Department of General and Endocrine Surgery, CHU Lille, Lille, France.
Univ Lille, Inserm, Institut Pasteur de Lille, CHU Lille, Lille, France.
Ann Surg. 2024 Nov 1;280(5):835-841. doi: 10.1097/SLA.0000000000006480. Epub 2024 Aug 7.
We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypocalcemia risk.
Postoperative hypocalcemia is frequent after total thyroidectomy. An early and accurate individualized prediction of the risk of hypocalcemia could guide the selective prescription of calcium supplementation only to patients most likely to present with hypocalcemia after total thyroidectomy.
This retrospective study enrolled all patients undergoing total thyroidectomy in a single referral center between November 2019 and March 2022 (derivation cohort) and April 2022 and September 2022 (validation cohort). The primary study outcome was postoperative hypocalcemia (serum calcium under 80 mg/L). Exposures were multiple clinical and biological variables prospectively collected and analyzed with various machine learning methods to develop and validate a multivariable prediction algorithm.
Among 610/118 participants in the derivation/validation cohorts, 100 (16.4%)/26 (22%) presented postoperative hypocalcemia. The most accurate prediction algorithm was obtained with random forest and combined intraoperative parathyroid hormone measurements with 3 clinical variables (age, sex, and body mass index) to calculate a postoperative hypocalcemia risk for each patient. After multiple cross-validation, the area under the receiver operative characteristic curve was 0.902 (0.829-0.970) in the derivation cohort, and 0.928 (95% CI: 0.86; 0.97) in the validation cohort. Postoperative hypocalcemia risk values of 7% (low threshold) and 20% (high threshold) had, respectively, a sensitivity of 92%, a negative likelihood ratio of 0.11, a specificity of 90%, and a positive of 7.6 for the prediction of postoperative hypocalcemia.
Using machine learning, we developed and validated a simple multivariable model that allowed the accurate prediction of postoperative hypocalcemia. The resulting algorithm could be used at the point of care to guide clinical management after total thyroidectomy.
我们利用机器学习开发并验证了一种多变量算法，以准确且早期预测术后低钙血症的风险。
甲状腺全切除术后常发生术后低钙血症。如果能够早期且准确地预测低钙血症的风险，就可以有针对性地指导选择性补充钙剂，仅针对甲状腺全切除术后最有可能发生低钙血症的患者。
本回顾性研究纳入了 2019 年 11 月至 2022 年 3 月（推导队列）和 2022 年 4 月至 9 月（验证队列）期间在一家转诊中心接受甲状腺全切除术的所有患者。主要研究结局是术后低钙血症（血清钙<80mg/L）。暴露因素是前瞻性收集的多个临床和生物学变量，并使用各种机器学习方法进行分析，以开发和验证多变量预测算法。
在推导/验证队列的 610/118 名参与者中，100（16.4%）/26（22%）人出现术后低钙血症。最准确的预测算法是随机森林算法，该算法结合术中甲状旁腺激素测量值和 3 个临床变量（年龄、性别和体重指数），为每位患者计算术后低钙血症风险。经过多次交叉验证，推导队列的受试者工作特征曲线下面积为 0.902（0.829-0.970），验证队列为 0.928（95%CI：0.86；0.97）。术后低钙血症风险值分别为 7%（低值）和 20%（高值）时，预测术后低钙血症的敏感性分别为 92%、负似然比为 0.11、特异性为 90%、阳性预测值为 7.6。
我们利用机器学习开发并验证了一种简单的多变量模型，该模型能够准确预测术后低钙血症。由此产生的算法可以在护理点使用，以指导甲状腺全切除术后的临床管理。
