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Objective: Ischemic mitral regurgitation is prevalent and associated with high surgical risk. With the less-invasive option of transcatheter edge-to-edge repair, the optimal patient selection for mitral valve operation for ischemic mitral regurgitation remains unclear. We sought to identify high-risk features in this group to guide patient selection. Methods: Using the Cardiothoracic Surgery Trial Network's severe ischemic mitral regurgitation trial data, we identified patient and echocardiographic characteristics associated with an increased risk of 2-year mortality using the support vector classifier and Cox proportional hazards model. We identified 6 high-risk features associated with 2-year survival. Patients were categorized into 3 groups, each having 1 or less, 2, or 3 or more of the 6 identified high-risk features. Results: Among the 251 patients, the median age was 69 (Q1 62, Q3 75) years, and 96 (38%) were female. Two-year mortality was 21% (n = 53). We identified 6 high-risk preoperative features: age 75 years or more (n = 69, 28%), prior sternotomy (n = 49, 20%), renal insufficiency (n = 69, 28%), gastrointestinal bleeding (n = 15, 6%), left ventricular ejection fraction less than 40% (n = 131, 52%), and ventricular end-systolic volume index less than 50 mL/m2 (n = 93, 37%). In patients who had 1 or less, 2, and 3 or more high-risk features, 90-day mortality was 4.2% (n = 5), 9.9% (n = 4), and 20.0% (n = 10), respectively (P = .006), and 2-year mortality was 10% (n = 12), 22% (n = 18), and 46% (n = 23) (P

Objective: Overprescribing of opioids has contributed to the opioid epidemic. Electronic medical records systems can auto-populate a default number of opioid pills that are prescribed at time of discharge. The aim of this study was to examine the association between lowered default pill counts with changed prescribing practices after cardiac surgery. Methods: On May 18, 2017, the default number of pills prescribers see in electronic medical records in the Yale New Haven Health System was lowered from 30 to 12. Patients undergoing coronary artery grafts, valve surgeries, and thoracic aortic aneurysm surgeries were included in this study. Data were gathered and stratified into 2 groups: 1 year before and 1 year following the default change. The amount of opioid prescribed was compared between the 2 groups. Results: A total of 1741 patient charts were reviewed, 832 before the change and 909 after the change. Significant changes were seen in prescribing practices, where the average amount of opioid prescribed was about 25% lower after the change. This amounted to about 15 fewer pills of 5 mg morphine for each patient. A linear regression model adjusting for other factors determined a prescribing difference of 75.2 morphine milligram equivalents per prescription (P

Background and Aims: Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods: We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models—including the deep learning–based DeepSurv model. Model performance was evaluated using Harrel’s c-index and was validated externally using the split-sample method. Results: The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. Conclusion: We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

This report describes the preparation and partial characterization of monoclonal antibodies that are reactive specifically with the nascently produced non-triple helical form of the type IV collagen α1 chain, designated as NTH α1(IV). These antibodies were nonreactive with the α1 chain of the type IV collagen in the triple-helical conformation. Three antibodies, #141, #179 and #370, with different epitopes in NTH α1(IV) were found to be reactive with the nascent polypeptide secreted from human normal cells and a human carcinoma cell line. The antibodies with different epitopes may provide a key method for elucidating the physiological function and tissue distribution of NTH α1(IV), which is distinct from the chain derived from triple-helical type IV collagen.

Type IV collagen with a triple-helical structure composed of three α chains is a major component of basement membrane. Previously, we reported that non-triple helical form of type IV collagen α1 chain (NTHα1(IV)) was isolated from human placenta and the culture media of human cells. In the present study, we report on the localization of NTH α1(IV) with a monoclonal antibody #370, exclusively reactive for the nascent chain, in the rabbit tissues. The staining was found on the basement membrane of blood vessels, of endomysium, of nerve, and of kidney but not on epithelial basement membrane. In a rabbit angiogenic model, #370 antibody staining was exclusively observed in neovascular tip region of endothelial cells, where no staining with anti-type IV collagen antibody was seen. Distinct localizations suggest that NTHα1(IV) is produced and stably deposited in endothelial cells and the surroundings under physiological conditions with some physiological roles in relation to the dynamics of vascular system.