Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement.

BACKGROUND: This study evaluated the performance of a machine learning (ML) algorithm in predicting outcomes of surgical aortic valve replacement (SAVR). METHODS: Adult patients undergoing isolated SAVR in The Society of Thoracic Surgeons (STS) National Database between 2007 and 2017 (n = 243,142) were randomly split 4:1 into training and validation sets. Outcomes that were evaluated were those for which STS models exist. The ML algorithm extreme gradient boosting (XGBoost) was used. Model calibration was measured by the ratio of observed to expected risk, calibration-in-the-large, and slope of calibration curve, and model discrimination was measured by the c-index. RESULTS: XGBoost demonstrated excellent calibration, with an average observed-to-expected ratio of 0.985, calibration-in-the-large of -0.017, and slope of calibration curve of 0.944. The c-index of XGBoost was significantly improved compared with STS models for 5 of 7 outcomes: operative mortality (77.1% [95% confidence interval {CI}, 75.8% to 78.4%] vs 76.2% [95% CI, 75.0% to 77.6%]; P = .007), prolonged ventilation (73.9% [95% CI, 73.1% to 74.6%] vs 72.6% [95% CI, 71.9% to 73.4%]; P < .001], acute renal failure (77.6% [95% CI, 76.3% to 78.7%] vs 73.7% [95% CI, 72.2% to 75.0%]; P < .001), reoperation (63.7% [95% CI, 62.7% to 64.8%] vs 62.6% [95% CI, 61.5% to 63.7%]; P = .01), and the composite of mortality or major morbidity (70.3% [95% CI, 69.6% to 70.9%] vs 69.0% [95% CI, 68.3% to 69.7%]; P < .001). For 2 outcomes the c-index was comparable: stroke (68.4% [95% CI, 66.6% to 70.3%] vs 67.6% [95% CI, 65.7% to 69.5%]; P .08) and deep sternal wound infection (59.9% [95% CI, 53.6% to 66.2%] vs 64.1% [95% CI, 57.5% to 70.1%]; P = .82). CONCLUSIONS: The ML algorithm XGBoost demonstrated excellent calibration and modest improvements in discriminatory ability compared with existing STS models in this study of isolated SAVR.

Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery.

BACKGROUND: This study evaluated the predictive utility of a machine learning algorithm in estimating operative mortality risk in cardiac surgery. METHODS: Index adult cardiac operations performed between 2011 and 2017 at a single institution were included. The primary outcome was operative mortality. Extreme gradient boosting (XGBoost) models were developed and evaluated using 10-fold cross-validation with 1000-replication bootstrapping. Model performance was assessed using multiple measures including precision, recall, calibration plots, area under the receiver-operating characteristic curve (C-index), accuracy, and F1 score. RESULTS: A total of 11,190 patients were included (7048 isolated coronary artery bypass grafting [CABG], 2507 isolated valves, and 1635 CABG plus valves). The Society of Thoracic Surgeons Predicted Risk of Mortality (STS PROM) was 3.2% ± 5.0%. Actual operative mortality was 2.8%. There was moderate correlation (r = 0.652) in predicted risk between XGBoost and STS PROM for the overall cohort and weak correlation (r = 0.473) in predicted risk between the models specifically in patients with operative mortality. XGBoost demonstrated improvements in all measures of model performance when compared with the STS PROM in the validation cohorts: mean average precision (0.221 XGBoost vs 0.180 STS PROM), C-index (0.808 XGBoost vs 0.795 STS PROM), calibration (mean observed-to-expected mortality: XGBoost 0.993 vs 0.956 STS PROM), accuracy (1%-3% improvement across discriminatory thresholds of 3%-10% risk), and F1 score (0.281 XGBoost vs 0.230 STS PROM). CONCLUSIONS: Machine learning algorithms such as XGBoost have promise in predictive analytics in cardiac surgery. The modest improvements in model performance demonstrated in the current study warrant further validation in larger cohorts of patients.

The eyes have it! Tamoxifen maculopathy revisited: a case report.

PURPOSE: To report on tamoxifen crystalline maculopathy in an 80-year-old patient and to review the ocular side effects of oral tamoxifen. METHODS: We report a case of an 80-year-old female patient who presented to our ophthalmic institute with painless gradual progressive diminution of vision in both eyes. She had a history of surgery for breast cancer after which she had been treated with oral tamoxifen citrate for 2 years before presentation. RESULTS: Our patient had profound visual impairment in both eyes. The anterior segments were found to be normal; in particular, the corneas were clear; the intraocular pressures in both eyes were 12 mm Hg. The perimacular region in both eyes showed deposits of multiple, fine crystalline material. Color vision was found to be impaired in both eyes and optical coherence tomography (OCT) confirmed the diagnosis of tamoxifen-induced maculopathy. CONCLUSION: Tamoxifen is a selective estrogen receptor modulator widely used in the treatment of hormone-responsive breast cancer. Ocular complications are rare with tamoxifen therapy and include cataract, vortex keratopathy, optic neuritis, and retinopathy. Crystalline maculopathy is one of the rare side effects of long-term tamoxifen use, which can be detected by noninvasive diagnostic tools such as OCT. Our patient is the oldest such patient reported in literature. Patients receiving tamoxifen therapy must be informed about the potential side-effects, and the need for serial ophthalmic examination to detect early signs of toxicity.