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The Society of Thoracic Surgeons National Intermacs Database Risk Model for Durable Left Ventricular Assist Device
Francis D Pagani1, Brandon Singletary2, Ryan Cantor2
1Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan.
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.
