARY OUTCOME MEASURE

The primary outcome was the refusal to enrol in the trial. Demographic and injury covariates were included in iterations of latent class models. The final model was selected based on a minimum Bayesian information criterion.

RESULTS

The final model identified three clusters with five covariates predictive of cluster membership (age, neighbourhood-based socioeconomic status, alcohol use, multiple fractures, multiple surgeries). The three clusters were associated with 22% (Cluster 1), 38% (Cluster 2) and 62% (Cluster 3) refusal rates, respectively. Members of Cluster 3 (n=84) were most commonly between 66 and 80 years of age (49% vs 6% (Cluster 1) and 21% (Cluster 2)), of high neighbourhood-based socioeconomic status (85% vs 63% (Cluster 1) and 8% (Cluster 2)), with isolated fractures (100% vs 80% (Cluster 1) and 92% (Cluster 2)), and were less likely to have multiple surgeries compared with the other clusters (28% vs 47% (Cluster 1) and 35% (Cluster 2)).

CONCLUSION

In this study, the likelihood of refusing to participate in the trial ranged from 22% to 62% in the three identified clusters. Elderly age, high socioeconomic status, and less severe injuries defined the cluster that was most likely to refuse trial participation.

TRIAL REGISTRATION NUMBER

NCT02984384.

Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Importance

Numerous studies have demonstrated that long-term outcomes after orthopedic trauma are associated with psychosocial and behavioral health factors evident early in the patient's recovery. Little is known about how to identify clinically actionable subgroups within this population.

Objectives

To examine whether risk and protective factors measured at 6 weeks after injury could classify individuals into risk clusters and evaluate whether these clusters explain variations in 12-month outcomes.

Design, Setting, and Participants

A prospective observational study was conducted between July 16, 2013, and January 15, 2016, among 352 patients with severe orthopedic injuries at 6 US level I trauma centers. Statistical analysis was conducted from October 9, 2017, to July 13, 2018.

Main Outcomes and Measures

At 6 weeks after discharge, patients completed standardized measures for 5 risk factors (pain intensity, depression, posttraumatic stress disorder, alcohol abuse, and tobacco use) and 4 protective factors (resilience, social support, self-efficacy for return to usual activity, and self-efficacy for managing the financial demands of recovery). Latent class analysis was used to classify participants into clusters, which were evaluated against measures of function, depression, posttraumatic stress disorder, and self-rated health collected at 12 months.

Results

Among the 352 patients (121 women and 231 men; mean [SD] age, 37.6 [12.5] years), latent class analysis identified 6 distinct patient clusters as the optimal solution. For clinical use, these clusters can be collapsed into 4 groups, sorted from low risk and high protection (best) to high risk and low protection (worst). All outcomes worsened across the 4 clinical groupings. Bayesian analysis shows that the mean Short Musculoskeletal Function Assessment dysfunction scores at 12 months differed by 7.8 points (95% CI, 3.0-12.6) between the best and second groups, by 10.3 points (95% CI, 1.6-20.2) between the second and third groups, and by 18.4 points (95% CI, 7.7-28.0) between the third and worst groups.

Conclusions and Relevance

This study demonstrates that during early recovery, patients with orthopedic trauma can be classified into risk and protective clusters that account for a substantial amount of the variance in 12-month functional and health outcomes. Early screening and classification may allow a personalized approach to postsurgical care that conserves resources and targets appropriate levels of care to more patients.

Postgraduate training for trauma prevention, injury surveillance and research, Uganda

Problem

The burden of trauma and injuries in Uganda is substantial and growing. Two important gaps that need addressing are the shortage of trained people and a lack of national data on noncommunicable diseases and their risk factors in Uganda.

Approach

We developed and implemented a new track within an existing master of public health programme, aimed at developing graduate-level capacity and promoting research on key national priorities for trauma and injuries. We also offered training opportunities to a wider audience and set up a high-level national injury forum to foster national dialogue on addressing the burden of trauma, injuries and disability.

Local setting

The Chronic Consequences of Trauma, Injuries and Disability in Uganda programme was implemented in 2012 at Makerere University School of Public Health in Kampala, Uganda, in conjunction with Johns Hopkins Bloomberg School of Public Health in Baltimore, United States of America.

Relevant changes

Over the years 2012 to 2017 we supported four cohorts of master’s students, with a total of 14 students (9 females and 5 males; mean age 30 years). Over 1300 individuals participated in workshops and seminars of the short-term training component of the programme. The forum hosted three research symposia and two national injury forums.

Lessons learnt

Institutional support and collaborative engagement is important for developing and implementing successful capacity development programmes. Integration of training components within existing academic structures is key to sustainability. Appropriate mentorship for highly motivated and talented students is valuable for guiding students through the programme.

Task-Specific Motor Rehabilitation Therapy After Stroke Improves Performance in a Different Motor Task: Translational Evidence

While the stroke survivor with a motor deficit strives for recovery of all aspects of daily life movements, neurorehabilitation training is often task specific and does not generalize to movements other than the ones trained. In rodent models of post-stroke recovery, this problem is poorly investigated as the training task is often the same as the one that measures motor function. The present study investigated whether motor training by pellet reaching translates into enhancement of different motor functions in rats after stroke. Adult rats were subjected to 60-min middle cerebral artery occlusion (MCAO). Five days after stroke, animals received either training consisting of 7 days of pellet reaching with the affected forelimb (n = 18) or no training (n = 18). Sensorimotor deficits were assessed using the sticky tape test and a composite neuroscore. Infarct volumes were measured by T2-weighted MRI on day 28. Both groups of rats showed similar lesion volume and forelimb impairment after stroke. Trained animals improved in the sticky tape test after day 7 post-stroke reaching peak performance on day 14. More reaching attempts during rehabilitation were associated with a better performance in the sticky tape removal time. Task-oriented motor training generalizes to other motor functions after experimental stroke. Training intensity correlates with recovery.

A new screening instrument for disability in low-income and middle-income settings: application at the Iganga-Mayuge Demographic Surveillance System (IM-DSS), Uganda

OBJECTIVE

The measurement of disability in low-income countries is recognised as a major deficiency in health information systems, especially in Africa. The Iganga and Mayuge Demographic Surveillance System (IM-DSS) in Uganda provides a special opportunity to develop population-based data to inform national health policies and evaluate innovations in assessing the burden of disability in Uganda. In this study, we apply a new instrument to screen for physical disabilities at the IM-DSS. The study utilised a modified version of the short set of questions proposed by the Washington Group on Disability Statistics. The instrument was applied at the household level and information was collected on all individuals over the age of 5, who were residents of the IM-DSS.

SETTING

The study was based at the IM-DSS, which covers the parts of Iganga and Mayuge districts in Eastern Uganda.

PARTICIPANTS

57,247 individuals were included in the survey, with 51% of the study population being women.

PRIMARY OUTCOMES

Activity limitations

RESULTS

The overall prevalence of physical disability at the IM-DSS was 9.4%, with vision being the most common type of difficulty reported in this population, and communication being least prevalent. Disability was less likely to be observed among males than their female counterparts (OR 0.75; 95% CI 0.71 to 0.81; p<0.001). Statistically significant associations were found between disability and increasing age, as well as disability and decreasing household wealth status.

CONCLUSIONS

This study shows that the modified short set of questions can be readily applied in a DSS setting to obtain estimates on the prevalence and types of disability at the population level. This instrument could be adapted for use to screen for disability in other LMIC settings, providing estimates that are comparable across different global regions and populations.

[Cognitive deficits and dementia in patients with multiple sclerosis: status quo and open questions]

Multiple sclerosis (MS) is the most common chronic immune-mediated disease which goes along with serious somatic and psychic symptoms. Whereas recent research rather focussed on the neurological symptoms of MS, there is nowadays an increasing interest among researchers in psychological symptoms of the disease. It is known that about half of the MS patients suffer from cognitive deficits, and that cognitive decline has a disadvantageous impact on lifestyle and quality of life in affected persons. So far, cognitive deficits in subjects with MS have been described as rather isolated, specific cognitive disturbances with otherwise intact intellectual abilities, while global deterioration of mental skills in terms of dementia is considered as being rather rare. In the present article, we describe cognitive domains which are typically impaired in subjects with MS and reflect on the question if the diagnosis of dementia might be underrepresented in MS patients due to several reasons (e.g., progression of cognitive deficits in the course of the disorder, adhering very closely to memory deficits as cardinal symptom of dementia, lack of longitudinal studies of cognitive performance levels). Furthermore, we recommend a multistage neuropsychological diagnostic procedure for clinical practice which aims at diagnosing cognitive deficits already in early stages of the illness. In so doing, practitioners may be able to offer adequate therapies to affected persons in all stages of the disorder in order to improve psychosocial functional levels.

A matched-pair cluster-randomized trial of guided care for high-risk older patients

BACKGROUND

Patients at risk for generating high health care expenditures often receive fragmented, low-quality, inefficient health care. Guided Care is designed to provide proactive, coordinated, comprehensive care for such patients.

OBJECTIVE

We hypothesized that Guided Care, compared to usual care, produces better functional health and quality of care, while reducing the use of expensive health services.

DESIGN

32-month, single-blind, matched-pair, cluster-randomized controlled trial of Guided Care, conducted in eight community-based primary care practices.

PATIENTS

The "Hierarchical Condition Category" (HCC) predictive model was used to identify high-risk older patients who were insured by fee-for-service Medicare, a Medicare Advantage plan or Tricare. Patients with HCC scores in the highest quartile (at risk for generating high health care expenditures during the coming year) were eligible to participate.

INTERVENTION

A registered nurse collaborated with two to five primary care physicians in providing eight services to participants: comprehensive assessment, evidence-based care planning, proactive monitoring, care coordination, transitional care, coaching for self-management, caregiver support, and access to community-based services.

MAIN MEASURES

Functional health was measured using the Short Form-36. Quality of care and health services utilization were measured using the Patient Assessment of Chronic Illness Care and health insurance claims, respectively.

KEY RESULTS

Of the eligible patients, 904 (37.8 %) gave written consent to participate; of these, 477 (52.8 %) completed the final interview, and 848 (93.8 %) provided complete claims data. In intention-to-treat analyses, Guided Care did not significantly improve participants' functional health, but it was associated with significantly higher participant ratings of the quality of care (difference = 0.27, 95 % CI = 0.08-0.45) and 29 % lower use of home care (95 % CI = 3-48 %).

CONCLUSIONS

Guided Care improves high-risk older patients' ratings of the quality of their care, and it reduces their use of home care, but it does not appear to improve their functional health.

Who participates in chronic disease self-management (CDSM) programs? Differences between participants and nonparticipants in a population of multimorbid older adults

BACKGROUND

Self-care management is recognized as a key component of care for multimorbid older adults; however, the characteristics of those most likely to participate in Chronic Disease Self-Management (CDSM) programs and strategies to maximize participation in such programs are unknown.

OBJECTIVES

To identify individual factors associated with attending CDSM programs in a sample of multimorbid older adults.

RESEARCH DESIGN

Participants in the intervention arm of a matched-pair cluster-randomized controlled trial of the Guided Care model were invited to attend a 6-session CDSM course. Logistic regression was used to identify factors independently associated with attendance.

SUBJECTS

All subjects (N = 241) were aged 65 years or older, were at high risk for health care utilization, and were not homebound.

MEASURES

Baseline information on demographics, health status, health activities, and quality of care was available for CDSM participants and nonparticipants. Participation was defined as attendance at 5 or more CDSM sessions.

RESULTS

A total of 22.8% of multimorbid older adults who were invited to CDSM courses participated in 5 or more sessions. Having better physical health (odds ratio [95% confidence interval] = 2.3 [1.1-4.8]) and rating one's physician poorly on support for patient activation (odds ratio [95% confidence interval] = 2.8 [1.3-6.0]) were independently associated with attendance.

CONCLUSIONS

Multimorbid older adults who are in better physical health and who are dissatisfied with their physicians' support for patient activation are more likely to participate in CDSM courses.

The effects of guided care on the perceived quality of health care for multi-morbid older persons: 18-month outcomes from a cluster-randomized controlled trial

BACKGROUND

The quality of health care for older Americans with chronic conditions is suboptimal.

OBJECTIVE

To evaluate the effects of "Guided Care" on patient-reported quality of chronic illness care.

DESIGN

Cluster-randomized controlled trial of Guided Care in 14 primary care teams.

PARTICIPANTS

Older patients of these teams were eligible to participate if, based on analysis of their recent insurance claims, they were at risk for incurring high health-care costs during the coming year. Small teams of physicians and their at-risk older patients were randomized to receive either Guided Care (GC) or usual care (UC).

INTERVENTION

"Guided Care" is designed to enhance the quality of health care by integrating a registered nurse, trained in chronic care, into a primary care practice to work with 2-5 physicians in providing comprehensive chronic care to 50-60 multi-morbid older patients.

MEASUREMENTS

Eighteen months after baseline, interviewers blinded to group assignment administered the Patient Assessment of Chronic Illness Care (PACIC) survey by telephone. Logistic and linear regression was used to evaluate the effect of the intervention on patient-reported quality of chronic illness care.

RESULTS

Of the 13,534 older patients screened, 2,391 (17.7%) were eligible to participate in the study, of which 904 (37.8%) gave informed consent and were cluster-randomized. After 18 months, 95.3% and 92.2% of the GC and UC recipients who remained alive and eligible completed interviews. Compared to UC recipients, GC recipients had twice greater odds of rating their chronic care highly (aOR = 2.13, 95% CI = 1.30-3.50, p = 0.003).

CONCLUSION

Guided Care improves self-reported quality of chronic health care for multi-morbid older persons.

Caregiving and chronic care: the guided care program for families and friends

BACKGROUND

The Guided Care Program for Families and Friends (GCPFF) is one component of "Guided Care" (GC), a model of primary care for chronically ill older adults that is facilitated by a registered nurse who has completed a supplemental educational curriculum.

METHODS

The GCPFF melds support for family caregivers with the delivery of coordinated and comprehensive chronic care and seeks to improve the health and well-being of both patients and their family caregivers. The GCPFF encompasses (a) an initial meeting between the nurse and the patient's primary caregiver, (b) education and referral to community resources, (c) ongoing "coaching," (d) a six-session group Caregiver Workshop, and (e) monthly Support Group meetings, all facilitated by the patient's GC nurse.

RESULTS

A cluster-randomized controlled trial of GC is underway in 14 primary care physician teams. Of 904 consented patients, 450 (49.8%) identified a primary caregiver; 308 caregivers met eligibility criteria, consented to participate, and completed a baseline interview. At 6-month follow-up, intervention group caregivers' mean Center for Epidemiological Studies Depression (CESD) and Caregiver Strain Index (CSI) scores were respectively 0.97 points (p = .14) and 1.14 points (p = .06) lower than control group caregivers'. Among caregivers who provided more than 14 hours of weekly assistance at baseline, intervention group caregivers' mean CESD and CSI scores were respectively 1.23 points (p = .20) and 1.83 points (p = .04) lower than control group caregivers'.

CONCLUSIONS

The GCPFF may benefit family caregivers of chronically ill older adults. Outcomes will continue to be monitored at 18-months follow-up.