狄诺塞麦用于治疗实体瘤骨转移患者，但有时会导致严重的低钙血症，因此谨慎的临床管理很重要。本研究旨在通过使用来自日本两个具有不同特征的机构的数据，对我们之前开发的狄诺塞麦引起的低钙血症风险预测模型进行外部验证，并开发一个具有改进性能和通用性的更新模型。在外部验证中，龟田综合医院的回顾性数据使用2013年6月至2022年6月期间的医院（KGH）和宫城癌症中心（MCC），主要评估受试者操作特征（ROC）-AUC。使用与之前使用的基于医院的管理数据库相同的数据集开发了基于评分的更新模型。与低钙血症预测相关的变量的选择基于外部验证的结果。对于外部验证，收集了来自235名KGH患者和224名MCC患者的数据。原始模型中的ROC-AUC值分别为0.879和0.774。由临床实验室测试（钙、白蛋白和碱性磷酸酶）组成的更新模型在两个设施中提供了相似的 ROC-AUC 值（KGH，0.837；MCC，0.856）。我们针对狄诺塞麦诱发的低钙血症开发了更新的风险预测模型，其中较小的接口差异。我们的结果表明，在广义预测模型的外部验证中使用来自具有不同特征的多个机构的数据的重要性，并且通常可能与风险预测模型的临床应用相关。我们的研究结果预计将有助于改善骨转移治疗的管理。

Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previously developed risk prediction model for denosumab-induced hypocalcemia by using data from two facilities with different characteristics in Japan and to develop an updated model with improved performance and generalizability.In the external validation, retrospective data of Kameda General Hospital (KGH) and Miyagi Cancer Center (MCC) between June 2013 and June 2022 were used and receiver operating characteristic (ROC)-AUC was mainly evaluated. A scoring-based updated model was developed using the same data set from a hospital-based administrative database as previously employed. Selection of variables related to prediction of hypocalcemia was based on the results of external validation.For the external validation, data from 235 KGH patients and 224 MCC patients were collected. ROC-AUC values in the original model were 0.879 and 0.774, respectively. The updated model consisting of clinical laboratory tests (calcium, albumin, and alkaline phosphatase) afforded similar ROC-AUC values in the two facilities (KGH, 0.837; MCC, 0.856).We developed an updated risk prediction model for denosumab-induced hypocalcemia with small interfacility differences. Our results indicate the importance of using data from plural facilities with different characteristics in the external validation of generalized prediction models and may be generally relevant to the clinical application of risk prediction models. Our findings are expected to contribute to improved management of bone metastasis treatment.