The severity of chronic obstructive pulmonary disease is associated with adverse outcomes after open thoracoabdominal aortic aneurysm repair.

OBJECTIVE: We assessed associations between outcomes after open thoracoabdominal aortic aneurysm (TAAA) repair and preoperative airflow limitation stratified by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric classification of chronic obstructive pulmonary disease (COPD) severity. METHODS: Among 2368 open elective TAAA repairs in patients with spirometric data, 1735 patients had COPD and 633 did not. Those with COPD were stratified by preoperative respiratory dysfunction as GOLD 1 (forced expiratory volume in the first second of expiration [FEV1] ≥80% of predicted; n = 228), GOLD 2 (50% ≤ FEV1 < 80% of predicted; n = 1215), GOLD 3 (30% ≤ FEV1 < 50% of predicted; n = 260), or GOLD 4 (FEV1 < 30% of predicted; n = 32). Early outcomes included operative mortality and adverse events (operative death or persistent stroke, spinal cord deficit, or renal failure requiring dialysis); associations of outcomes were determined using logistic regression models. Kaplan-Meier analysis compared late survival by the log-rank test. RESULTS: Pulmonary complications occurred in 38.4% of patients with COPD versus 30.0% without COPD (P < .001). Operative mortality and adverse events were more frequent in patients with COPD than without COPD (7.9% vs 3.8% [P < .001] and 14.9% vs 9.8% [P = .001], respectively). Worsening GOLD severity was independently associated with operative death and adverse event. Survival was poorer in patients with COPD than in those without (61.9% ± 1.2% vs 73.6% ± 1.8% at 5 years; P < .001), particularly in patients with increasing GOLD severity (68.7% ± 3.2% vs 63.7% ± 1.4% vs 51.4% ± 3.2% vs 31.3% ± 8.2% at 5 years; P < .001). CONCLUSIONS: Patients with COPD are at elevated risk for operative death and adverse events. Staging by GOLD severity aids preoperative risk stratification. Patients with airflow limitations may benefit from optimization before TAAA repair.

Coronary artery bypass grafting at safety-net versus non-safety-net hospitals.

Objectives: Safety-net hospitals (SNHs) provide essential services to predominantly underserved patients regardless of their ability to pay. We hypothesized that patients who underwent coronary artery bypass grafting (CABG) would have inferior observed outcomes at SNHs compared with non-SNHs but that matched cohorts would have comparable outcomes. Methods: We queried the Nationwide Readmissions Database for patients who underwent isolated CABG from 2016 to 2018. We ranked hospitals by the percentage of all admissions in which the patient was uninsured or insured with Medicaid; hospitals in the top quartile were designated as SNHs. We used propensity-score matching to mitigate the effect of confounding factors and compare outcomes between SNHs and non-SNHs. Results: A total of 525,179 patients underwent CABG, including 96,133 (18.3%) at SNHs, who had a greater burden of baseline comorbidities (median Elixhauser score 8 vs 7; P = .04) and more frequently required urgent surgery (57.1% vs 52.8%; P < .001). Observed in-hospital mortality (2.1% vs 1.8%; P = .004) and major morbidity, length of stay (9 vs 8 days; P < .001), cost ($46,999 vs $38,417; P < .001), and readmission rate at 30 (12.4% vs 11.3%) and 90 days (19.0% vs 17.7%) were greater at SNHs (both P < .001). After matching, none of these differences persisted except length of stay (9 vs 8 days) and cost ($46,977 vs $39,343) (both P < .001). Conclusions: After matching, early outcomes after CABG were comparable at SNHs and non-SNHs. Improved discharge resources could reduce length of stay and curtail cost, improving the value of CABG at SNHs.

Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery.

OBJECTIVE: Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification. METHODS: Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy. RESULTS: Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases. CONCLUSIONS: Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.

Development of a Machine Learning Model to Predict Outcomes and Cost After Cardiac Surgery.

BACKGROUND: Machine learning (ML) algorithms may enhance outcomes prediction and help guide clinical decision making. This study aimed to develop and validate a ML model that predicts postoperative outcomes and costs after cardiac surgery. METHODS: The Society of Thoracic Surgeons registry data from 4874 patients who underwent cardiac surgery (56% coronary artery bypass grafting, 42% valve surgery, 19% aortic surgery) at our institution were divided into training (80%) and testing (20%) datasets. The Extreme Gradient Boosting decision-tree ML algorithms were trained to predict three outcomes: operative mortality, major morbidity or mortality, and Medicare outlier high hospitalization cost. Algorithm performance was determined using accuracy, F1 score, and area under the precision-recall curve (AUC-PR). The ML algorithms were validated in index surgery cases with The Society of Thoracic Surgeons risk scores for mortality and major morbidities and with logistic regression and were then applied to nonindex cases. RESULTS: The ML algorithms with 25 input parameters predicted operative mortality (accuracy 95%; F1 0.31; AUC-PR 0.21), major morbidity or mortality (accuracy 71%, F1 0.47; AUC-PR 0.47), and high cost (accuracy 84%; F1 0.62; AUC-PR 0.65). Preoperative creatinine, complete blood count, patient height and weight, ventricular function, and liver dysfunction were important predictors for all outcomes. For patients undergoing nonindex cardiac operations, the ML model achieved an AUC-PR of 0.15 (95% CI, 0.05-0.32) for mortality and 0.59 (95% CI, 0.51-0.68) for major morbidity or mortality. CONCLUSIONS: The extreme gradient boosting ML algorithms can predict mortality, major morbidity, and high cost after cardiac surgery, including operations without established risk models. These ML algorithms may refine risk prediction after cardiac surgery for a wide range of procedures.

Racial/ethnic differences persist in treatment choice and outcomes in isolated intervention for coronary artery disease.

OBJECTIVE: Studies have noted racial/ethnic disparities in coronary artery disease intervention strategies. We investigated trends and outcomes of coronary artery disease treatment choice (coronary artery bypass grafting or percutaneous coronary intervention) stratified by race/ethnicity. METHODS: We queried the National Inpatient Sample for patients who underwent isolated coronary artery bypass grafting or percutaneous coronary intervention (2002-2017). Outcomes were stratified by race/ethnicity (White, African American, Hispanic, Asian). Multivariable logistic regression evaluated associations between race/ethnicity and receiving coronary artery bypass grafting versus percutaneous coronary intervention, in-hospital mortality, and costs. RESULTS: Over the 15-year period, 2,426,917 isolated coronary artery bypass grafting surgeries and 7,184,515 percutaneous coronary interventions were performed. Compared with White patients, African American patients were younger (62 [interquartile range, 53-70] vs 66 [interquartile range, 57-75] years), were more likely to have Medicaid insurance (12.2% vs 4.4%), and had more comorbidities (Charlson-Deyo index, 1.9 ± 1.6 vs 1.7 ± 1.6) (all P < .01). After adjustment for patient comorbidities, presence of acute myocardial infarction, insurance status, and geography, African Americans were the least likely of all racial/ethnic groups to undergo coronary artery bypass grafting (odds ratio, 0.76; P < .01), a consistent trend throughout the study. African American patients had higher risk-adjusted mortality after coronary artery bypass grafting (odds ratio, 1.09; P < .01). Race/ethnicity was not associated with increased mortality after percutaneous coronary intervention. African American patients had higher hospitalization costs for coronary artery bypass grafting (+$5816; P < .01) and percutaneous coronary intervention (+$856; P < .01) after controlling for confounders. CONCLUSIONS: In this contemporary national analysis, risk-adjusted frequency of coronary artery bypass grafting versus percutaneous coronary intervention for coronary artery disease differed by race/ethnicity. African American patients had lower odds of undergoing coronary artery bypass grafting and worse outcomes. Reasons for these differences merit further investigation to identify opportunities to reduce potential disparities.

Readmission After Bioprosthetic vs Mechanical Mitral Valve Replacement in the United States.

BACKGROUND: Choosing between a bioprosthetic and a mechanical mitral valve is an important decision for both patients and surgeons. We compared patient outcomes and readmission rates after bioprosthetic mitral valve replacement (Bio-MVR) vs mechanical mitral valve replacement (Mech-MVR). METHODS: The Nationwide Readmissions Database was queried to identify 31 474 patients who underwent isolated MVR (22 998 Bio-MVR, 8476 Mech-MVR) between January 1, 2016, and December 31, 2018. Propensity score matching by age, sex, elective status, and comorbidities was used to compare outcomes between matched cohorts by prosthesis type. Freedom from readmission within the first calendar year was estimated by Kaplan-Meier analysis and compared between matched cohorts. RESULTS: Bio-MVR patients were older (median age, 69 vs 57 years; P < .001) and had more comorbidities (median Elixhauser score, 14 vs 11; P < .001) compared with Mech-MVR patients. After propensity score matching (n = 15 549), Bio-MVR patients had similar operative mortality (3.5% vs 3.4%; P = .97) and costs ($50 958 vs $49 782; P = .16) but shorter lengths of stay (8 vs 9 days; P < .001) and fewer 30-day (16.0% vs 18.1%; P = .04) and 90-day (23.8% vs 26.8%; P = .01) readmissions compared with Mech-MVR patients. The difference in readmissions persisted at 1 year (P = .045). Readmission for bleeding or coagulopathy complications was less common with Bio-MVR (5.7% vs 10.1%; P < .001). CONCLUSIONS: Readmission was more common after Mech-MVR than after Bio-MVR. Identifying and closely observing patients at high risk for bleeding complications may bridge the readmissions gap between Bio-MVR and Mech-MVR.

Outcomes, Cost, and Readmission After Surgical Aortic or Mitral Valve Replacement at Safety-Net and Non-Safety-Net Hospitals.

BACKGROUND: Safety-net hospitals provide essential services to vulnerable patients with complex medical and socioeconomic circumstances. We hypothesized that matched patients at safety-net hospitals and non-safety-net hospitals would have comparable outcomes, costs, and readmission rates after isolated surgical aortic valve replacement (AVR) or mitral valve replacement (MVR). METHODS: The National Readmissions Database was queried to identify patients who underwent isolated AVR (n = 109 744) or MVR (n = 31 475) from 2016 to 2018. Safety-net burden was defined as the percentage of patients who were uninsured or insured with Medicaid, with hospitals in the top quartile designated as safety-net hospitals. After propensity score matching, outcomes for AVR and MVR at safety-net hospitals vs non-safety-net hospitals were compared. RESULTS: Overall, 17 925 AVRs (16%) and 5516 MVRs (18%) were performed at safety-net hospitals, and these patients had higher comorbidity rates, had lower socioeconomic status, and more frequently required urgent surgery. Observed inhospital mortality was similar between safety-net hospitals and non-safety-net hospitals (AVR 2.2% vs 2.1%, P = .4; MVR 4.8% vs 4.3%, P = .1). After matching, rates of inhospital mortality, major morbidity, and readmission were similar; however, safety-net hospitals had longer length of stay after AVR (7 vs 6 days, P = .001) and higher total cost after AVR ($49 015 vs $42 473, P < .001) and MVR ($59 253 vs $52 392, P < .001). CONCLUSIONS: Isolated surgical AVR and MVR are both performed at safety-net hospitals with outcomes comparable to those at non-safety-net hospitals, supporting efforts to expand access to these procedures for underserved populations. Investment in care coordination resources to reduce length of stay and curtail cost at safety-net hospitals is warranted.

Outcomes of Aortic Valve Replacement for Chronic Aortic Insufficiency: Analysis of the Society of Thoracic Surgeons Database.

BACKGROUND: This study evaluated outcomes and risk factors for surgical aortic valve replacement (SAVR) for aortic insufficiency (AI) in a national cohort. We analyzed the incidence, outcomes, and risk factors for SAVR for AI in the Society of Thoracic Surgeons Adult Cardiac Surgery Database. METHODS: The national database was queried for patients with moderate or greater AI undergoing isolated SAVR between July 2011 and December 2018. Patients with moderate or greater aortic stenosis, acute dissection, active endocarditis, concomitant procedures, or emergent operation were excluded. AI was staged using guideline criteria based on symptoms and ventricular remodeling. Operative mortality and morbidity were compared between stages, and risk factors for operative death were identified. RESULTS: A total of 12,564 patients underwent isolated SAVR for AI from 2011 to 2018. Patients were most frequently AI stage D (7019 [57.5%]), compared with B (1405 [11.2%]), C1 (1128 [9.0%]), or C2 (1325 [10.5%]). Operative mortality was 1.1% overall, and increased between stage C1, C2, and D (0.4% vs 0.7% vs 1.6%, respectively, P < .01), along with major morbidity (5.1% vs 7.5% vs 9.9%, respectively; P < .01). Mortality was higher in patients with severe ventricular dilation and an ejection fraction of less than 0.30 (2.7% vs 1.0%, P < .01). Risk factors for death were symptomatic AI, decreased ejection fraction, age, weight, body surface area, and dialysis. CONCLUSIONS: Operative mortality and morbidity for isolated SAVR for AI is very low in a national cohort, providing a benchmark for future transcatheter approaches. Operative risk increases with advanced ventricular remodeling. SAVR before development of ventricular remodeling may be appropriate in patients with severe AI.

Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.

BACKGROUND: Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative time points. METHODS: The Society of Thoracic Surgeons (STS) registry data elements from 2086 isolated CABG patients were divided into training and testing datasets and input into Extreme Gradient Boosting decision-tree machine learning algorithms. Two prediction models were developed based on data from preoperative (80 parameters) and postoperative (125 parameters) phases of care. Outcomes included operative mortality, major morbidity or mortality, high cost, and 30-day readmission. Machine learning and STS model performance were assessed using accuracy and the area under the precision-recall curve (AUC-PR). RESULTS: Preoperative machine learning models predicted mortality (accuracy, 98%; AUC-PR = 0.16; F1 = 0.24), major morbidity or mortality (accuracy, 75%; AUC-PR = 0.33; F1 = 0.42), high cost (accuracy, 83%; AUC-PR = 0.51; F1 = 0.52), and 30-day readmission (accuracy, 70%; AUC-PR = 0.47; F1 = 0.49) with high accuracy. Preoperative machine learning models performed similarly to the STS for prediction of mortality (STS AUC-PR = 0.11; P = .409) and outperformed STS for prediction of mortality or major morbidity (STS AUC-PR = 0.28; P < .001). Addition of intraoperative parameters further improved machine learning model performance for major morbidity or mortality (AUC-PR = 0.39; P < .01) and high cost (AUC-PR = 0.64; P < .01), with cross-clamp and bypass times emerging as important additive predictive parameters. CONCLUSIONS: Machine learning can predict mortality, major morbidity, high cost, and readmission after isolated CABG. Prediction based on the phase of care allows for dynamic risk assessment through the hospital course, which may benefit quality assessment and clinical decision-making.

Immune-modulatory alginate protects mesenchymal stem cells for sustained delivery of reparative factors to ischemic myocardium.

Paracrine factors secreted by mesenchymal stem cells (MSCs) have been previously shown to improve cardiac function following acute myocardial infarction (MI). However, cell therapy activates the innate immune response, leading to the rapid elimination of transplanted cells and only short-term therapeutic delivery. Herein, we describe a new strategy to deliver sustained paracrine-mediated MSC therapy to ischemic myocardium. Using an immune evasive, small molecule modified alginate, we encapsulated rat MSC cells in a core-shell hydrogel capsule and implanted them in the pericardial sac of post-MI rats. Encapsulated cells allowed diffusion of reparative paracrine factors at levels similar to non-encapsulated cells in vitro. Encapsulation enabled sustained cell survival with localization over the heart for 2 weeks. The effect of the experimental group on ventricular function and fibrosis was compared with blank (cell free) capsules and unencapsulated MSCs injected into infarcted myocardium. MSC capsules improved post-MI ventricular function ∼2.5× greater than MSC injection. After 4 weeks, post-MI fibrosis was reduced ∼2/3 with MSC capsules, but unchanged with MSC injection. MSC encapsulation with alginate core-shell capsules sustains cell survival and potentiates efficacy of therapy.

Effect of Laparoscopic Sleeve Gastrectomy on Heart Transplant Status in 4 Patients with Left Ventricular Assist Devices.

Bariatric surgery helps many morbidly obese patients lose substantial weight. However, few data exist on its long-term safety and effectiveness in patients who also have continuous-flow left ventricular assist devices and in whom heart transplantation is contemplated. We retrospectively identified patients at our institution who had undergone ventricular assist device implantation and subsequent laparoscopic sleeve gastrectomy from June 2015 through September 2017, and we evaluated their baseline demographic data, preoperative characteristics, and postoperative outcomes. Four patients (3 men), ranging in age from 32 to 44 years and in body mass index from 40 to 57, underwent sleeve gastrectomy from 858 to 1,849 days after left ventricular assist device implantation to treat nonischemic cardiomyopathy. All had multiple comorbidities. At a median follow-up duration of 42 months (range, 24-47 mo), median body mass index decreased to 31.9 (range, 28.3-44.3) at maximal weight loss, with a median percentage of excess body mass index lost of 72.5% (range, 38.7%-87.4%). After achieving target weight, one patient was listed for heart transplantation, another awaited listing, one was kept on destination therapy because of positive drug screens, and one regained weight and remained ineligible. On long-term follow-up, laparoscopic sleeve gastrectomy appears to be safe and feasible for morbidly obese patients with ventricular assist devices who must lose weight for transplantation consideration. Additional studies are warranted to evaluate this weight-loss strategy after transplantation and immunosuppression.