Nomogram for the Prediction of Intrahospital Mortality Risk of Patients with ST-Segment Elevation Myocardial Infarction Complicated with Hyperuricemia: A Multicenter Retrospective Study

Abstract

Purpose: This study aimed to establish an accurate and easy predictive model for ST-segment elevation myocardial infarction (STEMI) patients with hyperuricemia, using readily available features to estimate intrahospital mortality risk.

Patients and methods: This was a multicenter retrospective study involving the development of risk prediction models for intrahospital mortality among all STEMI patients with hyperuricemia from Zunyi Medical University Chest Pain Center's specialized alliance between January 1, 2016 and June 30, 2020. The primary outcome was intrahospital mortality. A total of 48 candidate variables were considered from demographic and clinical data. The least absolute shrinkage and selection operator (LASSO) was used to develop a nomogram. Concordance index values, decision curve analysis, the area under the curve (AUC), and clinical impact curves were examined. In this study, 489 patients with STEMI were included in the training dataset and an additional 209 patients from the 44 chest pain centers were included in the test cohort. B-type natriuretic peptides, α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C, out-of-hospital cardiac arrest (OHCA), shock index, and neutrophil-to-lymphocyte ratio were associated with intrahospital mortality and included in the nomogram.

Results: The model showed good discrimination power, and the AUC generated to predict survival in the training set was 0.875 (95% confidence interval, 0.825-0.925). In the validation set, the AUC of survival predictions was 0.87 (95% confidence interval, 0.792-0.947). Calibration plots and decision curve analysis showed good model performance in both datasets. A web-based calculator (https://bzxzmu.shinyapps.io/STEMI-with-Hyperuricemia-intrahospital-mortality/) was established based on the nomogram model, which was used to measure the levels of OHCA, neutrophil-to-lymphocyte ratio, shock index, α-HBDH, cystatin C, and B-type natriuretic peptides.

Conclusion: For practical applications, this model may prove clinically useful for personalized therapy management in patients with STEMI with hyperuricemia.

Keywords: STEMI; hyperuricemia; mortality; nomogram.

Figure 1

Flow chart outlining the patient inclusion process.

Figure 2

Demographic and clinical feature selection using the LASSO. (A) The minimum criterion of 10-fold cross-validation selects the optimal parameter (λ) in the LASSO model. (B) LASSO coefficient profiles of the 45 features. The coefficient profiles are drawn as a function of log(λ). (C) Intrahospital mortality nomogram. (D) Forest plot of odds ratio (OR) with confidence intervals.

Figure 3

Construction of a web-based calculator (https://bzxzmu.shinyapps.io/STEMI-with-Hyperuricemia-intrahospital-mortality/) for predicting intrahospital mortality based on the nomogram model. (A) Web mortality risk calculator. (B) 95% confidence interval of the web mortality rate.

Figure 4

Calibration plot of the nomogram in the training (A) and test cohorts (B). The dotted line represents the nomogram’s performance, whereas the solid line corrects any bias in the nomogram. The dashed line represents the reference line where an ideal nomogram would lie. Predictive accuracy of the LASSO model, GRACE model, shock index model for intrahospital mortality in the training (C) and test cohorts (D).Decision curve analysis of the nomogram in the training (E) and test cohorts (F). The x-axis indicates the threshold probability. The y-axis measures the net benefit. The gray line displays the net benefit of the strategy of treating all patients. The black line illustrates the net benefit of the strategy of treating no patients. The red line indicates the nomogram. Decision curve analysis is a specific method developed for evaluating the prognostic value of nomogram strategies. The net benefit of using a model to predict intrahospital mortality versus the strategies of “assuming all” or “assuming no” patients would be at high risk is shown for a different decision. The LASSO nomogram model (red) demonstrated an improved net benefit compared with the GRACE model.

Figure 5

Clinical impact curve for the LASSO nomogram model. The heavy red solid line shows the total number of patients out of 1000 who would be deemed high risk for each risk threshold. The blue dashed line shows how many of those would be true positives cases.