J Chest Surg.  2021 Apr;54(2):88-98. 10.5090/jcs.20.102.

A Risk Prediction Model for Operative Mortality after Heart Valve Surgery in a Korean Cohort

Affiliations
  • 1Departments of 1 Thoracic and Cardiovascular Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
  • 2Departments of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
  • 3Department of Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
  • 4Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
  • 5Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
  • 6Department of Cardiovascular Surgery, Sejong General Hospital, Bucheon, Korea
  • 7Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  • 8Department of Thoracic and Cardiovascular Surgery, Pusan National University Yangsan Hospital, Yangsan, Korea
  • 9Department of Thoracic and Cardiovascular Surgery, Wonju Severance Christian Hospital, Wonju, Korea
  • 10National Evidence-based Healthcare Collaborating Agency, Seoul, Korea
  • 11Department of Thoracic and Cardiovascular Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Korea

Abstract

Background
This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database.
Methods
We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities.
Results
Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%.
Conclusion
This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

Keyword

Risk prediction model; Mortality; Heart valve surgery
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