Citations

Neoadjuvant chemotherapy (NAC) increases rates of successful breast-conserving surgery (BCS) in patients with breast cancer. However, some studies suggest that BCS after NAC may confer an increased risk of locoregional recurrence (LRR). We assessed LRR rates and locoregional recurrence-free survival (LRFS) in patients enrolled on I-SPY2 (NCT01042379), a prospective NAC trial for patients with clinical stage II to III, molecularly high-risk breast cancer. Cox proportional hazards models were used to evaluate associations between surgical procedure (BCS vs mastectomy) and LRFS adjusted for age, tumor receptor subtype, clinical T category, clinical nodal status, and residual cancer burden (RCB). In 1462 patients, surgical procedure was not associated with LRR or LRFS on either univariate or multivariate analysis. The unadjusted incidence of LRR was 5.4% after BCS and 7.0% after mastectomy, at a median follow-up time of 3.5 years. The strongest predictor of LRR was RCB class, with each increasing RCB class having a significantly higher hazard ratio for LRR compared with RCB 0 on multivariate analysis. Triple-negative receptor subtype was also associated with an increased risk of LRR (hazard ratio: 2.91, 95% CI: 1.8–4.6, P < 0.0001), regardless of the type of operation. In this large multi-institutional prospective trial of patients completing NAC, we found no increased risk of LRR or differences in LRFS after BCS compared with mastectomy. Tumor receptor subtype and extent of residual disease after NAC were significantly associated with recurrence. These data demonstrate that BCS can be an excellent surgical option after NAC for appropriately selected patients.

Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).

Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Abstract Late acute graft-versus-host disease (GVHD) is defined as de novo acute GVHD presenting beyond 100 days after allogeneic hematopoietic cell transplantation (HCT) without manifestations of chronic GVHD. Data are limited regarding its characteristics, clinical course, and risk factors because of underrecognition and changes in classification. We evaluated 3542 consecutive adult recipients of first HCTs at 24 Mount Sinai Acute GVHD International Consortium (MAGIC) centers between January 2014 and August 2021 to better describe the clinical evolution and outcomes of late acute GVHD. The cumulative incidence of classic acute GVHD that required systemic treatment was 35.2%, and an additional 5.7% of patients required treatment for late acute GVHD. At the onset of symptoms, late acute GVHD was more severe than classic acute GVHD based on both clinical and MAGIC algorithm probability biomarker parameters and showed a lower overall response rate on day 28. Both clinical and biomarker grading at the time of treatment stratified the risk of nonrelapse mortality (NRM) in patients with classic and late acute GVHD, respectively, but long-term NRM and overall survival did not differ between patients with classic and late acute GVHD. Advanced age, female-to-male sex mismatch, and the use of reduced intensity conditioning were associated with the development of late acute GVHD, whereas the use of posttransplant cyclophosphamide–based GVHD prevention was protective mainly because of shifts in GVHD timing. Because overall outcomes were comparable, our findings, although not definitive, suggest that similar treatment strategies, including eligibility for clinical trials, based solely on clinical presentation at onset are appropriate.

AbstractObjectiveTo examine the experience of interracial anxiety among health professionals and how it may affect the quality of their interactions with patients from racially marginalized populations. We explored the influence of prior interracial exposure—specifically through childhood neighborhoods, college student bodies, and friend groups—on interracial anxiety among medical students and residents. We also examined whether levels of interracial anxiety change from medical school through residency.Data SourceWeb‐based longitudinal survey data from the Medical Student Cognitive Habits and Growth Evaluation Study.Study DesignWe used a retrospective longitudinal design with four observations for each trainee. The study population consisted of non‐Black US medical trainees surveyed in their 1st and 4th years of medical school and 2nd and 3rd years of residency. Mixed effects longitudinal models were used to assess predictors of interracial anxiety and assess changes in interracial anxiety scores over time.Principal FindingsIn total, 3155 non‐Black medical trainees were followed for 7 years. Seventy‐eight percent grew up in predominantly White neighborhoods. Living in predominantly White neighborhoods and having less racially diverse friends were associated with higher levels of interracial anxiety among medical trainees. Trainees' interracial anxiety scores did not substantially change over time; interracial anxiety was highest in the 1st year of medical school, lowest in the 4th year, and increased slightly during residency.ConclusionsNeighborhood and friend group composition had independent effects on interracial anxiety, indicating that premedical racial socialization may affect medical trainees' preparedness to interact effectively with diverse patient populations. Additionally, the lack of substantial change in interracial anxiety throughout medical training suggests the importance of providing curricular tools and structure (e.g., instituting interracial cooperative learning activities) to foster the development of healthy interracial relationships.

AbstractHuman lifespan continues to extend as an unprecedented number of people reach their seventh and eighth decades of life, unveiling chronic conditions that affect the older adult. Age-related skin conditions include senile purpura, seborrheic keratoses, pemphigus vulgaris, bullous pemphigoid, diabetic foot wounds and skin cancer. Current methods of drug testing prior to clinical trials require the use of pre-clinical animal models, which are often unable to adequately replicate human skin response. Therefore, a reliable model for aged human skin is needed. The current challenges in developing an aged human skin model include the intrinsic variability in skin architecture from person to person. An ideal skin model would incorporate innate functionality such as sensation, vascularization and regeneration. The advent of 3D bioprinting allows us to create human skin equivalent for use as clinical-grade surgical graft, for drug testing and other needs. In this review, we describe the process of human skin aging and outline the steps to create an aged skin model with 3D bioprinting using skin cells (i.e. keratinocytes, fibroblasts and melanocytes). We also provide an overview of current bioprinted skin models, associated limitations and direction for future research.

Abstract Introduction Prospective data about quality of life (QoL) in men with breast cancer (BC) are lacking. A prospective registry (EORTC10085) of men with all BC stages, including a QoL correlative study, was performed as part of the International Male Breast Cancer Program. Methods Questionnaires at BC diagnosis included the EORTC QLQ-C30 and BR23 (BC specific module), adapted for men. High functioning and global health/QoL scores indicate high functioning levels/high QoL; high symptom-focused measures scores indicate high symptoms/problems levels. EORTC reference data for healthy men and women with BC were used for comparisons. Results Of 422 men consenting to participate, 363 were evaluable. Median age was 67 years, and median time between diagnosis and survey was 1.1 months. A total of 114 men (45%) had node-positive early disease, and 28 (8%) had advanced disease. Baseline mean global health status score was 73 (SD: 21), better than in female BC reference data (62, SD: 25). Common symptoms in male BC were fatigue (22, SD: 24), insomnia (21, SD: 28), and pain (16, SD: 23), for which women’s mean scores indicated more burdensome symptoms at 33 (SD: 26), 30 (SD: 32), and 29 (SD: 29). Men’s mean sexual activity score was 31 (SD: 26), with less sexual activity in older patients or advanced disease. Conclusions QoL and symptom burden in male BC patients appears no worse (and possibly better) than that in female patients. Future analyses on impact of treatment on symptoms and QoL over time, may support tailoring of male BC management.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Breast Cancer address all aspects of management for breast cancer. The treatment landscape of metastatic breast cancer is evolving constantly. The therapeutic strategy takes into consideration tumor biology, biomarkers, and other clinical factors. Due to the growing number of treatment options, if one option fails, there is usually another line of therapy available, providing meaningful improvements in survival. This NCCN Guidelines Insights report focuses on recent updates specific to systemic therapy recommendations for patients with stage IV (M1) disease.

Abstract Objective Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self‐reported and electroencephalographically (EEG)‐confirmed seizures in ambulatory patients with epilepsy in their natural home environments. Methods Long‐term e‐surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e‐surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed‐effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Fifty‐four subjects returned 10 269 e‐survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12–3.61, AUC = .61, p = .02) was associated with increased relative odds of future self‐reported seizures. Multivariate analysis showed that previous self‐reported seizures (OR = 5.37, 95% CI = 3.53–8.16, AUC = .76, p < .001) were most strongly associated with future self‐reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87–5.95, AUC = .69, p < .001) remained significant when prior self‐reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e‐survey responses and subsequent EEG seizures. Significance Our results suggest that patients may tend to self‐forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self‐predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.