-
近年来,DTC的发病率呈逐年上升趋势,虽然其预后相对较好,但仍有30%左右的患者会出现复发,其病死率也呈上升态势,因此,给予患者及时且规范的治疗十分必要[1]。TSH抑制治疗是DTC治疗方案中不可或缺的环节,其可以有效地降低患者复发和病死的风险,提高患者的生存率、改善生存质量[2]。国内外指南皆推荐TSH抑制治疗药物首选左甲状腺素(levothyroxine,L-T4)口服制剂,推荐剂量为1.5~2.5 μg/(kg·d),并指出起始剂量视年龄及伴随疾病而异[3-4]。由于指南对L-T4最佳初始剂量并没有较为明确的标准,在临床工作中,医师往往根据个人经验决定,这导致很多患者经历较长的调药周期后TSH水平才能达标。调药周期过长会给患者带来一定的不良反应,如L-T4剂量不足会导致患者的甲状腺功能减退,造成患者乏力、嗜睡、水肿、血脂异常,甚至会增加肿瘤复发的风险等[5-6];L-T4剂量过大会加重患者心血管系统的负担,引起心绞痛或心律失常,加重绝经期女性的骨质疏松,进而增加其骨折风险等[7-8]。综上,构建DTC患者131I治疗后L-T4最佳初始剂量的预测模型,对于提高患者生活质量、改善预后都具有重要的临床意义。然而,截至目前尚未发现国内外有相关研究和文献报道。
本研究运用机器学习的方法为131I治疗后的DTC患者构建预测左甲状腺素钠片(优甲乐)的最佳初始剂量模型,提高首次达标率,有效缩短调药周期,以期减轻患者的经济负担,提高其生存质量,改善预后。
DTC患者131I治疗后左甲状腺素钠片最佳初始剂量预测模型探究
Exploration of the prediction model for the optimal initial dose of levothyroxine sodium tablets in patients with DTC after 131I treatment
-
摘要:
目的 应用机器学习方法为131I治疗后的分化型甲状腺癌(DTC)患者构建左甲状腺素钠片最佳初始剂量的预测模型。 方法 回顾性分析2019年11月至2020年11月在天津市肿瘤医院空港医院接受131I治疗后促甲状腺激素(TSH)抑制治疗最终达标的266例DTC患者[男性78例(男性组)、女性188例(女性组),年龄18~70(40.0+11.5)岁]的临床资料,包括一般资料、生化指标(共16项)和出院后定期复查的甲状腺功能相关数据及左甲状腺素钠片每次的调整剂量。通过计算随机森林模型重要度筛选出较为重要的临床特征;以筛选出的特征为自变量,以左甲状腺素钠片达标剂量为因变量构建多种回归模型,通过交叉验证选出准确率最高的模型。计数资料的组间比较采用独立样本的卡方检验。 结果 266 例患者的体重、身高、体重指数、体表面积、血红蛋白、平均红细胞体积、收缩压/ 舒张压、术后甲状旁腺激素、达标时左甲状腺素钠片剂量分别为(68.4±12.9) kg、(165.8±12.8) cm、 24.6±3.5、(1.9±0.2) m2、(140.1±19.1) g/L、(88.6±5.5) fl、(125.7±18.9) mm Hg/(82.7±12.4) mm Hg、(4.1±2.2) pmol/L、(117.0±30.1) μg/d。通过特征筛选,重要度排在前6位的临床特征依次为体表面积、体重、血红蛋白、身高、体重指数、年龄,其重要度均数依次为0.2805、0.1951、0.1315、0.1252、0.1080、0.0819。径向基核的支持向量回归(SVR)模型预测左甲状腺素钠片剂量的准确率(53.4%,142/266)最高,高于经验给药的首次达标率(15.0%,40/266);SVR模型在女性组中预测左甲状腺素钠片剂量的准确率高于男性组[60.6%(114/188)对35.9%(28/78)],且差异有统计学意义(χ2=13.51,P<0.001)。 结论 基于机器学习构建的SVR模型有望提高经131I治疗后的DTC患者左甲状腺素钠片的首次达标率,并且在女性患者中更有效。 Abstract:Objective To construct a prediction model for the optimal initial dose of levothyroxine sodium tablets in patients with differentiated thyroid cancer (DTC) after 131I treatment by machine learning. Methods A total of 266 DTC patients (78 males (male group) and 188 females (female group), aged 18 to 70 (40.0+11.5) years old) who received 131I treatment followed by thyroid stimulating hormone (TSH) suppressive therapy in the Department of Nuclear Medicine, Konggang Hospital, Tianjin Cancer Hospital between November 2019 and November 2020 were retrospectively analyzed for final compliance. A total of 16 clinical and biochemical indicators and data related to thyroid function were obtained, and each adjusted dose of levothyroxine sodium tablets was collected from patients with regular post-discharge rechecks. The indicators strongly correlated with the optimal dose of levothyroxine sodium tablets were screened by calculating random forest feature importance. A wide variety of regression models were constructed with the selected indicators and optimal dose of levothyroxine sodium tablets as independent and dependent variables, respectively. Selected the most accurate model using the cross-validation method. Counting data were compared between male and female groups using the chi-square test of independence. Results Body weight, height, body mass index, body surface area, hemoglobin, mean corpuscular volume, systolic/diastolic blood pressure, postoperative parathyroid hormone, and the reaching levothyroxine sodium tablets dose of 266 patients were (68.4±12.9) kg, (165.8±12.8) cm, 24.6±3.5, (1.9±0.2) m2, (140.1±19.1) g/L, (88.6±5.5) fl, (125.7±18.9) mm Hg/(82.7±12.4) mm Hg, (4.1±2.2) pmol/L, and (117.0±30.1) μg/d, respectively. Six indicators with a strong correlation with levothyroxine sodium tablets dose were screened using the feature selection method. According to the order of importance, the six indicators were body surface area, body weight, hemoglobin, height, body mass index, and age. Their average random forest importances were 0.2805, 0.1951, 0.1315, 0.1252, 0.1080 and 0.0819 respectively. The support vector regression (SVR) model using radial basis kernel had the highest accuracy (53.4%, 142/266) by cross-training validation. In addition, in this study, SVR's accuracy was significantly higher than the first success rate of empirical administration of levothyroxine sodium tablets (15.0%, 40/266). Moreover, the SVR model's accuracy was compared by dividing the patients into different subgroups according to gender. The results showed that the female patient group's accuracy was significantly higher than that of the male group (60.6% (114/188) vs. 35.9% (28/78)), with a statistically significant difference (χ2=13.51, P<0.001). Conclusions The SVR model is constructed based on machine learning and is expected to improve the first success rate of levothyroxine sodium tablets in DTC patients after being treated with 131I. It is more pronounced in female patients and helps to improve the quality of life and prognosis among DTC patients. -
Key words:
- Thyroid neoplasms /
- Iodine radioisotopes /
- Thyroxine /
- Machine learning /
- Prediction model
-
-
[1] Lim H, Devesa SS, Sosa JA, et al. Trends in thyroid cancer incidence and mortality in the United States, 1974−2013[J]. JAMA, 2017, 317(13): 1338−1348. DOI: 10.1001/jama.2017.2719. [2] Hovens GC, Stokkel MP, Kievit J, et al. Associations of serum thyrotropin concentrations with recurrence and death in differentiated thyroid cancer[J]. J Clin Endocrinol Metab, 2007, 92(7): 2610−2615. DOI: 10.1210/jc.2006-2566. [3] 中华医学会核医学分会. 131I治疗分化型甲状腺癌指南(2014版)[J]. 中华核医学与分子影像杂志, 2014, 34(4): 264−278. DOI: 10.3760/cma.j.issn.2095-2848.2014.04.002.
Chinese Society of Nuclear Medicine. Clinical guidelines for 131I treatment of differentiated thyroid cancer (2014 Edition)[J]. Chin J Nucl Med Mol Imaging, 2014, 34(4): 264−278. DOI: 10.3760/cma.j.issn.2095-2848.2014.04.002.[4] Haugen BR, Alexander EK, Bible KC, et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer[J]. Thyroid, 2016, 26(1): 1−133. DOI: 10.1089/thy.2015.0020. [5] Kotwal A, Cortes T, Genere N, et al. Treatment of thyroid dysfunction and serum lipids: a systematic review and meta-analysis[J]. J Clin Endocrinol Metab, 2020, 105(12): 3683−3694. DOI: 10.1210/clinem/dgaa672. [6] Biondi B, Cooper DS. The clinical significance of subclinical thyroid dysfunction[J]. Endocr Rev, 2008, 29(1): 76−131. DOI: 10.1210/er.2006-0043. [7] Cao CD, Wémeau JL. Risk-benefit ratio for TSH-suppressive levothyroxine therapy in differentiated thyroid cancer[J]. Ann Endocrinol, 2015, 76(1S1): S47−52. DOI: 10.1016/S0003-4266(16)30014-2. [8] Singh S, Duggal J, Molnar J, et al. Impact of subclinical thyroid disorders on coronary heart disease, cardiovascular and all-cause mortality: a meta-analysis[J]. Int J Cardiol, 2008, 125(1): 41−48. DOI: 10.1016/j.ijcard.2007.02.027. [9] Paragliola RM, Di Donna V, Locantore P, et al. Factors predicting time to TSH normalization and persistence of TSH suppression after total thyroidectomy for Graves' disease[J/OL]. Front Endocrinol, 2019, 10: 95[2021-11-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405427. DOI: 10.3389/fendo.2019.00095. [10] Miccoli P, Materazzi G, Rossi L. Levothyroxine therapy in thyrodectomized patients[J/OL]. Front Endocrinol (Lausanne), 2021, 11: 626268[2021-11-30]. https://pubmed.ncbi.nlm.nih.gov/33584551. DOI: 10.3389/fendo.2020.626268. [11] Benvenga S. L-T4 therapy in the presence of pharmacological interferents[J/OL]. Front Endocrinol (Lausanne), 2020, 11: 607446[2021-11-30]. https://pubmed.ncbi.nlm.nih.gov/33414765. DOI: 10.3389/fendo.2020.607446. [12] Jonklaas J. Sex and age differences in levothyroxine dosage requirement[J]. Endocr Pract, 2010, 16(1): 71−79. DOI: 10.4158/EP09257.OR. [13] Liwanpo L, Hershman JM. Conditions and drugs interfering with thyroxine absorption[J]. Best Pract Res Clin Endocrinol Metab, 2009, 23(6): 781−792. DOI: 10.1016/j.beem.2009.06.006. [14] Di Donna V, Santoro MG, de Waure C, et al. A new strategy to estimate levothyroxine requirement after total thyroidectomy for benign thyroid disease[J]. Thyroid, 2014, 24(12): 1759−1764. DOI: 10.1089/thy.2014.0111. [15] Zaborek NA, Cheng A, Imbus JR, et al. The optimal dosing scheme for levothyroxine after thyroidectomy: a comprehensive comparison and evaluation[J]. Surgery, 2019, 165(1): 92−98. DOI: 10.1016/j.surg.2018.04.097. [16] Chen SS, Zaborek NA, Doubleday AR, et al. Optimizing levothyroxine dose adjustment after thyroidectomy with a decision tree[J]. J Surg Res, 2019, 244: 102−106. DOI: 10.1016/j.jss.2019.06.025. [17] 李林通, 计成, 严思敏, 等. 分化型甲状腺癌患者术后左甲状腺素最佳初始剂量预测模型探讨[J]. 中国药房, 2019, 30(3): 387−391. DOI: 10.6039/j.issn.1001-0408.2019.03.21.
Li LT, Ji C, Yan SM, et al. Investigation of prediction model for optimal initial dose of levothyroxine in differentiated thyroid cancer patients after surgery[J]. China Pharm, 2019, 30(3): 387−391. DOI: 10.6039/j.issn.1001-0408.2019.03.21.