妇科恶性肿瘤患者的生存期预测

目的建立一个预测临终期妇科肿瘤患者生存时间的评分办法。方法回顾性分析不再适于接受任何抗肿瘤治疗,并于院内死亡的91例临终期妇科恶性肿瘤患者的临床资料。91例患者年龄中位数56岁(13~83岁),从入院起生存中位数27d(1~240d)。分析19项临床和生化指标与生存时间的关系。对单因素分析方法显示的9项显著影响生存时间的指标进行多元逻辑回归分析,以逐步后退方法建立一个包括5项指标的回归模型。将各项指标的回归系数转换为简单分数,相加后得到每一例患者的预后评分。依据本评分方法将患者分为两组,A组≤9·5分,B组≥10分。结果单因素分析显示,对生存时间有显著影响的9项指标为:呼吸困难、Karnofsky功能指数(KPS)、年龄、发热、肿瘤发展速度、有无并发症、血尿素氮、肌酐、血小板。多元逻辑回归结果建立了包括呼吸困难、KPS、年龄、发热和血尿素氮5项指标在内的回归方程。方程的正确分辨能力83·5%。A组37例,平均生存时间为(65±7)d。B组54例,平均生存时间为(19±2)d。A组存活≥30d者占83·8%(31/37)、B组存活≤29d者占85·2%(46/54)。两组生存曲线的差异有统计学意义(P<0·001)。结论依据本组资料建立的评分办法简单实用,是预测临终患者生存时间的有效方法。

Survival prediction for terminal gynecologic cancer patients

OBJECTIVE: To construct a scoring system of predicting the survival time of terminal gynecologic cancer patients. METHODS: The clinical data of 91 patients with terminal gynecologic cancers, aged 56 (13-83), who were not suitable to specific anti-cancer therapy and died in the hospital with a mean survival time of 27 days (1-240 days) were analyzed retrospectively. Initially univariate analysis was performed to evaluate the relationship between 19 clinico-biochemical indices and survival time. Multiple logistic regression was used to analyze 9 out of the 19 clinico-biochemical indices which had significant effects on the survival time, and a regression model with 5 indices was constructed by backward selection procedure. The regression coefficient of any category was divided by the maximum regression coefficient so as to get the score of this category. The cores were added together so as to get the overall score of an individual patient. RESULTS: Univariate analysis identified 9 factors independently and significantly influencing the survival time: short breath, Karnofsky performance status (KPS), age, high fever, speed of tumor growth, presence or absence of treatment-related complication, blood urea nitrogen (BUN), creatinine, and platelet. A regression equation was composed of 5 factors: short breath, KPS, age, higher fever, and BUN by multiple logistic regression with a correct classification ability of 83.5%. The 91 patients were then divided into 2 groups based on this prognostic score system: Group A (n = 37) with a score < or = 9.5 and the mean survival time of (65 +/- 7) days, and Group B (n = 54) with a score > or = 10 and the mean survival time of (19 +/- 2) days. 31 of the 37 patients in Group A survived > or = 30 days, and 46 of the 54 patients in Group B died within 29 days. The Kaplan-Meier survival curves of these 2 group were significantly different (P < 0.001). CONCLUSION: A simple, valid, and useful prognostic score system has been established.