Citations

AbstractWith an annual cumulative occurrence of approximately 15,000 in North America, all childhood cancers are rare. Very rare cancers as defined by both the European Cooperative Study Group for Rare Pediatric Cancers and the Children's Oncology Group fall into two principal categories: those so uncommon (fewer than 2 cases/million) that their study is challenging even through cooperative group efforts (e.g., pleuropulmonary blastoma and desmoplastic small round cell tumor) and those that are far more common in adults and therefore rarely studied in children (e.g., thyroid, melanoma, and gastrointestinal stromal tumor). Treatment strategies for these latter tumors are typically based on adult guidelines, although the pediatric variants of these tumors may harbor different genetic signatures and demonstrate different behavior. If melanoma and differentiated thyroid cancer are excluded, other rare cancer types account for only 2% of the cancers in children aged 0 to 14. This article highlights several of the most common rare tumor types.

Abstract Purpose: Incorporating patient-reported outcomes (PROs) into survivorship care may improve the comprehensiveness of follow-up. The objective was to engage stakeholders to develop a PRO assessment of survivors’ symptoms and concerns for use during breast cancer follow-up. Methods: We convened patient and oncologist stakeholder advisory groups to define a low-risk survivor cohort; develop an initial PRO assessment including survivorship domains of importance, measurement instruments, and clinically significant thresholds; and revise the assessment based on feedback from community focus groups and two rounds of iterative pilot testing with survivors. Results: The most commonly cited “low-risk” criteria was cancer stage. Stakeholders identified key domains and measurement instruments, including recurrence symptoms, treatment side-effects, adherence, mental health, and sexual health. Ninety-four survivors completed the PRO assessment in the initial pilot test (median age 62 years, median 2 years from diagnosis). Most (89/93) reported questions were easy to understand. The level of missingness was low. The most common threshold-level symptoms/concerns were hot flashes (46%), fatigue (35%), back pain (31%), joint pain (31%), and anxiety (30%). The PRO assessment was revised to clarify symptom causality and desire to discuss with a provider. In a follow-up pilot study with twenty survivors, the most common symptoms were chest/breast discomfort (50%) and anxiety (25%). Conclusions: We leveraged stakeholder input to develop a comprehensive PRO assessment to use to assess breast cancer survivors’ well-being. Our PRO assessment is acceptable to survivors. Future research will focus on the integration of the PRO assessment into clinical care.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Introduction Patient self-scheduling of medical appointments is becoming more common in many medical institutions. However, the complexity of scheduling multiple specialties, following scheduling guidelines, and managing appointment access requires a variety of processes for a diverse inventory of self-schedulable appointment types. Methods From 7 unique patient self-scheduling methods, we captured counts of successfully self-scheduled and completed appointments. A process map was created to show the paths of 5 different primary self-scheduling processes (new appointment self-scheduling) and 2 secondary self-scheduling processes (existing appointment self-rescheduling). Results There were 7 unique processes that led to 733,651 successfully self-scheduled completed visits from January 1 to December 31, 2023 at a multisite, multispecialty clinic. The self-scheduling processes consisted of the following: (1) Ticket offer (appointment “ticket” offers for specific visits generated by a provider order or system rules), the software “ticket” sent to the patient permits “admission” to self-schedule calendar templates (341,591 uses, 46.6%); (2) direct self-scheduled visit for prequalified visit types (203,593 uses, 27.6%); (3) self-reschedule option (patient option to reschedule existing appointment, 79,706 uses, 10.9%); (4) new patient self-scheduled visit via clinic website (does not require portal access, 54,367 uses, 7.4%). (5) automated waitlist self-rescheduled visit (38,649 uses, 5.3%); (6) automated waitlist self-scheduled visit of previously unscheduled visit (10,939 uses, 1.5%); and (7) self-triage self-scheduled visit (4806 uses, 0.7%). Conclusion The processes for self-scheduling are expanding. Our multispecialty clinic has implemented 7 different processes to help patients successfully self-schedule medical appointments. Some of the processes occur before initial scheduling (such as self-triage), and some are implemented after successful scheduling has already occurred (self-rescheduling option and self-rescheduling aided by an automated waitlist). Continued research is needed to look for measures of success beyond the ability to complete a self-scheduled visit, including the accuracy of the booking (right provider, location, and length of visit).

Abstract Extracellular vesicles (EVs) are key mediators of cell–cell communication and are involved in transferring specific biomolecular cargo to recipient cells to regulate their physiological functions. A major challenge in the understanding of EV function in vivo is the difficulty ascertaining the origin of the EV particles. The recent development of the “Snorkel-tag,” which includes EV-membrane-targeted CD81 fused to a series of extra-vesicular protein tags, can be used to mark EVs originating from a specific source for subsequent isolation and characterization. We developed an in vivo mouse model, termed “CAGS-Snorkel,” which expresses the Snorkel-tag under the control of the Cre-lox system, and crossed this mouse with either Prx1-Cre (mesenchymal progenitors) or Ocn-Cre (osteoblasts/osteocytes) and isolated Snorkel-tagged EVs from the mouse bone marrow plasma using a magnetic bead affinity column. miRNA-sequencing was performed on the isolated EVs, and although similar profiles were observed, a few key miRNAs involved in bone metabolism (miR-106b-5p, miRs-19b-3p, and miRs-219a-5p) were enriched in the Ocn-derived relative to the Prx1-derived EV subpopulations. To characterize the effects of these small EVs on a bone cell target, cultured mouse bone marrow stromal cells were treated with Prx1 or Ocn EVs, and mRNA-sequencing was performed. Pathways involved in ossification, bone development, and extracellular matrix interactions were regulated by both EV subpopulations, whereas a few pathways including advanced glycation end-products signaling were uniquely regulated in the Ocn EV subpopulation, underlying important biological effects of specific EV subpopulations within the bone marrow microenvironment. These data demonstrate that EV isolation in vivo using the CAGS-Snorkel mouse model is a useful tool in characterizing the cargo and understanding the biology of tissue-specific EVs. Moreover, while bone mesenchymal cell populations share a common EV secretory profile, we uncover key differences based on the stage of osteoblastic differentiation that may have important biological consequences.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

Neuroblastoma is the most common extracranial solid tumor diagnosed in children. This inaugural version of the NCCN Guidelines for Neuroblastoma provides recommendations for the diagnosis, risk classification, and treatment of neuroblastoma. The information in these guidelines was developed by the NCCN Neuroblastoma Panel, a multidisciplinary group of representatives with expertise in neuroblastoma, consisting of pediatric oncologists, radiologists, pathologists, surgeons, and radiation oncologists from NCCN Member Institutions. The evidence-based and consensus recommendations contained in the NCCN Guidelines are intended to guide clinicians in selecting the most appropriate treatments for their patients with this clinically heterogeneous disease.

The determination of an optimal treatment plan for an individual patient with rectal cancer is a complex process. In addition to decisions relating to the intent of rectal cancer surgery (ie, curative or palliative), consideration must also be given to the likely functional results of treatment, including the probability of maintaining or restoring normal bowel function/anal continence and preserving genitourinary functions. Particularly for patients with distal rectal cancer, finding a balance between curative-intent therapy while having minimal impact on quality of life can be challenging. Furthermore, the risk of pelvic recurrence is higher in patients with rectal cancer compared with those with colon cancer, and locally recurrent rectal cancer is associated with a poor prognosis. Careful patient selection and the use of sequenced multimodality therapy following a multidisciplinary approach is recommended. These NCCN Guidelines Insights detail recent updates to the NCCN Guidelines for Rectal Cancer, including the addition of endoscopic submucosal dissection as an option for early-stage rectal cancer, updates to the total neoadjuvant therapy approach based on the results of recent clinical trials, and the addition of a “watch-and-wait” nonoperative management approach for clinical complete responders to neoadjuvant therapy.

ABSTRACT Point-of-care ultrasonography (POCUS) has rapidly evolved from a niche technology to an indispensable tool across medical specialties, including nephrology. This evolution is driven by advancements in technology and the visionary efforts of clinicians in emergency medicine and beyond. Recognizing its potential, medical schools are increasingly integrating POCUS into training curricula, emphasizing its role in enhancing diagnostic accuracy and patient care. Despite these advancements, barriers such as limited faculty expertise and ‘lack of’ standardized guidelines hinder widespread adoption and regulation. The International Alliance for POCUS in Nephrology (IAPN), through this position statement, aims to guide nephrologists in harnessing the diagnostic power of POCUS responsibly and effectively. By outlining core competencies, recommending training modalities and advocating for robust quality assurance measures, we envision a future where POCUS enhances nephrology practice globally, ensuring optimal patient outcomes through informed, evidence-based decision-making. International collaboration and education are essential to overcome current challenges and realize the full potential of POCUS in nephrology and beyond.

Introduction: Vertebral compression fractures (VCFs) pose a considerable healthcare burden and are linked to elevated morbidity and mortality. Despite available anti-osteoporotic treatments (AOTs), guideline adherence is lacking. This study aims to evaluate subsequent hip fracture incidence after index VCF and to elucidate AOT prescribing patterns in VCF patients, further assessing the impact of surgical interventions on these patterns. Materials and Methods: Patients with index VCFs between 2010 and 2021 were identified using the PearlDiver database. Diagnostic and procedural data were recorded using International Classification of Diseases (ICD-9, ICD-10) and Current Procedural Terminology (CPT) codes. Patients under age 50 and follow-up <one year following index VCF were excluded. Patients were categorized based on whether they received AOT within one year, preceding and after index VCF, and were subsequently propensity-matched 1:3 based on age, sex, and Elixhauser Comorbidity Index (ECI) score to compare hip fracture incidence following index VCF. Sub-analysis was performed for operatively managed VCFs (kyphoplasty/vertebroplasty). Statistical tests included Chi-squared for categorical outcomes, and Kruskal–Wallis for continuous measures. Results: Of 637,701 patients, 72.6% were female. The overall subsequent hip fracture incidence was 2.6% at one year and 12.9% for all-time follow-up. Propensity-matched analysis indicated higher subsequent hip fracture rates in patients initiated on AOT post-index VCF (one year: 3.8% vs. 3.5%, p = 0.0013; all-time: 14.3% vs. 13.0%, p < 0.0001). Conclusions: The study reveals an unexpected increase in subsequent hip fractures among patients initiated on AOT post-index VCF, likely due to selection bias. These findings highlight the need for refined osteoporosis-management strategies to improve guideline adherence, thereby mitigating patient morbidity and mortality.