A Risk Prediction Model for Operative Mortality after Heart Valve Surgery in a Korean Cohort.
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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.
SUBMITTER: Kim HJ
PROVIDER: S-EPMC8038884 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveDetachment from the workforce following open heart valve surgery is a burden for the patient and society. The objectives were to examine patterns of employment status at different time points and to investigate factors associated with a lower likelihood of returning to the workforce within six months.MethodsA cohort study of patients aged 18-63 undergoing valvular surgery at a Danish tertiary centre from 2013-2017. Return to the workforce was defined as being employed, unemployed (still capable of working) or receiving paid leave of absence. The association between demographic-, clinical characteristics (including a surgical risk evaluation, EuroScore), and return to the workforce were investigated with a multivariable logistic regression model.ResultsIn total, 1,395 consecutive patients underwent surgery, 347 were between 18 and 63 years and eligible for inclusion. Of those, 282 were attached to the workforce before surgery and included in the study. At the time of surgery, 79% were on paid sick leave. After six months, 21% of the patients (being part of the workforce before surgery), were still on sick leave. In the regression model, prolonged sick leave prior to surgery (OR 0.43, 95% CI 0.23-0.79) and EuroScore ≥ 2.3 (OR 0.39, 95% CI 0.21-0.74) significantly reduced the likelihood of returning to the workforce.ConclusionOne-fifth of patients in the working-age were on sick leave six months after surgery. Prolonged sick leave prior to surgery and a EuroScore ≥2.3 were associated with a lower likelihood of returning to the workforce.
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