Correlation of demographic factors with length of treatment and survival in a palliative radiation oncology population.

Abstract

123 Background: Radiation is often used to palliate symptoms in patients with advanced cancer and the number of fractions used can vary significantly from as short as a single fraction (SF) to up to a multiple week course. No data currently exists regarding demographic factors and their influence on survival and fractionation decisions. The aim of this project is to investigate the association of demographic factors with survival, likelihood of SF use in patients (pts) receiving RT for bone metastases, and likelihood of hypofractionation (HF) (≤ 5 fx) in pts being treated for any palliative reason, excluding those receiving partial brain RT (PBRT) which includes stereotactic radiosurgery, and stereotactic radiation therapy. Methods: We retrospectively reviewed charts of pts treated with palliative RT between 1/2015 – 5/2017 at 2 tertiary centers and 4 community satellite practices. Demographic factors analyzed are included in table 1. Logistic regression was used to evaluate the associations between the factors and outcomes. Results: A total of 928 pts were included in the survival analysis. In the two subset analyses, 373 pts were included in the bone metastasis analysis and 745 were included in the analysis of all patients excluding pts receiving PBRT. In pts treated for bone metastases, consult type (inpatient vs. outpatient) was the only significant factor on multivariate analysis, with inpatient consults being more likely to get SF (HR = 2.169, =.025). In the pts receiving palliative RT (excluding PBRT), race and consult location (tertiary vs. community) were significant. Non-white pts (HR=0.527, p=.012) and pts treated in the community (HR=.778, p<.001) were less likely to get HF. Gender (male vs. female), consult location (tertiary vs. community), and consult type (inpatient vs. outpatient) were significant on multivariate analysis for survival (HR=1.243, p=.028; HR=1.602, p<.001; HR=2.301, p<.001, respectively). Conclusions: This analysis suggests that demographic factors may affect both survival and decision making regarding fractionation in patients receiving palliative radiation. Further investigation into the reasons for these differences is needed.

Maturity degree assessment of hospital ward system using integrated fuzzy AHP-TOPSIS model

“…Matrix falseZij1 represents a symmetric matrix where the elements on the diagonal are equal to 1. According to reference, [25] for the off-diagonal elements, the similarity between the 2 factors is too high if the correlation degree of 2 elements in falseZij1 is >0.95. In this case, it is necessary to consult with experts and remove the highly similar factors to determine the final factor system.…”

“…It was assumed that the factors h i, h j can be represented by vectors ) respectively, the similarity Z 1 ij between the factors h i and h j can be calculated using the formula below: Matrix Z 1 ij represents a symmetric matrix where the elements on the diagonal are equal to 1. According to reference, [25] for the off-diagonal elements, the similarity between the 2 factors is too high if the correlation degree of 2 elements in Z 1 ij is >0.95. In this case, it is necessary to consult with experts and remove the highly similar factors to determine the final factor system.…”

“…The similarity of corresponding vectors of each sample can be compared to judge whether the 2 factors are related. Based on the method in Reference, [ 25 ] the correlation degree of factors can be judged by comparing the angle between the vectors of 2 factors. The similarity analysis method based on vector cosine values can disregard the order of samples and accurately capture the true similarity level between 2 factors.…”