Patterns of primary and specialty care among children with sickle cell anemia.

BACKGROUND AND OBJECTIVE: National guidelines recommend that children with sickle cell anemia (SCA) be seen regularly by primary care providers (PCPs) as well as hematologists to receive comprehensive, multidisciplinary care. The objective is to characterize the patterns of primary and hematology care for children with SCA in Michigan. METHODS: Using validated claims definitions, children ages 1-17 years with SCA were identified using Michigan Medicaid administrative claims from 2010 to 2018. We calculated the number of outpatient PCP and hematologist visits per person-year, as well as the proportion of children with at least one visit to a PCP, hematologist, or both a PCP and hematologist annually. Negative binomial regression was used to calculate annual rates of visits for each provider type. RESULTS: A total of 875 children contributed 2889 person-years. Of the total 22,570 outpatient visits, 52% were with a PCP and 34% with a hematologist. Annually, 87%-93% of children had a visit with a PCP, and 63%-85% had a visit with a hematologist. Approximately 66% of total person-years had both visit types within a year. The annual rate ranged from 2.3 to 2.5 for hematologist visits and from 3.7 to 4.1 for PCP visits. CONCLUSIONS: Substantial gaps exist in the receipt of annual hematology care. Given that the majority of children with SCA see a PCP annually, strategies to leverage primary care visits experienced by this population may be needed to increase receipt of SCA-specific services.

Isotonic design for phase I cancer clinical trials with late-onset toxicities.

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion.

TrueNTH Sexual Recovery Intervention for couples coping with prostate cancer: Randomized controlled trial results.

BACKGROUND: Despite significant sexual dysfunction and distress after localized prostate cancer treatment, patients typically receive only physiologic erectile dysfunction management. The authors performed a randomized controlled trial of an online intervention supporting couples' posttreatment recovery of sexual intimacy. METHODS: Patients treated with surgery, radiation, or combined radiation and androgen deprivation therapy who had partners were recruited and randomized to an online intervention or a control group. The intervention, tailored to treatment type and sexual orientation, comprised 6 modules addressing expectations for sexual and emotional sequelae of treatment, rehabilitation, and guidance toward sexual intimacy recovery. Couples, recruited from 6 sites nationally, completed validated measures at the baseline and 3 and 6 months after treatment. Primary outcome group differences were assessed with t tests for individual outcomes. RESULTS: Among 142 randomized couples, 105 patients (mostly surgery) and 87 partners completed the 6-month survey; this reflected challenges with recruitment and attrition. There were no differences between the intervention and control arms in Patient-Reported Outcomes Measurement Information System Global Satisfaction With Sex Life scores 6 months after treatment (the primary outcome). Three months after treatment, intervention patients and partners reported more engagement in penetrative and nonpenetrative sexual activities than controls. More than 73% of the intervention participants reported high or moderate satisfaction with module content; more than 85% would recommend the intervention to other couples. CONCLUSIONS: Online psychosexual support for couples can help couples to connect and experience sexual pleasure early after treatment despite patients' sexual dysfunction. Participants' high endorsement of the intervention reflects the importance of sexual health support to couples after prostate cancer treatment. LAY SUMMARY: This study tested a web-based program supporting couples' sexual recovery of sexual intimacy after prostate cancer treatment. One hundred forty-two couples were recruited and randomly assigned to the program (n = 60) or to a control group (n = 82). The program did not result in improvements in participants' satisfaction with their sex life 6 months after treatment, but couples in the intervention group engaged in sexual activity sooner after treatment than couples in the control group. Couples evaluated the program positively and would recommend it to others facing prostate cancer treatment.

Depression in Female Adolescents with Heavy Menstrual Bleeding.

OBJECTIVE: To assess the degree to which heavy menstrual bleeding is associated with depression, independent of hormonal contraception. STUDY DESIGN: We performed a retrospective cohort study of 1168 female adolescents 9-18 years old presenting to general pediatricians for heavy menstrual bleeding or well visits. Depression was the primary outcome and defined as a diagnosis in the health record. Univariable and multivariable regression models were fit to the data to identify factors associated with depression diagnosis. RESULTS: In total, 581 adolescents with heavy menstrual bleeding and 587 without heavy menstrual bleeding were included. Depression diagnoses occurred with greater frequency in youth with heavy menstrual bleeding compared with those without heavy menstrual bleeding (50.9% vs 24.2% P < .001; risk ratio 1.67, 95% CI 1.39-2.01) but did not significantly differ between those taking vs not taking hormonal contraception (risk ratio 0.99; 95% CI 0.84-1.17). Most patients with depression and heavy menstrual bleeding developed depression following or concurrent with heavy menstrual bleeding (261/296, 88%). Of these, 199 of 261 (76%) were treated with hormonal contraception, but the majority (168/199; 84%) were diagnosed with depression before initiation. CONCLUSIONS: Heavy menstrual bleeding is associated with depression diagnosis in female adolescents. The use of hormonal contraception was not associated with depression diagnosis in multivariable analysis, covarying heavy menstrual bleeding, age, body mass index, anxiety, sexual activity, and substance use. As hormonal contraception is often used to treat heavy menstrual bleeding, heavy menstrual bleeding may be partially driving previous reports of increased depression risk in those taking hormonal contraception.

Extending the Continual Reassessment Method to accommodate step-up dosing in Phase I trials.

The Continual Reassessment Method (CRM) was developed for Phase I trials to identify a maximum-tolerated dose of an agent using a design in which each participant is treated with a single administration of the agent. We propose an extension of the CRM in which participants receive multiple administrations of an agent using a so-called step-up dosing procedure in which participants receive one or more administrations of lower doses of the agent before they receive their penultimate dose. We use methods developed for the CRM to model the probability of DLT for each administration, which leads to the use of conditional probability models to model the joint probability of DLT across multiple administrations. We compare our approach to two existing methods that use time-to-event modeling methods for modeling the probability of DLT. We demonstrate through simulations that our approach has operating characteristics similar to existing methods, but due to its foundations in the CRM, ours is simpler to implement than existing approaches and is therefore more likely to be adopted in practice.

Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial.

Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment. Data from both stages are used to determine the efficacy of the active treatment. We present a Bayesian approach where information is borrowed between stage 1 and stage 2. We compare our approach to standard methods using only stage 1 data and a log-linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that the Bayesian method has smaller root-mean-square-error and 95% credible interval widths than standard methods in the tested scenarios. We conclude that it is advantageous to utilize data from both stages for a primary efficacy analysis and that the specific snSMART design shown here can be used in the registration of a drug for the treatment of rare diseases.

A modular framework for early-phase seamless oncology trials.

BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND: To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown. OBJECTIVE: Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors. DESIGN: Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status. PATIENTS: 20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized. CONCLUSIONS: Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

A simulation-free approach to assessing the performance of the continual reassessment method.

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final "best" design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one-parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over- or under-estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.

Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials (snSMARTs) with three agents.

The small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) is a two-stage clinical trial design for rare diseases motivated by the comparison of three active treatments for isolated skin vasculitis in the ongoing clinical trial ARAMIS (a randomized multicenter study for isolated skin vasculitis, NCT09239573). In Stage 1, all patients are randomized to one of three treatments. In Stage 2, patients who respond to their initial treatment receive the same treatment again, while those who fail to respond are re-randomized to one of the two remaining treatments. A Bayesian method for estimating the response rate of each individual treatment in a three-arm snSMART demonstrated efficiency gains for a given sample size relative to other existing frequentist approaches. However, these efficiency gains are dependent upon knowing how many subjects are required to determine a specific difference in the treatment response rates. Because few sample size calculation methods for snSMARTs exist, we propose a Bayesian sample size calculation for an snSMART designed to distinguish the best treatment from the second-best treatment. Although our methods are based on asymptotic approximations, we demonstrate via simulations that our proposed sample size calculation approach produces the desired statistical power, even in small samples. Moreover, our methods and applet produce sample sizes quickly, thereby saving time relative to using simulations to determine the appropriate sample size. We compare our proposed sample size to an existing frequentist method based upon a weighted Z-statistic and demonstrate that the Bayesian method requires far fewer patients than the frequentist method for a study with the same design parameters.

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

Increased Presence of Perineural Invasion in the Tongue and Floor of the Mouth: Could It Represent a More Aggressive Oral Squamous Cell Carcinoma, or Do Larger Aggressive Tumors Cause Perineural Invasion?

PURPOSE: Despite data showing worse outcomes and aggressive disease behavior, perineural invasion (PNI) has not been well characterized in terms of tumor location, histopathologic features, or cervical lymph node status. The specific aims of this study were to measure correlations between PNI, tumor location, and other known histopathologic characteristics used to define aggressive disease. MATERIALS AND METHODS: This was a retrospective cohort study of adult patients with primary squamous cell carcinoma of the oral cavity who underwent neck dissection. We excluded patients whose neck was previously treated with surgery or radiation therapy. Demographic and histopathologic variables of interest were obtained from patient charts. The primary outcome of interest was PNI, and the predictors of interest included tumor location, histopathologic tumor characteristics, and cervical lymph node status. For continuous variables, mean differences were compared by t tests. For categorical variables, the differences in the distribution of the proportions were analyzed with the χ2 test. All variables were entered simultaneously into a multivariate logistic regression model to control for possible confounding. Statistical significance for the study was set at P < .05. RESULTS: Three hundred sixty-eight patients met the study criteria. PNI showed statistically significant correlations with lymph node status, tumor depth, and specific primary tumor location. PNI was more likely to be seen in tumors located in the tongue or floor of the mouth. Tumors with PNI had a deeper depth of invasion: 15.9 ± 10.9 mm versus 10.2 ± 10.0 mm (P < .001). PNI tumors had a higher mean total number of positive nodes: 2.85 ± 5.23 versus 0.83 ± 1.80 (P < .001). CONCLUSIONS: PNI is statistically correlated with tongue and floor-of-the-mouth subsites within the oral cavity, as well as larger tumors, deeper tumors, and disease that has progressed to the lymph nodes. Whether this correlation represents causation in either direction remains unknown.

Intraoperative Depth of Invasion Is Accurate in Early-Stage Oral Cavity Squamous Cell Carcinoma.

PURPOSE: Depth of invasion (DOI) is one predictor of nodal metastasis in oral cavity squamous cell carcinoma (OCSCC) and can facilitate the decision to complete an elective neck dissection (END) in early-stage disease with a clinically negative neck. The purpose of this study was to investigate the accuracy of DOI in intraoperative frozen specimens for T1N0 oral OCSCC. MATERIALS AND METHODS: To compare the accuracy of DOI in frozen versus permanent specimens, we completed a prospective, blinded study of 30 patients with cT1N0 OCSCC who presented between October 2016 and December 2017. RESULTS: DOI in frozen specimens was 96.8% accurate in predicting the need for END with a sensitivity of 90.9%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 95.2%. A strong correlation was found between DOIs in frozen and permanent specimens measured by head and neck (HN) pathologists (r = 0.96; 95% confidence interval [CI], 0.93 to 0.97), between HN pathologists using frozen specimens (r = 0.98; 95% CI, 0.95 to 0.99) and permanent specimens (r = 0.95; 95% CI, 0.91 to 0.98), and in DOIs in frozen specimens communicated intraoperatively versus measured by HN pathologist 1 (r = 0.93; 95% CI, 0.86 to 0.97) and HN pathologist 2 (r = 0.95; 95% CI, 0.89 to 0.98). Only 1 patient who did not undergo an END based on frozen specimens was undertreated owing to upgrading of the DOI in permanent specimens. CONCLUSIONS: DOI in intraoperative frozen sections has an accuracy of 96.8% and may be reliably used as a clinical tool to determine the need for END in early-stage OCSCC.

Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals.

In contrast with typical Phase III clinical trials, there is little existing methodology for determining the appropriate numbers of patients to enroll in adaptive Phase I trials. And, as stated by Dennis Lindley in a more general context, "[t]he simple practical question of 'What size of sample should I take' is often posed to a statistician, and it is a question that is embarrassingly difficult to answer." Historically, simulation has been the primary option for determining sample sizes for adaptive Phase I trials, and although useful, can be problematic and time-consuming when a sample size is needed relatively quickly. We propose a computationally fast and simple approach that uses Beta distributions to approximate the posterior distributions of DLT rates of each dose and determines an appropriate sample size through posterior coverage rates. We provide sample sizes produced by our methods for a vast number of realistic Phase I trial settings and demonstrate that our sample sizes are generally larger than those produced by a competing approach that is based upon the nonparametric optimal design.

A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs).

Designing clinical trials to study treatments for rare diseases is challenging because of the limited number of available patients. A suggested design is known as the small n sequential multiple assignment randomized trial (snSMART), in which patients are first randomized to one of multiple treatments (stage 1). Patients who respond to their initial treatment continue the same treatment for another stage, while those who fail to respond are rerandomized to one of the remaining treatments (stage 2). The data from both stages are used to compare the efficacy between treatments. Analysis approaches for snSMARTs are limited, and we propose a Bayesian approach that allows for borrowing of information across both stages. Through simulation, we compare the bias, root-mean-square error, width, and coverage rate of 95% confidence/credible interval of estimators from of our approach to estimators produced from (i) standard approaches that only use the data from stage 1, and (ii) a log-Poisson model using data from both stages whose parameters are estimated via generalized estimating equations. We demonstrate the root-mean-square error and width of 95% confidence/credible intervals of our estimators are smaller than the other approaches in realistic settings, so that the collection and use of stage 2 data in snSMARTs provide improved inference for treatments of rare diseases.

Modeling adverse event counts in phase I clinical trials of a cytotoxic agent.

Background/Aims The goal of phase I clinical trials for cytotoxic agents is to find the maximum dose with an acceptable risk of severe toxicity. The most common designs for these dose-finding trials use a binary outcome indicating whether a patient had a dose-limiting toxicity. However, a patient may experience multiple toxicities, with each toxicity assigned an ordinal severity score. The binary response is then obtained by dichotomizing a patient's richer set of data. We contribute to the growing literature on new models to exploit this richer toxicity data, with the goal of improving the efficiency in estimating the maximum tolerated dose. Methods We develop three new, related models that make use of the total number of dose-limiting and low-level toxicities a patient experiences. We use these models to estimate the probability of having at least one dose-limiting toxicity as a function of dose. In a simulation study, we evaluate how often our models select the true maximum tolerated dose, and we compare our models with the continual reassessment method, which uses binary data. Results Across a variety of simulation settings, we find that our models compare well against the continual reassessment method in terms of selecting the true optimal dose. In particular, one of our models which uses dose-limiting and low-level toxicity counts beats or ties the other models, including the continual reassessment method, in all scenarios except the one in which the true optimal dose is the highest dose available. We also find that our models, when not selecting the true optimal dose, tend to err by picking lower, safer doses, while the continual reassessment method errs more toward toxic doses. Conclusion Using dose-limiting and low-level toxicity counts, which are easily obtained from data already routinely collected, is a promising way to improve the efficiency in finding the true maximum tolerated dose in phase I trials.

Infectious Risk after Allogeneic Hematopoietic Cell Transplantation Complicated by Acute Graft-versus-Host Disease.

The occurrence of infections after allogeneic hematopoietic stem cell transplantation (HCT) is nearly universal. However, the relationship between infections and graft-versus-host disease (GVHD) is complex and attribution of infectious-related mortality is highly inconsistent, making comparison of infectious complication rates across allogeneic HCT clinical studies difficult. We categorized infectious complications from diagnosis or 1 year before HCT (whichever occurred later) through 2 years after HCT according to timing, frequency, causative organism, severity, and contribution to mortality for 431 consecutive patients who underwent allogeneic HCT from 2008 to 2011. We then assessed the contribution of risk factors, such as the frequency of pre-HCT infections and post-HCT GVHD, on post-HCT infection frequency and severity. We found that each pre-HCT bacterial infection/year leads to an additional 2.15 post-HCT bacterial infection/year (P = .004). Pre-HCT viral and fungal infections were not predictors for post-HCT infections. Acute GVHD (aGVHD) significantly increased the risk of developing life-threatening (hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.33 to 2.90) and fatal (HR, 2.8; 95% CI, 1.10 to 7.08) infections. Furthermore, patients who develop aGVHD experienced ~60% more infections than patients who never develop aGVHD. Quantification of infection frequency and severity for patients with and without GVHD may facilitate comparison of infectious outcomes across allogeneic HCT trials.

Variation in the use of advanced imaging at the time of breast cancer diagnosis in a statewide registry.

BACKGROUND: Although national guidelines do not recommend extent of disease imaging for patients with newly diagnosed early stage breast cancer given that the harm outweighs the benefits, high rates of testing have been documented. The 2012 Choosing Wisely guidelines specifically addressed this issue. We examined the change over time in imaging use across a statewide collaborative, as well as the reasons for performing imaging and the impact on cost of care. METHODS: Clinicopathologic data and use of advanced imaging tests (positron emission tomography, computed tomography, and bone scan) were abstracted from the medical records of patients treated at 25 participating sites in the Michigan Breast Oncology Quality Initiative (MiBOQI). For patients diagnosed in 2014 and 2015, reasons for testing were abstracted from the medical record. RESULTS: Of the 34,078 patients diagnosed with stage 0-II breast cancer between 2008 and 2015 in MiBOQI, 6853 (20.1%) underwent testing with at least 1 imaging modality in the 90 days after diagnosis. There was considerable variability in rates of testing across the 25 sites for all stages of disease. Between 2008 and 2015, testing decreased over time for patients with stage 0-IIA disease (all P < .001) and remained stable for stage IIB disease (P = .10). This decrease in testing over time resulted in a cost savings, especially for patients with stage I disease. CONCLUSION: Use of advanced imaging at the time of diagnosis decreased over time in a large statewide collaborative. Additional interventions are warranted to further reduce rates of unnecessary imaging to improve quality of care for patients with breast cancer. Cancer 2017;123:2975-83. © 2017 American Cancer Society.

Associations between use of the 21-gene recurrence score assay and chemotherapy regimen selection in a statewide registry.

BACKGROUND: The 21-gene recurrence score (RS) assay predicts response to adjuvant chemotherapy in patients with early-stage, hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative invasive breast cancer, but to the authors' knowledge, the role of the assay in guiding the selection of chemotherapy regimen has not been established. The current study was conducted to examine patterns of use of the RS assay for selecting chemotherapy regimens across a statewide registry from 2006 through 2013. METHODS: Demographic, pathologic, and treatment data were abstracted from medical records for 16,666 women with breast cancer who were treated at 25 hospital systems across Michigan that were participating in the Michigan Breast Oncology Quality Initiative. Treatment patterns were examined based on the RS assay test result. RESULTS: Approximately 25% of patients with lymph node-negative disease who underwent testing with the RS assay and who were treated with chemotherapy received an anthracycline-based regimen, compared with 49% of patients with lymph node-negative disease who were treated with chemotherapy and who had not undergone testing with the RS assay. Of those patients with lymph node-positive disease who underwent testing with the RS assay and who received chemotherapy, 31% received an anthracycline-based regimen. In comparison, 71% of patients with lymph node-positive, chemotherapy-treated disease who did not undergo testing received an anthracycline. From 2006 through 2013, there was a statistically significant decrease in the use of anthracycline-containing regimens in both patients with lymph node-negative and lymph node-positive disease. CONCLUSIONS: Use of anthracycline-containing chemotherapy regimens in eligible patients appears to vary with use of the RS assay, despite the lack of evidence supporting use of the assay to guide regimen selection. Results of ongoing prospective trials should help to define the role of the RS assay in this setting. Cancer 2017;123:948-56. © 2016 American Cancer Society.

Modest Waning of Influenza Vaccine Efficacy and Antibody Titers During the 2007-2008 Influenza Season.

BACKGROUND: Antibody titers decrease with time following influenza vaccination, raising concerns that vaccine efficacy might wane. However, the relationship between time since vaccination and protection is unclear. METHODS: Time-varying vaccine efficacy (VE[t]) was examined in healthy adult participants (age range, 18-49 years) in a placebo-controlled trial of inactivated influenza vaccine (IIV) and live-attenuated influenza vaccine (LAIV) performed during the 2007-2008 influenza season. Symptomatic respiratory illnesses were laboratory-confirmed as influenza. VE(t) was estimated by fitting a smooth function based on residuals from Cox proportional hazards models. Subjects had blood samples collected immediately prior to vaccination, 30 days after vaccination, and at the end of the influenza season for testing by hemagglutination inhibition and neuraminidase inhibition assays. RESULTS: Overall efficacy was 70% (95% confidence interval [CI], 50%-82%) for IIV and 38% (95% CI, 5%-59%) for LAIV. Statistically significant waning was detected for IIV (P = .03) but not LAIV (P = .37); however, IIV remained significantly efficacious until data became sparse at the end of the season. Similarly, antibody titers against influenza virus hemagglutinin and neuraminidase significantly decreased over the season among IIV recipients. CONCLUSIONS: Both vaccines were efficacious but LAIV less so. IIV efficacy decreased slowly over time, but the vaccine remained significantly efficacious for the majority of the season.