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Multicenter Study
. 2025 Apr;119(4):907-916.
doi: 10.1016/j.athoracsur.2024.11.039. Epub 2025 Jan 24.

The Society of Thoracic Surgeons National Intermacs Database Risk Model for Durable Left Ventricular Assist Device Implantation

Affiliations
Multicenter Study

The Society of Thoracic Surgeons National Intermacs Database Risk Model for Durable Left Ventricular Assist Device Implantation

Francis D Pagani et al. Ann Thorac Surg. 2025 Apr.

Erratum in

  • Corrigendum.
    [No authors listed] [No authors listed] Ann Thorac Surg. 2025 Jul;120(1):187. doi: 10.1016/j.athoracsur.2025.05.001. Epub 2025 May 22. Ann Thorac Surg. 2025. PMID: 40407760 No abstract available.

Abstract

Background: Statistical risk models for durable left ventricular assist device (LVAD) implantation inform candidate selection, quality improvement, and evaluation of provider performance. This study developed a 90-day mortality risk model using The Society of Thoracic Surgeons National Intermacs Database (STS Intermacs).

Methods: STS Intermacs was queried for primary durable LVAD implants from January 2019 to September 2023. Multivariable logistic regression was used to derive a model based on preimplant risk factors by using derivation (2019-2021 implants) and validation (2022-2023 implants) cohorts. Model performance (derivation and validation cohorts) was assessed using C-statistics, Brier scores, and calibration plots. A refined model (all patients) was generated to calculate observed-to-expected (O/E; 95% CI) ratios for each center.

Results: The study population consisted of 11,342 patients from 2019 to 2023 who were sequentially divided in time into derivation (n = 6775) and validation (n = 4567) cohorts. Ninety-day mortality was 8.0% (9.2% in the derivation cohort vs 7.4% in the validation cohort; P = .001). Logistic regression applied to derivation and validation cohorts produced similar discrimination (area under the curve [AUC], 0.714 [95% CI, 0.69-0.74]; and AUC, 0.707; [95% CI, 0.67-0.72], respectively) and calibration (Brier score, .08 vs .07), with overestimation of risk among patients with a predicted risk >0.4. The O/E analysis identified 22 (12.5%) centers with worse than expected mortality with a 95% CI >1.0 and 14 centers (8.0%) with better than expected mortality with a 95% CI <1.0 (all P < .05).

Conclusions: The STS Intermacs risk model demonstrated satisfactory discrimination and calibration. This tool may be used to inform candidate selection, facilitate quality improvement, and assess provider performance.

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Conflict of interest statement

Disclosures Francis D. Pagani has served as a noncompensated ad hoc scientific advisor for Abbott, BrioHealth Solutions, Berlin Heals, FineHeart, and Medtronic; has served as a noncompensated medical monitor for Abiomed; has served as a member of the Data Safety Monitoring Board for Carmat; and has received travel support from BrioHealth Solutions. Aditi Nayak has served on the clinical advisory board of Pumpinheart; and has reported consulting for Alira Health. Jeffrey Teuteberg has reported consulting for Abbott, Broadview Ventures, and Medtronic; has served on the advisory board for Abiomed, Care Dx, Medtronic, and Takeda; and has served as a speaker for CareDx, Medtronic, and Paragonix. Palak Shah has reported consulting for Procyrion, Merck, Natera, Ortho Clinical Diagnostics, Tosoh Biosciences, and JVP Labs. J. David Vega has reported membership on the Clinical Events Committee for BrioHealth INNOVATE IDE clinical trial. Josef Stehlik has reported consulting for Medtronic, Natera, and TransMedics. All other authors declare that they have no conflicts of interest.

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