ABSTRACT
INTRODUCTION: The aim of this study was to perform a Surveillance, Epidemiology, and End Results (SEER) analysis on the effect of radiotherapy (RT) on survival among patients with prostate ductal adenocarcinoma (DA), a rare variant of prostate cancer. PATIENTS AND METHODS: Cases of T1 to 4 N0 M0 prostate DA diagnosed between 2004 and 2013 were extracted from SEER. The association between categorical variables and radiation therapy was assessed for statistical significance using the χ2 test or Fisher exact test. Difference in continuous variables across the RT groups was assessed for statistical significance using the 2-sample t test or non-parametric test. The distribution of overall survival (OS) and disease-specific survival (DSS) between the RT groups was assessed using the Kaplan-Meier method and the log rank test and after propensity matching. The association between hazards of death (HR) and covariates was examined using Cox proportional hazards model. A 2-sided P-value of .05 was used to determine statistical significance. RESULTS: A total of 205 patients met inclusion criteria. On univariate analysis, RT was associated with significant improvement in OS and DSS. On multivariate Cox regression, RT significantly decreased risk of death for both OS and DSS (HR, 0.516; 95% confidence interval [CI], 0.273-0.978 and HR, 0.232; 95% CI, 0.082-0.658, respectively). After propensity score matching, RT demonstrated a persistent improvement in both OS and DSS. CONCLUSIONS: RT decreased risk of death for both OS and DSS in patients with node-negative, nonmetastatic prostate DA on multivariable analysis. RT was also associated with improved OS and DSS after propensity matching.
Subject(s)
Adenocarcinoma , Prostatic Neoplasms , Humans , Kaplan-Meier Estimate , Male , Proportional Hazards Models , Prostatic Neoplasms/radiotherapy , Radiotherapy, Adjuvant , SEER ProgramABSTRACT
BACKGROUND: Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator. METHODS: Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America. RESULTS: Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk. CONCLUSION: The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications.