Perioperative Changes in Plasma Nitrite and IL-6 Levels Predict Postoperative Atrial Fibrillation (POAF) and Acute Kidney Injury (AKI) after Cardiac Surgery.

Background: Postoperative atrial fibrillation (POAF) and acute kidney injury (AKI) are common yet significant complications after cardiac surgery, with incidences of up to 40% for each. Here, we assessed plasma nitrite and serum interleukin-6 (IL-6) levels before and after cardiac surgery to quantify the extent to which oxidative stress and inflammation contribute to POAF and AKI occurrence. Methods: We prospectively enrolled 206 cardiac surgical patients. Plasma nitrite and serum IL-6 levels were determined preoperatively and at 24 h, 48 h and 72 h postoperatively. The patients had continuous EKG monitoring for occurrence of POAF, while daily serum creatinine was measured for determination of stage 1 + AKI. Results: Postoperatively, 78 (38%) patients experienced AF, and 47 (23%) patients experienced stage 1 + AKI. POAF analysis: Age, ACE-inhibitor use, valve surgery and percent change in baseline plasma nitrite at 24 h postoperatively were associated with POAF in multiple logistic regression analysis. The inclusion of this new biomarker significantly improved the POAF prediction model (AUC 0.77 for clinical risk factors alone, to AUC 0.81). AKI analysis: A history of diabetes mellitus was associated with AKI in multiple logistic regression analysis, and the addition of preoperative IL-6 levels improved the prediction model for AKI occurrence (AUC 0.69 to AUC 0.74). Conclusions: We previously observed selective upregulation of NADPH oxidase isoform 4 (NOX4) in patients with AF, a critical causal role of NOX4 for AF in zebrafish and a robust inhibitory effect of nitric oxide (NO) on NOX4. Our data innovatively demonstrate that a reduction in circulating nitrite levels, likely implicative of elevated NOX4-mediated oxidative stress, independently associates with POAF and improves POAF prediction, whereas the inclusion of circulating IL-6 levels improves the prediction model for AKI. Therefore, therapeutic strategies to mitigate these pathophysiological sequalae of surgical stress may reduce the incidence of severe postoperative complications of POAF and AKI.

Functionally graded hydrogels with opposing biochemical cues for osteochondral tissue engineering.

Osteochondral tissue (OC) repair remains a significant challenge in the field of musculoskeletal tissue engineering. OC tissue displays a gradient structure characterized by variations in both cell types and extracellular matrix components, from cartilage to the subchondral bone. These functional gradients observed in the native tissue have been replicated to engineer OC tissuein vitro. While diverse fabrication methods have been employed to create these microenvironments, emulating the natural gradients and effective regeneration of the tissue continues to present a significant challenge. In this study, we present the design and development of CMC-silk interpenetrating (IPN) hydrogel with opposing dual biochemical gradients similar to native tissue with the aim to regenerate the complete OC unit. The gradients of biochemical cues were generated using an in-house-built extrusion system. Firstly, we fabricated a hydrogel that exhibits a smooth transition of sulfated carboxymethyl cellulose (sCMC) and TGF-ß1 (SCT gradient hydrogel) from the upper to the lower region of the IPN hydrogel to regenerate the cartilage layer. Secondly, a hydrogel with a hydroxyapatite (HAp) gradient (HAp gradient hydrogel) from the lower to the upper region was fabricated to facilitate the regeneration of the subchondral bone layer. Subsequently, we developed a dual biochemical gradient hydrogel with a smooth transition of sCMC + TGF-ß1 and HAp gradients in opposing directions, along with a blend of both biochemical cues in the middle. The results showed that the dual biochemical gradient hydrogels with biochemical cues corresponding to the three zones (i.e. cartilage, interface and bone) of the OC tissue led to differentiation of bone-marrow-derived mesenchymal stem cells to zone-specific lineages, thereby demonstrating their efficacy in directing the fate of progenitor cells. In summary, our study provided a simple and innovative method for incorporating gradients of biochemical cues into hydrogels. The gradients of biochemical cues spatially guided the differentiation of stem cells and facilitated tissue growth, which would eventually lead to the regeneration of the entire OC tissue with a smooth transition from cartilage (soft) to bone (hard) tissues. This promising approach is translatable and has the potential to generate numerous biochemical and biophysical gradients for regeneration of other interface tissues, such as tendon-to-muscle and ligament-to-bone.

A Novel Digital Health Platform With Health Coaches to Optimize Surgical Patients: Feasibility Study at a Large Academic Health System.

BACKGROUND: Pip is a novel digital health platform (DHP) that combines human health coaches (HCs) and technology with patient-facing content. This combination has not been studied in perioperative surgical optimization. OBJECTIVE: This study's aim was to test the feasibility of the Pip platform for deploying perioperative, digital, patient-facing optimization guidelines to elective surgical patients, assisted by an HC, at predefined intervals in the perioperative journey. METHODS: We conducted an institutional review board-approved, descriptive, prospective feasibility study of patients scheduled for elective surgery and invited to enroll in Pip from 2.5 to 4 weeks preoperatively through 4 weeks postoperatively at an academic medical center between November 22, 2022, and March 27, 2023. Descriptive primary end points were patient-reported outcomes, including patient satisfaction and engagement, and Pip HC evaluations. Secondary end points included mean or median length of stay (LOS), readmission at 7 and 30 days, and emergency department use within 30 days. Secondary end points were compared between patients who received Pip versus patients who did not receive Pip using stabilized inverse probability of treatment weighting. RESULTS: A total of 283 patients were invited, of whom 172 (60.8%) enrolled in Pip. Of these, 80.2% (138/172) patients had ≥1 HC session and proceeded to surgery, and 70.3% (97/138) of the enrolled patients engaged with Pip postoperatively. The mean engagement began 27 days before surgery. Pip demonstrated an 82% weekly engagement rate with HCs. Patients attended an average of 6.7 HC sessions. Of those patients that completed surveys (95/138, 68.8%), high satisfaction scores were recorded (mean 4.8/5; n=95). Patients strongly agreed that HCs helped them throughout the perioperative process (mean 4.97/5; n=33). The average net promoter score was 9.7 out of 10. A total of 268 patients in the non-Pip group and 128 patients in the Pip group had appropriate overlapping distributions of stabilized inverse probability of treatment weighting for the analytic sample. The Pip cohort was associated with LOS reduction when compared to the non-Pip cohort (mean 2.4 vs 3.1 days; median 1.9, IQR 1.0-3.1 vs median 3.0, IQR 1.1-3.9 days; mean ratio 0.76; 95% CI 0.62-0.93; P=.009). The Pip cohort experienced a 49% lower risk of 7-day readmission (relative risk [RR] 0.51, 95% CI 0.11-2.31; P=.38) and a 17% lower risk of 30-day readmission (RR 0.83, 95% CI 0.30-2.31; P=.73), though these did not reach statistical significance. Both cohorts had similar 30-day emergency department returns (RR 1.06, 95% CI 0.56-2.01, P=.85). CONCLUSIONS: Pip is a novel mobile DHP combining human HCs and perioperative optimization content that is feasible to engage patients in their perioperative journey and is associated with reduced hospital LOS. Further studies assessing the impact on clinical and patient-reported outcomes from the use of Pip or similar DHPs HC combinations during the perioperative journey are required.

A Global Health Survey of People Who Vape but Never Smoked: Protocol for the VERITAS (Vaping Effects: Real-World International Surveillance) Study.

BACKGROUND: There is only limited information about the health effects of regular vaping. Research on the health status of people who used to smoke faces the challenge that previous smoking may have caused unknown health effects. Only studies of people who vape but have never smoked combustible cigarettes can enable the detection of harms attributable to vaping. Large prospective studies of well-characterized electronic cigarette users with and without a history of combustible cigarette smoking are warranted to establish the long-term effects of regular vaping on respiratory health. OBJECTIVE: We will conduct a global cross-sectional survey of individuals from 6 world regions. Respiratory symptoms will be assessed using a validated questionnaire-the Respiratory Symptom Experience Scale (RSES). Current vapers who are nonusers of other tobacco or nicotine products will be compared with matched controls who are nonusers of vapes and other tobacco or nicotine products. METHODS: This will be a multicountry, cross-sectional internet-based survey of 750 adults aged ≥18 years who satisfy the criteria for inclusion in either a cohort of people who exclusively vape and who are nonusers of other tobacco or nicotine products ("vapers cohort"; target N=500) or a cohort of nonvapers who are also nonusers of other tobacco or nicotine products ("controls cohort"; target N=250). The primary end point of the study is the RSES score. RSES scores of people in the "vapers cohort" will be compared with those of people in the "controls cohort." Additionally, the study will collect data to characterize patterns of vaping product use among the vapers cohort. Data collection will include information about the age initiation of using vape products, reasons for starting and continuing the use of vape products, specific types of products used, flavors and nicotine strengths of recently used products, as well as the frequency and intensity of product use in the past 30 days. RESULTS: Participant recruitment started in April 2023, and enrollment was completed by November 2023 with 748 participants. Results will be reported in 2024. CONCLUSIONS: This will be the first study providing key insights into respiratory health effects associated with using electronic cigarettes in people who vape with no established use of combustible cigarettes or other tobacco or nicotine products. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54236.

Ventricular arrhythmia inducibility in porcine infarct model after stereotactic body radiation therapy.

BACKGROUND: Ventricular arrhythmia (VA) is the primary mechanism of sudden death in patients with structural heart disease. Cardiac stereotactic body radiation therapy (SBRT) delivered to the scar in the left ventricle significantly reduces the burden of VA. OBJECTIVE: The goal of this study was to investigate the impact of SBRT on scar morphology and VA inducibility in a porcine infarct model. METHODS: Myocardial infarction (MI) was created in 10 Yorkshire pigs involving the left anterior descending artery territory. Cardiac positron emission tomography and computed tomography were performed for targeted SBRT. Alternative pigs received SBRT at 25 Gy in a single fraction. The terminal experiment included endocardial mapping, programmed ventricular stimulation, and tissue harvesting. RESULTS: Of the 10 pigs infarcted, 2 died prematurely after MI and 8 (4 MI and 4 MI+SBRT) survived. Mean time from MI to SBRT was 48 ± 12 days, and mean time from SBRT to harvest was 32 ± 12 days. Scar was localized on intracardiac mapping in all pigs, and the scar was denser in the MI+SBRT compared with the MI-only group (33% ± 20% vs 14% ± 11%; P = .07). All 4 MI pigs had inducible VA during programmed stimulation, whereas only 1 of 4 pigs had inducible VA in the MI+SBRT arm (100% vs 25%; P = .07). No myocardial fibrosis was seen in the remote areas in either group. CONCLUSION: SBRT reduced VA inducibility in pigs with scarring after MI. Endocardial mapping revealed denser scar in pigs receiving SBRT compared with those that did not, suggesting that SBRT suppresses VA inducibility through better scar homogenization.

REMAP Periop: a randomised, embedded, multifactorial adaptive platform trial protocol for perioperative medicine to determine the optimal enhanced recovery pathway components in complex abdominal surgery patients within a US healthcare system.

INTRODUCTION: Implementation of enhanced recovery pathways (ERPs) has resulted in improved patient-centred outcomes and decreased costs. However, there is a lack of high-level evidence for many ERP elements. We have designed a randomised, embedded, multifactorial, adaptive platform perioperative medicine (REMAP Periop) trial to evaluate the effectiveness of several perioperative therapies for patients undergoing complex abdominal surgery as part of an ERP. This trial will begin with two domains: postoperative nausea/vomiting (PONV) prophylaxis and regional/neuraxial analgesia. Patients enrolled in the trial will be randomised to arms within both domains, with the possibility of adding additional domains in the future. METHODS AND ANALYSIS: In the PONV domain, patients are randomised to optimal versus supraoptimal prophylactic regimens. In the regional/neuraxial domain, patients are randomised to one of five different single-injection techniques/combination of techniques. The primary study endpoint is hospital-free days at 30 days, with additional domain-specific secondary endpoints of PONV incidence and postoperative opioid consumption. The efficacy of an intervention arm within a given domain will be evaluated at regular interim analyses using Bayesian statistical analysis. At the beginning of the trial, participants will have an equal probability of being allocated to any given intervention within a domain (ie, simple 1:1 randomisation), with response adaptive randomisation guiding changes to allocation ratios after interim analyses when applicable based on prespecified statistical triggers. Triggers met at interim analysis may also result in intervention dropping. ETHICS AND DISSEMINATION: The core protocol and domain-specific appendices were approved by the University of Pittsburgh Institutional Review Board. A waiver of informed consent was obtained for this trial. Trial results will be announced to the public and healthcare providers once prespecified statistical triggers of interest are reached as described in the core protocol, and the most favourable interventions will then be implemented as a standardised institutional protocol. TRIAL REGISTRATION NUMBER: NCT04606264.

Converse modulation of Wnt/ß-catenin signaling during expansion and differentiation phases of Infrapatellar fat pad-derived MSCs for improved engineering of hyaline cartilage.

Mesenchymal stem cells (MSCs) are potential candidates in cell-based therapy for cartilage repair and regeneration. However, during chondrogenic differentiation, MSCs undergo undesirable hypertrophic maturation. This poses a risk of ossification in the neo-tissue formed that eventually impedes the clinical use of MSCs for cartilage repair. TGF-ß is a potent growth factor used for chondrogenic differentiation of MSCs, however, its role in hypertrophy remains ambiguous. In the present work, we decipher that TGF-ß activates Wnt/ß-catenin signaling through SMAD3 and increases the propensity of Infrapatellar fat pad derived MSCs (IFP-MSCs) towards hypertrophy. Notably, inhibiting TGF-ß induced Wnt/ß-catenin signaling suppresses hypertrophic progression and enhances chondrogenic ability of IFP-MSCs in plasma hydrogels. Additionally, we demonstrate that activating Wnt signaling during expansion phase, promotes proliferation and reduces senescence, while improving stemness of IFP-MSCs. Thus, conversely modulating Wnt signaling in vitro during expansion and differentiation phases generates hyaline-like cartilage with minimal hypertrophy. Importantly, pre-treatment of IFP-MSCs encapsulated in plasma hydrogel with Wnt modulators followed by subcutaneous implantation in nude mice resulted in formation of a cartilage tissue with negligible calcification. Overall, this study provides technological advancement on targeting Wnt/ß-catenin pathway in a 3D scaffold, while maintaining the standard chondro-induction protocol to overcome the challenges associated with the clinical use of MSCs to engineer hyaline cartilage.

Development and Validation of a Machine Learning Model to Identify Patients Before Surgery at High Risk for Postoperative Adverse Events.

Importance: Identifying patients at high risk of adverse outcomes prior to surgery may allow for interventions associated with improved postoperative outcomes; however, few tools exist for automated prediction. Objective: To evaluate the accuracy of an automated machine-learning model in the identification of patients at high risk of adverse outcomes from surgery using only data in the electronic health record. Design, Setting, and Participants: This prognostic study was conducted among 1 477 561 patients undergoing surgery at 20 community and tertiary care hospitals in the University of Pittsburgh Medical Center (UPMC) health network. The study included 3 phases: (1) building and validating a model on a retrospective population, (2) testing model accuracy on a retrospective population, and (3) validating the model prospectively in clinical care. A gradient-boosted decision tree machine learning method was used for developing a preoperative surgical risk prediction tool. The Shapley additive explanations method was used for model interpretability and further validation. Accuracy was compared between the UPMC model and National Surgical Quality Improvement Program (NSQIP) surgical risk calculator for predicting mortality. Data were analyzed from September through December 2021. Exposure: Undergoing any type of surgical procedure. Main Outcomes and Measures: Postoperative mortality and major adverse cardiac and cerebrovascular events (MACCEs) at 30 days were evaluated. Results: Among 1 477 561 patients included in model development (806 148 females [54.5%; mean [SD] age, 56.8 [17.9] years), 1 016 966 patient encounters were used for training and 254 242 separate encounters were used for testing the model. After deployment in clinical use, another 206 353 patients were prospectively evaluated; an additional 902 patients were selected for comparing the accuracy of the UPMC model and NSQIP tool for predicting mortality. The area under the receiver operating characteristic curve (AUROC) for mortality was 0.972 (95% CI, 0.971-0.973) for the training set and 0.946 (95% CI, 0.943-0.948) for the test set. The AUROC for MACCE and mortality was 0.923 (95% CI, 0.922-0.924) on the training and 0.899 (95% CI, 0.896-0.902) on the test set. In prospective evaluation, the AUROC for mortality was 0.956 (95% CI, 0.953-0.959), sensitivity was 2148 of 2517 patients (85.3%), specificity was 186 286 of 203 836 patients (91.4%), and negative predictive value was 186 286 of 186 655 patients (99.8%). The model outperformed the NSQIP tool as measured by AUROC (0.945 [95% CI, 0.914-0.977] vs 0.897 [95% CI, 0.854-0.941], for a difference of 0.048), specificity (0.87 [95% CI, 0.83-0.89] vs 0.68 [95% CI, 0.65-0.69]), and accuracy (0.85 [95% CI, 0.82-0.87] vs 0.69 [95% CI, 0.66, 0.72]). Conclusions and Relevance: This study found that an automated machine learning model was accurate in identifying patients undergoing surgery who were at high risk of adverse outcomes using only preoperative variables within the electronic health record, with superior performance compared with the NSQIP calculator. These findings suggest that using this model to identify patients at increased risk of adverse outcomes prior to surgery may allow for individualized perioperative care, which may be associated with improved outcomes.

Enhanced Recovery Protocols Reduce Mortality Across Eight Surgical Specialties at Academic and University-affiliated Community Hospitals.

OBJECTIVE: To determine if implementation of a simplified ERP across multiple surgical specialties in different hospitals is associated with improved short and long-term mortality. Secondary aims were to examine ERP effect on length of stay, 30-day readmission, discharge disposition, and complications. SUMMARY BACKGROUND DATA: Enhanced recovery after surgery and various derivative ERPs have been successfully implemented. These protocols typically include elaborate sets of multimodal and multidisciplinary approaches, which can make implementation challenging or are variable across different specialties. Few studies have shown if a simplified version of ERP implemented across multiple surgical specialties can improve clinical outcomes. METHODS: A simplified ERP with 7 key domains (minimally invasive surgical approach when feasible, pre-/intra-operative multimodal analgesia, postoperative multimodal analgesia, postoperative nausea and vomiting prophylaxis, early diet advancement, early ambulation, and early removal of urinary catheter) was implemented in 5 academic and community hospitals within a single health system. Patients who underwent nonemergent, major orthopedic or abdominal surgery including hip/knee replacement, hepatobiliary, colorectal, gynecology oncology, bariatric, general, and urological surgery were included. Propensity-matched, retrospective case-control analysis was performed on all eligible surgical patients between 2014 and 2017 after ERP implementation or in the 12 months preceding ERP implementation (control population). RESULTS: A total of 9492 patients (5185 ERP and 4307 controls) underwent ERP eligible surgery during the study period. Three thousand three hundred sixty-seven ERP patients were matched by surgical specialty and hospital site to control non-ERP patients. Short and long-term mortality was improved in ERP patients: 30 day: ERP 0.2% versus control 0.6% ( P = 0.002); 1-year: ERP 3.9% versus control 5.1% ( P < 0.0001); 2-year: ERP 6.2% versus control 9.0% ( P < 0.0001). Length of stay was significantly lower in ERP patients (ERP: 3.9 ± 3.8 days; control: 4.8 ± 5.0 days, P < 0.0001). ERP patients were also less likely to be discharged to a facility (ERP: 11.3%; control: 14.8%, P < 0.0001). There was no significant difference for 30-day readmission. All complications except venous thromboembolism were significantly reduced in the ERP population (P < 0.02). CONCLUSIONS: A simplified ERP can uniformly be implemented across multiple surgical specialties and hospital types. ERPs improve short and long-term mortality, clinical outcomes, length of stay, and discharge disposition to home.

Preoperative Identification of Patient-Dependent Blood Pressure Targets Associated With Low Risk of Intraoperative Hypotension During Noncardiac Surgery.

BACKGROUND: Intraoperative hypotension (IOH) is strongly linked to organ system injuries and postoperative death. Blood pressure itself is a powerful predictor of IOH; however, it is unclear which pressures carry the lowest risk and may be leveraged to prevent subsequent hypotension. Our objective was to develop a model that predicts, before surgery and according to a patient's unique characteristics, which intraoperative mean arterial pressures (MAPs) between 65 and 100 mm Hg have a low risk of IOH, defined as an MAP <65 mm Hg, and may serve as testable hemodynamic targets to prevent IOH. METHODS: Adult, noncardiac surgeries under general anesthesia at 2 tertiary care hospitals of the University of Pittsburgh Medical Center were divided into training and validation cohorts, then assigned into smaller subgroups according to preoperative risk factors. Primary outcome was hypotension risk, defined for each intraoperative MAP value from 65 to 100 mm Hg as the proportion of a value's total measurements followed by at least 1 MAP <65 mm Hg within 5 or 10 minutes, and calculated for all values in each subgroup. Five models depicting MAP-associated IOH risk were compared according to best fit across subgroups with proportions whose confidence interval was <0.05. For the best fitting model, (1) performance was validated, (2) low-risk MAP targets were identified according to applied benchmarks, and (3) preoperative risk factors were evaluated as predictors of model parameters. RESULTS: A total of 166,091 surgeries were included, with 121,032 and 45,059 surgeries containing 5.4 million and 1.9 million MAP measurements included in the training and validation sets, respectively. Thirty-six subgroups with at least 21 eligible proportions (confidence interval <0.05) were identified, representing 92% and 94% of available MAP measurements, respectively. The exponential with theta constant model demonstrated the best fit (weighted sum of squared error 0.0005), and the mean squared error of hypotension risk per MAP did not exceed 0.01% in validation testing. MAP targets ranged between 69 and 90 mm Hg depending on the subgroup and benchmark used. Increased age, higher American Society of Anesthesiologists physical status, and female sexindependently predicted ( P < .05) hypotension risk curves with less rapid decay and higher plateaus. CONCLUSIONS: We demonstrate that IOH risk specific to a given MAP is patient-dependent, but predictable before surgery. Our model can identify intraoperative MAP targets before surgery predicted to reduce a patient's exposure to IOH, potentially allowing clinicians to develop more personalized approaches for managing hemodynamics.

Development of Highly Sensitive and Humidity Independent Room Temeprature NO2 Gas Sensor Using Two Dimensional Ti3C2Tx Nanosheets and One Dimensional WO3 Nanorods Nanocomposite.

Room temperature gas sensors have been widely explored in gas sensor technology for real-time applications. However, humidity has found to affect the room temperature sensing and the sensor life, necessitating the development of novel sensing materials with high sensitivity and stability under humid conditions at room temperature. In this work, the room temperature sensing performance of a Ti3C2Tx decorated, WO3 nanorods based nanocomposite has been investigated. The hydrothermally synthesized WO3/Ti3C2Tx nanocomposite has been investigated for structural, morphological, and electrical studies using X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Brunanuer-Emmett-Teller techniques. The WO3/Ti3C2Tx sensors have been found to be highly selective to NO2 at room temperature and exhibit much higher sensitivity in comparison to pristine WO3 nanorods. Furthermore, sodium l-ascorbate treated Ti3C2Tx sheets in WO3/Ti3C2Tx enhanced the stability and reversibility of the sensor toward NO2 even under variable humidity conditions (0-99% relative humidity). This study shows the potential room temperature sensing application of a WO3/Ti3C2Tx nanocomposite-based sensor for detecting NO2 at sub-ppb level. Further, a plausible sensing mechanism based on WO3/Ti3C2Tx nanocomposite has been proposed to explain the improved sensing characteristics.

Isolation, Expansion, and Differentiation of Mesenchymal Stem Cells from the Infrapatellar Fat Pad of the Goat Stifle Joint.

The IFP, present in the knee joint, serves as a promising source of MSCs. The IFP is an easily accessible tissue as it is routinely resected and discarded during arthroscopic procedures and knee replacement surgeries. Additionally, its removal is associated with minimal donor site morbidity. Recent studies have demonstrated that IFP-MSCs do not lose their proliferation capacity during in vitro expansion and have age-independent osteogenic differentiation potential. IFP-MSCs possess superior chondrogenic differentiation potential compared to bone marrow-derived MSCs (BMSCs) and adipose-derived stem cells (ADSCs). Although these cells are easily obtainable from aged and diseased patients, their effectiveness is limited. Hence, using IFP-MSCs from healthy donors is important to determine their efficacy in biomedical applications. As access to a healthy human donor is challenging, animal models could be a better alternative to enable fundamental understanding. Large animals such as dogs, horses, sheep, and goats play a crucial role in translational research. Amongst these, the goat could be a preferred model since the stifle joint of the goat has the closest anatomy to the human knee joint. Moreover, goat-IFP can fulfill the higher MSC numbers needed for tissue regeneration applications. Furthermore, low cost, availability, and compliance with the 3R principles for animal research make them an attractive model. This study demonstrates a simple protocol for isolating IFP-MSCs from the stifle joint of goats and in vitro culture conditions for their expansion and differentiation. The aseptically isolated IFP from the goat was washed, minced, and digested enzymatically. After filtration and centrifugation, the collected cells were cultured. These cells were adherent, had MSCs-like morphology, and demonstrated remarkable clonogenic ability. Further, they differentiated into adipogenic, chondrogenic, and osteogenic lineages, demonstrating their multipotency. In conclusion, the study demonstrates the isolation and expansion of MSCs, which show potential in tissue engineering and regenerative medicine applications.

DNA Methylation-Based Prediction of Post-operative Atrial Fibrillation.

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF. Methods: We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort. Results: A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort. Conclusions: We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Comparison of accuracy of two uncalibrated pulse contour cardiac output monitors in off-pump coronary artery bypass surgery patients using pulmonary artery catheter-thermodilution as a reference.

BACKGROUND: Cardiac output (CO) is a key measure of adequacy of organ and tissue perfusion, especially in critically ill or complex surgical patients. CO monitoring technology continues to evolve. Recently developed CO monitors rely on unique algorithms based on pulse contour analysis of an arterial blood pressure (ABP) waveform. The objective of this investigation was to compare the accuracy of two monitors using different methods of pulse contour analysis - the Retia Argos device and the Edwards Vigileo-FloTrac device - with pulmonary artery catheter (PAC)-thermodilution as a reference. METHODS: Fifty-eight patients undergoing off-pump coronary artery bypass surgery formed the study cohort. A total of 572 triplets of CO measurements from each device - Argos, Vigileo-FloTrac (third generation), and thermodilution - were available before and after interventions (e.g., vasopressors, fluids, and inotropes). Bland-Altman analysis accounting for repeated measurements per subject and concordance analysis were applied to assess the accuracy of the CO values and intervention-induced CO changes of each pulse contour device against thermodilution. Cluster bootstrapping was employed to statistically compare the root-mean-squared-errors (RMSE = √(µ2 + σ2), where µ and σ are the Bland-Altman bias and precision errors) and concordance rates of the two devices. RESULTS: The RMSE (mean (95% confidence intervals)) for CO values was 1.16 (1.00-1.32) L/min for the Argos device and 1.54 (1.33-1.77) L/min for the Vigileo-FloTrac device; the concordance rate for intervention-induced CO changes was 87 (82-92)% for the Argos device and 72 (65-78)% for the Vigileo-FloTrac device; and the RMSE for the CO changes was 17 (15-19)% for the Argos device and 21 (19-23)% for the Vigileo-FloTrac device (p < 0.0167 for all comparisons). CONCLUSIONS: In comparison with CO measured by the PAC, the Argos device proved to be more accurate than the Vigileo-FloTrac device in CO trending and absolute CO measurement in patients undergoing off-pump coronary artery bypass surgery.

The impact of socioeconomic status in patients with left ventricular assist devices (LVADs).

BACKGROUND: Socioeconomic status (SES) can be a powerful predictor of adverse outcomes among heart failure patients but its impact on survival and readmission following left ventricular assist device (LVAD) implantation surgery is poorly understood. We investigated if the LVAD recipients from more deprived neighborhoods experienced higher mortality and readmission rate after device implantation as compared to those from less deprived areas. METHODS: This is a single center, retrospective analysis evaluating adults who received Heartmate III and Heartware HVAD implants between 2009 and 2018. SES indicators were area of deprivation index (ADI), race and income. Our cohort was grouped by ADI quartiles from least deprived (Q1), Q2, Q3 to the most deprived (Q4). Outcomes included overall mortality and readmission following surgery. RESULTS: A total of 191 patients were included in the study. Demographics by SES indicators demonstrated that least deprived (Q1) patients were older than the most deprived (65 vs. 57, p < .01), African-American patients originated from more deprived neighborhoods than Caucasians (ADI 87 vs. 62, p < .001), and high-income patients had higher preoperative BUN and creatinine. Outcome differences included a decreased risk of death in most deprived patients (Q4) compared to the least deprived (Q1), however after adjusting for age, LVAD indication, and INTERMACS profile this was no longer significant. No differences in survival or readmission by race or income was observed CONCLUSION: SES does not independently impact survival and readmission after Heartware HVAD and Heartmate III LVAD implantation. More studies are needed to evaluate if other SES factors affect these outcomes.

Anesthesiologists' Role in Value-based Perioperative Care and Healthcare Transformation.

Health care is undergoing major transformation with a shift from fee-for-service care to fee-for-value. The advent of new care delivery and payment models is serving as a driver for value-based care. Hospitals, payors, and patients increasingly expect physicians and healthcare systems to improve outcomes and manage costs. The impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical and procedural practices further highlights the urgency and need for anesthesiologists to expand their roles in perioperative care, and to impact system improvement. While there have been substantial advances in anesthesia care, perioperative complications and mortality after surgery remain a key concern. Anesthesiologists are in a unique position to impact perioperative health care through their multitude of interactions and influences on various aspects of the perioperative domain, by using the surgical experience as the first touchpoint to reengage the patient in their own health care. Among the key interventions that are being effectively instituted by anesthesiologists include proactive engagement in preoperative optimization of patients' health; personalization and standardization of care delivery by segmenting patients based upon their complexity and risk; and implementation of best practices that are data-driven and evidence-based and provide structure that allow the patient to return to their optimal state of functional, cognitive, and psychologic health. Through collaborative relationships with other perioperative stakeholders, anesthesiologists can consolidate their role as clinical leaders driving value-based care and healthcare transformation in the best interests of patients.

Platelet transfusion is associated with 90-day and 1-year mortality for adult patients requiring veno-arterial extracorporeal membrane oxygenation.

BACKGROUND: Studies examining one-year mortality respecting component blood transfusion are sparse. We hypothesize that component blood product transfusions are negatively associated with 90-day and 1-year survival for all patients requiring veno-arterial (VA) or veno-venous (VV) ECMO. STUDY DESIGN AND METHODS: This was an IRB-approved retrospective cohort analysis of 676 consecutive patients requiring ECMO at the University of Pittsburgh between 2005 and 2016. Patients were analysed both as an entire cohort and as two subsets with respect to ECMO modality (VA vs. VV). Additional data collected and analysed included patient characteristics, laboratory values and blood product transfusion. RESULTS: Multivariable analysis revealed that platelet transfusion was associated with 90-day mortality (OR: 1·05, P = 0·037) and one-year mortality for the entire cohort (OR = 1·05, P = 0·046,). Platelet transfusion volume was also associated with mortality in the VA-ECMO subset of patients at both 90 days (OR = 1·08, P = 0·03) and one year (OR: 1·11, P = 0·014). Age, peak International Normalized Raton ECMO, nadir haemoglobin (on ECMO) and final haemoglobin (after ECMO) were significantly associated with mortality for patients requiring VA-ECMO. For VV-ECMO patients, age, INR and peak creatinine on ECMO were associated with mortality. No individual component blood product was associated with one-year mortality for patients requiring VV-ECMO. CONCLUSION: Platelet transfusion was associated with increased 90-day and 1-year mortality for patients requiring VA-ECMO.

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge. METHODS: Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features. RESULTS: Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge. CONCLUSIONS: A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. METHODS: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. RESULTS: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955). CONCLUSIONS: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.