This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make endof-life decisions. The clinician’s prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician’s prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

Purpose: We aimed to develop a prognostic model to assist palliative care referral at least 3 months before death in advanced cancer patients treated at an outpatient medical oncology clinic. Materials and Methods: In this prospective cohort study, a total of 200 patients were enrolled at a tertiary cancer center in South Korea. The major eligibility criterion was an expected survival of less than a year as estimated by their oncologists. We analyzed the influences of known prognostic factors along with chemotherapy status, mid-arm circumference, and triceps skinfold thickness on survival time. Results: The mean age of the patients was 64.5 years, 36% were female, and the median survival time was 7.6 months. In the multivariate analysis, we found 6 significant factors related to poor survival: a poor Eastern Cooperative Oncology Group (ECOG) performance status (≥2), not undergoing chemotherapy, anorexia, a low lymphocyte level (<12%), a high lactate dehydrogenase (LDH) level (≥300 IU/L), and a low mid-arm circumference (<23 cm). We developed a prognostic model (score, 0-8.0) to predict 3-month survival based on the multivariate analysis. Patients who scored ≥4.0 points had a short survival of less than 3 months (p<0.001). The discriminating ability of the prognostic model using the area under the receiver operating characteristic curve (AUC) was 0.88. Conclusion The prognostic model using ECOG performance status, chemotherapy status, anorexia, lymphocytes, LDH, and mid-arm circumference can predict 3-month survival in medical oncology outpatients. It can alert oncologists to refer patients to palliative care specialists before it is too late.