Sepsis is a life-threatening condition that poses a significant challenge in emergency and critical care medicine worldwide. It occurs when the body's immune response to an infection becomes dysregulated, leading to organ dysfunction. Despite advancements in medical care, sepsis remains a leading cause of death among emergency and critically ill patients, with an alarming global incidence and mortality rate.
In this article, we delve into the development and validation of a risk prediction model for sepsis-induced myocardial injury (SMCI). SMCI is a severe complication of sepsis, characterized by myocardial damage and dysfunction. The occurrence of SMCI not only complicates treatment but also significantly worsens the prognosis of sepsis patients.
The early identification of high-risk patients for SMCI is crucial to improving patient outcomes. Risk prediction models, which integrate multiple factors, provide clinicians with individualized risk assessments, aiding in early decision-making. These models have proven valuable in risk stratification and prognostic evaluation for various diseases.
Previous studies have explored risk factors for SMCI, identifying lactate levels, septic shock, age, and hemoglobin levels as independent risk factors. However, these analyses lacked a visual and intuitive approach to calculating the risk of SMCI occurrence, limiting their practical application in clinical settings.
To address this gap, we developed a nomogram, a visual tool that demonstrates the relationship between clinical variables and the probability of SMCI. By creating a user-friendly digital interface, our nomogram enables rapid risk assessment of myocardial injury, facilitating timely interventions to reduce morbidity and mortality.
Our study systematically collected initial clinical data from sepsis patients upon their presentation to the emergency department. This data included indicators reflecting immunity, cytokine storm, tissue perfusion, and other commonly used clinical parameters. Using these indicators, we constructed an SMCI risk prediction model and evaluated its discrimination, calibration, and clinical applicability through internal validation.
The model's performance was assessed using various statistical methods. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model's discriminative ability, with an AUC of 0.856 in the training cohort and 0.853 in the internal validation cohort, indicating good performance. Calibration curves and the Hosmer-Lemeshow test confirmed the model's good calibration. Additionally, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the model's clinical applicability.
Our study has several advantages. We employed LASSO regression to mitigate multicollinearity risks, and subsequent variance inflation factor (VIF) analysis confirmed the absence of multicollinearity among the predictors. We also calculated an events-per-variable (EPV) value of 30.3, providing strong evidence for the model's stability and low risk of overfitting.
Furthermore, we collected commonly used clinical data within 24 hours of admission for sepsis patients in the emergency resuscitation area. This approach offers practical advantages, as sepsis patients typically present in the emergency department, allowing for early prediction and intervention.
However, our study also has limitations. As a retrospective analysis, the model lacks real-time applicability, and biases cannot be prospectively controlled. Additionally, factors such as fluid resuscitation, blood transfusion, antibiotic use, and oxygen therapy were not incorporated into the model. Our study also did not include traumatic sepsis, limiting our understanding of these indicators' predictive value in traumatic sepsis-induced myocardial injury.
Despite these limitations, our proposed nomogram based on Log Myo, Log BNP, and Log IL-6 shows promise as a practical tool for early risk assessment of myocardial injury in sepsis. However, external validation is necessary before clinical implementation to assess the model's generalizability.
In conclusion, our study contributes to the development of a risk prediction model for SMCI, offering a valuable tool for clinicians to identify high-risk patients early and implement targeted interventions to improve patient survival. Further research, including prospective, multicenter, and longitudinal studies, is warranted to refine and validate the model, incorporating additional variables such as treatment-related factors, hemodynamics, novel inflammatory markers, and cytokines.
