构建和验证一个风险预测模型的症状集群痛苦在脑瘤患者:一个横截面研究
Li Ying1,2, Su Yangmei1,2, Xiang Qing1,3
1State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China.
BMC cancer
|November 22, 2025
概括
一种新的名图模型有效地预测了脑瘤患者的症状集群痛苦 (SCD),有助于早期干预. 该工具有助于医务人员识别有风险的个体,以改善症状管理和生活质量.
科学领域:
- 在瘤学瘤学.
- 护理 护理 护理
- 生物统计学 生物统计学
背景情况:
- 大脑瘤患者经常经历症状集群困扰 (SCD),对生活质量和康复产生负面影响.
- 在这群人群中,缺乏护士领导的预测工具来管理SCD.
研究的目的:
- 确定脑瘤患者中SCD的决定因素和风险指标.
- 为SCD构建和验证基于名录的预测模型.
- 为了促进SCD的早期识别和管理.
主要方法:
- 分析了300名脑瘤患者的队列,使用单变量和多变量逻辑回归.
- 使用R软件和rms包,开发了一个风险预测名ogram模型.
- 用ROC曲线,校准曲线和Hosmer-Lemeshow测试来评估模型的性能.
主要成果:
- 开发了一个预测模型,结合了八个因素:年龄,支付方式,疾病持续时间,卡诺夫斯基绩效状态 (KPS),财务毒性 (COST),自我有效性 (GSES) 和医疗应对模式 (MCMQ).
- 该模型表现出良好的预测准确性,AUC为0.813,灵敏度为0.717,特异性为0.791.
- 校准分析表明模型很适合,预测和实际SCD发病率之间没有显著差异 (P=0.061).
结论:
- 开发的诺莫格拉姆模型在脑瘤患者中表现出强大的SCD预测能力.
- 该工具可以帮助医疗保健专业人员早期识别患有SCD风险的患者.
- 基于这种模式的及时干预可以改善症状管理和患者的治疗结果.
相关概念视频
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Mouse Models of Cancer Study
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...


