Scholarlink

Background <p>Tumor-Related Sepsis requires a novel, straightforward model for early and precise prognosis prediction due to inadequate current assessments.</p> Methods <p>This retrospective study utilized data from the MIMIC-IV 3.0 database for model development and internal validation. External validation was performed using datasets from different centers to enhance the model’s generalizability. Three machine learning techniques were employed for variable selection. After comparing multiple models, the best-performing one was selected and used to develop a clinically applicable nomogram.</p> Results <p>This study included 3777 cases for the development of the model. Four distinct prediction models were developed by integrating various machine learning techniques and clinical characterization methods. These models incorporated 8, 14, 8, and 9 variables, respectively. During internal validation, Model 4 demonstrated acceptable discriminative performance, with AUC values of 0.771 (95% CI: 0.750–0.791) for the training set and 0.769 (95% CI: 0.738–0.799) for the test set. Compared to other models, Model 4 exhibited comparable calibration accuracy and higher clinical utility. It also yielded higher AUC values than the APACHE II, SOFA, and LODS scoring systems. In external validation, Model 4 maintained consistent performance, achieving AUC values of 0.703 (95% CI: 0.676–0.730) for the EICU-CARD database and 0.714 (95% CI: 0.633–0.796) for the Guangxi tertiary hospital dataset. A nomogram was developed to facilitate clinical interpretation and decision-making.</p> Conclusions <p>Despite the retrospective design, this study developed a concise nomogram prediction model using multiple machine learning approaches and multi-database validation. The model demonstrated moderate discriminative ability in both internal and external validation, suggesting potential clinical utility that requires further prospective evaluation.</p> Clinical trial registration <p>This study was registered with the Chinese Clinical Trial Registry on April 22, 2025 (registration number ChiCTRPID270259).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

查看全文
  • Lingyu Jiang,
  • Luming Zhang,
  • Weisheng Chen,
  • Yonglong Zhong,
  • Junlin Zhang,
  • Miao Hu,
  • Lu Huang,
  • Lin Han,
  • Shulin Xiang,
  • Bin Xiong,
  • Haiyan Yin

摘要

Background

Tumor-Related Sepsis requires a novel, straightforward model for early and precise prognosis prediction due to inadequate current assessments.

Methods

This retrospective study utilized data from the MIMIC-IV 3.0 database for model development and internal validation. External validation was performed using datasets from different centers to enhance the model’s generalizability. Three machine learning techniques were employed for variable selection. After comparing multiple models, the best-performing one was selected and used to develop a clinically applicable nomogram.

Results

This study included 3777 cases for the development of the model. Four distinct prediction models were developed by integrating various machine learning techniques and clinical characterization methods. These models incorporated 8, 14, 8, and 9 variables, respectively. During internal validation, Model 4 demonstrated acceptable discriminative performance, with AUC values of 0.771 (95% CI: 0.750–0.791) for the training set and 0.769 (95% CI: 0.738–0.799) for the test set. Compared to other models, Model 4 exhibited comparable calibration accuracy and higher clinical utility. It also yielded higher AUC values than the APACHE II, SOFA, and LODS scoring systems. In external validation, Model 4 maintained consistent performance, achieving AUC values of 0.703 (95% CI: 0.676–0.730) for the EICU-CARD database and 0.714 (95% CI: 0.633–0.796) for the Guangxi tertiary hospital dataset. A nomogram was developed to facilitate clinical interpretation and decision-making.

Conclusions

Despite the retrospective design, this study developed a concise nomogram prediction model using multiple machine learning approaches and multi-database validation. The model demonstrated moderate discriminative ability in both internal and external validation, suggesting potential clinical utility that requires further prospective evaluation.

Clinical trial registration

This study was registered with the Chinese Clinical Trial Registry on April 22, 2025 (registration number ChiCTRPID270259).