Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study.

BACKGROUND: Healthcare providers currently calculate risk of the composite outcome of morbidity or mortality associated with a coronary artery bypass grafting (CABG) surgery through manual input of variables into a logistic regression-based risk calculator. This study indicates that automated artificial intelligence (AI)-based techniques can instead calculate risk. Specifically, we present novel numerical embedding techniques that enable NLP (natural language processing) models to achieve higher performance than the risk calculator using a single preoperative surgical note. METHODS: The most recent preoperative surgical consult notes of 1,738 patients who received an isolated CABG from July 1, 2014 to November 1, 2022 at a single institution were analyzed. The primary outcome was the Society of Thoracic Surgeons defined composite outcome of morbidity or mortality (MM). We tested three numerical-embedding techniques on the widely used TextCNN classification model: 1a) Basic embedding, treat numbers as word tokens; 1b) Basic embedding with a dataloader that Replaces out-of-context (ROOC) numbers with a tag, where context is defined as within a number of tokens of specified keywords; 2) ScaleNum, an embedding technique that scales in-context numbers via a learned sigmoid-linear-log function; and 3) AttnToNum, a ScaleNum-derivative that updates the ScaleNum embeddings via multi-headed attention applied to local context. Predictive performance was measured via area under the receiver operating characteristic curve (AUC) on holdout sets from 10 random-split experiments. For eXplainable-AI (X-AI), we calculate SHapley Additive exPlanation (SHAP) values at an ngram resolution (SHAP-N). While the analyses focus on TextCNN, we execute an analogous performance pipeline with a long short-term memory (LSTM) model to test if the numerical embedding advantage is robust to model architecture. RESULTS: A total of 567 (32.6%) patients had MM following CABG. The embedding performances are as follows with the TextCNN architecture: 1a) Basic, mean AUC 0.788 [95% CI (confidence interval): 0.768-0.809]; 1b) ROOC, 0.801 [CI: 0.788-0.815]; 2) ScaleNum, 0.808 [CI: 0.785-0.821]; and 3) AttnToNum, 0.821 [CI: 0.806-0.834]. The LSTM architecture produced a similar trend. Permutation tests indicate that AttnToNum outperforms the other embedding techniques, though not statistically significant verse ScaleNum (p-value of .07). SHAP-N analyses indicate that the model learns to associate low blood urine nitrate (BUN) and creatinine values with survival. A correlation analysis of the attention-updated numerical embeddings indicates that AttnToNum learns to incorporate both number magnitude and local context to derive semantic similarities. CONCLUSION: This research presents both quantitative and clinical novel contributions. Quantitatively, we contribute two new embedding techniques: AttnToNum and ScaleNum. Both can embed strictly positive and bounded numerical values, and both surpass basic embeddings in predictive performance. The results suggest AttnToNum outperforms ScaleNum. With regards to clinical research, we show that AI methods can predict outcomes after CABG using a single preoperative note at a performance that matches or surpasses the current risk calculator. These findings reveal the potential role of NLP in automated registry reporting and quality improvement.

Partial Heart Transplantation in Adult Cardiac Surgery.

Many young adults require heart valve replacements. Current options for valve replacement in adults include mechanical valves, bioprosthetic valves, or the Ross procedure. Of these, mechanical and bioprosthetic valves are the most common options, although mechanical valve usage predominates in younger adults due to durability, while bioprosthetic valve usage predominates in older adults. Partial heart transplantation is a new method of valvular replacement that can deliver durable and self-repairing valves and allow adult patients freedom from anticoagulation therapy. This procedure involves transplantation of donor heart valves only, permitting expanded utilization of donor hearts as compared with orthotopic heart transplantation. In this review, we discuss the potential benefits of this procedure in adults who elect against the anticoagulation regimen required of mechanical valve replacements, although it has not yet been clinically established. Partial heart transplantation is a promising new therapy for the treatment of pediatric valvular dysfunction. This is a novel technique in the adult population with potential utility for valve replacement in young patients for whom anticoagulation therapy is problematic, such as women who wish to become pregnant, patients with bleeding disorders, and patients with active lifestyles.

Partial heart transplantation can ameliorate donor organ utilization.

BACKGROUND: The treatment of babies with unrepairable heart valve dysfunction remains an unsolved problem because there are no growing heart valve implants. However, orthotopic heart transplants are known to grow with recipients. AIM: Partial heart transplantation is a new approach to delivering growing heart valve implants, which involves transplantation of the part of the heart containing the valves only. In this review, we discuss the benefits of this procedure in children with unrepairable valve dysfunction. CONCLUSION: Partial heart transplantation can be performed using donor hearts with poor ventricular function and slow progression to donation after cardiac death. This should ameliorate donor heart utilization and avoid both primary orthotopic heart transplantation in children with unrepairable heart valve dysfunction and progression of these children to end-stage heart failure.