Clinical features and prognostic factors in adults with brain abscess.

Studies on brain abscess are hampered by single-centre design with limited sample size and incomplete follow-up. Thus, robust analyses on clinical prognostic factors remain scarce. This Danish nationwide, population-based cohort study included clinical details of all adults (≥18 years) diagnosed with brain abscess in the Danish National Patient Registry from 2007 through 2014 and the prospective clinical database of the Danish Study Group of Infections of the Brain covering all Danish departments of infectious diseases from 2015 through 2020. All patients were followed for 6 months after discharge. Prognostic factors for mortality at 6 months after discharge were examined by adjusted modified Poisson regression to compute relative risks with 95% confidence intervals (CI). Among 485 identified cases, the median age was 59 years [interquartile range (IQR 48-67)] and 167 (34%) were female. The incidence of brain abscess increased from 0.4 in 2007 to 0.8 per 100 000 adults in 2020. Immuno-compromise was prevalent in 192/485 (40%) and the clinical presentation was predominated by neurological deficits 396/485 (82%), headache 270/411 (66%), and fever 208/382 (54%). The median time from admission until first brain imaging was 4.8 h (IQR 1.4-27). Underlying conditions included dental infections 91/485 (19%) and ear, nose and throat infections 67/485 (14%), and the most frequent pathogens were oral cavity bacteria (59%), Staphylococcus aureus (6%), and Enterobacteriaceae (3%). Neurosurgical interventions comprised aspiration 356/485 (73%) or excision 7/485 (1%) and was preceded by antibiotics in 377/459 (82%). Fatal outcome increased from 29/485 (6%) at discharge to 56/485 (12%) 6 months thereafter. Adjusted relative risks for mortality at 6 months after discharge was 3.48 (95% CI 1.92-6.34) for intraventricular rupture, 2.84 (95% CI 1.45-5.56) for immunocompromise, 2.18 (95% CI 1.21-3.91) for age >65 years, 1.81 (95% CI 1.00-3.28) for abscess diameter >3 cm, and 0.31 (95% CI 0.16-0.61) for oral cavity bacteria as causative pathogen. Sex, neurosurgical treatment, antibiotics before neurosurgery, and corticosteroids were not associated with mortality. This study suggests that prevention of rupture of brain abscess is crucial. Yet, antibiotics may be withheld until neurosurgery, if planned within a reasonable time period (e.g. 24 h), in some clinically stable patients. Adjunctive corticosteroids for symptomatic perifocal brain oedema was not associated with increased mortality.

Overall survival and causes of death in elderly patients with Hodgkin lymphoma: a Norwegian population-based case-control study.

Elderly Hodgkin Lymphoma (HL) patients are poorly characterized and underrepresented in studies. In this national population-based study, we investigated cause-specific survival using competing-risk analysis in elderly HL patients compared to the normal population. Patients ≥ 60 years diagnosed between 2000-2015 were identified by Cancer Registry of Norway, records reviewed in detail and compared to data from Norwegian Cause of Death Registry for patients and cancer-free controls. Of 492 patients, 81 (17%) were ineligible for treatment directed specifically towards HL, mostly because of an underlying other lymphoma entity, whereas 74 (15%) and 337 (69%) were treated with palliative or curative intent, respectively. Median overall survival in patients ineligible for assessment of HLdirected therapies was 0.5 years (95% confidence interval [CI] 0.4-0.6), and for palliatively and curatively treated patients 0.8 (0.4-1.2) and 9.1 (7.5-10.7) years, respectively. After correction of discrepancies in registry data, with 359 deaths, 108 (30%) died of HL, the most common cause of death. In curatively treated patients, treatment-related mortality was 6.5% and the risk-difference of dying from HL compared to controls was 28% (95% CI 23-33%) after 10 years. These numbers indicate disease control in a majority of elderly patients eligible for curative treatment, compared to risk-differences for death from HL of 59% (48-71%) and 42% (31-53%) after 10 years in the palliative and ineligible groups, respectively. There was an increased risk of dying from hematological malignancies other than HL in all groups, but not from other competing causes of death, showing no excess mortality from long-term treatment complications.

The younger the better: importance of age in treatment of childhood obesity.

Children living with obesity are prevalent worldwide. It is an established finding that many children who start a lifestyle intervention tend to leave prematurely. The aim of this study was to identify characteristics in children with obesity who prematurely leave a lifestyle intervention. The cohort study includes children living with obesity aged 4-17, treated in a Danish family-centered lifestyle intervention between 2014 and 2017. Data were collected from patient records. BMI-SDS was calculated using an external Danish reference population and multivariable regression analysis was used to answer the research question. Of the 159 children included, 64 children who left the intervention within the first 1.5 years were older compared to those who stayed in the intervention (10.2 years ± 2.9 vs 11.5 years ± 3.1, p = 0.005). Older participants (> 66.6th percentile) had a shorter treatment duration (489 days) compared to the youngest (190 days 95% CI: 60; 320, p = 0.005) and middle third (224 days 95% CI: 89; 358, p = 0.001). Additionally, an inverse association was found between duration of treatment and age at baseline (-31 days, 95% CI (-50; -13), p = 0.001).   Conclusion: The risk of leaving a lifestyle intervention prematurely was primarily dependent on the age of the participants, emphasizing the importance of including children early in lifestyle interventions. What is Known: • Lifestyle interventions for childhood obesity that are shorter in duration often lead to short-term weight reductions only. Limited knowledge exists on why some children prematurely leave these interventions. What is New: • This study observes a solid inverse correlation and association between age and time spent in the interventions, when treating childhood obesity. We hereby suggest age as an important determinant for the adherence to lifestyle interventions and emphasize the importance of treatment early in life.

Complications After Volar Locking Plate Fixation of Distal Radius Fractures: A Retrospective Study of 822 Patients.

PURPOSE: With the current routine use of volar locking plates as the preferred surgical treatment option for distal radius fractures, the purpose of this study was to investigate the incidence of postoperative complications following surgery and, second, investigate the correlation between demographic factors and the risk of complications. METHODS: We retrospectively reviewed all patients who had been surgically treated for a distal radius fracture with open reduction and internal fixation using volar plating and screws during a 3-year period. Relevant demographic information and all postoperative complications of the 822 patients eligible for inclusion were recorded, with a mean follow-up time of 2.8 years. RESULTS: We identified an overall complication rate of 12.3% (101 of the 822 patients), with 4.8% defined as experiencing major complications and 7.5% defined as experiencing minor complications. The most frequent were complications that led to hardware removal, observed in 2.7% (n = 22) of the patients; wound-related problems that did not require surgical revision, observed in 2.2% (n = 18) of the patients; and carpal tunnel syndrome, observed in 1.9% (n = 16) of the patients. Binary logistic regression modeling showed no correlation between demographic factors and the risk of complications. CONCLUSIONS: In conclusion, a low overall complication rate of 12.3% was found. Further, 4.8% of the patients experienced a major complication and 7.5% of the patients experienced a minor complication following open reduction and internal fixation using volar plating of distal radius fractures. Age, sex, fracture type, and time from trauma to surgery were not found to be associated with an increased risk of postoperative complications. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic IV.

Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas.

User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents' evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human-computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users. Supplementary Information: The online version contains supplementary material available at 10.1007/s10799-022-00373-9.

Training Thoracic Ultrasound Skills: A Randomized Controlled Trial of Simulation-Based Training versus Training on Healthy Volunteers.

INTRODUCTION: As ultrasound becomes more accessible, the use of point-of-care ultrasound examinations performed by clinicians has increased. Sufficient theoretical and practical skills are prerequisites to integrate thoracic ultrasound into a clinical setting and to use it as supplement in the clinical decision-making. Recommendations on how to educate and train clinicians for these ultrasound examinations are debated, and simulation-based training may improve clinical performance. OBJECTIVES: The aim of this study was to explore the effect of simulation-based training in thoracic ultrasound compared to training on healthy volunteers. METHOD: A total of 66 physicians with no previous experience in thoracic ultrasound completed a training program and assessment of competences from November 2018 to May 2019. After a theoretical session in ultrasound physics, sonoanatomy, and thoracic ultrasound, the physicians were randomized into one of three groups for practical training: (1) simulation-based training, (2) training on a healthy volunteer, or (3) no training (control group). Primary outcome was difference in the clinical performance score after the training period. RESULTS: Using a multiple comparison, ANOVA with Bonferroni correction for multiplicity, there was no statistical significant difference between the two trained groups' performance score: 45.1 points versus 41.9 points (minimum 17 points, maximum 68 points; p = 0.38). The simulation-based training group scored significantly higher than the control group without hands-on training, 36.7 points (p = 0.009). CONCLUSIONS: The use of simulation-based training in thoracic ultrasound does not improve the clinical performance score compared to conventional training on healthy volunteers. As focused, thoracic ultrasound is a relatively uncomplicated practical procedure when taught; focus should mainly be on the theoretical part and the supervised clinical training in a curriculum. However, simulation can be used instead or as an add-on to training on simulated patients.

Weed Classification Using Explainable Multi-Resolution Slot Attention.

In agriculture, explainable deep neural networks (DNNs) can be used to pinpoint the discriminative part of weeds for an imagery classification task, albeit at a low resolution, to control the weed population. This paper proposes the use of a multi-layer attention procedure based on a transformer combined with a fusion rule to present an interpretation of the DNN decision through a high-resolution attention map. The fusion rule is a weighted average method that is used to combine attention maps from different layers based on saliency. Attention maps with an explanation for why a weed is or is not classified as a certain class help agronomists to shape the high-resolution weed identification keys (WIK) that the model perceives. The model is trained and evaluated on two agricultural datasets that contain plants grown under different conditions: the Plant Seedlings Dataset (PSD) and the Open Plant Phenotyping Dataset (OPPD). The model represents attention maps with highlighted requirements and information about misclassification to enable cross-dataset evaluations. State-of-the-art comparisons represent classification developments after applying attention maps. Average accuracies of 95.42% and 96% are gained for the negative and positive explanations of the PSD test sets, respectively. In OPPD evaluations, accuracies of 97.78% and 97.83% are obtained for negative and positive explanations, respectively. The visual comparison between attention maps also shows high-resolution information.

Sustainable weight loss over three years in children with obesity: a pragmatic family-centered lifestyle intervention.

INTRODUCTION: Childhood obesity has psychological consequences and increases the risk of continuous obesity into adulthood, associated with development of non-communicable disease (e.g. type 2 diabetes). Short-term weight loss intervention studies show good results but long-term studies are limited. METHODS: One hundred ninety-nine obese children (4-18 years of age), with a BMI-SDS (standard deviation score) above + 2 SDS were enrolled into a multifactorial family-centered lifestyle intervention study. The children had yearly visits in the outpatient clinic for anthropometrics, blood samples and DXA-scans, and 6-8 meeting with community health workers between these visits. The children followed the intervention up to 3 years. RESULTS: After a follow-up of 26.7 ± 17.5 months a reduction in BMI-SDS of - 0.25 SDS (p < 0.001) was observed. The 57 children who were adherent to the intervention for ≥ 2 years had significantly reduced BMI-SDS compared to the 142 children with shorter intervention (BMI-SDS: - 0.38 ± 0.67 vs. - 0.20 ± 0.50, p = 0.036). All weight loss was accompanied by decrease in fat mass and increase in muscle mass (p < 0.001). CONCLUSION: The intervention was found to induce long-term reduction in BMI-SDS in obese children, with beneficial change in body composition. Children who followed the intervention the longest had the greatest reduction in BMI-SDS. LEVEL OF EVIDENCE: Level III, longitudinal cohort study.

Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks.

Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation is typically unfeasible, the potential of targeted management remains an open research area. In this work, we propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network. Data collection has been performed at four separate experimental plot trial sites over three growing seasons. The method is thoroughly evaluated by predicting the biomass composition of different grass clover mixtures using only an image of the canopy. With a relative biomass clover content prediction of R2 = 0.91, we present new state-of-the-art results across the largely varying sites. Combining the algorithm with an all terrain vehicle (ATV)-mounted image acquisition system, we demonstrate a feasible method for robust coverage and species distribution mapping of 225 ha of mixed crops at a median capacity of 17 ha per hour at 173 images per hectare.

A Novel Locating System for Cereal Plant Stem Emerging Points' Detection Using a Convolutional Neural Network.

Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs. The penalty regions were defined based on fault prediction of the trained model without penalty region assignment. By adding penalty regions to the training, the network's ability to precisely locate emergence points of the cereal plants was enhanced significantly. A coefficient of determination of about 87 percent between the predicted PSEP number of each image and the manually marked one implies the ability of the system to count PSEPs. With regard to the obtained results, it was concluded that the developed model can give a reliable clue about the quality of PSEPs' distribution and the performance of seed drills in fields.

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.

A bioluminescence resonance energy transfer 2 (BRET2) assay for monitoring seven transmembrane receptor and insulin receptor crosstalk.

The angiotensin AT1 receptor is a seven transmembrane (7TM) receptor, which mediates the regulation of blood pressure. Activation of angiotensin AT1 receptor may lead to impaired insulin signaling indicating crosstalk between angiotensin AT1 receptor and insulin receptor signaling pathways. To elucidate the molecular mechanisms behind this crosstalk, we applied the BRET2 technique to monitor the effect of angiotensin II on the interaction between Rluc8 tagged insulin receptor and GFP2 tagged insulin receptor substrates 1, 4, 5 (IRS1, IRS4, IRS5) and Src homology 2 domain-containing protein (Shc). We demonstrate that angiotensin II reduces the interaction between insulin receptor and IRS1 and IRS4, respectively, while the interaction with Shc is unaffected, and this effect is dependent on Gαq activation. Activation of other Gαq-coupled 7TM receptors led to a similar reduction in insulin receptor and IRS4 interactions whereas Gαs- and Gαi-coupled 7TM receptors had no effect. Furthermore, we used a panel of kinase inhibitors to show that angiotensin II engages different pathways when regulating insulin receptor interactions with IRS1 and IRS4. Angiotensin II inhibited the interaction between insulin receptor and IRS1 through activation of ERK1/2, while the interaction between insulin receptor and IRS4 was partially inhibited through protein kinase C dependent mechanisms. We conclude that the crosstalk between angiotensin AT1 receptor and insulin receptor signaling shows a high degree of specificity, and involves Gαq protein, and activation of distinct kinases. Thus, the BRET2 technique can be used as a platform for studying molecular mechanisms of crosstalk between insulin receptor and 7TM receptors.

Designing and Testing a UAV Mapping System for Agricultural Field Surveying.

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

FieldSAFE: Dataset for Obstacle Detection in Agriculture.

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360 ∘ camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates.

Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks.

Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops.

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

Automated detection and recognition of wildlife using thermal cameras.

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3-10 m and an accuracy of 75.2% for an altitude range of 10-20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3-10 of meters and 77.7% in an altitude range of 10-20 m.

MRD-driven treatment with venetoclax-R2 in mantle cell lymphoma: the Nordic Lymphoma Group MCL7 VALERIA trial.

ABSTRACT: Despite improvements in treatment of mantle cell lymphoma (MCL), most patients eventually relapse. In this multicenter phase 1b/2 trial, we evaluated safety and efficacy of minimal residual disease (MRD)-driven venetoclax, lenalidomide, and rituximab (venetoclax-R2) in relapsed/refractory (R/R) MCL and explored the feasibility of stopping treatment in molecular remission. The primary end point was overall response rate (ORR) at 6 months. After dose escalation, the recommended phase 2 dose was lenalidomide 20 mg daily, days 1 to 21; venetoclax 600 mg daily after ramp-up; and rituximab 375 mg/m2 weekly for 4 weeks, then every 8 weeks. MRD monitoring by RQ-PCR was performed every 3 months. When MRD-negativity in the blood was reached, treatment was continued for another 3 months; if MRD-negativity was then confirmed, treatment was stopped. In total, 59 patients were enrolled, with a median age of 73 years. At 6 months, the ORR was 63% (29 complete remission [CR], 8 partial remission [PR]), and 40% (4 CR, 2 PR) for patients previously failing a Bruton tyrosine kinase (BTK) inhibitor. Median progression-free survival (PFS) was 21 months, with median overall survival of 31 months. TP53 mutation was associated with inferior PFS (P < .01). Overall, 28 patients (48%) discontinued treatment in molecular remission, and 25 remain MRD negative after a median of 17.4 months. Hematological toxicity was frequent, with 52 of 59 (88%) patients with G3-4 neutropenia and 21 of 59 (36%) patients with G3-4 thrombocytopenia. To conclude, MRD-driven venetoclax-R2 is feasible and tolerable and shows efficacy in R/R MCL, also after BTK inhibitor failure. This trial was registered at www.ClinicalTrials.gov as #NCT03505944.

Work Disability and Return to Work After Treatment for Acute Lymphoblastic Leukemia: A Danish Nationwide Cohort Study.

Purpose: Most adult patients diagnosed with acute lymphoblastic leukemia (ALL) are below retirement age. The overall survival of patients with ALL has improved with implementation of high intensity pediatric-inspired treatment protocols. However, this treatment comes with a risk of long-term complications, which could affect the ability to work. The aim of this study was to investigate the risk of disability pension (DP) and return to work (RTW) for patients with ALL. Patients and Methods: Patients aged 18-60 years diagnosed with ALL between 2005 and 2019 were identified in the Danish National Acute Leukemia Registry. Each patient was matched with five comparators from the general population on birth year, sex, and Charlson Comorbidity Index. The Aalen-Johansen estimator was used to calculate the cumulative risk of DP for patients and comparators from index date (defined as 1 year after diagnosis) with competing events (transplantation or relapse, death, retirement pension, or early retirement pension). Differences in cumulative incidences were calculated using Gray's test. RTW was calculated as proportions one, three, and five years after the index date for patients holding a job before diagnosis. Results: A total of 154 patients with ALL and 770 matched comparators were included. The 5-year cumulative risk of DP was increased fivefold for patients with ALL compared with the general population. RTW was 41.7%, 65.7%, and 60.7% one, three, and five years after the index date, respectively. Conclusion: The risk of DP in patients with ALL increased significantly compared with the general population. Five years after the index date, RTW was 60.7% for patients with ALL.