Pfizer Ltd., Endocrine Care, Walton Oaks, Dorking Road, Tadworth, Surrey, KT20 7NS, UK.
Appl Health Econ Health Policy. 2013 Jun;11(3):237-49. doi: 10.1007/s40258-013-0030-4.
Response to growth hormone (GH) therapy may vary between individual patients. Therefore the use of GH in children should be closely monitored to avoid over, under, or ineffective treatment regimens. The treatment response can be evaluated using growth prediction models. In an effort to improve the accuracy of these prediction models, Ranke et al. (J Clin Endocrinol Metab 95(3):1229-37) proposed a novel 'data-driven' approach based on a quantitative analysis of a large cohort of patients from the Pfizer International Growth Database (KIGS) treated with Genotropin (human growth hormone). This model allows physicians to predict and evaluate the level of growth response and responsiveness for their patients so they can adapt treatment accordingly. By comparing the actually observed and the predicted growth response the ability of an individual to respond to GH (responsiveness) can be estimated and further treatment can be adapted accordingly
To determine the potential population level reduction in the amount of GH used and impact on height outcome of using this data-driven approach to guide treatment decisions, compared to conventional, 'experience-based' GH treatment in prepubertal patients with growth hormone deficiency (GHD) or Turner syndrome (TS).
A model was developed to study the height outcome and the total amount of GH used in the presence or absence of data-driven treatment decisions. The proportion of patients for whom height outcome could be improved or GH use could be reduced (i.e. for low compliance, high or low responder) was estimated using the KIGS cohort. The analysis assumed that this segmentation allows physicians to tailor dosage to the individual patient's needs or even to discontinue therapy when it is not effective. The analysis used a 4-year time horizon, with Germany as an example country, but results are extendable to other countries. Only the total amount of GH used was included, and effects were defined as the height outcome after 4 years.
The analysis estimated that an evidence-driven approach may reduce the total amount of GH utilized by 7.0 % over 4 years for the treatment of short stature in prepubertal patients with GHD and TS in Germany. Despite the reduction in drug use the average growth outcomes remained unaffected with the new treatment approach. Univariate and probabilistic sensitivity analyses showed that the results are robust.
Our analysis showed that using a data-driven approach to guide treatment decisions for children with GHD or TS is estimated to result in efficiencies in the amount of GH used, without reducing the average growth in the population.
生长激素(GH)治疗的反应可能因个体患者而异。因此,应密切监测儿童使用 GH 的情况,以避免过度、不足或无效的治疗方案。可以使用生长预测模型来评估治疗反应。为了提高这些预测模型的准确性,Ranke 等人(J Clin Endocrinol Metab 95(3):1229-37)提出了一种新的“数据驱动”方法,该方法基于对辉瑞国际生长数据库(KIGS)中接受 Genotropin(人生长激素)治疗的大量患者进行的定量分析。该模型允许医生预测和评估其患者的生长反应和反应能力,以便他们能够相应地调整治疗。通过比较实际观察到的和预测的生长反应,可以估计个体对 GH 的反应能力(反应性),并进一步相应地调整治疗。
与传统的基于经验的 GH 治疗相比,确定使用这种数据驱动方法指导治疗决策在多大程度上可以降低人群中 GH 的使用量,并影响生长激素缺乏症(GHD)或特纳综合征(TS)的青春期前患者的身高结局。
建立了一个模型来研究存在或不存在数据驱动的治疗决策时的身高结局和 GH 的总使用量。使用 KIGS 队列估计可以改善身高结局或减少 GH 使用量(即低依从性、高或低反应者)的患者比例。该分析假设,这种细分允许医生根据个体患者的需求调整剂量,甚至在治疗无效时停止治疗。该分析使用了 4 年的时间范围,以德国为例,但结果可扩展到其他国家。仅包括 GH 的总使用量,并且将影响定义为 4 年后的身高结局。
该分析估计,在德国,使用基于证据的方法可能会在 4 年内减少 GHD 和 TS 青春期前矮小症患者总 GH 用量的 7.0%。尽管药物使用减少,但新治疗方法的平均生长结果保持不变。单变量和概率敏感性分析表明结果是稳健的。
我们的分析表明,使用数据驱动的方法来指导 GHD 或 TS 儿童的治疗决策预计将提高 GH 使用量的效率,而不会降低人群的平均生长速度。