A prospective, multicenter cohort study to validate a simple performance status–based survival prediction system for oncologists

Abstract

In predicting the prognosis of patients with advanced cancer, the PS-PPI was as accurate as the PPI. The PS-PPI was useful for short- and long-term survival prediction and for the prediction of survival for patients undergoing chemotherapy. Cancer 2017;123:1442-1452. © 2016 American Cancer Society.

Validation of the Palliative Prognostic Index, Performance Status–Based Palliative Prognostic Index and Chinese Prognostic Scale in a home palliative care setting for patients with advanced cancer in China

“…In the previous study, [ 31 ] the PS-PPI was as accurate as the PPI to predict the survival of advanced cancer patients, which paralleled our findings that the AUROCs of the PPI and PS-PPI were not significantly different from each other. Survival time among the three risk groups of the PS-PPI was significantly different from each other.…”

“…Using the Eastern Cooperative Oncology Group (ECOG) PS to assess advanced cancer patients’ performance status instead of the Palliative Performance Scale (PPS), the PS-PPI was recently developed by Takeshi et al in 2016 to predict the survival of advanced cancer patients [ 31 ]. The PS-PPI score was calculated by summing the scores of the ECOG PS scores, oral intake, delirium, dyspnea at rest, and edema [ 35 ].…”

“…However, there is no study to support the validity of the PPI in the HPC setting in mainland China [ 24 , 25 ]. Recently, Takeshi et al [ 31 ] reported the development of the Performance Status–Based Palliative Prognostic Index (PS-PPI), a brief version of the PPI, in which the performance status was based on the Eastern Cooperative Oncology Group (ECOG) PS instead of the Palliative Performance Scale (PPS). The sensitivity of the PS-PPI for advanced cancer patients was higher than that of the PPI.…”

Factors contributing to the ceiling effect of the EQ-5D-5L: an analysis of patients with prostate cancer judged “no-problems”

“…Model 5 showed the lowest AIC among our models, and almost all inputted variables were excluded, which indicates that the LASSO selections in our analysis work well. Lastly, statistically significant ORs of dummy variables for registered hospitals in a few of the models may have occurred due to the different [32][33][34], CTCAE common terminology criteria for adverse events [35], NA Factors not assessed by logistic regression models due to the results of LASSO selections-factors not input into a LASSO selection a Registered hospitals were set as dummy variables in all LASSO selections (Hospital A to D were set in four binomial (0 or 1) variables; An instance of all variables being 0 is represented by Hospital E) tendency for disease progression of patients registered in each hospital [26]. We also cannot exclude regional differences between the hospitals and/or judgments by physicians in charge.…”

“…Medical information was provided by their physicians in charge after informed consent was given, and the filled-out questionnaires were sent by mail and returned by patients to the data center [26]. The provided information included the presence or absence of cancer progression, prostate-specific antigen (PSA) concentration [29][30][31], number of days from last treatment, the presence or absence of other diseases, and Eastern Cooperative Oncology Group (ECOG) performance status (PS) [32][33][34], and common terminology criteria for adverse events (CTCAE) version 4.0 [35] were collected. In addition, we added information regarding received treatments (i.e., treatments previously undertaken and ongoing treatments) in order to examine therapies related to a CE for this analysis.…”