A Comparison of the Accuracy of Clinician Prediction of Survival Versus the Palliative Prognostic Index

Paige Farinholt, Minjeong Park, Ying Guo, Eduardo Bruera, David Hui

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)792-797
Number of pages6
JournalJournal of pain and symptom management
Volume55
Issue number3
DOIs
StatePublished - Mar 2018

Keywords

  • Clinical prediction rule
  • forecasting
  • neoplasms
  • prognosis
  • statistical data analysis
  • survival

ASJC Scopus subject areas

  • General Nursing
  • Clinical Neurology
  • Anesthesiology and Pain Medicine

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

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