Surgical management of CRS was associated with significant improvement on objective and QOL measures; however, specific patient factors, in particular ASA and depression, predict poorer outcome. Preoperative CT may be a predictor of endoscopic and QOL outcome and deserves further study.

The quality of physician-patient communication is a critical factor in treatment outcomes and patient satisfaction with care. To date, few studies have specifically conducted an in-depth evaluation of the effect of telemedicine (TM) on physician-patient communication in a medical setting. Our objective was to determine whether physical separation and technology used during TM have a negative effect on physician-patient communication. In this noninferiority randomized clinical trial, patients were randomized to receive a single consultation with one of 9 physicians, either in person (IP) or via TM. Patients (n = 221) were recruited from pulmonary, endocrine, and rheumatology clinics at a Midwestern Veterans Administration hospital. Physician-patient communication was measured using a validated self-report questionnaire consisting of 33 items measuring satisfaction with visit convenience and physician's patient-centered communication, clinical competence, and interpersonal skills. Satisfaction for physician's patient-centered communication was similar for both consultation types (TM = 3.76 versus IP = 3.61), and noninferiority of TM was confirmed (noninferiority t-test p = 0.002). Patient satisfaction with physician's clinical competence (TM = 4.63 versus IP = 4.52) and physician's interpersonal skills (TM = 4.79 versus IP = 4.74) were similar, and noninferiority of TM was confirmed (noninferiority t-test p = 0.006 and p = 0.04, respectively). Patients reported greater satisfaction with convenience for TM as compared to IP consultations (TM = 4.41 versus IP = 2.37, noninferiority t-test p < 0.001). Patients were equally satisfied with physician's ability to develop rapport, use shared decision making, and promote patient-centered communication during TM and IP consultations. Our data suggest that, despite physical separation, physician-patient communication during TM is not inferior to communication during IP consultations.

Surgeries to correct nasal airway obstruction (NAO) often have less than desirable outcomes, partly due to the absence of an objective tool to select the most appropriate surgical approach for each patient. Computational fluid dynamics (CFD) models can be used to investigate nasal airflow, but variables need to be identified that can detect surgical changes and correlate with patient symptoms. CFD models were constructed from pre- and post-surgery computed tomography scans for 10 NAO patients showing no evidence of nasal cycling. Steady-state inspiratory airflow, nasal resistance, wall shear stress, and heat flux were computed for the main nasal cavity from nostrils to posterior nasal septum both bilaterally and unilaterally. Paired t-tests indicated that all CFD variables were significantly changed by surgery when calculated on the most obstructed side, and that airflow, nasal resistance, and heat flux were significantly changed bilaterally as well. Moderate linear correlations with patient-reported symptoms were found for airflow, heat flux, unilateral allocation of airflow, and unilateral nasal resistance as a fraction of bilateral nasal resistance when calculated on the most obstructed nasal side, suggesting that these variables may be useful for evaluating the efficacy of nasal surgery objectively. Similarity in the strengths of these correlations suggests that patient-reported symptoms may represent a constellation of effects and that these variables should be tracked concurrently during future virtual surgery planning.

Objective. To develop and validate a clinically informed algorithm that uses solely Medicare claims to identify, with a high positive predictive value, incident breast cancer cases. Data Source. Population-based Surveillance, Epidemiology, and End Results (SEER) Tumor Registry data linked to Medicare claims, and Medicare claims from a 5 percent random sample of beneficiaries in SEER areas. Study Design. An algorithm was developed using claims from 1995 breast cancer patients from the SEER-Medicare database, as well as 1995 claims from Medicare control subjects. The algorithm was validated on claims from breast cancer subjects and controls from 1994. The algorithm development process used both clinical insight and logistic regression methods. Data Extraction. Training set: Claims from 7,700 SEER-Medicare breast cancer subjects diagnosed in 1995, and 124,884 controls. Validation set: Claims from 7,607 SEER-Medicare breast cancer subjects diagnosed in 1994, and 120,317 controls. Principal Findings. A four-step prediction algorithm was developed and validated. It has a positive predictive value of 89 to 93 percent, and a sensitivity of 80 percent for identifying incident breast cancer. The sensitivity is 82-87 percent for stage I or II, and lower for other stages. The sensitivity is 82-83 percent for women who underwent either breast-conserving surgery or mastectomy, and is similar across geographic sites. A cohort identified with this algorithm will have 89-93 percent incident breast cancer cases, 1.5-6 percent cancer-free cases, and 4-5 percent prevalent breast cancer cases. Conclusions. This algorithm has better performance characteristics than previously proposed algorithms. The ability to examine national patterns of breast cancer care using Medicare claims data would open new avenues for the assessment of quality of care.

SUMMARY We consider the problem of selecting one model from a large class of plausible models. A predictive Bayesian viewpoint is advocated to avoid the specification of prior probabilities for the candidate models and the detailed interpretation of the parameters in each model. Using criteria derived from a certain predictive density and a prior specification that emphasizes the observables, we implement the proposed methodology for three common problems arising in normal linear models: variable subset selection, selection of a transformation of predictor variables and estimation of a parametric variance function. Interpretation of the relative magnitudes of the criterion values for various models is facilitated by a calibration of the criteria. Relationships between the proposed criteria and other well‐known criteria are examined.

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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.