Quality Measurement for Soft Tissue Sarcomas in Germany: First Results of the Certified Sarcoma Centres.

INTRODUCTION: Soft tissue sarcomas (STSs) are rare diseases. A high level of standardization and centralization was lacking in Germany until 2018. METHODS: By developing an evidence-based guideline and a certification system for sarcoma centres, foundations for structured, guideline-based, and centralized sarcoma care were defined. First results of the certified sarcoma centres are presented. RESULTS: The first 3 years of data collection show good results for case volume, presentation rates in pretherapeutic and postoperative tumour boards, psycho-oncological counselling, and study rates. However, other indicators (e.g., preoperative or postoperative radiotherapy for operated high-risk STS without GIST, counselling rates social services) still have potential for improvement. Based on these results, the set of indicators could be further improved. CONCLUSIONS: A sarcoma-specific quality assurance scheme that includes guideline-derived quality indicators was developed. In future, a broader database will allow further insights into sarcoma care in Germany.

[S3 guideline on skin cancer prevention: evaluation and updating procedure]. / S3-Leitlinie Prävention von Hautkrebs: Evaluation und Aktualisierungsprozess.

BACKGROUND: Originally published in 2014, the S3 guideline "Prevention of skin cancer" is the first evidence-based guideline available exclusively for primary and secondary prevention, which summarizes interprofessional consented recommendations for skin cancer risk reduction and early detection. Due to the large number of new publications and expanding focus, an update was deemed necessary. METHODS: After a structured needs assessment, key questions were prioritized. The resulting systematic literature search resulted in a three-stage screening process. Recommendations formulated in working groups were approved in a formal consensus process, taking into account conflicts of interest, and finalized after a 6­week public consultation process. RESULTS: The needs assessment identified "skin cancer screening" (60.1%), "individual risk avoidance behaviors" (44.20%), and "risk factors" (43.48%) as topics of greatest interest. The prioritization phase resulted in 41 new key questions. A total of 22 key issues were re-evaluated in an evidence-based manner using 93 publications. As part of comprehensive guideline restructuring, 61 recommendations were newly developed and 43 were modified. The consultation phase resulted in no changes to recommendations and 33 changes to background material. CONCLUSION: The identified need for change resulted in extensive modification and redrafting of recommendations. As the target group "nononcology patients" cannot be identified via cancer registries or certification systems, no quality indicators can be derived from the guideline. To transfer the guideline to health care, innovative, addressee-specific concepts are required, which will be discussed and implemented during the preparation of the patient guideline.

Linking profiles of pathway activation with clinical motor improvements - A retrospective computational study.

BACKGROUND: Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity. OBJECTIVE: The aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement. METHODS: We used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways. RESULTS: The obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort. CONCLUSION: Pathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.

StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming.

BACKGROUND: Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. OBJECTIVE: We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. METHODS: Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. RESULTS: Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10-10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. CONCLUSION: We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

CLOVER-DBS: Algorithm-Guided Deep Brain Stimulation-Programming Based on External Sensor Feedback Evaluated in a Prospective, Randomized, Crossover, Double-Blind, Two-Center Study.

BACKGROUND: Recent technological advances in deep brain stimulation (DBS) (e.g., directional leads, multiple independent current sources) lead to increasing DBS-optimization burden. Techniques to streamline and facilitate programming could leverage these innovations. OBJECTIVE: We evaluated clinical effectiveness of algorithm-guided DBS-programming based on wearable-sensor-feedback compared to standard-of-care DBS-settings in a prospective, randomized, crossover, double-blind study in two German DBS centers. METHODS: For 23 Parkinson's disease patients with clinically effective DBS, new algorithm-guided DBS-settings were determined and compared to previously established standard-of-care DBS-settings using UPDRS-III and motion-sensor-assessment. Clinical and imaging data with lead-localizations were analyzed to evaluate characteristics of algorithm-derived programming compared to standard-of-care. Six different versions of the algorithm were evaluated during the study and 10 subjects programmed with uniform algorithm-version were analyzed as a subgroup. RESULTS: Algorithm-guided and standard-of-care DBS-settings effectively reduced motor symptoms compared to off-stimulation-state. UPDRS-III scores were reduced significantly more with standard-of-care settings as compared to algorithm-guided programming with heterogenous algorithm versions in the entire cohort. A subgroup with the latest algorithm version showed no significant differences in UPDRS-III achieved by the two programming-methods. Comparing active contacts in standard-of-care and algorithm-guided DBS-settings, contacts in the latter had larger location variability and were farther away from a literature-based optimal stimulation target. CONCLUSION: Algorithm-guided programming may be a reasonable approach to replace monopolar review, enable less trained health-professionals to achieve satisfactory DBS-programming results, or potentially reduce time needed for programming. Larger studies and further improvements of algorithm-guided programming are needed to confirm these results.

Quality assurance in melanoma care: guideline-based quality indicators for melanoma - implementation, evaluation and update process.

BACKGROUND: In 2018, an update of the German evidence-based (S3) guidelines "Diagnosis, Therapy and Follow-up of Melanoma" first issued in 2013 was published under the auspices of the German Guideline Program in Oncology. The update also included a revision of existing guideline-based quality indicators (QIs). PATIENTS AND METHODS: Using a standardized multi-step process, the guideline-derived QIs were revised by a multidisciplinary, interprofessional working group based on the strong (level A) recommendations contained in the guideline update as well as on a systematic literature search for international indicators and on the outcomes of existing QIs as reported by certified German skin cancer centers. RESULTS: Based on the original set of twelve guideline-based QIs agreed upon in 2013, the working group developed an updated set containing nine indicators. Four QIs were kept unchanged; two were modified; two were removed; and three new QIs were added. Unlike 2013, the working group was now able to incorporate the outcomes of QIs previously implemented at the various skin cancer centers. CONCLUSIONS: Close cooperation between guideline group and certification commission allows for the implementation of guideline-based QIs in cancer care. Measured outcomes form the basis for updating both the guidelines and the QI development process. They provide information about the care of cancer patients in a real-world setting as well as on guideline adherence and the feasibility of QIs themselves. This is a dynamic process that can be described in a transparent manner and that requires regular updating.

Left Prefrontal Connectivity Links Subthalamic Stimulation with Depressive Symptoms.

OBJECTIVE: Subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease (PD) not only stimulates focal target structures but also affects distributed brain networks. The impact this network modulation has on non-motor DBS effects is not well-characterized. By focusing on the affective domain, we systematically investigate the impact of electrode placement and associated structural connectivity on changes in depressive symptoms following STN-DBS, which have been reported to improve, worsen, or remain unchanged. METHODS: Depressive symptoms before and after STN-DBS surgery were documented in 116 patients with PD from 3 DBS centers (Berlin, Queensland, and Cologne). Based on individual electrode reconstructions, the volumes of tissue activated (VTAs) were estimated and combined with normative connectome data to identify structural connections passing through VTAs. Berlin and Queensland cohorts formed a training and cross-validation dataset used to identify structural connectivity explaining change in depressive symptoms. The Cologne data served as the test-set for which depressive symptom change was predicted. RESULTS: Structural connectivity was linked to depressive symptom change under STN-DBS. An optimal connectivity map trained on the Berlin cohort could predict changes in depressive symptoms in Queensland patients and vice versa. Furthermore, the joint training-set map predicted changes in depressive symptoms in the independent test-set. Worsening of depressive symptoms was associated with left prefrontal connectivity. INTERPRETATION: Fibers connecting the electrode with left prefrontal areas were associated with worsening of depressive symptoms. Our results suggest that for the left STN-DBS lead, placement impacting fibers to left prefrontal areas should be avoided to maximize improvement of depressive symptoms. ANN NEUROL 2020;87:962-975.

Deep brain stimulation induced normalization of the human functional connectome in Parkinson's disease.

Neuroimaging has seen a paradigm shift away from a formal description of local activity patterns towards studying distributed brain networks. The recently defined framework of the 'human connectome' enables global analysis of parts of the brain and their interconnections. Deep brain stimulation (DBS) is an invasive therapy for patients with severe movement disorders aiming to retune abnormal brain network activity by local high frequency stimulation of the basal ganglia. Beyond clinical utility, DBS represents a powerful research platform to study functional connectomics and the modulation of distributed brain networks in the human brain. We acquired resting-state functional MRI in 20 patients with Parkinson's disease with subthalamic DBS switched on and off. An age-matched control cohort of 15 subjects was acquired from an open data repository. DBS lead placement in the subthalamic nucleus was localized using a state-of-the art pipeline that involved brain shift correction, multispectral image registration and use of a precise subcortical atlas. Based on a realistic 3D model of the electrode and surrounding anatomy, the amount of local impact of DBS was estimated using a finite element method approach. On a global level, average connectivity increases and decreases throughout the brain were estimated by contrasting on and off DBS scans on a voxel-wise graph comprising eight thousand nodes. Local impact of DBS on the motor subthalamic nucleus explained half the variance in global connectivity increases within the motor network (R = 0.711, P < 0.001). Moreover, local impact of DBS on the motor subthalamic nucleus could explain the degree to how much voxel-wise average brain connectivity normalized towards healthy controls (R = 0.713, P < 0.001). Finally, a network-based statistics analysis revealed that DBS attenuated specific couplings known to be pathological in Parkinson's disease. Namely, coupling between motor thalamus and motor cortex was increased while striatal coupling with cerebellum, external pallidum and subthalamic nucleus was decreased by DBS. Our results show that resting state functional MRI may be acquired in DBS on and off conditions on clinical MRI hardware and that data are useful to gain additional insight into how DBS modulates the functional connectome of the human brain. We demonstrate that effective DBS increases overall connectivity in the motor network, normalizes the network profile towards healthy controls and specifically strengthens thalamo-cortical connectivity while reducing striatal control over basal ganglia and cerebellar structures.

Connectivity Predicts deep brain stimulation outcome in Parkinson disease.

OBJECTIVE: The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. METHODS: A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. RESULTS: In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p < 0.001). This same connectivity profile predicted response in an independent patient cohort (p < 0.01). Structural and functional connectivity were independent predictors of clinical improvement (p < 0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. INTERPRETATION: Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. Ann Neurol 2017;82:67-78.