Pirovano M, Maltoni M, Nanni O, Marinari M, Indelli M, Zaninetta G, Petrella V, Barni S, Zecca E, Scarpi E, Labianca R, Amadori D, Luporini G
Divisione di Oncologia Medica, Ospedale S. Carlo Borromeo, Milan, Italy.
J Pain Symptom Manage. 1999 Apr;17(4):231-9. doi: 10.1016/s0885-3924(98)00145-6.
In recent years, extensive research has been performed to identify prognostic factors that predict survival in terminally ill cancer patients. This study describes the construction of a simple prognostic score based on factors identified in a prospective multicenter study of 519 patients with a median survival of 32 days. An exponential multiple regression model was adopted to evaluate the joint effect of some clinico-biological variables on survival. From an initial model containing 36 variables, a final parsimonious model was obtained by means of a backward selection procedure. The Palliative Prognostic Score (PaP Score) is based on the final model and includes the following variables: Clinical Prediction of Survival (CPS), Karnofsky Performance Status (KPS), anorexia, dyspnea, total white blood count (WBC) and lymphocyte percentage. A numerical score was given to each variable, based on the relative weight of the independent prognostic significance shown by each single category in the multivariate analysis. The sum of the single scores gives the overall PaP Score for each patient and was used to subdivide the study population into three groups, each with a different probability of survival at 30 days: (1) group A: probability of survival at 30 days > 70%, with patient score < or = 5.5; (2) group B: probability of survival at 30 days 30-70%, with patient score 5.6-11.0; and (3) group C: probability of survival at 30 days < 30%, with patient score > 11.0. Using this method, 178/519 (34.3%) patients were classified in risk group A, 205 (39.5%) patients were in risk group B, and 136 (26.2%) patients were in risk group C. The patients classified in the three risk groups had a very different survival experience (logrank = 294.8, P < 0.001), with a median survival of 64 days for group A, 32 days for group B, and 11 days for group C. The PaP Score based on simple clinical and biohumoral variables proved to be statistically significant in a multivariate analysis. The score is valid in this population (training set). An independent validation on another patient series (testing set) is required and is the object of a companion paper.
近年来，人们开展了广泛研究，以确定能预测晚期癌症患者生存情况的预后因素。本研究描述了基于一项对519例患者进行的前瞻性多中心研究中所确定因素构建的一个简单预后评分。采用指数多元回归模型来评估一些临床生物学变量对生存的联合影响。从一个包含36个变量的初始模型出发，通过向后选择程序得到了一个最终的简约模型。姑息预后评分（PaP评分）基于最终模型，包括以下变量：生存临床预测（CPS）、卡氏功能状态（KPS）、厌食、呼吸困难、白细胞总数（WBC）和淋巴细胞百分比。根据多变量分析中每个单一类别所显示的独立预后意义的相对权重，给每个变量赋予一个数值评分。各个评分的总和即为每个患者的总体PaP评分，并用于将研究人群分为三组，每组在30天时具有不同的生存概率：（1）A组：30天时生存概率>70%，患者评分≤5.5；（2）B组：30天时生存概率30 - 70%，患者评分5.6 - 11.0；（3）C组：30天时生存概率<30%，患者评分>11.0。采用这种方法，519例患者中有178例（34.3%）被归类为A风险组，205例（39.5%）患者被归类为B风险组，136例（26.2%）患者被归类为C风险组。归入三个风险组的患者生存经历差异很大（对数秩检验=294.8，P<0.001），A组的中位生存期为64天，B组为32天，C组为11天。基于简单临床和生物体液变量的PaP评分在多变量分析中被证明具有统计学意义。该评分在这一人群（训练集）中有效。需要在另一个患者系列（测试集）上进行独立验证，这是一篇配套论文的主题。
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