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Background <p>Identifying patients at high risk of adverse events is crucial in chimeric antigen receptor T-cell (CAR-T) therapy to enable early intervention. Despite the development of numerous prediction models, their methodological quality and performance remain systematically unassessed.</p> Methods <p>We conducted a systematic search across seven databases and Google Scholar, covering all records up to October 29, 2025. The risk of bias and applicability were assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only the Area Under the Receiver Operating Characteristic Curve (AUC) results from external validation cohorts were included in the meta-analysis. Subgroup and sensitivity analyses were conducted to explore potential sources of heterogeneity.</p> Results <p>Twenty-nine studies were included, reporting 26 distinct prediction models. The most common predictors were platelet count, C-reactive protein, and interleukin-6. Ten models underwent external validation, and six reported calibration. The PROBAST assessment indicated a high risk of bias across all studies. Pooled AUCs from external validation cohorts ranged from 0.60 to 0.79.</p> Conclusion <p>Pooled AUC estimates indicate moderate discrimination of the included models. However, these estimates reflect discriminative ability under uncertainty rather than direct clinical readiness.</p>

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Prediction models for CAR-T Cell therapy-related adverse events in hematologic malignancies: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

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  • Linrui Ye,
  • Luqing Liao,
  • Lulu Wang,
  • Xuejian Zhao,
  • Jitao Zeng,
  • Xuehu Xu

摘要

Background

Identifying patients at high risk of adverse events is crucial in chimeric antigen receptor T-cell (CAR-T) therapy to enable early intervention. Despite the development of numerous prediction models, their methodological quality and performance remain systematically unassessed.

Methods

We conducted a systematic search across seven databases and Google Scholar, covering all records up to October 29, 2025. The risk of bias and applicability were assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only the Area Under the Receiver Operating Characteristic Curve (AUC) results from external validation cohorts were included in the meta-analysis. Subgroup and sensitivity analyses were conducted to explore potential sources of heterogeneity.

Results

Twenty-nine studies were included, reporting 26 distinct prediction models. The most common predictors were platelet count, C-reactive protein, and interleukin-6. Ten models underwent external validation, and six reported calibration. The PROBAST assessment indicated a high risk of bias across all studies. Pooled AUCs from external validation cohorts ranged from 0.60 to 0.79.

Conclusion

Pooled AUC estimates indicate moderate discrimination of the included models. However, these estimates reflect discriminative ability under uncertainty rather than direct clinical readiness.