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A Comparison of the Accuracy of Clinician Prediction of Survival Versus the Palliative Prognostic Index
Authors:Paige Farinholt  Minjeong Park  Ying Guo  Eduardo Bruera  David Hui
Affiliation:1. Department of Internal Medicine, Baylor College of Medicine, Houston, Texas, USA;2. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA;3. Department of Palliative Care, Rehabilitation and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
Abstract:

Context

Survival predictions for advanced cancer patients impact many aspects of care, but the accuracy of clinician prediction of survival (CPS) is low. Prognostic tools such as the Palliative Prognostic Index (PPI) have been proposed to improve accuracy of predictions. However, it is not known if PPI is better than CPS at discriminating survival.

Objective

We compared the prognostic accuracy of CPS to PPI in patients with advanced cancer.

Methods

This was a prospective study in which palliative care physicians at our tertiary care cancer center documented both the PPI and CPS in hospitalized patients with advanced cancer. We compared the discrimination of CPS and PPI using concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index, and integrated discrimination improvement for 30-day survival and 100-day survival.

Results

Two hundred fifteen patients were enrolled with a median survival of 109 days and a median follow-up of 239 days. The AUC for 30-day survival was 0.76 (95% CI 0.66–0.85) for PPI and 0.58 (95% CI 0.47–0.68) for CPS (P < 0.0001). Using the net reclassification index, 67% of patients were correctly reclassified using PPI instead of CPS for 30-day survival (P = 0.0005). CPS and PPI had similar accuracy for 100-day survival (AUC 0.62 vs. 0.64; P = 0.58).

Conclusion

We found that PPI was more accurate than CPS when used to discriminate survival at 30 days, but not at 100 days. This study highlights the reason and timing for using PPI to facilitate survival predictions.
Keywords:Clinical prediction rule  forecasting  prognosis  neoplasms  statistical data analysis  survival
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