Reporting and analysis of sex in vascular surgery research.

OBJECTIVE: To examine sex in human vascular surgery research by quantifying the inclusion and analysis of sex-based data in high-impact vascular surgery journals. METHODS: A bibliographic review of original articles published in the European Journal of Vascular and Endovascular Surgery, Journal of Vascular Surgery, JVS: Venous and Lymphatic Disorders, Journal of Endovascular Therapy, and Annals of Vascular Surgery from January 1, 2018, to December 31, 2020, and from January 1, 2023, to December 31, 2023, was conducted. Abstracted data included sex-based data analysis, inclusion of sex as a variable in multivariable analysis, inclusion of sex as an independent variable, and a discussion of sex-based results. RESULTS: Of the 3762 articles that included human, animal, or cell subjects, 249 (6.6%) did not state sex. Of those 249 articles, 183 included human subjects, 55 included animal subjects, and 11 used cell lines as the subjects. These were removed from analysis as well as the remaining 68 articles with animal subjects. In addition, 23 researched a sex-specific pathology and were removed from analysis. Of the remaining 3422 articles included in our study, 42.3% analyzed sex, 46.9% included sex in multivariable analysis, 4.8% included sex as an independent variable, and 26.6% included a discussion of sex. There were no significant differences in all four sex variables between 2018, 2019, and 2020. Between 2018-2020 and 2023, there were significant increases in all four sex variables. Multicenter studies had significantly higher rates of independent analysis of sex over single-center studies (7.4% vs 3.3%, P < .001). There was no significant difference in independent analysis of sex between U.S.-based and non-U.S.-based studies. Only 191 articles (5.6%) had 90% or greater matching of men and women in their study. CONCLUSIONS: Equitable inclusion and analysis of sex is rare in vascular surgery research. Less than 5% of articles included an independent analysis of data by sex, and few studies included males and females equally. Clinical research is the basis for evidence-based medicine; therefore, it is important to strive for equitable inclusion, analysis, and reporting of data to foster generalizability of clinical research to men and women.

Timing and co-occurrence of symptoms prior to a diagnosis of light chain (AL) amyloidosis.

It is well-established that most patients with systemic light chain (AL) amyloidosis have multi-organ involvement and are often diagnosed after a lag period of increasing symptoms. We leverage electronic health record (EHR) data from the TriNetX research network to describe the incidence, timing, and co-occurrence of precursor conditions of interests in a cohort of AL amyloidosis patients identified between October 2015-December 2020. Nineteen precursor diagnoses of interest representing features of AL amyloidosis were identified using ICD codes up to 36 months prior to AL amyloidosis diagnosis. Among 1,401 patients with at least 36 months of EHR data prior to AL amyloidosis diagnosis, 46% were females, 16% were non-Hispanic Black, and 6% were Hispanic. The median age was 71 (range, 21-91) years. The median number of precursor diagnoses was 5 with dyspnea and fatigue being the most prevalent. The time from the first occurrence of a precursor to AL diagnosis ranged from 3.2 to 21.4 months. Analyses of pairwise co-occurrence of specific diagnoses indicated a high association (Cole's coefficient >0.6) among the examined precursor diagnoses. These findings provide novel information about the timing and co-occurrence of key precursor conditions and could be used to develop algorithms for early identification of AL amyloidosis.

Optimal Donor Selection Across Multiple Outcomes For Hematopoietic Stem Cell Transplantation By Bayesian Nonparametric Machine Learning.

Allogeneic hematopoietic cell transplantation (HCT) is one of the only curative treatment options for patients suffering from life-threatening hematologic malignancies; yet, the possible adverse complications can be serious even fatal. Matching between donor and recipient for 4 of the HLA genes is widely accepted and supported by the literature. However, among 8/8 allele matched unrelated donors, there is less agreement among centers and transplant physicians about how to prioritize donor characteristics like additional HLA loci (DPB1 and DQB1), donor sex/parity, CMV status, and age to optimize transplant outcomes. This leads to varying donor selection practice from patient to patient or via center protocols. Furthermore, different donor characteristics may impact different post transplant outcomes beyond mortality, including disease relapse, graft failure/rejection, and chronic graft-versus-host disease (components of event-free survival, EFS). We develop a general methodology to identify optimal treatment decisions by considering the trade-offs on multiple outcomes modeled using Bayesian nonparametric machine learning. We apply the proposed approach to the problem of donor selection to optimize overall survival and event-free survival, using a large outcomes registry of HCT recipients and their actual and potential donors from the Center for International Blood and Marrow Transplant Research (CIBMTR). Our approach leads to a donor selection strategy that favors the youngest male donor, except when there is a female donor that is substantially younger.

Timing and co-occurrence of symptoms prior to a diagnosis of light chain (AL) amyloidosis.

It is well-established that light chain (AL) amyloidosis patients have multi-organ involvement and are often diagnosed after a lag period of increasing symptoms. We leverage electronic health record (EHR) data from the TriNetX research network to describe the incidence, timing, and co-occurrence of precursor conditions of interests in a cohort of AL amyloidosis patients identified between October 2015-December 2020. Nineteen precursor diagnoses of interest representing features of AL amyloidosis were identified using ICD codes up to 36 months prior to AL amyloidosis diagnosis. Among 1,401 patients with at least 36 months of EHR data prior to AL amyloidosis diagnosis, 46% were females, 16% were non-Hispanic Black, and 6% were Hispanic. The median age was 71 (range, 21-91) years. The median number of precursor diagnoses was 5 with dyspnea and fatigue being the most prevalent. The time from the first occurrence of a precursor to AL diagnosis ranged from 3.2 to 21.4 months. Analyses of pairwise co-occurrence of specific diagnoses indicated a high association (Cole's coefficient > 0.6) among the examined precursor diagnoses. These findings provide novel information about the timing and co-occurrence of key precursor conditions and could be used to develop algorithms for early identification of AL amyloidosis.

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time-to-event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate-dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user-friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood-borne cancer, where heteroskedasticity and nonproportional hazards are likely present.

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design.

BACKGROUND: The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. OBJECTIVES: To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. DESIGN: This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.

Impact of Reintervention During Stage 1 Palliation Hospitalization: A National, Multicenter Study.

BACKGROUND: Stage 1 palliation (S1P) for hypoplastic left heart syndrome remains associated with high morbidity and mortality. Previous studies on burden of reinterventions did not include patients who remain hospitalized before stage 2 palliation (S2P). This study described the rate of reintervention during S1P hospitalization and sought to determine the impact of reintervention on outcomes. METHODS: All participants enrolled in phase II of the National Pediatric Cardiology Quality Improvement Collaborative after S1P were included in this study. The primary outcome was the rate of reintervention during hospitalization after S1P and before hospital discharge or S2P. Reintervention was defined as 1 or more unplanned interventional cardiac catheterizations or surgical reoperations. RESULTS: Between March 1, 2016 and October 1, 2019, 1367 participants underwent S1P and 339 (24.8%) had a reintervention; most commonly to address the source of pulmonary blood flow. Gestational age, weight at S1P, atrioventricular septal defect, heterotaxy, preoperative pulmonary artery bands, hybrid S1P, and an additional bypass run or early extracorporeal membrane oxygenation were significantly associated with reintervention. Participants in the reintervention group experienced higher rates of nearly all postoperative complications, were less likely to be discharged before S2P (57.1% vs 86%; P < .001), and more likely to experience in-hospital mortality (17% vs 5%; P < .001). CONCLUSIONS: Unplanned reintervention during hospitalization after S1P palliation occurred in 25% of participants in a large, registry-based national cohort. Participants who underwent reintervention were more likely to remain as inpatient and were less likely to survive to S2P. Reintervention was associated with a multitude of postoperative complications that affect survival and long-term outcome.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Lisinopril Mitigates Radiation-Induced Mitochondrial Defects in Rat Heart and Blood Cells.

The genetic bases and disparate responses to radiotherapy are poorly understood, especially for cardiotoxicity resulting from treatment of thoracic tumors. Preclinical animal models such as the Dahl salt-sensitive (SS) rat can serve as a surrogate model for salt-sensitive low renin hypertension, common to African Americans, where aldosterone contributes to hypertension-related alterations of peripheral vascular and renal vascular function. Brown Norway (BN) rats, in comparison, are a normotensive control group, while consomic SSBN6 with substitution of rat chromosome 6 (homologous to human chromosome 14) on an SS background manifests cardioprotection and mitochondrial preservation to SS rats after injury. In this study, 2 groups from each of the 3 rat strains had their hearts irradiated (8 Gy X 5 fractions). One irradiated group was treated with the ACE-inhibitor lisinopril, and a separate group in each strain served as nonirradiated controls. Radiation reduced cardiac end diastolic volume by 9-11% and increased thickness of the interventricular septum (11-16%) and left ventricular posterior wall (14-15%) in all 3 strains (5-10 rats/group) after 120 days. Lisinopril mitigated the increase in posterior wall thickness. Mitochondrial function was measured by the Seahorse Cell Mitochondrial Stress test in peripheral blood mononuclear cells (PBMC) at 90 days. Radiation did not alter mitochondrial respiration in PBMC from BN or SSBN6. However, maximal mitochondrial respiration and spare capacity were reduced by radiation in PBMC from SS rats (p=0.016 and 0.002 respectively, 9-10 rats/group) and this effect was mitigated by lisinopril (p=0.04 and 0.023 respectively, 9-10 rats/group). Taken together, these results indicate injury to the heart by radiation in all 3 strains of rats, although the SS rats had greater susceptibility for mitochondrial dysfunction. Lisinopril mitigated injury independent of genetic background.

Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning.

PURPOSE: Donor selection practices for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) vary, and the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models has not been studied. METHODS: We trained a Bayesian ML model in 10,318 patients who underwent MUD HCT from 1999 to 2014 to provide patient- and donor-specific predictions of clinically severe (grade 3 or 4) acute graft-versus-host disease or death by day 180. The model was validated in 3,501 patients from 2015 to 2016 with archived records of potential donors at search. Donor selection optimizing predicted outcomes was implemented over either an unlimited donor pool or the donors in the search archives. Posterior mean differences in outcomes from optimal donor selection versus actual practice were summarized per patient and across the population with 95% intervals. RESULTS: Event rates were 33% (training) and 37% (validation). Among donor features, only age affected outcomes, with the effect consistent regardless of patient features. The median (interquartile range) difference in age between the youngest donor at search and the selected donor was 6 (1-10) years, whereas the number of donors per patient younger than the selected donor was 6 (1-36). Fourteen percent of the validation data set had an approximate 5% absolute reduction in event rates from selecting the youngest donor at search versus the actual donor used, leading to an absolute population reduction of 1% (95% interval, 0 to 3). CONCLUSION: We confirmed the singular importance of selecting the youngest available MUD, irrespective of patient features, identified potential for improved HCT outcomes by selecting a younger MUD, and demonstrated use of novel ML models transferable to optimize other complex treatment decisions in a patient-specific way.

Endothelial Rap1 (Ras-Association Proximate 1) Restricts Inflammatory Signaling to Protect From the Progression of Atherosclerosis.

OBJECTIVE: Small GTPase Rap1 (Ras-association proximate 1) is a novel, positive regulator of NO release and endothelial function with a potentially key role in mechanosensing of atheroprotective, laminar flow. Our objective was to delineate the role of Rap1 in the progression of atherosclerosis and its specific functions in the presence and absence of laminar flow, to better define its role in endothelial mechanisms contributing to plaque formation and atherogenesis. Approach and Results: In a mouse atherosclerosis model, endothelial Rap1B deletion exacerbates atherosclerotic plaque formation. In the thoracic aorta, where laminar shear stress-induced NO is otherwise atheroprotective, plaque area is increased in Athero-Rap1BiΔEC (atherogenic endothelial cell-specific, tamoxifen-inducible Rap1A+Rap1B knockout) mice. Endothelial Rap1 deficiency also leads to increased plaque size, leukocyte accumulation, and increased CAM (cell adhesion molecule) expression in atheroprone areas, whereas vascular permeability is unchanged. In endothelial cells, in the absence of protective laminar flow, Rap1 deficiency leads to an increased proinflammatory TNF-α (tumor necrosis factor alpha) signaling and increased NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) activation and elevated inflammatory receptor expression. Interestingly, this increased signaling to NF-κB activation is corrected by AKTVIII-an inhibitor of Akt (protein kinase B) translocation to the membrane. Together, these data implicate Rap1 in restricting Akt-dependent signaling, preventing excessive cytokine receptor signaling and proinflammatory NF-κB activation. CONCLUSIONS: Via 2 distinct mechanisms, endothelial Rap1 protects from the atherosclerosis progression in the presence and absence of laminar flow; Rap1-stimulated NO release predominates in laminar flow, and restriction of proinflammatory signaling predominates in the absence of laminar flow. Our studies provide novel insights into the mechanisms underlying endothelial homeostasis and reveal the importance of Rap1 signaling in cardiovascular disease.

Nonparametric competing risks analysis using Bayesian Additive Regression Trees.

Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation.

Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.

Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.

Variations in 30-day readmissions and length of stay among spine surgeons: a national study of elective spine surgery among US Medicare beneficiaries.

OBJECTIVE Pay-for-performance programs are targeting hospital readmissions. These programs have an underlying assumption that readmissions are due to provider practice patterns that can be modified by a reduction in reimbursement. However, there are limited data to support the role of providers in influencing readmissions. To study this, the authors examined variations in readmission rates by spine surgeon within 30 days among Medicare beneficiaries undergoing elective lumbar spine surgery for degenerative conditions. METHODS The authors applied validated ICD-9-CM algorithms to 2003-2007 Medicare data to select beneficiaries undergoing elective inpatient lumbar spine surgery for degenerative conditions. Mixed models, adjusting for patient demographics, comorbidities, and surgery type, were used to estimate risk of 30-day readmission by the surgeon. Length of stay (LOS) was also studied using these same models. RESULTS A total of 39,884 beneficiaries were operated on by 3987 spine surgeons. The mean readmission rate was 7.2%. The mean LOS was 3.1 days. After adjusting for patient characteristics and surgery type, 1 surgeon had readmission rates significantly below the mean, and only 5 surgeons had readmission rates significantly above the mean. In contrast, for LOS, the patients of 288 surgeons (7.2%) had LOS significantly lower than the mean, and the patients of 397 surgeons (10.0%) had LOS significantly above the mean. These findings were robust to adjustments for surgeon characteristics and clustering by hospital. Similarly, hospital characteristics were not significantly associated with readmission rates, but LOS was associated with hospital for-profit status and size. CONCLUSIONS The authors found almost no variations in readmission rates by surgeon. These findings suggest that surgeon practice patterns do not affect the risk of readmission. Likewise, no significant variation in readmission rates by hospital characteristics were found. Strategies to reduce readmissions would be better targeted at factors other than providers.

Prevalence and Consequences of Axillary Lymph Node Dissection in the Era of Sentinel Lymph Node Biopsy for Breast Cancer.

BACKGROUND: Despite clear guidelines for its use and wide adoption, no population-based study has examined the extent to which patients with early stage breast cancer are benefiting from sentinel lymph node biopsy (SLNB) by being spared a potentially avoidable axillary lymph node dissection (ALND) and its associated morbidity. OBJECTIVE: Examine variation in type of axillary surgery performed by surgeon volume; investigate the extent and consequences of potentially avoidable ALND. RESEARCH DESIGN/SUBJECTS: Observational study of older women with pathologically node-negative stage I-II invasive breast cancer who underwent surgery in a SEER state in 2008-2009. MEASURES: Surgeon annual volume of breast cancer cases and type of axillary surgery were determined by Medicare claims. An estimated probability of excess lymphedema due to ALND was calculated. RESULTS: Among 7686 pathologically node-negative women, 49% underwent ALND (either initially or after SLNB) and 25% were operated on by low-volume surgeons. Even after adjusting for demographic and tumor characteristics, women treated by higher volume surgeons were less likely to undergo ALND [medium volume: odds ratio, 0.69 (95% confidence interval, 0.51-0.82); high volume: odds ratio, 0.59 (95% confidence interval, 0.45-0.76)]. Potentially avoidable ALND cases were estimated to represent 21% of all expected lymphedema cases. CONCLUSIONS: In this pathologically node-negative population-based breast cancer cohort, only half underwent solely SLNB. Patients treated by low-volume surgeons were more likely to undergo ALND. Resources and guidelines on the appropriate training and competency of surgeons to assure the optimal performance of SLNB should be considered to decrease rates of potentially avoidable ALND and lymphedema.

Effect of hospital volume on processes of breast cancer care: A National Cancer Data Base study.

BACKGROUND: The purpose of this study was to examine variations in delivery of several breast cancer processes of care that are correlated with lower mortality and disease recurrence, and to determine the extent to which hospital volume explains this variation. METHODS: Women who were diagnosed with stage I-III unilateral breast cancer between 2007 and 2011 were identified within the National Cancer Data Base. Multiple logistic regression models were developed to determine whether hospital volume was independently associated with each of 10 individual process of care measures addressing diagnosis and treatment, and 2 composite measures assessing appropriateness of systemic treatment (chemotherapy and hormonal therapy) and locoregional treatment (margin status and radiation therapy). RESULTS: Among 573,571 women treated at 1755 different hospitals, 38%, 51%, and 10% were treated at high-, medium-, and low-volume hospitals, respectively. On multivariate analysis controlling for patient sociodemographic characteristics, treatment year and geographic location, hospital volume was a significant predictor for cancer diagnosis by initial biopsy (medium volume: odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.05-1.25; high volume: OR = 1.30, 95% CI = 1.14-1.49), negative surgical margins (medium volume: OR = 1.15, 95% CI = 1.06-1.24; high volume: OR = 1.28, 95% CI = 1.13-1.44), and appropriate locoregional treatment (medium volume: OR = 1.12, 95% CI = 1.07-1.17; high volume: OR = 1.16, 95% CI = 1.09-1.24). CONCLUSIONS: Diagnosis of breast cancer before initial surgery, negative surgical margins and appropriate use of radiation therapy may partially explain the volume-survival relationship. Dissemination of these processes of care to a broader group of hospitals could potentially improve the overall quality of care and outcomes of breast cancer survivors. Cancer 2017;123:957-66. © 2016 American Cancer Society.

The interplay between hospital and surgeon factors and the use of sentinel lymph node biopsy for breast cancer.

BACKGROUND: Several surgeon characteristics are associated with the use of sentinel lymph node biopsy (SLNB) for breast cancer. No studies have systematically examined the relative contribution of both surgeon and hospital factors on receipt of SLNB. OBJECTIVE: To evaluate the relationship between surgeon and hospital characteristics, including a novel claims-based classification of hospital commitment to cancer care (HC), and receipt of SLNB for breast cancer, a marker of quality care. DATA SOURCES/STUDY DESIGN: Observational prospective survey study was performed in a population-based cohort of Medicare beneficiaries who underwent incident invasive breast cancer surgery, linked to Medicare claims, state tumor registries, American Hospital Association Annual Survey Database, and American Medical Association Physician Masterfile. Multiple logistic regression models determined surgeon and hospital characteristics that were predictors of SLNB. RESULTS: Of the 1703 women treated at 471 different hospitals by 947 different surgeons, 65% underwent an initial SLNB. Eleven percent of hospitals were high-volume and 58% had a high commitment to cancer care. In separate adjusted models, both high HC (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.12-2.10) and high hospital volume (HV, OR 1.90, 95% CI 1.28-2.79) were associated with SLNB. Adding surgeon factors to a model including both HV and HC minimally modified the effect of high HC (OR 1.34, 95% CI 0.95-1.88) but significantly weakened the effect of high HV (OR 1.25, 95% CI 0.82-1.90). Surgeon characteristics (higher volume and percentage of breast cancer cases) remained strong independent predictors of SLNB, even when controlling for various hospital characteristics. CONCLUSIONS: Hospital factors are associated with receipt of SLNB but surgeon factors have a stronger association. Since regionalization of breast cancer care in the U.S. is unlikely to occur, efforts to improve the surgical care and outcomes of breast cancer patients must focus on optimizing patient access to SLNB by ensuring hospitals have the necessary resources and training to perform SLNB, staffing hospitals with surgeons who specialize/focus in breast cancer and referring patients who do not have access to SLNB to an experienced center.

Outcomes of Allogeneic Hematopoietic Cell Transplantation in Children and Young Adults with Chronic Myeloid Leukemia: A CIBMTR Cohort Analysis.

Chronic myeloid leukemia (CML) in children and young adults is uncommon. Young patients have long life expectancies and low morbidity with hematopoietic cell transplantation (HCT). Prolonged tyrosine kinase inhibitor (TKI) use may cause significant morbidity. In addition, indication for HCT in patients in the first chronic phase is not established. We hence retrospectively evaluated outcomes in 449 CML patients with early disease receiving myeloablative HCT reported to the CIBMTR. We analyzed various factors affecting outcome, specifically the effect of age and pre-HCT TKI in pediatric patients (age < 18 years, n = 177) and young adults (age 18 to 29 years, n = 272) with the goal of identifying prognostic factors. Post-HCT probability rates of 5-year overall survival (OS) and leukemia-free survival (LFS) were 75% and 59%, respectively. Rates of OS and LFS were 76% and 57% in <18-year and 74% and 60% in 18- to 29-year group, respectively, by univariate analysis (P = .1 and = .6). Five-year rates of OS for HLA matched sibling donor (MSD) and bone marrow (BM) stem cell source were 83% and 80%, respectively. In multivariate analysis there was no effect of age (<18 versus 18 to 29) or pre-HCT TKI therapy on OS, LFS, transplant related mortality, or relapse. Favorable factors for OS were MSD (P < .001) and recent HCT (2003 to 2010; P = .04). LFS was superior with MSD (P < .001), BM as graft source (P = .001), and performance scores > 90 (P = .03) compared with unrelated or mismatched peripheral blood stem cells donors and recipients with lower performance scores. Older age was associated with increased incidence of chronic graft-versus-host disease (P = .0002). In the current era, HCT outcomes are similar in young patients and children with early CML, and best outcomes are achieved with BM grafts and MSD.

Nonparametric survival analysis using Bayesian Additive Regression Trees (BART).

Bayesian additive regression trees (BART) provide a framework for flexible nonparametric modeling of relationships of covariates to outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, and exceeding that of its competitors. Software is also readily available for such outcomes. In this article, we introduce modeling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis. Simulation studies of one-sample and two-sample scenarios, in comparison with long-standing traditional methods, establish face validity of the new approach. We then demonstrate the model's ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario with crossing survival functions and survival function estimation in a scenario where hazards are multiplicatively modified by a highly nonlinear function of the covariates. Using data from a recently published study of patients undergoing hematopoietic stem cell transplantation, we illustrate the use and some advantages of the proposed method in medical investigations. Copyright © 2016 John Wiley & Sons, Ltd.