Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high‐interindividual differences in effective dosage and the narrow therapeutic window. Model‐informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision‐support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty‐one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

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在一般实践中使用机器学习进行模型知情的左旋甲状腺素精确剂量：开发、验证和临床模拟试验

左旋甲状腺素是西方世界最常用的药物之一。由于有效剂量存在较大个体差异且治疗窗较窄，因此给药具有挑战性。使用机器学习的模型知情精确剂量（MIPD）可以帮助全科医生（GP），但初级保健不存在这样的模型。此外，由于开发人员和临床医生的观点之间存在巨大差距，决策支持算法在医疗保健领域的引入受到限制。我们报告了第一个用于初级保健的 MIPD 应用程序的开发、验证和临床模拟试验。左旋甲状腺素的稳定维持剂量是模型目标。多类别模型针对不同的剂量类别生成针对个体患者的预测。随机森林在国家初级保健数据库上进行了培训和测试（n= 19,004），即使在亚临床甲状腺功能减退症中，各给药方案的最终加权 AUC 均为 0.71。 TSH、fT4、体重和年龄最具预测性。为了评估 MIPD 对左旋甲状腺素的安全性、可行性和临床影响，我们在全科医生中进行了临床模拟研究，并将 MIPD 与传统处方进行了比较。 51 名全科医生为 20 名原发性甲状腺功能减退症患者（不伴有 MIPD）和 2 周后伴有 MIPD 的患者选择了起始剂量。剂量过量和剂量不足定义为相对于稳定维持剂量高于和低于 12.5 μg。 MIPD 减少了药物过量数量（30.5% 至 23.9%，P <0.01）和量值（中位数 50 至 37.5 μg，磷< 0.01）并增加了最佳起始剂量（18.3% 至 30.2%，磷< 0.01）。全科医生更频繁地使用 MIPD 考虑实验室结果，并且大多数人会经常使用该模型。本研究证明了 MIPD 在初级保健中使用左旋甲状腺素的临床相关性、安全性和有效性。