Safety of Early Discharge After Coronary Artery Bypass Grafting: A Nationwide Readmissions Analysis.

BACKGROUND: We determined the safety of early discharge after coronary artery bypass grafting (CABG) in patients with uncomplicated postoperative courses and compared outcomes to routine discharge in a national cohort. We identified preoperative factors associated with readmission following early discharge after CABG. METHODS: The Nationwide Readmissions Database was queried to identify patients undergoing CABG from 01/2016-12/2018. Patients were stratified based on length of stay (LOS) as early (≤4 days) versus routine (5-10 days) discharge. Patients were excluded with hospital courses indicative of complicated stays (emergent procedures, LOS>10 days, discharge to extended care facility or with home health, index-hospitalization mortality). Propensity-score matching was performed to compare outcomes between cohorts. Multivariable logistic regression models were used to identify factors associated with readmission following early discharge. RESULTS: A total of 91,861 patients underwent CABG with an uncomplicated postoperative course during the study period (≈20% of CABG population). Of these 31% (28,790/91,861) were discharged early and 69% (63,071/91,861) routine. After propensity-score matching, patients discharged early had lower readmission rates at 30-days, 90-days, and up to one year (P<.001, all). Index-hospitalization cost was lower with early discharge ($26,676 versus $32,859; P<.001). Early discharge was associated with a lower incidence of nosocomial infection at index-hospitalization (0.17% versus 0.81%, P<.001) and readmission from infection (14.5% versus 18%, P=.016). CONCLUSIONS: Early discharge after uncomplicated CABG can be considered in a highly selective patient population. Early discharge patients are readmitted less frequently than matched routine discharge patients, with a lower incidence of readmission from infection. Appropriate post-discharge processes to facilitate early discharge after CABG should be further pursued.

Machine learning in cardiac surgery: a narrative review.

Background and Objective: Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support. Methods: We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery". Key Content and Findings: ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited. Conclusions: Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.

Outcomes after bioprosthetic versus mechanical mitral valve replacement for infective endocarditis in the United States.

Objective: In patients who underwent mitral valve replacement for infectious endocarditis, we evaluated the association of prosthesis choice with readmission rates and causes (the primary outcomes), as well as with in-hospital mortality, cost, and length of stay (the secondary outcomes). Methods: Patients with infectious endocarditis who underwent isolated mitral valve replacement from January 2016 to December 2018 were identified in the United States Nationwide Readmissions Database and stratified by valve type. Propensity score matching was used to compare adjusted outcomes. Results: A weighted total of 4206 patients with infectious endocarditis underwent bioprosthetic mitral valve replacement (n = 3132) and mechanical mitral valve replacement (n = 1074) during the study period. Patients in the bioprosthetic mitral valve replacement group were older than those in the mechanical mitral valve replacement group (median 57 vs 46 y, P < .001). After propensity matching, the bioprosthetic mitral valve replacement group (n = 1068) had similar in-hospital mortality, length of stay, and costs compared with the mechanical mitral valve replacement group (n = 1056). Overall, 90-day readmission rates were high (28.9%) and comparable for bioprosthetic mitral valve replacement (30.5%) and mechanical mitral valve replacement (27.5%, P = .4). Likewise, there was no difference in readmissions over a calendar year by prosthesis type. Readmissions for infection and bleeding were common for both bioprosthetic mitral valve replacement and mechanical mitral valve replacement groups. Conclusions: Outcomes and readmission rates were similar for mechanical mitral valve replacement and bioprosthetic mitral valve replacement in infectious endocarditis, suggesting that valve choice should not be determined by endocarditis status. Additionally, strategies to mitigate readmission for infection and bleeding are needed for both groups.

Development of a Sensor Technology to Objectively Measure Dexterity for Cardiac Surgical Proficiency.

BACKGROUND: Technical skill is essential for good outcomes in cardiac surgery. However, no objective methods exist to measure dexterity while performing surgery. The purpose of this study was to validate sensor-based hand motion analysis (HMA) of technical dexterity while performing a graft anastomosis within a validated simulator. METHODS: Surgeons at various training levels performed an anastomosis while wearing flexible sensors (BioStamp nPoint, MC10 Inc) with integrated accelerometers and gyroscopes on each hand to quantify HMA kinematics. Groups were stratified as experts (n = 8) or novices (n = 18). The quality of the completed anastomosis was scored using the 10 Point Microsurgical Anastomosis Rating Scale (MARS10). HMA parameters were compared between groups and correlated with quality. Logistic regression was used to develop a predictive model from HMA parameters to distinguish experts from novices. RESULTS: Experts were faster (11 ± 6 minutes vs 21 ± 9 minutes; P = .012) and used fewer movements in both dominant (340 ± 166 moves vs 699 ± 284 moves; P = .003) and nondominant (359 ± 188 moves vs 567 ± 201 moves; P = .02) hands compared with novices. Experts' anastomoses were of higher quality compared with novices (9.0 ± 1.2 MARS10 vs 4.9 ± 3.2 MARS10; P = .002). Higher anastomosis quality correlated with 9 of 10 HMA parameters, including fewer and shorter movements of both hands (dominant, r = -0.65, r = -0.46; nondominant, r = -0.58, r = -0.39, respectively). CONCLUSIONS: Sensor-based HMA can distinguish technical dexterity differences between experts and novices, and correlates with quality. Objective quantification of hand dexterity may be a valuable adjunct to training and education in cardiac surgery training programs.

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.

Readmissions After Surgical Aortic Valve Replacement: Influence of Prosthesis Type.

INTRODUCTION: Prosthesis choice during aortic valve replacement (AVR) weighs lifelong anticoagulation with mechanical valves (M-AVR) against structural valve degeneration in bioprosthetic valves (B-AVR). METHODS: The Nationwide Readmissions Database was queried to identify patients who underwent isolated surgical AVR between January 1, 2016 and December 31, 2018, stratifying by prothesis type. Propensity score matching was used to compare risk-adjusted outcomes. Readmission at 1 y was estimated with Kaplan-Meier (KM) analysis. RESULTS: Patients (n = 109,744) who underwent AVR (90,574 B-AVR and 19,170 M-AVR) were included. B-AVR patients were older (median 68 versus 57 y; P < 0.001) and had more comorbidities (mean Elixhauser score: 11.8 versus 10.7; P < 0.001) compared to M-AVR patients. After matching (n = 36,951), there was no difference in age (58 versus 57 y; P = 0.6) and Elixhauser score (11.0 versus 10.8; P = 0.3). B-AVR patients had similar in-hospital mortality (2.3% versus 2.3%; P = 0.9) and cost (mean: $50,958 versus $51,200; P = 0.4) compared with M-AVR patients. However, B-AVR patients had shorter length of stay (8.3 versus 8.7 d; P < 0.001) and fewer readmissions at 30 d (10.3% versus 12.6%; P < 0.001) and 90 d (14.8% versus 17.8%; P < 0.001), and 1 y (P < 0.001, KM analysis). Patients undergoing B-AVR were less likely to be readmitted for bleeding or coagulopathy (5.7% versus 9.9%; P < 0.001) and effusions (9.1% versus 11.9%; P < 0.001). CONCLUSIONS: B-AVR patients had similar early outcomes compared to M-AVR patients, but lower rates of readmission. Bleeding, coagulopathy, and effusions are drivers of excess readmissions in M-AVR patients. Readmission reduction strategies targeting bleeding and improved anticoagulation management are warranted in the first year following AVR.

Coronary artery bypass grafting after acute ST-elevation myocardial infarction.

OBJECTIVES: The study objectives were to describe the trends and outcomes of isolated coronary artery bypass grafting after ST-elevation myocardial infarction using a nationwide database. METHODS: We queried the 2002-2016 National Inpatient Sample database for hospitalized patients with ST-elevation myocardial infarction who underwent isolated coronary artery bypass grafting. We report temporal trends, predictors, and outcomes of coronary artery bypass grafting in the early (2002-2010) and recent (2011-2016) cohorts. RESULTS: Of 3,347,470 patients hospitalized for ST-elevation myocardial infarction, 7.7% underwent isolated coronary artery bypass grafting. The incidence of isolated coronary artery bypass grafting after ST-elevation myocardial infarction decreased over time (9.2% in 2002 vs 5.5% in 2016, Ptrend < .001), whereas perioperative crude in-hospital mortality did not change (5.1% in 2002 vs 4.2% in 2016, Ptrend = .66), coinciding with an increase in the burden of comorbidities. There was an increase in performing isolated coronary artery bypass grafting on hospitalization day 3 or more, as well as an increase in the use of mechanical support devices and precoronary artery bypass grafting percutaneous coronary intervention. In the early cohort, isolated coronary artery bypass grafting on days 1 and 2 was associated with higher in-hospital mortality. In the recent cohort, coronary artery bypass grafting on day 2 had similar in-hospital mortality compared with day 3 or more and lower rates of acute kidney injury, ischemic stroke, ventricular arrhythmia, and length of hospital stay. CONCLUSIONS: In this nationwide analysis, there has been a decline in the use of isolated coronary artery bypass grafting after ST-elevation myocardial infarction. Isolated coronary artery bypass grafting on day 1 was performed in sicker patients and was associated with higher in-hospital mortality than coronary artery bypass grafting performed on day 3 or more. In the recent cohort, isolated coronary artery bypass grafting on day 2 had similar in-hospital mortality compared with day 3 or more.

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.

Considerations for Left Atrial Appendage Occlusion During Cardiac Surgery.

Atrial fibrillation is associated with an increased risk of stroke secondary to thrombus formation in the left atrial appendage. Left atrial appendage occlusion (LAAO) is an effective method of reducing the risk of stroke in patients with atrial fibrillation. Although LAAO does not remove the requirement for anticoagulation, it reduces the risk of stroke when compared to anticoagulation alone. We critically analyze the data on LAAO in cardiac surgery. We also discuss the methods of LAAO, the risks of LAAO, and patient populations that could benefit from LAAO. We discuss high-level evidence that LAAO at the time of cardiac surgery reduces the risk of stroke in patients with a history of atrial fibrillation. In patients without a history of atrial fibrillation undergoing cardiac surgery, we suggest that LAAO should be considered in select patients at high risk of atrial fibrillation and stroke, when technically feasible.

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.

Socioeconomic disparities in procedural choice and outcomes after aortic valve replacement.

Objective: To identify potential socioeconomic disparities in the procedural choice of patients undergoing surgical aortic valve replacement (SAVR) versus transcatheter aortic valve replacement (TAVR) and in readmission outcomes after SAVR or TAVR. Methods: The Nationwide Readmissions Database was queried to identify a total of 243,691 patients who underwent isolated SAVR and TAVR between January 2016 and December 2018. Patients were stratified according to a tiered socioeconomic status (SES) metric comprising patient factors including education, literacy, housing, employment, insurance status, and neighborhood median income. Multivariable analyses were used to assess the effect of SES on procedural choice and risk-adjusted readmission outcomes. Results: SAVR (41.4%; 100,833 of 243,619) was performed less frequently than TAVR (58.6%; 142,786 of 243,619). Lower SES was more frequent among patients undergoing SAVR (20.2% [20,379 of 100,833] vs 19.4% [27,791 of 142,786]; P < .001). Along with such variables as small hospital size, drug abuse, arrhythmia, and obesity, lower SES was independently associated with SAVR relative to TAVR (adjusted odds ratio [aOR], 1.17; 95% confidence interval [CI], 1.11 to 1.24). After SAVR, but not after TAVR, lower SES was independently associated with increased readmission at 30 days (aOR, 1.19; 95% CI, 1.07-1.32), 90 days (aOR, 1.27; 95% CI, 1.15-1.41), and 1 year (adjusted hazard ratio, 1.19; 95% CI, 1.11 to 1.28; P < .05 for all). Conclusions: Our study findings indicate that socioeconomic disparities exist in the procedural choice for patients undergoing AVR. Patients with lower SES had increased odds of undergoing SAVR, as well as increased odds of readmission after SAVR, but not after TAVR, supporting that health inequities exist in the surgical care of socioeconomically disadvantaged patients.

Influence of concomitant ablation of nonparoxysmal atrial fibrillation during coronary artery bypass grafting on mortality and readmissions.

Objective: We determined the utilization rate of surgical ablation (SA) during coronary artery bypass grafting (CABG) and compared outcomes between CABG with or without SA in a national cohort. Methods: The January 2016 to December 2018 Nationwide Readmissions Database was searched for all patients undergoing isolated CABG with preoperative persistent or chronic atrial fibrillation by using the International Classification of Diseases, 10th Revision classification. Propensity score matching and multivariate logistic regressions were performed to compare outcomes, and Cox proportional hazards model was used to assess risk factors for 1-year readmission. Results: Of 18,899 patients undergoing CABG with nonparoxysmal atrial fibrillation, 78% (n = 14,776) underwent CABG alone and 22% (n = 4123) underwent CABG with SA. In the propensity score-matched cohort (n = 8116), CABG with SA (n = 4054) (vs CABG alone [n = 4112]) was not associated with increased in-hospital mortality (3.4% [139 out of 4112] vs 3.9% [159 ut of 4054]; P = .4), index-hospitalization length of stay (10 days vs 10 days; P = .3), 30-day readmission (19.1% [693 out of 3362] vs 17.2% [609 out of 3537]; P = .2), or 90-day readmission (28.9% [840 out of 2911] vs 26.2% [752 out of 2875]; P = .1). Index hospitalization costs were significantly higher for those undergoing SA ($52,556 vs $47,433; P < .001). Rates of readmission at 300 days were similar between patients receiving SA (43.8%) and no SA (42.8%; log-rank P = .3). The 3 most common causes of readmission were not different between groups and included heart failure (24.3% [594 out of 2444]; P = .6), infection (16.8% [411 out of 2444]; P = .5), and arrhythmia (11.7% [286 out of 2444]; P = .2). Conclusions: In patients with nonparoxysmal atrial fibrillation, utilization of SA during CABG remains low. SA during CABG did not adversely influence mortality or short-term readmissions. These findings support increased use of SA during CABG.

Nationwide database analysis of one-year readmission rates after open surgical or thoracic endovascular repair of Stanford Type B aortic dissection.

Objective: We examined readmissions and resource use during the first postoperative year in patients who underwent thoracic endovascular aortic repair or open surgical repair of Stanford type B aortic dissection. Methods: The Nationwide Readmissions Database (2016-2018) was queried for patients with type B aortic dissection who underwent thoracic endovascular aortic repair or open surgical repair. The primary outcome was readmission during the first postoperative year. Secondary outcomes included 30-day and 90-day readmission rates, in-hospital mortality, length of stay, and cost. A Cox proportional hazards model was used to determine risk factors for readmission. Results: During the study period, type B aortic dissection repair was performed in 6456 patients, of whom 3517 (54.5%) underwent thoracic endovascular aortic repair and 2939 (45.5%) underwent open surgical repair. Patients undergoing thoracic endovascular aortic repair were older (63 vs 59 years; P < .001) with fewer comorbidities (Elixhauser score of 11 vs 17; P < .001) than patients undergoing open surgical repair. Thoracic endovascular aortic repair was performed electively more often than open surgical repair (29% vs 20%; P < .001). In-hospital mortality was 9% overall and lower in the thoracic endovascular aortic repair cohort than in the open surgical repair cohort (5% vs 13%; P < .001). However, the 90-day readmission rate was comparable between the thoracic endovascular aortic repair and open surgical repair cohorts (28% vs 27%; P = .7). Freedom from readmission for up to 1 year was also similar between cohorts (P = .6). Independent predictors of 1-year readmission included length of stay more than 10 days (P = .005) and Elixhauser comorbidity risk index greater than 4 (P = .033). Conclusions: Approximately one-third of all patients with type B aortic dissection were readmitted within 90 days after aortic intervention. Surprisingly, readmission during the first postoperative year was similar in the open surgical repair and thoracic endovascular aortic repair cohorts, despite marked differences in preoperative patient characteristics and interventions.

Activation of Adaptive and Innate Immune Cells via Localized IL2 Cytokine Factories Eradicates Mesothelioma Tumors.

PURPOSE: IL2 immunotherapy has the potential to elicit immune-mediated tumor lysis via activation of effector immune cells, but clinical utility is limited due to pharmacokinetic challenges as well as vascular leak syndrome and other life-threatening toxicities experienced by patients. We developed a safe and clinically translatable localized IL2 delivery system to boost the potency of therapy while minimizing systemic cytokine exposure. EXPERIMENTAL DESIGN: We evaluated the therapeutic efficacy of IL2 cytokine factories in a mouse model of malignant mesothelioma. Changes in immune populations were analyzed using time-of-flight mass cytometry (CyTOF), and the safety and translatability of the platform were evaluated using complete blood counts and serum chemistry analysis. RESULTS: IL2 cytokine factories enabled 150× higher IL2 concentrations in the local compartment with limited leakage into the systemic circulation. AB1 tumor burden was reduced by 80% after 1 week of monotherapy treatment, and 7 of 7 of animals exhibited tumor eradication without recurrence when IL2 cytokine factories were combined with anti-programmed cell death protein 1 (aPD1). Furthermore, CyTOF analysis showed an increase in CD69+CD44+ and CD69-CD44+CD62L- T cells, reduction of CD86-PD-L1- M2-like macrophages, and a corresponding increase in CD86+PD-L1+ M1-like macrophages and MHC-II+ dendritic cells after treatment. Finally, blood chemistry ranges in rodents demonstrated the safety of cytokine factory treatment and reinforced its potential for clinical use. CONCLUSIONS: IL2 cytokine factories led to the eradication of aggressive mouse malignant mesothelioma tumors and protection from tumor recurrence, and increased the therapeutic efficacy of aPD1 checkpoint therapy. This study provides support for the clinical evaluation of this IL2-based delivery system. See related commentary by Palanki et al., p. 5010.

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.