Development and validation of a nomogram to predict survival in septic patients with heart failure in the intensive care unit

Abstract

Heart failure is a common complication in patients with sepsis, and individuals who experience both sepsis and heart failure are at a heightened risk for adverse outcomes. This study aims to develop an effective nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the intensive care unit (ICU). This study extracted the pertinent clinical data of septic patients with heart failure from the Critical Medical Information Mart for Intensive Care (MIMIC-IV) database. Patients were then randomly allocated into a training set and a test set at a ratio of 7:3. Cox proportional hazards regression analysis was used to determine independent risk factors influencing patient prognosis and to develop a nomogram model. The model's efficacy and clinical significance were assessed through metrics such as the concordance index (C-index), time-dependent receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). A total of 5,490 septic patients with heart failure were included in the study. A nomogram model was developed to predict short-term survival probabilities, using 13 variables: age, pneumonia, endotracheal intubation, mechanical ventilation, potassium (K), anion gap (AG), lactate (Lac), activated partial thromboplastin time (APTT), white blood cell count (WBC), red cell distribution width (RDW), hemoglobin-to-red cell distribution width ratio (HRR), Sequential Organ Failure Assessment (SOFA) score, and Charlson Comorbidity Index (CCI). The C-index was 0.730 (95% CI 0.719-0.742) for the training set and 0.761 (95% CI 0.745-0.776) for the test set, indicating strong model accuracy, indicating good model accuracy. Evaluations via the ROC curve, calibration curve, and decision curve analyses further confirmed the model's reliability and utility. This study effectively developed a straightforward and efficient nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the ICU. The implementation of treatment strategies that address the risk factors identified in the model can enhance patient outcomes and increase survival rates.

Keywords: Heart failure; MIMIC-IV database; Nomogram model; Retrospective analysis; Sepsis.

Fig. 1

Selecting Flowchart.

Fig. 2

COX regression analysis. SOFA: Sequential Organ Failure Assessment; CCI: Charlson comorbidity Index; WBC: white blood cell; RDW: red blood cell distribution width; HRR: hemoglobin-red cell distribution width ratio; K: potassium; Lac: lactic acid; AG: anion gap; APTT: activated partial thromboplastin time.

Fig. 3

Nomogram model for short-term survival probabilities in septic patients with HF. This model is utilized for estimating the 7, 15, and 30-day survival probabilities of patients with this condition. By assigning scores on a scale, the changes in each variable are represented through forest plots, followed by the calculation of a cumulative score to forecast the likelihood of an event occurrence. SOFA: Sequential Organ Failure Assessment; CCI: Charlson comorbidity Index; WBC: white blood cell; RDW: red blood cell distribution width; HRR: hemoglobin-red cell distribution width ratio; K: potassium; Lac: lactic acid; AG: anion gap; APTT: activated partial thromboplastin time.

Fig. 4

The time-dependent ROC curve of the nomogram. (A) Training set; (B) testing set. To evaluate the accuracy of a model in predicting the 7-day, 15-day, and 30-day survival probabilities for septic patients with heart failure within both the training set (A) and the test set (B), the ROC curve was utilized. The model demonstrated robust predictive accuracy.

Fig. 5

Prognostic calibration curve plot. (A) Training set; (B) testing set. This figure displays the calibration curves for the established nomogram, indicating the agreement between predicted and observed survival probabilities in both the training set (A) and the test set (B).

Fig. 6

Prognostic DCA plot. (A-C) Training set; (D-F) testing set. This figure presents the DCA for predicting survival probabilities in septic patients with heart failure within both the training set (A-C) and the test set (D-F). The curves evaluate the clinical utility of the model, demonstrating that our model yields substantial net clinical benefit in both the test set and the training set.