Outcomes of Conversion Surgery for Patients With Low-Risk Papillary Thyroid Carcinoma.

Importance: The outcomes of patients with low-risk thyroid cancer who undergo surgery following a period of active surveillance (AS) are not well-defined. Objective: To evaluate surgical, pathologic, and oncologic outcomes among patients undergoing conversion surgery (CS) following AS for low-risk papillary thyroid carcinoma. Design, Setting, and Participants: In this cohort study, patients who underwent CS for disease progression were compared with patients who underwent CS without disease progression and with a propensity score-matched cohort of patients who underwent initial surgery (IS). The median (IQR) postsurgical follow-up time was 40.3 (18.0-59.0) months. Patients were treated at a quaternary cancer referral center in the United States. Exposures: Surgery. Main Outcomes and Measures: Surgical complications, pathologic characteristics, overall survival (OS), and recurrence-free survival (RFS). Results: Of 550 patients who underwent AS, 55 (10.0%) had CS, of whom 39 (7.1%) had surgery due to suspected disease progression (median [IQR] age, 48 [39-56] years; 32 [82.1%] female). There were no clinically meaningful differences in rates of surgical sequalae between the progression CS group (12 of 39 [30.7%]) and the nonprogression CS group (7 of 16 [43.8%]) (Cramer V, 0.2; 95% CI, 0.01-0.5). The 5-year OS was 100% (95% CI, 100%-100%) in both the disease-progression CS cohort and the IS cohort. Although the cohort of patients undergoing CS after disease progression was by definition a subset with more aggressive tumor behavior, no clinically meaningful differences were observed in the rates of regional recurrence (2 of 39 [5.1%] vs 0 of 39 patients with IS), local recurrence (0 patients), distant metastasis (0 patients), or disease-specific mortality (0 patients) when compared with the matched IS group. Five-year RFS rates were similar: 100% in the IS group and 86% (95% CI, 70%-100%) in the CS group. Conclusions and Relevance: In this cohort study, CS for suspected disease progression was associated with surgical and oncologic outcomes similar to IS, supporting the feasibility and safety of AS for patients with low-risk papillary thyroid carcinoma.

Analyzing mastectomy and reconstruction weight in immediate autologous breast reconstruction: A preliminary study.

BACKGROUND: This study aims to explore the ideal breast size by assessing the relationship between mastectomy to free flap weight ratio and complications as well as patient-reported outcomes in autologous breast reconstruction (ABR). METHOD: A retrospective review of patients undergoing bilateral immediate ABR with mastectomy and flap weights available was completed. Patients were divided into three groups based on the ratio of mastectomy to flap weights. The patients were grouped as "maintained" if the flap weight was within 10% of the mastectomy weight. Patients with a weight difference greater than 10% were used to declare "downsized" or "upsized." Outcomes included complications and four domains of the BREAST-Q at 1-year postoperatively. RESULTS: Three hundred and fifty-nine patients were included in the analysis, of which 112 were downsized, 91 maintained, and 156 upsized, respectively. Presence of complications did not significantly differ among the groups. At 1-year postoperatively, Sexual Well-being significantly differed (p = 0.033). Between preoperative and 1 year, patients who upsized experienced an improvement in Satisfaction with Breasts by 16 points (p < 0.001), while patients who downsized experienced a decline in Physical Well-being of the Chest by 7 points (p = 0.016). Multivariable linear regression model showed that Sexual Well-being was 13 points lower in the downsized cohort than in the maintained cohort (ß = -13, 95% confidence interval: -21 to -5.4; p = 0.001). CONCLUSION: Although complication rates do not significantly differ between the three cohorts, patients who downsize may have lower Sexual Well-being postoperatively. Surgeons should consider our preliminary findings to counsel patients preoperatively about the predicted breast size and the impact of downsizing on sexual health.

Nonresponse data in sexual well-being among breast reconstruction patients-who are we overlooking?

BACKGROUND: Missing data can affect the representativeness and accuracy of survey results, and sexual health-related surveys are especially at a higher risk of nonresponse due to their sensitive nature and stigma. The purpose of this study was to evaluate the proportion of patients who do not complete the BREAST-Q Sexual Well-being relative to other BREAST-Q modules and compare responders versus nonresponders of Sexual Well-being. We secondarily examined variables associated with Sexual Well-being at 1-year. METHODS: A retrospective analysis of patients who underwent breast reconstruction from January 2018 to December 2021 and completed any of the BREAST-Q modules postoperatively at 1-year was performed. RESULTS: The 2941 patients were included. Of the four BREAST-Q domains, Sexual Well-being had the highest rate of nonresponse (47%). Patients who were separated (vs. married, OR = 0.69), whose primary language was not English (vs. English, OR = 0.60), and had Medicaid insurance (vs. commercial, OR = 0.67) were significantly less likely to complete the Sexual Well-being. Postmenopausal patients were significantly more likely to complete the survey than premenopausal patients. Lastly, autologous reconstruction patients were 2.93 times more likely to respond than implant-based reconstruction patients (p < 0.001) while delayed (vs. immediate, OR = 0.70, p = 0.022) and unilateral (vs. bilateral, OR = 0.80, p = 0.008) reconstruction patients were less likely to respond. History of psychiatric diagnosis, aromatase inhibitors, and immediate breast reconstruction were significantly associated with lower Sexual Well-being at 1-year. CONCLUSION: Sexual Well-being is the least frequently completed BREAST-Q domain, and there are demographic and clinical differences between responders and nonresponders. We encourage providers to recognize patterns in nonresponse data for Sexual-Well-being to ensure that certain patient population's sexual health concerns are not overlooked.

Disparity Reduction in United States Breast Reconstruction: An Analysis From 2005-2017 Using 3 Nationwide Data Sets.

BACKGROUND: Following passage of the Women's Health and Cancer Rights Act (WHCRA), a steady rise in breast reconstruction rates was reported; however, a recent update is lacking. This study aimed to evaluate longitudinal trends in breast reconstruction (BR) rates in the U.S. and relevant sociodemographic factors. METHODS: Mastectomy cases with/without BR from 2005 through 2017 were abstracted from the National Surgical Quality Improvement Program (NSQIP), Surveillance, Epidemiology, and End Results (SEER) Program, and National Cancer Database (NCDB). BR rates were examined using Poisson regression. Multivariable logistic regression analysis of NCDB data was used to identify predictors of reconstruction. Race and insurance distributions were evaluated over time. RESULTS: Of 1,554,381 mastectomy patients, 507,631 (32.7%) received BR. Annual reconstruction rates per 1000 mastectomies increased from 2005 to 2012 (NSQIP: Incidence Rate Ratio (IRR) 1.077; SEER: 1.090; NCDB: 1.092) and stabilized from 2013 to 2017. NCDB data showed that patients who were younger (≤59 years), privately insured, had fewer comorbidities, and underwent contralateral prophylactic mastectomy were more likely to undergo BR (all p<0.001). Over time, the increase in BR rates was higher among Black (252.3%) and Asian (366.4%) patients than White patients (137.3%). BR rates increased more among Medicaid (418.6%) and Medicare (302.8%) patients than privately insured (125.3%) patients. CONCLUSIONS: This analysis demonstrates stabilization in immediate BR rates over the last decade; reasons behind this stabilization are likely multifactorial. Disparities based on race and insurance type have decreased, with a more equitable distribution of BR rates.

Social Determinants of Health and Patient-reported Outcomes Following Autologous Breast Reconstruction, Using Insurance as a Proxy.

BACKGROUND: Insurance type can serve as a surrogate marker for social determinants of health and can influence many aspects of the breast reconstruction experience. We aimed to examine the impact of insurance coverage on patients reported outcomes with the BREAST-Q (patient reported outcome measure for breast reconstruction patients, in patients receiving) in patients receiving deep inferior epigastric artery perforator (DIEP) flap breast reconstruction. METHODS: We retrospectively examined patients who received DIEP flaps at our institution from 2010 to 2019. Patients were divided into categories by insurance: commercial, Medicaid, or Medicare. Demographic factors, surgical factors, and complication data were recorded. Descriptive statistics, Fisher's exact, Kruskal-Wallis rank sum tests, and generalized estimating equations were performed to identify associations between insurance status and five domains of the BREAST-Q Reconstructive module. RESULTS: A total of 1,285 patients were included, of which 1,011 (78.7%) had commercial, 89 (6.9%) had Medicaid, and 185 (14.4%) had Medicare insurances. Total flap loss rates were significantly higher in the Medicare and Medicaid patients as compared to commercial patients; however, commercial patients had a higher rate of wound dehiscence as compared to Medicare patients. With all other factors controlled for, patients with Medicare had lower Physical Well-being of the Chest (PWBC) than patients with commercial insurance (ß = - 3.1, 95% confidence interval (CI): -5.0, -1.2, p = 0.002). There were no significant associations between insurance classification and other domains of the BREAST-Q. CONCLUSION: Patients with government-issued insurance had lower success rates of autologous breast reconstruction. Further, patients with Medicare had lower PWBC than patients with commercial insurance regardless of other factors, while other BREAST-Q metrics did not differ. Further investigation as to the causes of such variation is warranted in larger, more diverse cohorts.

Practical considerations for sandwich variance estimation in two-stage regression settings.

We present a practical approach for computing the sandwich variance estimator in two-stage regression model settings. As a motivating example for two-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has been rarely applied in regression calibration, despite it requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is likely due to requiring specialized statistical coding. We first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich to a resampling variance approach for both settings. Finally, we further compare these two variance estimation approaches for data examples from the Women's Health Initiative (WHI) and Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or over-covered in certain settings, particularly when there was high correlation between covariates or large measurement error.

Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma.

BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

An augmented likelihood approach for the Cox proportional hazards model with interval-censored auxiliary and validated outcome data-with application to the Hispanic Community Health Study/Study of Latinos.

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Nutritional Blood Concentration Biomarkers in the Hispanic Community Health Study/Study of Latinos: Measurement Characteristics and Power.

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes.

An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures.

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self-reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error.