A 2-week prognostic prediction model for terminal cancer patients in a palliative care unit at a Japanese general hospital

作者：Ohde Sachiko^*; Hayashi Akitoshi; Takahasi Osamu; Yamakawa Sen; Nakamura Megumi; Osawa Ayako; Shapiro Mina L; Deshpande Gautam A; Tokuda Yasuharu; Omata Fumio; Ishida Yasushi; Soejima Kumiko; Hinohara Shigeaki; Fukui Tsuguya

来源：Palliative Medicine, 2011, 25(2): 170-176.

DOI：10.1177/0269216310383741

• Palliative care
• prediction model
• prognosis
• survival
• terminal cancer

Objective: We aimed to develop a prognostic prediction model for 2-week survival among patients with terminal cancer in a palliative care unit (PCU).
Methods: A prospective cohort study was conducted on terminal cancer patients in the PCU for 11 months at a general hospital in Tokyo, Japan. We collected data regarding demographics, treatment history, performance status, symptoms, and laboratory results. Patients who survived more than 2 weeks were labeled 'long survivors' and those who died within 2 weeks were grouped as 'short survivors'. Stepwise logistic regression model was constructed for the model development and bootstrapping was used for the internal model validation.
Results: In 158 subjects whose data were available for the analysis, 109 (69%) subjects were categorized as long survivors and 49 (31%) subjects as short survivors. A prognostic prediction model with a total score of 8 points was constructed as follows: 2 points each for anorexia, dyspnea, and edema; 1 point each for blood urea nitrogen > 25 mg/dl and platelets < 260,000/mm(3). Area under the receiver operating characteristic (ROC) curve of this model was 83.2% (95% CI: 75.3-91.0%). Bootstrapped validation beta coefficients of the predictors were similar to the original cohort beta coefficients.
Conclusion: Our prognostic prediction model for estimating 14-day survival for patients with terminal cancer on the PCU ward included five clinical predictors that are readily available in the clinical setting and showed a relatively high accuracy. External validation is needed to confirm the model's generalizability.

• 出版日期2011-3