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BACKGROUND: Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. OBJECTIVE: This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. METHODS: We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. RESULTS: Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. CONCLUSIONS: We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

Original publication

DOI

10.1089/jpm.2013.0368

Type

Journal article

Journal

J Palliat Med

Publication Date

05/2014

Volume

17

Pages

545 - 552

Keywords

Analysis of Variance, China, Female, Hospice Care, Humans, Karnofsky Performance Status, Linear Models, Male, Middle Aged, Neoplasms, Pain Measurement, Prognosis, Proportional Hazards Models, Prospective Studies, Quality of Life, Terminally Ill, Time Factors