Huber Markus, Luedi Markus M, Schubert Gerrit A, Musahl Christian, Tortora Angelo, Frey Janine, Beck Jürgen, Mariani Luigi, Christ Emanuel, Andereggen Lukas
Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Department of Neurosurgery, Kantonsspital Aarau, Aarau, Switzerland.
Front Surg. 2024 Mar 13;11:1363431. doi: 10.3389/fsurg.2024.1363431. eCollection 2024.
In clinical practice, the size of adenomas is crucial for guiding prolactinoma patients towards the most suitable initial treatment. Consequently, establishing guidelines for serum prolactin level thresholds to assess prolactinoma size is essential. However, the potential impact of gender differences in prolactin levels on estimating adenoma size (micro- vs. macroadenoma) is not yet fully comprehended.
To introduce a novel statistical method for deriving gender-specific prolactin thresholds to discriminate between micro- and macroadenomas and to assess their clinical utility.
We present a novel, multilevel Bayesian logistic regression approach to compute observationally constrained gender-specific prolactin thresholds in a large cohort of prolactinoma patients ( = 133) with respect to dichotomized adenoma size. The robustness of the approach is examined with an ensemble machine learning approach (a so-called super learner), where the observed differences in prolactin and adenoma size between female and male patients are preserved and the initial sample size is artificially increased tenfold.
The framework results in a global prolactin threshold of 239.4 μg/L (95% credible interval: 44.0-451.2 μg/L) to discriminate between micro- and macroadenomas. We find evidence of gender-specific prolactin thresholds of 211.6 μg/L (95% credible interval: 29.0-426.2 μg/L) for women and 1,046.1 μg/L (95% credible interval: 582.2-2,325.9 μg/L) for men. Global (that is, gender-independent) thresholds result in a high sensitivity (0.97) and low specificity (0.57) when evaluated among men as most prolactin values are above the global threshold. Applying male-specific thresholds results in a slightly different scenario, with a high specificity (0.99) and moderate sensitivity (0.74). The male-dependent prolactin threshold shows large uncertainty and features some dependency on the choice of priors, in particular for small sample sizes. The augmented datasets demonstrate that future, larger cohorts are likely able to reduce the uncertainty range of the prolactin thresholds.
The proposed framework represents a significant advancement in patient-centered care for treating prolactinoma patients by introducing gender-specific thresholds. These thresholds enable tailored treatment strategies by distinguishing between micro- and macroadenomas based on gender. Specifically, in men, a negative diagnosis using a universal prolactin threshold can effectively rule out a macroadenoma, while a positive diagnosis using a male-specific prolactin threshold can indicate its presence. However, the clinical utility of a female-specific prolactin threshold in our cohort is limited. This framework can be easily adapted to various biomedical settings with two subgroups having imbalanced average biomarkers and outcomes of interest. Using machine learning techniques to expand the dataset while preserving significant observed imbalances presents a valuable method for assessing the reliability of gender-specific threshold estimates. However, external cohorts are necessary to thoroughly validate our thresholds.
在临床实践中，腺瘤大小对于指导泌乳素瘤患者选择最合适的初始治疗至关重要。因此，制定血清泌乳素水平阈值指南以评估泌乳素瘤大小至关重要。然而，泌乳素水平的性别差异对估计腺瘤大小（微腺瘤与大腺瘤）的潜在影响尚未完全明了。
引入一种新的统计方法来推导区分微腺瘤和大腺瘤的性别特异性泌乳素阈值，并评估其临床实用性。
我们提出一种新颖的多级贝叶斯逻辑回归方法，以计算在一大群泌乳素瘤患者（n = 133）中针对二分法腺瘤大小的观察性约束性别特异性泌乳素阈值。该方法的稳健性通过集成机器学习方法（所谓的超级学习器）进行检验，其中保留了女性和男性患者之间观察到的泌乳素和腺瘤大小差异，并将初始样本量人为增加了十倍。
该框架得出区分微腺瘤和大腺瘤的总体泌乳素阈值为239.4 μg/L（95%可信区间：44.0 - 451.2 μg/L）。我们发现女性的性别特异性泌乳素阈值为211.6 μg/L（95%可信区间：29.0 - 426.2 μg/L），男性为1046.1 μg/L（95%可信区间：582.2 - 2325.9 μg/L）。总体（即与性别无关）阈值在男性中评估时导致高敏感性（0.97）和低特异性（0.57），因为大多数泌乳素值高于总体阈值。应用男性特异性阈值会导致略有不同的情况，具有高特异性（0.99）和中等敏感性（0.74）。男性依赖的泌乳素阈值显示出较大的不确定性，并且对先验选择有一定依赖性，特别是对于小样本量。扩充后的数据集表明，未来更大的队列可能能够缩小泌乳素阈值的不确定性范围。
所提出的框架通过引入性别特异性阈值，在以患者为中心的泌乳素瘤患者治疗方面取得了重大进展。这些阈值通过基于性别区分微腺瘤和大腺瘤来实现量身定制的治疗策略。具体而言，在男性中，使用通用泌乳素阈值进行阴性诊断可有效排除大腺瘤，而使用男性特异性泌乳素阈值进行阳性诊断可表明其存在。然而，我们队列中女性特异性泌乳素阈值的临床实用性有限。该框架可以很容易地适用于各种生物医学环境，其中两个亚组的平均生物标志物和感兴趣的结果不均衡。使用机器学习技术在保留显著观察到的不平衡的同时扩展数据集，为评估性别特异性阈值估计的可靠性提供了一种有价值的方法。然而，需要外部队列来彻底验证我们的阈值。
