Isolated surgical vs. transcatheter aortic valve replacement: a propensity score analysis

Almedimigh, Abdulmalik A.; Albabtain, Monirah A; AlFayez, Latifa A; Alsubaie, Faisal F.; Almoghairi, Abdulrahman; AlOtaiby, Mohammad; Alkhushail, Abdullah; Ismail, Huda; Pragliola, Claudio; Adam, Adam I; Arafat, Amr A.

doi:10.1186/s43057-022-00094-3

Cardiothorac Surg

2023

DOI: 10.1186/s43057-022-00094-3

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Isolated surgical vs. transcatheter aortic valve replacement: a propensity score analysis

Abdulmalik A. Almedimigh

Monirah A Albabtain

Latifa A AlFayez

et al.

Abstract: Background The debate about the optimal approach for aortic valve replacement continues. We compared the hospital and long-term outcomes (survival, aortic valve reintervention, heart failure readmissions, and stroke) between transcatheter vs. surgical (TAVR vs. SAVR) aortic valve replacement. The study included 789 patients; 293 had isolated SAVR, and 496 had isolated TAVR. Patients with concomitant procedures were excluded. Propensity score matching identified 53 matched pairs. … Show more

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Applying machine learning methods to predict operative mortality after tricuspid valve surgery

Arafat,

Alamro,

AlRasheed

et al. 2023

Cardiothorac Surg

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Background EuroSCORE stratifies surgical risk in cardiac surgery; however, it is not explicitly for tricuspid valve surgery. Therefore, we aimed to apply machine learning (ML) methods to predict operative mortality after tricuspid valve surgery and compare the predictive ability of these models to EuroSCORE. This retrospective analysis included 1161 consecutive patients who underwent tricuspid valve surgery at a single center from 2009 to 2021. The study outcome was operative mortality (n=112), defined as mortality occurring within 30 days of surgery or the same hospital admission. Random forest, LASSO, elastic net, and logistic regression were used to identify predictors of operative mortality. Results EuroSCORE was significantly higher in patients who had operative mortality [8.52 (4.745–20.035) vs.4.11 (2.29–6.995), P<0.001] [AUC=0.73]. Random forest identified eight variables predicting operative mortality with an accuracy of 92% in the test set (age≥70 years, heart failure, emergency surgery, chronic kidney disease grade IV, diabetes mellitus, tricuspid valve replacement, hypertension, and redo surgery). The classification error rate in the training data was 9%, and in the testing data, it was 4.8%. Logistic regression identified eight variables with an AUC of 0.76. LASSO identified 13 variables with an AUC of 0.78, and elastic net identified 17 variables (AUC=0.795). The AUCs of the elastic net (P=0.048) and random forest (P<0.001) models were significantly higher than that of EuroSCORE. Conclusions ML effectively predicted TV surgery mortality more accurately than the traditional risk-scoring method. Incorporating ML in cardiac surgery risk scoring with comprehensive inclusion of all possible variables is recommended.

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Applying machine learning methods to predict operative mortality after tricuspid valve surgery

Arafat,

Alamro,

AlRasheed

et al. 2023

Cardiothorac Surg

View full text Add to dashboard Cite

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Age-Specific Outcomes of Bioprosthetic vs. Mechanical Aortic Valve Replacement: Balancing Reoperation Risk with Anticoagulation Burden

Alhijab,

Alfayez,

Hassan

et al. 2024

JCDD

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Background: The choice of prosthesis for aortic valve replacement (AVR) remains challenging. The risk of anticoagulation complications vs. the risk of aortic valve reintervention should be weighed. This study compared the outcomes of bioprosthetic vs. mechanical AVR in patients older and younger than 50. Methods: This retrospective study was conducted from 2009 to 2019 and involved 292 adult patients who underwent isolated AVR. The patients were divided according to their age (above 50 years or 50 years and younger) and the type of valves used in each age group. The outcomes of bioprosthetic valves (Groups 1a (>50 years) and 1b (≤50 years)) were compared with those of mechanical valves (Groups 2a (>50 years) and 2b (≤50 years)) in each age group. Results: The groups had nearly equal rates of preexisting comorbidities except for Group 1b, in which the rate of hypertension was greater (32.6% vs. 14.7%; p = 0.025). This group also had higher rates of old stroke (8.7% vs. 0%, p = 0.011) and higher creatinine clearance (127.62 (108.82–150.23) vs. 110.02 (84.87–144.49) mL/min; p = 0.026) than Group 1b. Patients in Group 1a were significantly older than Group 2a (64 (58–71) vs. 58 (54–67) years; p = 0.002). There was no significant difference in the NYHA class between the groups. The preoperative ejection fraction and other echocardiographic parameters did not differ significantly between the groups. Re-exploration for bleeding was more common in patients older than 50 years who underwent mechanical valve replacement (p = 0.021). There was no difference in other postoperative complications between the groups. The groups had no differences in survival, stroke, or bleeding rates. Aortic valve reintervention was significantly greater in patients ≤ 50 years old with bioprosthetic valves. There were no differences between groups in the changes in left ventricular mass, ejection fraction, or peak aortic valve pressure during the 5-year follow-up. Conclusions: The outcomes of mechanical and bioprosthetic valve replacement were comparable in patients older than 50 years. Using bioprosthetic valves in patients younger than 50 years was associated with a greater rate of valve reintervention, with no beneficial effect on the risk of bleeding or stroke.

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Isolated surgical vs. transcatheter aortic valve replacement: a propensity score analysis

Cited by 2 publications

References 32 publications

Applying machine learning methods to predict operative mortality after tricuspid valve surgery

Applying machine learning methods to predict operative mortality after tricuspid valve surgery

Age-Specific Outcomes of Bioprosthetic vs. Mechanical Aortic Valve Replacement: Balancing Reoperation Risk with Anticoagulation Burden

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