title: Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?
abstract: Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer.
  
  
  
  In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: 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.
  
  
  
  A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71-133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47-0.67) for PaP-CPS, 0.78 (95% CI 0.7-0.87) for PaP-without CPS, and 0.73 (95% CI 0.64-0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P < 0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P < 0.05).
  
  
  
  We found that PaP score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.
authors.affiliation: Department of Palliative Care, Rehabilitation and Integrative Medicine, MD Anderson Cancer Center, Houston, 77030, USA. Electronic address: dhui@mdanderson.org.
authors.forename: David
authors.initials: D
authors.lastname: Hui
authors.affiliation: Department of Biostatistics, MD Anderson Cancer Center, Houston, 77030, USA.
authors.forename: Minjeong
authors.initials: M
authors.lastname: Park
authors.affiliation: Department of Biostatistics, MD Anderson Cancer Center, Houston, 77030, USA.
authors.forename: Diane
authors.initials: D
authors.lastname: Liu
authors.affiliation: Department of Clinical Oncology, Barretos Cancer Hospital, Barretos, São Paulo, Brazil.
authors.forename: Carlos Eduardo
authors.initials: CE
authors.lastname: Paiva
authors.affiliation: Department of Medicine, Dongguk University, Seoul, South Korea.
authors.forename: Sang-Yeon
authors.initials: SY
authors.lastname: Suh
authors.affiliation: Department of Palliative and Supportive Care, Palliative Care Team and Seirei Hospice, Seirei Mikatahara General Hospital, Shizuoka, Japan.
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Morita
authors.affiliation: Department of Palliative Care, Rehabilitation and Integrative Medicine, MD Anderson Cancer Center, Houston, 77030, USA.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Bruera
mesh_terms: D000328:Adult; D000368:Aged; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D017567:Karnofsky Performance Status; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis* / Q000401:mortality*; D010166:Palliative Care / Q000379:methods; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D016019:Survival Analysis; D055815:Young Adult
keywords: Clinical prediction rule; Forecasting; Neoplasms; Prognosis; Statistical data analysis; Survival