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Updated: May 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A
Yuxin Qiu1, Menglei Li2, Xiubao Song3
1Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
Background:
Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients.
Methods:
Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).
Results:
The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems.
Conclusion:
This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).
Insights
A new nomogram accurately predicts 28-day mortality in mitral valve disorder (MVD) patients by identifying key risk factors. This tool aids in improving patient prognosis and guiding early interventions like transcatheter mitral valve replacement.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Mitral valve disorder (MVD) is the most common valvular heart disease.
- A validated clinical index for predicting MVD mortality is currently lacking.
- Accurate mortality prediction is crucial for patient management and treatment decisions.
Purpose of the Study:
- To develop and validate a nomogram for predicting 28-day mortality risk in MVD patients.
- To identify independent risk factors associated with short-term mortality in MVD.
- To compare the nomogram's performance against existing scoring systems.
Main Methods:
- Utilized ICD-9 codes from the MIMIC-III database to identify MVD patients.
- Employed LASSO and multivariate logistic regression to identify risk factors and construct the nomogram.
- Assessed nomogram performance using AUC, calibration curves, Hosmer-Lemeshow test, IDI, NRI, and DCA.
Main Results:
- Included 2771 MVD patients in the analysis.
- Identified age, anion gap, creatinine, glucose, BUN, urine output, SBP, respiratory rate, SpO2, GCS, and metastatic cancer as independent risk factors.
- The nomogram demonstrated adequate calibration and favorable discrimination, outperforming SAPSII, APSIII, and SOFA scores.
Conclusions:
- Successfully developed a nomogram to predict 28-day mortality in MVD patients.
- The nomogram identifies key independent risk factors, aiding in prognosis enhancement.
- This tool can support decisions for early interventional treatments like TMVR and TMVI.
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