Meta-analysis of Survival Prediction with Palliative Performance Scale

Abstract

This paper aims to reconcile the use of Palliative Performance Scale (PPSv2) for survival prediction in palliative care through an international collaborative study by five research groups. The study involves an individual patient data meta-analysis on 1,808 patients from four original datasets to reanalyze their survival patterns by age, gender, cancer status, and initial PPS score. Our findings reveal a strong association between PPS and survival across the four datasets. The Kaplan-Meier survival curves show each PPS level as distinct, with a strong ordering effect in which higher PPS levels are associated with increased length of survival. Using a stratified Cox proportional hazard model to adjust for study differences, we found females lived significantly longer than males, with a further decrease in hazard for females not diagnosed with cancer. Further work is needed to refine the reporting of survival times/probabilities and to improve prediction accuracy with the inclusion of other variables in the models. Resume / Cet article vise a reconcilier !'usage de l'echelle de performance en soins palliatifs [EPSP] comme facteur predlctlf de survie grace aun projet international commun auquel ont particlpe cinq groupes de recherche. Cette etude comporte une meta-analyse des donnees recueillies aupres des 1,808 patients provenant de quatre cohortes differentes. On a done refait I'analyse du pronostic de survie selon I'age, Ie genre, Ie stade de la maladie et Ie score initial de I'EPSP. Nos resultats revelent qu'il y a une relation importante entre I'EPSP et Ie facteur predictif de survie atravers I'ensemble des donnees des quatre groupes. Les courbes de survie Kaplan-Meier demontrent que chaque niveau de I'EPSP est distinct et que les plus hauts scores de I'EPSP sont assocles aune plus longue periods de survie. A I'aide du modele de hasard proportionnel de Cox, nous avons realuste les differences qui existaient entre les etudes et nous avons decouvert que les femmes, de tacon significative, vivent plus longtemps que les hommes. De plus, ces probabilites sont encore plus elevees chez les femmes n'ayant pas ete diagnostiquees du cancer. II nous faudra d'autres etudes pour perfectionner nos previsions sur Ie temps de survie/probabilite et pour ameliorer "exactitude des predictions tout en y ajoutant d'autres variables.

Nurse and physician inter-rater agreement of three performance status measures in palliative care outpatients

“…However, for the ECOG, some patients who would have qualified for the trial according to physicians would be discounted by nurses. The PPS is increasingly used in clinical palliative care settings [19], for prognostication in hospice populations [18,24,25], and for the creation of collaborative care plans based on level of function [32]. A PPS of ≤60 is increasingly being used to assess eligibility for home care services based on the observation that this population requires more home support [1].…”

Predictors of Health Service Use Over the Palliative Care Trajectory

“…10 The AHCR has been used in numerous other studies, for a wide range of patient populations and clinical settings 7,[11][12][13][14][15][16][17] The Palliative Performance Scale (PPS) was used to collect data on functional and cognitive status of the patient during each caregiver interview. 18 The PPS assesses the physical functioning in ambulation, activity, and evidence of disease, ability for self-care, intake (i.e., person's eating habits), and level of consciousness. The possible values range from 100% (full ambulation and healthy) to 0% (death), decreasing in 10% decrements.…”

Survival Duration among Patients with a Noncancer Diagnosis Admitted to a Palliative Care Unit: A Retrospective Study

“…[2][3][4] We also found a correlation between PPS and survival. 5,[9][10][11][12] It was not surprising, therefore, to find that noncancer patients had both a lower PPS and a lower survival. However, we did not expect to find a lower survival even when comparing patients with similar prognoses and PPS scores, or that the diagnosis of cancer was not correlated with survival on multivariable analysis.…”

“…We measured correlations between LOS and patient characteristics using Pearson's test. To perform multivariable regression analyses, we grouped PPS scores into low (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), medium (30-40) and high (50 + ). 8 We constructed a multivariable regression model for LOS that included age and gender, as well as all factors that correlated with LOS on bivariate analysis with a p < 0.05 (the PPS and prognostic categories as well as a cancer diagnosis).…”