Dataset Information

Development and validation of a nomogram for predicting immune-related pneumonitis after sintilimab treatment.

ABSTRACT:

Background

Immune-related pneumonitis is a rare and potentially fatal adverse event associated with sintilimab. We aimed to develop and validate a nomogram for predicting the risk of immune-related pneumonitis in patients treated with sintilimab.

Methods

The least absolute shrinkage and selection operator (LASSO) regression was used to determine risk factors. Multivariable logistic regression was used to establish a prediction model. Its clinical validity was evaluated using calibration, discrimination, decision, and clinical impact curves. Internal validation was performed against the validation set and complete dataset.

Results

The study included 632 patients; 59 were diagnosed with immune-related pneumonitis. LASSO regression analysis identified that the risk factors for immune-related pneumonitis were pulmonary metastases (odds ratio [OR], 4.015; 95% confidence interval [CI]: 1.725-9.340) and metastases at >3 sites (OR, 2.687; 95% CI: 1.151-6.269). The use of combined antibiotics (OR, 0.247; 95% CI: 0.083-0.738) and proton pump inhibitors (OR, 0.420; 95% CI: 0.211-0.837) were protective factors. The decision and clinical impact curves showed that the nomogram had clinical value for patients treated with sintilimab.

Conclusions

We have developed and validated a practical nomogram model of sintilimab-associated immune-related pneumonitis, which provides clinical value for determining the risk of immune-related pneumonitis and facilitating the safe administration of sintilimab therapy.

SUBMITTER: Hong B

PROVIDER: S-EPMC10905226 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a nomogram for predicting immune-related pneumonitis after sintilimab treatment.

Hong Baohui B Chen Rong R Zheng Caiyun C Liu Maobai M Yang Jing J

Cancer medicine 20240112 3

<h4>Background</h4>Immune-related pneumonitis is a rare and potentially fatal adverse event associated with sintilimab. We aimed to develop and validate a nomogram for predicting the risk of immune-related pneumonitis in patients treated with sintilimab.<h4>Methods</h4>The least absolute shrinkage and selection operator (LASSO) regression was used to determine risk factors. Multivariable logistic regression was used to establish a prediction model. Its clinical validity was evaluated using calib ...[more]

PMID: 38214102

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Dataset Information

Development and validation of a nomogram for predicting immune-related pneumonitis after sintilimab treatment.

Background

Methods

Results

Conclusions

Publications

Development and validation of a nomogram for predicting immune-related pneumonitis after sintilimab treatment.

Similar Datasets

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