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PROgnostic Model for Advanced Cancer (PRO-MAC)
Allyn Hum1,2, Yoko Kin Yoke Wong3, Choon Meng Yee1,2
1Palliative Care Centre for Excellence in Research and Education, Singapore.
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.
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