Grover S A, Coupal L, Zowall H, Rajan R, Trachtenberg J, Elhilali M, Chetner M, Goldenberg L
Centre for the Analysis of Cost-Effective Care, Montreal General Hospital, Que.
CMAJ. 2000 Apr 4;162(7):977-83.
The incidence of prostate cancer is increasing, as is the number of diagnostic and therapeutic interventions to manage this disease. We developed a Markov state-transition model--the Montreal Prostate Cancer Model--for improved forecasting of the health care requirements and outcomes associated with prostate cancer. We then validated the model by comparing its forecasted outcomes with published observations for various cohorts of men.
We combined aggregate data on the age-specific incidence of prostate cancer, the distribution of diagnosed tumours according to patient age, clinical stage and tumour grade, initial treatment, treatment complications, and progression rates to metastatic disease and death. Five treatments were considered: prostatectomy, radiation therapy, hormonal therapies, combination therapies and watchful waiting. The resulting model was used to calculate age-, stage-, grade- and treatment-specific clinical outcomes such as expected age at prostate cancer diagnosis and death, and metastasis-free, disease-specific and overall survival.
We compared the model's forecasts with available cohort data from the Surveillance, Epidemiology and End Results (SEER) Program, based on over 59,000 cases of localized prostate cancer. Among the SEER cases, the 10-year disease-specific survival rates following prostatectomy for tumour grades 1, 2 and 3 were 98%, 91% and 76% respectively, as compared with the model's estimates of 96%, 92% and 84%. We also compared the model's forecasts with the grade-specific survival among patients from the Connecticut Tumor Registry (CTR). The 10-year disease-specific survival among the CTR cases for grades 1, 2 and 3 were 91%, 76% and 54%, as compared with the model's estimates of 91%, 73% and 37%.
The Montreal Prostate Cancer Model can be used to support health policy decision-making for the management of prostate cancer. The model can also be used to forecast clinical outcomes for individual men who have prostate cancer or are at risk of the disease.
前列腺癌的发病率在上升，用于管理该疾病的诊断和治疗干预措施的数量也在增加。我们开发了一种马尔可夫状态转换模型——蒙特利尔前列腺癌模型，以更好地预测与前列腺癌相关的医疗保健需求和结果。然后，我们通过将模型预测结果与已发表的不同男性队列观察结果进行比较，对该模型进行了验证。
我们综合了以下数据：前列腺癌的年龄特异性发病率、根据患者年龄、临床分期和肿瘤分级诊断出的肿瘤分布、初始治疗、治疗并发症以及转移至疾病和死亡的进展率。考虑了五种治疗方法：前列腺切除术、放射治疗、激素治疗、联合治疗和观察等待。所得模型用于计算年龄、分期、分级和治疗特异性的临床结果，如前列腺癌诊断和死亡时的预期年龄，以及无转移、疾病特异性和总体生存率。
我们将该模型的预测结果与监测、流行病学和最终结果（SEER）计划中超过59000例局限性前列腺癌病例的可用队列数据进行了比较。在SEER病例中，肿瘤分级为1、2和3的前列腺切除术后10年疾病特异性生存率分别为98%、91%和76%，而模型估计值分别为96%、92%和84%。我们还将该模型的预测结果与康涅狄格肿瘤登记处（CTR）患者的分级特异性生存率进行了比较。CTR病例中分级为1、2和3的10年疾病特异性生存率分别为91%、76%和54%，而模型估计值分别为91%、73%和37%。
蒙特利尔前列腺癌模型可用于支持前列腺癌管理的卫生政策决策。该模型还可用于预测患有前列腺癌或有患该疾病风险的个体男性的临床结果。
