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目的:探讨血液系统恶性肿瘤患者跌倒的危险因素，建立列线图预测模型，为跌倒风险评估和精准管理提供参考。方法:采用前瞻性研究设计，于2022年1月至2023年6月便利抽样法选取山东大学齐鲁医院510例血液系统恶性肿瘤患者为调查对象，收集患者人口学特征、疾病治疗、药物等相关资料。通过LASSO-Logistic回归筛选血液系统恶性肿瘤患者跌倒的危险因素，建立列线图预测模型。采用受试者工作特征曲线、校准曲线评估模型预测效果，Bootstrap重抽样法对模型进行内部验证。结果:510例血液系统恶性肿瘤患者中，男273例，女237例，年龄53.0（41.0，63.0）岁。血液系统恶性肿瘤患者跌倒风险预测模型共纳入6个危险因素，分别为疾病类型（ OR = 0.185，95% CI 0.061 ~ 0.562）、体温≥38 ℃（ OR = 2.239，95% CI 1.128 ~ 4.445）、疼痛（ OR = 15.581，95% CI 6.592 ~ 36.829）、贫血（ OR = 4.097，95% CI 1.536 ~ 10.927）、骨髓抑制天数（ OR = 3.341，95% CI 1.619 ~ 6.893）、日常生活自理能力评估（ OR = 3.160，95% CI 1.051 ~ 9.506）（均 P<0.05），该模型受试者工作特征曲线下面积为0.884（95% CI 0.841～0.927），最佳临界值为0.248，灵敏度为87.4%，特异度为75.6%，内部验证C统计量为0.873，校准曲线和理想曲线几乎重合，模型Brier得分为0.080。 结论:构建的跌倒风险预测模型具有良好的预测效果，可高效、客观量化跌倒发生风险，为血液系统恶性肿瘤患者跌倒的早期评估及有效预防提供参考。

Objective:To construct a fall risk prediction model for patients with hematologic malignancies and to provide a reference for the risk assessment and accurate management of falls.Methods:The prospective study design was adopted to facilitate the selection of 510 patients with hematologic malignant in Qilu Hospital of Shandong University for investigation, and relevant data such as patient demographic characteristics, disease treatment and drugs were collected. The LASSO-Logistic regression was used to screen the risk factors of falls in patients with hematologic malignancies, to construct a nomogram risk prediction model. The receiver operating characteristic curve (ROC) and calibration curve were used to evaluate the predictive performance of the model. Bootstrap resampling were used to validate internal validation of the model.Results:Among 510 patients with hematological malignancies, there were 273 males and 237 females, aged 53.0 (41.0, 63.0) years old. A total of 6 risk factors were included in the fall risk prediction model for patients with hematological malignancies, which were disease type ( OR = 0.185, 95% CI 0.061 - 0.562), body temperature ≥38 ℃ ( OR = 2.239, 95% CI 1.128 - 4.445), pain ( OR = 15.581, 95% CI 6.592 - 36.829), anemia ( OR = 4.097, 95% CI 1.536 - 10.927), days of bone marrow suppression ( OR = 3.341, 95% CI 1.619 - 6.893), and assessment of daily self-care ability ( OR = 3.160, 95% CI 1.051 - 9.506)(all P<0.05). The ROC curve of the fall risk prediction model was 0.884 (95% CI 0.841-0.927). The optimal threshold, sensitivity, and specificity of the risk prediction model were 0.248, 87.4% and 75.6%. The internal validation C statistic was 0.873. The Calibration curve was almost coincides with the ideal curve, and the model Brier score was 0.080. Conclusions:The constructed fall risk prediction model has good predictive performance, which can efficiently and objectively quantify the risk of falls, and provide a reference for the early assessment and effective prevention of falls in patients with hematological malignancies.