The impact of a statewide payment reform on transcatheter aortic valve replacement (TAVR) utilization and readmissions.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an increasingly used but relatively expensive procedure with substantial associated readmission rates. It is unknown how cost-constrictive payment reform measures, such as Maryland's All Payer Model, impact TAVR utilization given its relative expense. This study investigated the impact of Maryland's All Payer Model on TAVR utilization and readmissions among Maryland Medicare beneficiaries. METHODS: This was a quasi-experimental investigation of Maryland Medicare patients undergoing TAVR between 2012 and 2018. New Jersey data were used for comparison. Longitudinal interrupted time series analyses were used to study TAVR utilization and difference-in-differences analyses were used to investigate post-TAVR readmissions. RESULTS: During the first year of payment reform (2014), TAVR utilization among Maryland Medicare beneficiaries dropped by 8% (95% confidence interval [CI]: -9.2% to -7.1%; p < 0.001), with no concomitant change in TAVR utilization in New Jersey (0.2%, 95% CI: 0%-1%, p = 0.09). Longitudinally, however, the All Payer Model did not impact TAVR utilization in Maryland compared to New Jersey. Difference-in-differences analyses demonstrated that implementation of the All Payer Model was not associated with significantly greater declines in 30-day post-TAVR readmissions in Maryland versus New Jersey (-2.1%; 95% CI: -5.2% to 0.9%; p =0.1). CONCLUSIONS: Maryland's All Payer Model resulted in an immediate decline in TAVR utilization, likely a result of hospitals adjusting to global budgeting. However, beyond this transition period, this cost-constrictive reform measure did not limit Maryland TAVR utilization. In addition, the All Payer Model did not reduce post-TAVR 30-day readmissions. These findings may help inform expansion of globally budgeted healthcare payment structures.

Late onset atrial fibrillation in patients undergoing surgical aortic valve replacement.

OBJECTIVE: Aortic valve disease is a risk factor for atrial fibrillation (AF), and AF is associated with increased late mortality and morbidity after cardiac surgery. The evolution of alternative approaches to AF prophylaxis, including less invasive technologies and medical therapies, has altered the balance between risk and potential benefit for prophylactic intervention at the time of surgical aortic valve replacement (SAVR). Such interventions impose incremental risk, however, making an understanding of predictors of new onset AF that persists beyond the perioperative episode relevant. METHODS: We conducted a retrospective single-institution cohort analysis of patients undergoing SAVR with no history of preoperative AF (n = 1014). These patients were cross-referenced against an institutional electrocardiogram (ECG) database to identify those with ECGs 3-12 months after surgery. Logistic regression was used to identify predictors of late AF. RESULTS: Among the 401 patients (40%), who had ECGs in our institution 3-12 months after surgery, 16 (4%) had late AF. Patients with late AF were older than patients without late AF (73 vs. 65, p = .025), and underwent procedures that were more urgent/emergent (38% vs. 15%, p = .015), with higher predicted risk of mortality (2.2% vs. 1.3%, p = .012). Predictors associated with the development of late AF were advanced age, higher preoperative creatinine level and urgent/emergent surgery. CONCLUSIONS: The incidence of late AF 3-12 months after SAVR, is low. Prophylactic AF interventions at the time of SAVR may not be warranted.

Red blood cell storage duration and peri-operative outcomes in paediatric cardiac surgery.

BACKGROUND: Prior research on red blood cell (RBC) storage duration and clinical outcomes in paediatric cardiac surgery has shown conflicting results. The purpose of this study was to evaluate whether blood stored for a longer duration is harmful in these patients. METHODS: We performed a retrospective cohort study of paediatric patients undergoing cardiac surgery at our institution between January 2011 and June 2015. Patients were stratified based on whether they were transfused RBCs stored for ≤15 days (fresher blood) or >15 days (older blood). The primary outcome was composite morbidity, with prolonged length of stay (LOS) as a secondary outcome. Subgroup analyses were performed after stratification by RBC transfusion volume (≤2 vs. >2 RBC units). Multivariable logistic regression models were used to assess the impact of RBC storage duration on composite morbidity and prolonged LOS. RESULTS: Of 461 patients, 122 (26·5%) received fresher blood and 339 (73·5%) received older blood. The overall rate of composite morbidity was 18·0% (n = 22) for patients receiving fresher blood and 13·6% (n = 46) for patients receiving older blood (P = 0·24). In the risk-adjusted model, patients receiving older blood did not exhibit an increased risk of composite morbidity (OR: 0·74, 95% CI: 0·37-1·47, P = 0·40) or prolonged LOS (OR: 0·72, 95% CI: 0·38-1·35, P = 0·30) compared to patients receiving fresher blood. Similar results were seen after stratification by RBC transfusion volume. CONCLUSIONS: Transfusing RBCs stored for a longer duration was not associated with an increased risk of morbidity or prolonged LOS in paediatric cardiac surgery patients.

Enhancement of human iPSC-derived cardiomyocyte maturation by chemical conditioning in a 3D environment.

Recent advances in the understanding and use of pluripotent stem cells have produced major changes in approaches to the diagnosis and treatment of human disease. An obstacle to the use of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) for regenerative medicine, disease modeling and drug discovery is their immature state relative to adult myocardium. We show the effects of a combination of biochemical factors, thyroid hormone, dexamethasone, and insulin-like growth factor-1 (TDI) on the maturation of hiPSC-CMs in 3D cardiac microtissues (CMTs) that recapitulate aspects of the native myocardium. Based on a comparison of the gene expression profiles and the structural, ultrastructural, and electrophysiological properties of hiPSC-CMs in monolayers and CMTs, and measurements of the mechanical and pharmacological properties of CMTs, we find that TDI treatment in a 3D tissue context yields a higher fidelity adult cardiac phenotype, including sarcoplasmic reticulum function and contractile properties consistent with promotion of the maturation of hiPSC derived cardiomyocytes.

Trends and Updates on Cardiopulmonary Bypass Setup in Pediatric Cardiac Surgery.

Perfusion strategies for cardiopulmonary bypass have direct consequences on pediatric cardiac surgery outcomes. However, inconsistent study results and a lack of uniform evidence-based guidelines for pediatric cardiopulmonary bypass management have led to considerable variability in perfusion practices among, and even within, institutions. Important aspects of cardiopulmonary bypass that can be optimized to improve clinical outcomes of pediatric patients undergoing cardiac surgery include extracorporeal circuit components, priming solutions, and additives. This review summarizes the current literature on circuit components and priming solution composition with an emphasis on crystalloid, colloid, and blood-based primes, as well as mannitol, bicarbonate, and calcium.

3D bioprinting using stem cells.

Recent advances have allowed for three-dimensional (3D) printing technologies to be applied to biocompatible materials, cells and supporting components, creating a field of 3D bioprinting that holds great promise for artificial organ printing and regenerative medicine. At the same time, stem cells, such as human induced pluripotent stem cells, have driven a paradigm shift in tissue regeneration and the modeling of human disease, and represent an unlimited cell source for tissue regeneration and the study of human disease. The ability to reprogram patient-specific cells holds the promise of an enhanced understanding of disease mechanisms and phenotypic variability. 3D bioprinting has been successfully performed using multiple stem cell types of different lineages and potency. The type of 3D bioprinting employed ranged from microextrusion bioprinting, inkjet bioprinting, laser-assisted bioprinting, to newer technologies such as scaffold-free spheroid-based bioprinting. This review discusses the current advances, applications, limitations and future of 3D bioprinting using stem cells, by organ systems.

Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review.

Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice.

Historical Accounts of Congenital Heart Surgery.

We present historical accounts of congenital heart surgery since the early 1900s, as our specialty evolved from individual heroic efforts into an established and sophisticated surgical specialty with consistent and excellent results. We highlight colleagues and intrepid pioneers who have strived to solve seemingly insurmountable problems during this remarkable journey and celebrate continued success into the 21st century with surgical advances that have resulted in innovative operations, database inquiries, quality measures, new techniques of medical illustration, and the establishment of the Congenital Heart Surgeons' Society, which has become the leading organization dedicated to congenital heart surgery in North America.

Impact of intraoperative blood products, fluid administration, and persistent hypothermia on bleeding leading to reexploration after cardiac surgery.

OBJECTIVE: Risk factors for severe postoperative bleeding after cardiac surgery remain multiple and incompletely elucidated. We evaluated the impact of intraoperative blood product transfusions, intravenous fluid administration, and persistently low core body temperature (CBT) at intensive care unit arrival on risk of perioperative bleeding leading to reexploration. METHODS: We retrospectively queried our tertiary care center's Society of Thoracic Surgeons Institutional Database for all index, on-pump, adult cardiac surgery patients between July 2016 and September 2022. Intraoperative fluid (crystalloid and colloid) and blood product administrations, as well as perioperative CBT data, were harvested from electronic medical records. Linear and nonlinear mixed models, treating surgeon as a random effect to account for inter-surgeon practice differences, were used to assess the association between above factors and reexploration for bleeding. RESULTS: Of 4037 patients, 151 (3.7%) underwent reexploration for bleeding. Reexplored patients experienced remarkably greater postoperative morbidity (23% vs 6%, P < .001) and 30-day mortality (14% vs 2%, P < .001). In linear models, progressively increasing IV crystalloid administration (adjusted odds ratio, 1.11, 95% confidence interval, 1.03-1.19) and decreasing CBT on intensive care unit arrival (adjusted odds ratio, 1.20; 95% confidence interval, 1.05-1.37) were associated with greater risk of bleeding leading to reexploration. Nonlinear analysis revealed increasing risk after ∼6 L of crystalloid administration and a U-shaped relationship between CBT and reexploration risk. Intraoperative blood product transfusion of any kind was not associated with reexploration. CONCLUSIONS: We found evidence of both dilution- and hypothermia-related effects associated with perioperative bleeding leading to reexploration in cardiac surgery. Interventions targeting modification of such risk factors may decrease the rate this complication.

Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning.

OBJECTIVE: We aimed to learn the causal determinants of postoperative length of stay in cardiac surgery patients undergoing isolated coronary artery bypass grafting or aortic valve replacement surgery. METHODS: For patients undergoing isolated coronary artery bypass grafting or isolated aortic valve replacement surgeries between 2011 and 2016, we used causal graphical modeling on electronic health record data. The Fast Causal Inference (FCI) algorithm from the Tetrad software was used on data to estimate a Partial Ancestral Graph (PAG) depicting direct and indirect causes of postoperative length of stay, given background clinical knowledge. Then, we used the latent variable intervention-calculus when the directed acyclic graph is absent (LV-IDA) algorithm to estimate strengths of causal effects of interest. Finally, we ran a linear regression for postoperative length of stay to contrast statistical associations with what was learned by our causal analysis. RESULTS: In our cohort of 2610 patients, the mean postoperative length of stay was 219 hours compared with the Society of Thoracic Surgeons 2016 national mean postoperative length of stay of approximately 168 hours. Most variables that clinicians believe to be predictors of postoperative length of stay were found to be causes, but some were direct (eg, age, diabetes, hematocrit, total operating time, and postoperative complications), and others were indirect (including gender, race, and operating surgeon). The strongest average causal effects on postoperative length of stay were exhibited by preoperative dialysis (209 hours); neuro-, pulmonary-, and infection-related postoperative complications (315 hours, 89 hours, and 131 hours, respectively); reintubation (61 hours); extubation in operating room (-47 hours); and total operating room duration (48 hours). Linear regression coefficients diverged from causal effects in magnitude (eg, dialysis) and direction (eg, crossclamp time). CONCLUSIONS: By using retrospective electronic health record data and background clinical knowledge, causal graphical modeling retrieved direct and indirect causes of postoperative length of stay and their relative strengths. These insights will be useful in designing clinical protocols and targeting improvements in patient management.

Arterial oxygen and carbon dioxide tension and acute brain injury in extracorporeal cardiopulmonary resuscitation patients: Analysis of the extracorporeal life support organization registry.

BACKGROUND: Acute brain injury (ABI) remains common after extracorporeal cardiopulmonary resuscitation (ECPR). Using a large international multicenter cohort, we investigated the impact of peri-cannulation arterial oxygen (PaO2) and carbon dioxide (PaCO2) on ABI occurrence. METHODS: We retrospectively analyzed adult (≥18 years old) ECPR patients in the Extracorporeal Life Support Organization registry from 1/2009 through 12/2020. Composite ABI included ischemic stroke, intracranial hemorrhage (ICH), seizures, and brain death. The registry collects 2 blood gas data pre- (6 hours) and post- (24 hours) cannulation. Blood gas parameters were classified as: hypoxia (<60mm Hg), normoxia (60-119mm Hg), and mild (120-199mm Hg), moderate (200-299mm Hg), and severe hyperoxia (≥300mm Hg); hypocarbia (<35mm Hg), normocarbia (35-44mm Hg), mild (45-54mm Hg) and severe hypercarbia (≥55mm Hg). Missing values were handled using multiple imputation. Multivariable logistic regression analysis was used to assess the relationship of PaO2 and PaCO2 with ABI. RESULTS: Of 3,125 patients with ECPR intervention (median age=58, 69% male), 488 (16%) experienced ABI (7% ischemic stroke; 3% ICH). In multivariable analysis, on-ECMO moderate (aOR=1.42, 95%CI: 1.02-1.97) and severe hyperoxia (aOR=1.59, 95%CI: 1.20-2.10) were associated with composite ABI. Additionally, severe hyperoxia was associated with ischemic stroke (aOR=1.63, 95%CI: 1.11-2.40), ICH (aOR=1.92, 95%CI: 1.08-3.40), and in-hospital mortality (aOR=1.58, 95%CI: 1.21-2.06). Mild hypercarbia pre-ECMO was protective of composite ABI (aOR=0.61, 95%CI: 0.44-0.84) and ischemic stroke (aOR=0.56, 95%CI: 0.35-0.89). CONCLUSIONS: Early severe hyperoxia (≥300mm Hg) on ECMO was a significant risk factor for ABI and mortality. Careful consideration should be given in early oxygen delivery in ECPR patients who are at risk of reperfusion injury.

Early Reexploration for Bleeding Is Associated With Improved Outcome in Cardiac Surgery.

BACKGROUND: Reexploration after cardiac surgery, most frequently for bleeding, is a quality metric used to assess surgical performance. This may cause surgeons to delay return to the operating room in favor of attempting nonoperative management. This study investigated the impact of the timing of reexploration on morbidity and mortality. METHODS: This study was a single-institution retrospective review of all adult cardiac surgery patients from July 2010 to June 2020. Time to reexploration was assessed, and outcomes were compared across increasing time intervals. Reported bleeding sites were classified into 5 groups, and bleeding rate (chest tube output) was compared across bleeding sites. Univariable analysis was performed using the Fisher exact and Kruskal-Wallis tests. Multivariable logistic regression models were used for risk-adjusted analyses. RESULTS: Of 10 070 eligible patients, 251 (2.5%) required reexploration for postoperative bleeding. The most common site of bleeding was "any suture line" (n = 70; 28%). Interestingly, in 30% of cases (n = 75) "no active bleeding" site was reported. The highest rate of bleeding (mL/h) was observed in the "any mediastinal structure" group (median, 450; interquartile range [IQR], 185, 8878), and the lowest rate was noted in the "no active bleeding" group (median, 151.2; IQR, 102, 270). Both morbidity rates (0-4 hours, 12.3% vs 25-48 hours, 37.5%; P = .001) and mortality rates (0-4 hours, 3.1% vs 25-48 hours, 43.8%; P = .001) escalated significantly with increasing time to reexploration. CONCLUSIONS: Delayed reexploration for bleeding after cardiac surgery is associated with increased risk for morbidity and mortality. Early surgical intervention, particularly within 4 hours, may improve outcomes. Implications from using reoperation as a performance metric may lead to unnecessary delay and patient harm.

Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

BACKGROUND: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater than 60 inputs. We hypothesized that deep learning can estimate postoperative mortality risk based on a preoperative chest radiograph for cardiac surgeries in which STS-PROM scores were available (STS index procedures) or unavailable (non-STS index procedures). METHODS: We developed a deep learning model (CXR-CTSurgery) to predict postoperative mortality based on preoperative chest radiographs in 9283 patients at Massachusetts General Hospital (MGH) having cardiac surgery before April 8, 2014. CXR-CTSurgery was tested on 3615 different MGH patients and externally tested on 2840 patients from Brigham and Women's Hospital (BWH) having surgery after April 8, 2014. Discrimination for mortality was compared with the STS-PROM using the C-statistic. Calibration was assessed using the observed-to-expected ratio (O/E ratio). RESULTS: For STS index procedures, CXR-CTSurgery had a C-statistic similar to STS-PROM at MGH (CXR-CTSurgery: 0.83 vs STS-PROM: 0.88; P = .20) and BWH (0.74 vs 0.80; P = .14) testing cohorts. The CXR-CTSurgery C-statistic for non-STS index procedures was similar to STS index procedures in the MGH (0.87 vs 0.83) and BWH (0.73 vs 0.74) testing cohorts. For STS index procedures, CXR-CTSurgery had better calibration than the STS-PROM in the MGH (O/E ratio: 0.74 vs 0.52) and BWH (O/E ratio: 0.91 vs 0.73) testing cohorts. CONCLUSIONS: CXR-CTSurgery predicts postoperative mortality based on a preoperative CXR with similar discrimination and better calibration than the STS-PROM. This may be useful when the STS-PROM cannot be calculated or for non-STS index procedures.

Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores.

OBJECTIVE: Current cardiac surgery risk models do not address a substantial fraction of procedures. We sought to create models to predict the risk of operative mortality for an expanded set of cases. METHODS: Four supervised machine learning models were trained using preoperative variables present in the Society of Thoracic Surgeons (STS) data set of the Massachusetts General Hospital to predict and classify operative mortality in procedures without STS risk scores. A total of 424 (5.5%) mortality events occurred out of 7745 cases. Models included logistic regression with elastic net regularization (LogReg), support vector machine, random forest (RF), and extreme gradient boosted trees (XGBoost). Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration slope and expected-to-observed event ratio. External validation was performed using STS data sets from Brigham and Women's Hospital (BWH) and the Johns Hopkins Hospital (JHH). RESULTS: Models performed comparably with the highest mean AUC of 0.83 (RF) and expected-to-observed event ratio of 1.00. On external validation, the AUC was 0.81 in BWH (RF) and 0.79 in JHH (LogReg/RF). Models trained and applied on the same institution's data achieved AUCs of 0.81 (BWH: LogReg/RF/XGBoost) and 0.82 (JHH: LogReg/RF/XGBoost). CONCLUSIONS: Machine learning models trained on preoperative patient data can predict operative mortality at a high level of accuracy for cardiac surgical procedures without established risk scores. Such procedures comprise 23% of all cardiac surgical procedures nationwide. This work also highlights the value of using local institutional data to train new prediction models that account for institution-specific practices.

Effect of concurrent mitral valve surgery for secondary mitral regurgitation upon mortality after aortic valve replacement or coronary artery bypass surgery.

Objectives: It is uncertain whether concurrent mitral valve repair or replacement for moderate or greater secondary mitral regurgitation at the time of coronary artery bypass graft or aortic valve replacement surgery improves long-term survival. Methods: Patients undergoing coronary artery bypass graft and/or aortic valve replacement surgery with moderate or greater secondary mitral regurgitation were reviewed. The effect of concurrent mitral valve repair or replacement upon long-term mortality was assessed while accounting for patient and operative characteristics and mitral regurgitation severity. Results: Of 1,515 patients, 938 underwent coronary artery bypass graft or aortic valve replacement surgery alone and 577 underwent concurrent mitral valve repair or replacement. Concurrent mitral valve repair or replacement did not alter the risk of postoperative mortality for patients with moderate mitral regurgitation (hazard ratio = 0.93; 0.75-1.17) or more-than-moderate mitral regurgitation (hazard ratio = 1.09; 0.74-1.60) in multivariable regression. Patients with more-than-moderate mitral regurgitation undergoing coronary artery bypass graft-only surgery had a survival advantage from concurrent mitral valve repair or replacement in the first two postoperative years (P = 0.028) that did not persist beyond that time. Patients who underwent concurrent mitral valve repair or replacement had a higher rate of later mitral valve operation or reoperation over the five subsequent years (1.9% vs. 0.2%; P = 0.0014) than those who did not. Conclusions: These observations suggest that mitral valve repair or replacement for more-than-moderate mitral regurgitation at the time of coronary artery bypass grafting may be reasonable in a suitably selected coronary artery bypass graft population but not for aortic valve replacement, with or without coronary artery bypass grafting. Our findings are supportive of 2021 European guidelines that severe secondary mitral regurgitation "should" or be "reasonabl[y]" intervened upon at the time of coronary artery bypass grafting but do not support 2020 American guidelines for performing mitral valve repair or replacement concurrent with aortic valve replacement, with or without coronary artery bypass grafting.

Neuromonitoring detects brain injury in patients receiving extracorporeal membrane oxygenation support.

OBJECTIVE: There is limited evidence on standardized protocols for optimal neurological monitoring methods in patients receiving extracorporeal membrane oxygenation (ECMO). We previously introduced protocolized noninvasive multimodal neuromonitoring using serial neurological examinations, electroencephalography, transcranial Doppler ultrasound, and somatosensory evoked potentials. The purpose of this study was to examine if standardized neuromonitoring is associated with detection of acute brain injury (ABI) and improved patient outcomes. METHODS: A retrospective analysis of ECMO patients who received neurocritical care consultation was performed and outcomes were reviewed. The cohort was stratified according to those who did not receive standardized neuromonitoring (era 1: 2016-2017) and those who received standardized neuromonitoring (era 2: 2017-2020). Multivariable logistic regression was used to evaluate the association between standardized neuromonitoring and ABI. RESULTS: A total of 215 patients (mean age, 54 years; 60% male) underwent ECMO (71% venoarterial-ECMO) in our institution, 70 in era 1 and 145 in era 2. The proportion of patients diagnosed with ABI were 23% in era 1 and 33% in era 2 (P = .12). In multivariable logistic regression, standardized neuromonitoring (odds ratio, 2.24; 95% CI, 1.12-4.48; P = .02) and pre-ECMO cardiac arrest (odds ratio, 2.17; 95% CI, 1.14-4.14; P = .02) were independently associated with ABI. There was a greater proportion of patients with good neurological outcomes when discharged alive in era 2 (54% vs 30%; P = .04). CONCLUSIONS: Standardized neuromonitoring was associated with increased ABIs in ECMO patients. Although neuromonitoring does not prevent ABI from occurring, it might prevent worsening with timely interventions (eg, anticoagulation management, optimizing oxygen delivery and blood pressure), leading to improved neurological outcomes at discharge.

Recruited macrophages elicit atrial fibrillation.

Atrial fibrillation disrupts contraction of the atria, leading to stroke and heart failure. We deciphered how immune and stromal cells contribute to atrial fibrillation. Single-cell transcriptomes from human atria documented inflammatory monocyte and SPP1+ macrophage expansion in atrial fibrillation. Combining hypertension, obesity, and mitral valve regurgitation (HOMER) in mice elicited enlarged, fibrosed, and fibrillation-prone atria. Single-cell transcriptomes from HOMER mouse atria recapitulated cell composition and transcriptome changes observed in patients. Inhibiting monocyte migration reduced arrhythmia in Ccr2-∕- HOMER mice. Cell-cell interaction analysis identified SPP1 as a pleiotropic signal that promotes atrial fibrillation through cross-talk with local immune and stromal cells. Deleting Spp1 reduced atrial fibrillation in HOMER mice. These results identify SPP1+ macrophages as targets for immunotherapy in atrial fibrillation.

Leveraging Machine Learning to Predict 30-Day Hospital Readmission After Cardiac Surgery.

BACKGROUND: Hospital readmission within 30 days of discharge is a well-studied outcome. Predicting readmission after cardiac surgery, however, is notoriously challenging; the best-performing models in the literature have areas under the curve around .65. A reliable predictive model would enable clinicians to identify patients at risk for readmission and to develop prevention strategies. METHODS: We analyzed The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database at our institution, augmented with electronic medical record data. Predictors included demographics, preoperative comorbidities, proxies for intraoperative risk, indicators of postoperative complications, and time series-derived variables. We trained several machine learning models, evaluating each on a held-out test set. RESULTS: Our analysis cohort consisted of 4924 cases from 2011 to 2016. Of those, 723 (14.7%) were readmitted within 30 days of discharge. Our models included 141 STS-derived and 24 electronic medical records-derived variables. A random forest model performed best, with test area under the curve 0.76 (95% confidence interval, 0.73 to 0.79). Using exclusively preoperative variables, as in STS calculated risk scores, degraded the area under the curve, to 0.64 (95% confidence interval, 0.60 to 0.68). Key predictors included length of stay (12.5 times more important than the average variable) and whether the patient was discharged to a rehabilitation facility (11.2 times). CONCLUSIONS: Our approach, augmenting STS variables with electronic medical records data and using flexible machine learning modeling, yielded state-of-the-art performance for predicting 30-day readmission. Separately, the importance of variables not directly related to inpatient care, such as discharge location, amplifies questions about the efficacy of assessing care quality by readmissions.

Extruded poly (glycerol sebacate) and polyglycolic acid vascular graft forms a neoartery.

In the ongoing search for the optimal biomaterial for tissue engineered vascular grafts (TEVGs), poly (glycerol sebacate) (PGS) has emerged as a new potential candidate. We have utilized a novel method to create unique, pore-free, extruded PGS grafts with and without a supportive exterior layer of polyglycolic acid (PGA). The 1 mm diameter by 5 mm length TEVGs were implanted in a rat model of infrarenal abdominal aorta interposition grafting. Three months after implantation, TEVGs comprised of extruded PGS with an external PGA braid demonstrated a patency rate of 9/10 (90%) with no signs of dilatation, dehiscence, or rupture. The PGS/PGA graft was remodeled into a neoartery with complete endothelialization of the neoartery lumen and formation of smooth muscle actinin multilayers as demonstrated via immunohistochemistry. Formation and maturation of extracellular matrix material were also observed, with amounts of elastin and collagen comparable to native rat aorta. No significant host inflammatory response was observed. These findings suggest the combination of an extruded PGS tube with an external reinforcing PGA braid is a promising material for small diameter TEVGs.

Resting Energy Expenditure of Patients on Venovenous Extracorporeal Membrane Oxygenation for Adult Respiratory Distress Syndrome: A Pilot Study.

OBJECTIVES: The objectives of this study were to 1) in patients without pulmonary function, determine resting energy expenditure (REE) in venovenous extracorporeal membrane oxygenation (ECMO) acute respiratory distress syndrome (ARDS) patients by paralysis status and 2) determine the threshold tidal volume (TV) associated with meaningful gas exchange as determined by measurable end-tidal carbon dioxide elimination (etV̇co2). DESIGN: Retrospective observational study. SETTING: A tertiary high ECMO volume academic institution. PATIENTS/SUBJECTS: Ten adult ARDS patients on venovenous ECMO. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The modified Weir equation, Fick principle, Henderson-Hasselbalch equation, ECMO flow, hemoglobin, and pre and post oxygenator blood gases were used to determine ECMO carbon dioxide production (V̇co2), ECMO oxygen consumption, and REE. REE values were matched to patients' paralysis status based on medication flowsheets and compared using a paired t test. Linear regression was performed to determine the threshold TV normalized to ideal body weight (IBW) associated with measurable ventilator etV̇co2, above which meaningful ventilation occurs. When lungs were not functioning, patients had significantly lower mean REE when paralyzed (23.4 ± 2.8 kcal/kg/d) than when not paralyzed (29.2 ± 5.8 kcal/kg/d) (p = 0.02). Furthermore, mean REE was not similar between patients and varied as much as 1.7 times between patients when paralyzed and as much as 1.4 times when not paralyzed. Linear regression showed that ventilator V̇co2 was measurable and increased linearly when TV was greater than or equal to 0.7 mL/kg. CONCLUSIONS: REE is patient-specific and varies significantly with and without patient paralysis. When TV exceeds 0.7 mL/kg IBW, ventilator V̇co2 increases measurably and must be considered in determining total REE.