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Development and internal validation of an ICU mortality prediction model for patients with concurrent sepsis and
1The Third Clinical Medical College of Changzhi Medical College, Changzhi City, Shanxi Province, China.
Insights
This study developed a nomogram to predict intensive care unit (ICU) mortality in patients with both sepsis and heart failure. The model, using eight clinical variables, showed good predictive accuracy, aiding risk stratification for this high-risk group.
Area of Science:
- Critical Care Medicine
- Cardiology
- Medical Informatics
Background:
- Sepsis and heart failure are common, life-threatening ICU conditions.
- Their coexistence complicates management and increases mortality risk.
- Effective risk stratification is crucial for clinical decision-making.
Purpose of the Study:
- To develop and validate a practical nomogram for predicting ICU mortality.
- To aid in risk stratification for patients with both sepsis and heart failure.
- To facilitate clinical decision-making in this complex patient population.
Main Methods:
- Retrospective cohort study using the eICU-CRD database.
- Patients with sepsis and heart failure were randomly assigned to training (70%) and validation (30%) sets.
- Least Absolute and Selective Operator (LASSO) regression was used for variable selection and nomogram construction.
Main Results:
- A total of 1,394 patients were included.
- The final model incorporated eight independent predictors: mechanical ventilation, lactate, respiratory rate, white blood cell count, age, platelet count, systolic blood pressure, and oxygen saturation.
- The nomogram demonstrated good discriminatory performance (AUC training: 0.826, validation: 0.798) and calibration.
Conclusions:
- An internally validated predictive nomogram for ICU mortality in sepsis and heart failure patients was developed.
- The model shows acceptable discrimination and calibration based on routine clinical variables.
- External validation is necessary before clinical application due to the broad case definition and lack of heart failure-specific variables.
Background:
Sepsis and heart failure are common critical conditions within the ICU, and their coexistence significantly increases the difficulty of patient management and mortality risk. This study aims to develop and validate a practical nomogram for predicting ICU mortality, thereby facilitating risk stratification and clinical decision-making for patients coded as having both sepsis and heart failure.
Methods:
This study constitutes a retrospective cohort investigation. Patients meeting inclusion criteria for both sepsis and heart failure were identified via the eICU-CRD database, subsequently randomised in a 7:3 ratio to form training and validation cohorts. Variable selection employed the Least Absolute and Selective Operator (LASSO) regression, with nomogram analysis constructed. Model discriminatory ability was assessed via area under the receiver operating characteristic curve (AUC). Calibration was evaluated using calibration curves and the Hosmer-Lemeshow test. Decision curves and clinical impact curves were plotted to evaluate the model's net benefit and clinical applicability.
Results:
A total of 1,394 patients were included and divided into a training set (975 cases) and a validation set (419 cases) at a ratio of 7:3. The final model integrated eight independent predictors: mechanical ventilation, lactate, respiratory rate, white blood cell count, age, platelet count, systolic blood pressure, and oxygen saturation. The model demonstrated acceptable to good discriminatory performance in both training and validation cohorts (AUC 0.826 [95% CI 0.789-0.863] and 0.798 [95% CI 0.732-0.864], respectively), with good calibration (Brier scores: training cohort 0.090, validation cohort 0.091; Hosmer-Lemeshow goodness-of-fit test P-values > 0.05), and demonstrated clear clinical net benefit.
Conclusions:
This study developed and internally validated a predictive nomogram based on eight routine clinical variables for ICU mortality in patients coded for both sepsis and heart failure. The model demonstrated acceptable discrimination and calibration. However, given the broad, code-based case definition and the lack of heart failure-specific variables, this tool should be reframed as an internally validated ICU mortality prediction model for this high-risk population, rather than as a cardiology-specific or clinically actionable nomogram. External validation is required before any clinical application.