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Development of peripheral biomarker-based prognostic nomograms for short-term and long-term survival in immune
Zhengkun Guan1, Tiezhu Yao1, Guang Liu1
1Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Background:
Immune checkpoint inhibitor-associated myocarditis (ICI myocarditis) is a rare but highly fatal immune-related adverse reaction. This study aimed to develop nomogram prognostic models for both short-term and long-term survival outcomes in patients with ICI myocarditis based on key biomarkers in peripheral blood.
Methods:
In this single-center retrospective study, we included 90 patients with ICI myocarditis at the Fourth Hospital of Hebei Medical University. Critical peripheral biomarkers associated with 40-day and 1-year overall survival (OS) were identified. Two prognostic models were developed and evaluation of the models were performed with receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA).
Results:
A total of 24 patients (26.7%) succumbed within 40 days, while 40 patients (44.4%) died within one year. Cardiac troponin-I (cTnI), N-terminal pro-brain natriuretic peptide (NTBNP) and lactic dehydrogenase-to-albumin ratio (LAR) were identified as critical prognostic factors for 40-day OS in patients with ICI myocarditis and utilized to develop a nomogram model. The model demonstrates an area under the curve (AUC) of 0.867 [95% confidence interval (CI): 0.774-0.960] and a C-index of 0.824. Another predictive model for the 1-year OS was developed based on cTnI, NTBNP, LAR and systemic inflammatory response index (SIRI) with an AUC of 0.765 (95% CI: 0.664-0.866) and a C index of 0.742. The calibration curve demonstrates that both models exhibit strong consistency. The results of the DCA further indicate that both nomograms possess substantial clinical utility.
Conclusions:
These two prediction models will enable clinicians to more effectively utilize readily available peripheral blood biomarkers for the convenient and efficient identification of high-risk patients with poor prognoses, thereby facilitating early intervention.
