Citations

Abstract The cumulative impact of baseline comorbidities on outcomes of chimeric antigen receptor T-cell (CAR-T) therapy is not well established. Therefore, we developed and validated a Cellular Therapy Comorbidity Index (CT-CI) to predict outcomes following CD19-directed CAR-T therapy for large B-cell lymphoma (LBCL). Patients aged 18 or older receiving commercial CAR-T therapy for LBCL during 2017 to 2020 were selected from the Center for International Blood and Marrow Transplant Research registry. Patients were randomly assigned to training or validation cohorts. Comorbidities given weighted scores comprised the CT-CI, which was then validated for overall survival (OS) prognostication. A total of 1916 patients from 97 medical centers were included, with a median age of 64 years (19-91 years). About 70% of patients had comorbidities, such as cardiac disease (12%); diabetes (14%); hepatic dysfunction (mild, 8%; moderate to severe, 2%); psychiatric disturbance (18%); and pulmonary dysfunction (moderate, 15%; severe, 12%). The CT-CI was calculated, stratified patients in 3 categories, and was associated with increased mortality. Patients with higher CT-CI scores had worse OS (CT-CI 1: hazard ratio [HR], 1.37 [95% confidence interval [CI], 1.16-1.62; P< .001]; CT-CI 2: HR, 1.49 [95% CI, 1.17-1.89; P = .001]; CT-CI ≥ 3: HR, 2.55 [95% CI, 1.90-3.42; P< .001]). Higher CT-CI scores predicted treatment-related mortality and relapse. There was no correlation between the CT-CI score and CAR-T–related toxicities. The novel CT-CI score stratifies the effect of patient comorbidities on survival after CAR-T therapy and can be used for clinical decision-making and treatment selection in high-risk populations. However, comorbidities and fear of increased toxicity should not preclude patients from this effective therapy.

Abstract Outcomes in classic Hodgkin lymphoma (cHL) have steadily improved; however, additional therapies are needed for patients who relapse or do not respond to novel agents. Here, we report the efficacy and safety of camidanlumab tesirine (Cami), an anti-CD25 antibody-drug conjugate, in patients with relapsed/refractory cHL after brentuximab vedotin/programmed cell death protein 1 inhibitor therapies from the phase 2 ADCT-301-201 study. Eligible patients were adults with cHL who had received ≥3 previous lines of systemic therapy (or ≥2 if ineligible for hematopoietic stem cell transplant). Patients received 45 μg/kg Cami (IV, once every 3 weeks) in cycles 1 to 2, followed by 30 μg/kg IV once every 3 weeks for ≤1 year. The primary end point was overall response rate (ORR) per 2014 Lugano classification. Secondary end points included complete response rate (CRR), progression-free survival (PFS), and overall survival (OS). In total, 117 patients were enrolled with a median age of 37.0 years (range, 19-87). The ORR was 70.1% (95% confidence interval [CI], 60.9-78.2), with a CRR of 33.3% (95% CI, 24.9-42.6). The median PFS was 9.13 months (95% CI, 5.3-15.0); median OS was not reached. Thirty-three (28.2%) patients discontinued treatment because of treatment-emergent adverse events; the most common reasons were skin and subcutaneous tissue disorders (n = 10 [8.5%] patients), infections and infestations (n = 5 [4.3%]), and nervous systems disorders (n = 5 [4.3%]). Guillain-Barré–type or polyradiculopathy-type events occurred in 8 (6.8%) patients. Cami was efficacious in this heavily pretreated population; however, the efficacy was overshadowed by substantial issues with the safety profile. This trial was registered at www.clinicaltrials.gov as #NCT04052997.

Interaction between breast milk antibodies and gut microbes shape immune responses during weaning

Restless legs syndrome (RLS) is a frequent sleep-related sensorimotor disorder defined by an urge to move the legs in the evening while resting. Severe RLS symptoms can negatively impact sleep, mood, and quality of life. Periodic leg movements during sleep and wakefulness are found in 60% to 80% of patients with RLS. The pathophysiology of RLS and periodic leg movement is still poorly understood and involves brain iron deficiency, dopamine dysregulation, and genetic predisposition. Over the past decades, several cross-sectional and longitudinal studies have reported an association between RLS, cardiovascular disease, and hypertension although the magnitude, direction, and underlying mechanisms of these associations remain inconclusive. Periodic leg movements during sleep are concomitant with an increase in blood pressure and heart rate, which may affect the physiological nocturnal blood pressure dip and, therefore, lead to an increased incidence of cardiovascular disease. Insomnia and sleep deprivation linked to sensory discomfort could also increase the sympathetic tone and contribute to an increase in cardiovascular risk. Comorbidities and associated sleep disorders may also synergically increase cardiovascular risk. This narrative review details the current knowledge of RLS, a still underdiagnosed and poorly recognized disorder, and examines the epidemiological relationship between RLS, cardiovascular disease and hypertension, potential mechanisms underlying this association, and strategies to reduce the risk of cardiovascular disease in patients with moderate-to-severe RLS. Greater multidisciplinary collaborations between cardiologists and sleep specialists are needed to better identify, understand, and manage patients with sleep disorders, including those with RLS.

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, characterized by recurrent upper airway obstruction during sleep, resulting in intermittent hypoxia, increased sympathetic activation, and sleep deficiency. Over the past 2 decades, there has been a robust body of evidence to support a strong link between OSA and cardiometabolic diseases. Obesity is an important risk factor for OSA. Observational studies indicate that OSA is a strong risk factor for the development of hypertension and diabetes. Moreover, clinical and experimental studies support a causal role of OSA in hypertension and impairments in glucose metabolism, beyond excess weight. Notably, OSA is particularly underdiagnosed and undertreated in women, which may heighten the cardiometabolic risk. OSA is often overlooked during pregnancy and has been linked to adverse cardiometabolic outcomes in observational studies. In randomized clinical trials, treatment of OSA with continuous positive airway pressure reduces blood pressure in individuals with hypertension, but the beneficial effects of continuous positive airway pressure on glycemic outcomes are less convincing. Inconsistent cardiometabolic response to OSA treatment can be partly explained by failure to consider heterogeneity in OSA and variable continuous positive airway pressure adherence among diverse populations. In this review, we summarize the relationships between OSA and cardiometabolic conditions with a particular focus on obesity, hypertension, and diabetes. We review the current knowledge on the heterogeneity in OSA and discuss potential underlying mechanisms for impairments in blood pressure and glucose metabolism in OSA. We also provide a clinical perspective for OSA management considering current research gaps and emerging approaches for the prevention and treatment of cardiometabolic disease.

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, with its incidence rising due to aging populations, obesity, and advancements in diagnostic modalities. The interplay between sleep disorders and AF is increasingly recognized, with obstructive sleep apnea (OSA) serving as a well-established risk factor. However, emerging evidence implicates additional sleep disturbances—including central sleep apnea, insomnia, and restless legs syndrome—in AF pathogenesis and progression. Despite compelling observational data, interventional studies evaluating the impact of sleep disorder treatment on AF outcomes have yielded mixed results. Although continuous positive airway pressure therapy in patients with OSA mitigates AF recurrence, randomized controlled trials have yet to confirm a definitive causal benefit. This review synthesizes epidemiological, mechanistic, and interventional data linking sleep disorders to AF.

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.