Development and Validation of a Nomogram for Predicting Survival in Patients With Cardiogenic Shock

AbstractBackgroundThere is currently a lack of easy-to-use tools for assessing the severity of cardiogenic shock (CS) patients. This study aims to develop a nomogram for evaluating severity in CS patients regardless of the underlying cause.Methods and ResultsThe MIMIC-IV database was used to identify 1923 CS patients admitted to the ICU. A multivariate Cox model was developed in the training cohort (70%) based on LASSO regression results. Factors such as age, systolic blood pressure, arterial oxygen saturation, hemoglobin, serum creatinine, blood glucose, arterial pH, arterial lactate, and norepinephrine use were incorporated into the final model. This model was visualized as a Cardiogenic Shock Survival Nomogram (CSSN) to predict 30-day survival rates. The model’s c-statistic was 0.75 (95% CI 0.73–0.77) in the training cohort and 0.73 (95% CI 0.70– 0.77) in the validation cohort, demonstrating good predictive accuracy. The AUC of the CSSN for 30-day survival probabilities was 0.76 in the training cohort and 0.73 in the validation cohort. Calibration plots showed strong concordance between predicted and actual survival rates, and decision curve analysis (DCA) affirmed the model’s clinical utility. The CSSN outperformed the Cardiogenic Shock Score (CSS) in various metrics, including c-statistic, time-dependent ROC, calibration plots, and DCA (c-statistic: 0.75 versus 0.72; AUC: 0.76 versus 0.73). Sensitivity analyses confirmed the model’s robustness across both AMI-CS and non-AMI-CS subgroups.ConclusionsThe CSSN was developed to predict 30-day survival rates in CS patients irrespective of the underlying cause, showing good performance and potential clinical utility in managing CS.CLINICAL PERSPECTIVEWhat Is New?This study presents the development and validation of a novel nomogram, the Cardiogenic Shock Survival Nomogram (CSSN), specifically designed for predicting 30-day survival rates in patients with cardiogenic shock (CS), regardless of the underlying cause.Utilizing a large cohort from the MIMIC-IV database, this research integrates multiple available clinical factors—such as age, systolic blood pressure, arterial oxygen saturation, hemoglobin levels, serum creatinine, blood glucose, arterial pH, arterial lactate, and norepinephrine use—into a convenient and easy to use the prognostic tool.What Are the Clinical Implications?Enhanced Decision-Making: Clinicians can utilize the CSSN as a practical tool to assess the severity and prognosis of CS patients, enabling more informed decision-making regarding treatment strategies.Improved Risk Stratification: By incorporating easily obtainable clinical parameters such as age, systolic blood pressure, arterial oxygen saturation, and biochemical markers, the CSSN provides a straightforward method for risk stratification, facilitating the identification of high-risk patients who may benefit from more intensive monitoring or advanced therapeutic interventions.