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Morbidity and Mortality in Adult Congenital Heart Surgery: Physiologic Component Augments Risk Prediction
Brittany G Abt1, Ramsey S Elsayed1, Markian Bojko1
1Division of Cardiac Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California.
Insights
The anatomic and physiologic stratification system (ACAP) score improves mortality prediction in adult congenital heart disease (ACHD) cardiac surgery. Adding physiologic severity enhances risk models for better patient outcomes.
Area of Science:
- Cardiology
- Cardiac Surgery
- Congenital Heart Disease
Background:
- The 2018 American College of Cardiology/American Heart Association guidelines introduced the Anatomic and Physiologic Stratification System (ACAP) score.
- Evaluation of the ACAP score's efficacy in predicting outcomes after cardiac surgery for adult congenital heart disease (ACHD) is crucial.
Purpose of the Study:
- To assess if the ACAP score better estimates mortality and morbidity in ACHD patients undergoing cardiac operations.
- To compare the predictive performance of ACAP with existing risk stratification systems.
Main Methods:
- The ACAP score was calculated for 318 ACHD patients undergoing heart surgery (2001-2019).
- Primary endpoint was perioperative mortality; secondary endpoint was a composite of mortality, prolonged ventilation, and renal failure.
- Logistic regression and receiver operating characteristic curves were used to compare predictive abilities.
Main Results:
- The ACAP score, incorporating anatomic and physiologic components, achieved an area under the curve of 0.81 for perioperative mortality.
- Adding physiologic severity significantly improved the predictive ability of other scores (ACHS and STAT) for the composite outcome.
- The predictive power of ACAP was comparable to enhanced ACHS and STAT scores.
Conclusions:
- Physiologic severity significantly enhances the prediction of mortality and morbidity following cardiac surgery in ACHD patients.
- Current risk models require further development to be more robust and specific for ACHD populations.
Background:
We sought to evaluate whether the anatomic and physiologic stratification system (ACAP score), released as part of the American College of Cardiology/American Heart Association updated guidelines for management of adult congenital heart disease (ACHD) in 2018, better estimated mortality and morbidity after cardiac operations for ACHD.
Methods:
The ACAP score was determined for 318 patients (age ≥18 years) with ACHD undergoing heart surgery at our institution between December 2001 and August 2019. The primary end point was perioperative mortality. The secondary aim was to evaluate the performance of the ACAP, The Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) Congenital Heart Surgery Mortality Categories, and ACHS mortality scores/categories at predicting a composite adverse outcome of perioperative mortality, prolonged ventilation, and renal failure requiring replacement therapy. Logistic regression models were built to estimate mortality and the composite outcome using anatomic and physiologic components independently and together. Receiver operating characteristic curves were created, and area under the curves were compared using the Delong test.
Results:
The median age was 37 years (interquartile range, 26.3-50.0 years). There were 9 perioperative mortalities (2.8%). With respect to perioperative mortality, the area under the curve using the anatomic component only was 0.74, which improved to 0.81 after including physiologic severity (P = .05). When physiologic severity was added to the model for the composite outcome, the discriminatory abilities of the ACHS mortality score and the STAT categories increased significantly to 0.83 (95% CI, 0.75-0.91; P = .02) and 0.82 (95% CI, 0.73-0.90; P = .04), comparable to the predictive power of ACAP.
Conclusions:
Physiologic severity augments ability to predict mortality and morbidity after cardiac surgery for ACHD. There is need for more robust ACHD-specific risk models.
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