Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database

Yuxin Qiu; Menglei Li; Xiubao Song; Zihao Li; Ao Ma; Zhichao Meng; Yanfei Li; Minghui Tan

doi:10.1016/j.ijcard.2024.132105

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database

Int J Cardiol. 2024 Apr 25:407:132105. doi: 10.1016/j.ijcard.2024.132105. Online ahead of print.

Authors

Yuxin Qiu¹, Menglei Li², Xiubao Song³, Zihao Li⁴, Ao Ma⁵, Zhichao Meng⁵, Yanfei Li⁶, Minghui Tan⁷

Affiliations

¹ Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
² College of Life Science and Technology, Jinan University, Guangzhou 510630, China.
³ Department of Recovery, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
⁴ Department of Pharmacy, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
⁵ Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
⁶ Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China. Electronic address: 973517560@qq.com.
⁷ Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China. Electronic address: tanminghui@jnu.edu.cn.

PMID: 38677334
DOI: 10.1016/j.ijcard.2024.132105

Abstract

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).

Keywords: 28-day mortality; MIMIC III database; Mitral valve disorder; Nomogram; Prognosis.