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PROgnostic Model for Advanced Cancer (PRO-MAC)
  1. Allyn Hum1,2,
  2. Yoko Kin Yoke Wong3,
  3. Choon Meng Yee1,2,
  4. Chung Seng Lee1,2,
  5. Huei Yaw Wu1,2 and
  6. Mervyn Yong Hwang Koh1,2
  1. 1 Palliative Care Centre for Excellence in Research and Education, Singapore
  2. 2 Department of Palliative Medicine, Tan Tock Seng Hospital, Singapore
  3. 3 Epidemiology, Singapore Clinical Research Institute, Singapore
  1. Correspondence to Dr Yoko Kin Yoke Wong, Department of Epidemiology, Singapore Clinical Research Institute, Singapore 138669, Singapore; yoko.wong{at}


Objective To develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting.

Design Prospective cohort study with 2 years’ follow-up.

Setting Single tertiary teaching hospital in Singapore.

Participants The study includes consecutive patients diagnosed with advanced cancer who were referred to a palliative care unit between 2013 and 2015 (N=840). Data were randomly split into training (n=560) and validation (n=280) sets.

Results 743 (88.5%) patients died with a mean follow-up of 97.0 days (SD 174.0). Cox regression modelling was used to build a prognostic model, cross-validating with six randomly split dataset pairs. Predictor variables for the model included functional status (Palliative Performance Scale, PPS V.2), symptoms (Edmonton Symptom Assessment System, ESASr), clinical assessment (eg, the number of organ systems with metastasis, serum albumin and total white cell count level) and patient demographics. The area under the receiver operating characteristic curve using the final averaged prognostic model was between 0.69 and 0.75. Our model classified patients into three prognostic groups, with a median survival of 79.0 days (IQR 175.0) for the low-risk group (0–1.5 points), 42.0 days (IQR 75.0) for the medium-risk group (2.0–5.5 points), and 15.0 days (IQR 28.0) for the high-risk group (6.0–10.5 points).

Conclusions PROgnostic Model for Advanced Cancer (PRO-MAC) takes into account patient and disease-related factors and identify high-risk patients with 90-day mortality. PPS V.2 and ESASr are important predictors. PRO-MAC will help physicians identify patients earlier for supportive care, facilitating multidisciplinary, shared decision-making.

  • prediction model
  • advanced cancer
  • palliative care

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  • AH and YKYW are joint first authors.

  • AH and YKYW contributed equally.

  • Contributors AH and MYHK contributed to the concept, design and implementation of the research. YKYW performed data analysis and model building. AH took the lead in writing the manuscript with input from all authors. All authors provided critical feedback and helped shape the research, analysis and manuscript.

  • Funding This study was funded by the Singapore Millennium Foundation.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethical approval for the conduct of the study was obtained from the institutional ethics board.

  • Provenance and peer review Not commissioned; externally peer reviewed.