Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

Abstract

Objective: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival.

Design: Prospective multicentre observational cohort study.

Setting: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams).

Participants: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services.

Main outcome measures: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions.

Results: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival.

Conclusions: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival.

Fig 1 Selection of study sample

Fig 2 Kaplan-Meier survival curves for PiPS-A models. Graph shows survival curves for three prognostic groups identified by PiPS-A scores. Vertical lines indicate survival at specific “cut-off points” of 14 and 56 days. Harrell’s C index is defined as proportion of pairs of participants in which predictions and outcomes are concordant, and C=0.6894 indicates that PiPS-A scores can correctly order survival times for pairs of participants 68.9% of time

Fig 3 Kaplan-Meier survival curves for PiPS-B models. Graph shows survival curves for three prognostic groups identified by PiPS-B scores. Vertical lines indicate survival at specific “cut-off points” of 14 and 56 days. Harrell’s C index is defined as proportion of pairs of participants in which predictions and outcomes are concordant, and C=0.6745 indicates that PiPS-B scores can correctly order survival times for pairs of participants 67.5% of time