Construction and Validation of a Risk Prediction Model for Sepsis-induced Myocardial Injury: A Comprehensive Analysis and Clinical Application
Introduction:
Sepsis, a critical global health challenge, poses a life-threatening risk through its impact on organ dysfunction. This study delves into the development and validation of a risk prediction model for sepsis-induced myocardial injury (SMCI), aiming to enhance early identification and intervention. We explore the limitations of existing research and introduce a novel approach to address these gaps.
The Challenge of Sepsis and SMCI:
Sepsis, with its high incidence and mortality rates, has become a leading cause of death among emergency and critically ill patients. SMCI, a severe complication, significantly increases the difficulty of treatment and is closely linked to poor prognosis. Early identification of high-risk patients is crucial for timely interventions.
Methodology and Findings:
We conducted a retrospective analysis on 370 sepsis patients, focusing on comprehensive clinical data within 24 hours of admission. The study included indicators reflecting immunity, cytokine storm, tissue perfusion, and commonly used clinical parameters. Through LASSO regression and logistic regression analysis, we identified Log Myo, Log BNP, and Log IL-6 as independent risk factors for SMCI. A nomogram was developed, demonstrating moderate discriminative ability (AUC: 0.856) and good calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) further validated the model's clinical applicability.
Addressing Limitations and Enhancing Clinical Practice:
This study addresses limitations in previous research by incorporating a broader range of indicators and focusing on early identification in the emergency department. We emphasize the importance of timely intervention to reduce morbidity and mortality in sepsis patients. The nomogram provides a practical tool for early risk assessment, enabling clinicians to make informed decisions and tailor interventions.
Conclusion:
The proposed nomogram, based on Log Myo, Log BNP, and Log IL-6, offers a valuable resource for early risk assessment of SMCI. Further external validation is recommended to ensure its clinical implementation and contribute to improved patient outcomes in sepsis management.
