Preoperative GLP-1 Receptor Agonist Use and Risk of Postoperative Respiratory Complications.

This cohort study evaluates the risk of postoperative respiratory complications among patients with diabetes undergoing surgery who had vs those who had not a prescription fill for glucagon-like peptide 1 receptor agonists.

Variability in Intraoperative Opioid and Nonopioid Utilization During Intracranial Surgery: A Multicenter, Retrospective Cohort Study.

BACKGROUND: Key goals during intracranial surgery are to facilitate rapid emergence and extubation for early neurologic evaluation. Longer-acting opioids are often avoided or administered at subtherapeutic doses due to their perceived risk of sedation and delayed emergence. However, inadequate analgesia and increased postoperative pain are common after intracranial surgery. In this multicenter study, we describe variability in opioid and nonopioid administration patterns in patients undergoing intracranial surgery. METHODS: This was a multicenter, retrospective observational cohort study using the Multicenter Perioperative Outcomes Group database. Opioid and nonopioid practice patterns in 31,217 cases undergoing intracranial surgery across 11 institutions in the United States are described. RESULTS: Across all 11 institutions, total median [interquartile range] oral morphine equivalents, normalized to weight and anesthesia duration was 0.17 (0.08 to 0.3) mg.kg.min-1. There was a 7-fold difference in oral morphine equivalents between the lowest (0.05 [0.02 to 0.13] mg.kg.min-1) and highest (0.36 [0.18 to 0.54] mg.kg.min-1) prescribing institutions. Patients undergoing supratentorial surgery had higher normalized oral morphine equivalents compared with those having infratentorial surgery [0.17 [0.08-0.31] vs. 0.15 [0.07-0.27] mg/kg/min-1; P<0.001); however, this difference is clinically small. Nonopioid analgesics were not administered in 20% to 96.8% of cases across institutions. CONCLUSION: This study found wide variability for both opioid and nonopioid utilization at an institutional level. Future work on practitioner-level opioid and nonopioid use and its impact on outcomes after intracranial surgery should be conducted.

Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche.

Partial reprogramming (pulsed expression of reprogramming transcription factors) improves the function of several tissues in old mice. However, it remains largely unknown how partial reprogramming impacts the old brain. Here we use single-cell transcriptomics to systematically examine how partial reprogramming influences the subventricular zone neurogenic niche in aged mouse brains. Whole-body partial reprogramming mainly improves neuroblasts (cells committed to give rise to new neurons) in the old neurogenic niche, restoring neuroblast proportion to more youthful levels. Interestingly, targeting partial reprogramming specifically to the neurogenic niche also boosts the proportion of neuroblasts and their precursors (neural stem cells) in old mice and improves several molecular signatures of aging, suggesting that the beneficial effects of reprogramming are niche intrinsic. In old neural stem cell cultures, partial reprogramming cell autonomously restores the proportion of neuroblasts during differentiation and blunts some age-related transcriptomic changes. Importantly, partial reprogramming improves the production of new neurons in vitro and in old brains. Our work suggests that partial reprogramming could be used to rejuvenate the neurogenic niche and counter brain decline in old individuals.

A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs.

The intricate molecular environment of the native membrane profoundly influences every aspect of membrane protein (MP) biology. Despite this, the most prevalent method of studying MPs uses detergent-like molecules that disrupt and remove this vital local membrane context. This severely impedes our ability to quantitatively decipher the local molecular context and comprehend its regulatory role in the structure, function, and biogenesis of MPs. Using a library of membrane-active polymers we have developed a platform for the high-throughput analysis of the membrane proteome. The platform enables near-complete spatially resolved extraction of target MPs directly from their endogenous membranes into native nanodiscs that maintain the local membrane context. We accompany this advancement with an open-access quantitative database that provides the most efficient extraction conditions of 2065 unique mammalian MPs. Our method enables rapid and near-complete extraction and purification of target MPs directly from their endogenous organellar membranes at physiological expression levels while maintaining the nanoscale local membrane environment. Going beyond the plasma membrane proteome, our platform enables extraction from any target organellar membrane including the endoplasmic reticulum, mitochondria, lysosome, Golgi, and even transient organelles such as the autophagosome. To further validate this platform we took several independent MPs and demonstrated how our resource can enable rapid extraction and purification of target MPs from different organellar membranes with high efficiency and purity. Further, taking two synaptic vesicle MPs, we show how the database can be extended to capture multiprotein complexes between overexpressed MPs. We expect these publicly available resources to empower researchers across disciplines to capture membrane 'nano-scoops' containing a target MP efficiently and interface with structural, functional, and other bioanalytical approaches. We demonstrate an example of this by combining our extraction platform with single-molecule TIRF imaging to demonstrate how it can enable rapid determination of homo-oligomeric states of target MPs in native cell membranes.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.

Principled and interpretable alignability testing and integration of single-cell data.

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features. SMAI provides a statistical test to robustly assess the alignability between datasets to avoid misleading inference and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

A Cost and Waste-Savings Comparison Between Single-Use and Reusable Pulse Oximetry Sensors Across US Operating Rooms.

BACKGROUND: Operating room (OR) expenditures and waste generation are a priority, with several professional societies recommending the use of reprocessed or reusable equipment where feasible. The aim of this analysis was to compare single-use pulse oximetry sensor stickers ("single-use stickers") versus reusable pulse oximetry sensor clips ("reusable clips") in terms of annual cost savings and waste generation across all ORs nationally. METHODS: This study did not involve patient data or research on human subjects. As such, it did not meet the requirements for institutional review board approval. An economic model was used to compare the relative costs and waste generation from using single-use stickers versus reusable clips. This model took into account: (1) the relative prices of single-use stickers and reusable clips, (2) the number of surgeries and ORs nationwide, (3) the workload burden of cleaning the reusable clips, and (4) the costs of capital for single-use stickers and reusable clips. In addition, we also estimated differences in waste production based on the raw weight plus unit packaging of single-use stickers and reusable clips that would be disposed of over the course of the year, without any recycling interventions. Estimated savings were rounded to the nearest $0.1 million. RESULTS: The national net annual savings of transitioning from single-use stickers to reusable clips in all ORs ranged from $510.5 million (conservative state) to $519.3 million (favorable state). Variability in savings estimates is driven by scenario planning for replacement rate of reusable clips, workload burden of cleaning (ranging from an additional expense of $618k versus a cost savings of $309k), and cost of capital-interest gained on investment of capital that is freed up by the monetary savings of a transition to reusable clips contributes between $541k (low-interest rates of 2.85%) and $1.3 million (high-interest rates of 7.08%). The annual waste that could be diverted from landfill by transitioning to reusable clips was found to be between 587 tons (conservative state) up to 589 tons (favorable state). If institutions need to purchase new vendor monitors or cables to make the transition, that may increase the 1-time capital disbursement. CONCLUSIONS: Using reusable clips versus single-use stickers across all ORs nationally would result in appreciable annual cost savings and waste generation reduction impact. As both single-use stickers and reusable clips are equally accurate and reliable, this cost and waste savings could be instituted without a compromise in clinical care.

Association of Patient Race and Hospital with Utilization of Regional Anesthesia for Treatment of Postoperative Pain in Total Knee Arthroplasty: A Retrospective Analysis Using Medicare Claims.

BACKGROUND: Regional anesthesia for total knee arthroplasty has been deemed high priority by national and international societies, and its use can serve as a measure of healthcare equity. The association between utilization of regional anesthesia for postoperative pain and (1) race and (2) hospital in patients undergoing total knee arthroplasty was estimated. The hypothesis was that Black patients would be less likely than White patients to receive regional anesthesia, and that variability in regional anesthesia would more likely be attributable to the hospital where surgery occurred than race. METHODS: This study used Medicare fee-for-service claims for patients aged 65 yr or older who underwent primary total knee arthroplasty between January 1, 2011, and December 31, 2016. The primary outcome was administration of regional anesthesia for postoperative pain, defined as any peripheral (femoral, lumbar plexus, or other) or neuraxial (spinal or epidural) block. The primary exposure was self-reported race (Black, White, or Other). Clinical significance was defined as a relative difference of 10% in regional anesthesia administration. RESULTS: Data from 733,406 cases across 2,507 hospitals were analyzed: 90.7% of patients were identified as White, 4.7% as Black, and 4.6% as Other. Median hospital-level prevalence of use of regional anesthesia was 51% (interquartile range, 18 to 79%). Black patients did not have a statistically different probability of receiving a regional anesthetic compared to White patients (adjusted estimates: Black, 53.3% [95% CI, 52.5 to 54.1%]; White, 52.7% [95% CI, 52.4 to 54.1%]; P = 0.132). Findings were robust to alternate specifications of the exposure and outcome. Analysis of variance revealed that 42.0% of the variation in block administration was attributable to hospital, compared to less than 0.01% to race, after adjusting for other patient-level confounders. CONCLUSIONS: Race was not associated with administration of regional anesthesia in Medicare patients undergoing primary total knee arthroplasty. Variation in the use of regional anesthesia was primarily associated with the hospital where surgery occurred.

Association between "Balance Billing" Legislation and Anesthesia Payments in California: A Retrospective Analysis.

BACKGROUND: Insured patients who receive out-of-network care may receive a "balance bill" for the difference between the practitioner's charge and their insurer's contracted rate. In 2017, California banned balance billing for anesthesia care. This study examined the association between California's law and subsequent payments for anesthesia care. The authors hypothesized that, after the law's implementation, there would be no change in in-network payment amounts, and that out-of-network payment amounts and the portion of claims occurring out-of-network would decline. METHODS: The study used average, quarterly, California county-level payment data (2013 to 2020) derived from a claims database of commercially insured patients. Using a difference-in-differences approach, the change was estimated in payment amounts for intraoperative or intrapartum anesthesia care, along with the portion of claims occurring out-of-network, after the law's implementation. The comparison group was office visit payments, expected to be unaffected by the law. The authors prespecified that they would refer to differences of 10% or greater as policy significant. RESULTS: The sample consisted of 43,728 procedure code-county-quarter-network combinations aggregated from 4,599,936 claims. The law's implementation was associated with a significant 13.6% decline in payments for out-of-network anesthesia care (95% CI, -16.5 to -10.6%; P < 0.001), translating to an average $108 decrease across all procedures (95% CI, -$149 to -$64). There was a statistically significant 3.0% increase in payments for in-network anesthesia care (95% CI, 0.9 to 5.1%; P = 0.007), translating to an average $87 increase (95% CI, $64 to $110), which may be notable in some circumstances but did not meet the study threshold for identifying a change as policy significant. There was a nonstatistically significant increase in the portion of claims occurring out-of-network (10.0%, 95% CI, -4.1 to 24.2%; P = 0.155). CONCLUSIONS: California's balance billing law was associated with significant declines in out-of-network anesthesia payments in the first 3 yr after implementation. There were mixed statistical and policy significant results for in-network payments and the proportion of out-of-network claims.

Chromatin accessibility dynamics of neurogenic niche cells reveal defects in neural stem cell adhesion and migration during aging.

The regenerative potential of brain stem cell niches deteriorates during aging. Yet the mechanisms underlying this decline are largely unknown. Here we characterize genome-wide chromatin accessibility of neurogenic niche cells in vivo during aging. Interestingly, chromatin accessibility at adhesion and migration genes decreases with age in quiescent neural stem cells (NSCs) but increases with age in activated (proliferative) NSCs. Quiescent and activated NSCs exhibit opposing adhesion behaviors during aging: quiescent NSCs become less adhesive, whereas activated NSCs become more adhesive. Old activated NSCs also show decreased migration in vitro and diminished mobilization out of the niche for neurogenesis in vivo. Using tension sensors, we find that aging increases force-producing adhesions in activated NSCs. Inhibiting the cytoskeletal-regulating kinase ROCK reduces these adhesions, restores migration in old activated NSCs in vitro, and boosts neurogenesis in vivo. These results have implications for restoring the migratory potential of NSCs and for improving neurogenesis in the aged brain.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Across eleven benchmark datasets, TISSUE provides well-calibrated prediction intervals for predicted expression values. Moreover it consistently reduces false discovery rates for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics, and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers. TISSUE is publicly available as a flexible wrapper method for existing spatial gene expression prediction methods to assist researchers with implementing uncertainty-aware analyses of spatial transcriptomics data.

Modular adjuvant-free pan-HLA-DR-immunotargeting subunit vaccine against SARS-CoV-2 elicits broad sarbecovirus-neutralizing antibody responses.

Subunit vaccines typically require co-administration with an adjuvant to elicit protective immunity, adding development hurdles that can impede rapid pandemic responses. To circumvent the need for adjuvant in a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit vaccine, we engineer a thermostable immunotargeting vaccine (ITV) that leverages the pan-HLA-DR monoclonal antibody 44H10 to deliver the viral spike protein receptor-binding domain (RBD) to antigen-presenting cells. X-ray crystallography shows that 44H10 binds to a conserved epitope on HLA-DR, providing the basis for its broad HLA-DR reactivity. Adjuvant-free ITV immunization in rabbits and ferrets induces robust anti-RBD antibody responses that neutralize SARS-CoV-2 variants of concern and protect recipients from SARS-CoV-2 challenge. We demonstrate that the modular nature of the ITV scaffold with respect to helper T cell epitopes and diverse RBD antigens facilitates broad sarbecovirus neutralization. Our findings support anti-HLA-DR immunotargeting as an effective means to induce strong antibody responses to subunit antigens without requiring an adjuvant.

Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain.

The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop 'aging clocks' based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of 28 mice, tiling ages from young to old. We trained single-cell-based regression models to predict chronological age and biological age (neural stem cell proliferation capacity). These aging clocks are generalizable to independent cohorts of mice, other regions of the brains, and other species. To determine if these aging clocks could quantify transcriptomic rejuvenation, we generated single-cell transcriptomic datasets of neurogenic regions for two interventions-heterochronic parabiosis and exercise. Aging clocks revealed that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions, but in different ways. This study represents the first development of high-resolution aging clocks from single-cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

A spectral method for assessing and combining multiple data visualizations.

Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it important to evaluate their relative performance, and to leverage and combine their individual strengths. This paper proposes a spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure - the visualization eigenscore - of the relative performance of the visualizations for preserving the structure around each data point. It also generates a consensus visualization, having improved quality over individual visualizations in capturing the underlying structure. Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple real-world datasets to demonstrate the effectiveness of the method. We also provide theoretical justifications based on a general statistical framework, yielding several fundamental principles along with practical guidance.

Association Between Naloxone Coprescription Mandates and Postoperative Outcomes.

INTRODUCTION: The opioid epidemic is a public health issue in the United States. The objective of this study was to evaluate the association between naloxone coprescription mandates and postoperative outcomes. BACKGROUND: Data on naloxone coprescription mandates show mixed evidence for fatal overdoses in the broader population. How these mandates have impacted surgical patients has not been fully explored. METHODS: Healthcare claims data were used to identify all patients undergoing 1 of 50 common procedures between January 1, 2004, and June 30, 2019, and categorized as high risk for opioid overdose. The primary outcomes were an emergency department visit or hospital admission within 30 postoperative days. To reduce confounding, the association between this outcome and the implementation of naloxone coprescription mandates was estimated using a difference-in-differences approach. RESULTS: The study included 429,878 surgical patients with an average age of 54.8 years (SD=15.9 years) and with 257,728 females (60.0%). There was no significant association between naloxone prescribing mandates and the primary outcomes. After adjustment for potential confounders, the incidence of hospital admission was 3.26% after implementation of a naloxone coprescription mandate compared with 3.33% before (difference change: -0.08%, 95% CI: -0.44% to 0.29%, P =0.68). The incidence of an emergency department visit was 7.06% after implementation of a naloxone coprescription mandate compared with 7.73% before (difference: -0.67%, 95% CI: -1.39% to 0.05%, P =0.07). These results were robust to a variety of sensitivity and subgroup analyses. CONCLUSIONS: Naloxone coprescription mandates were not associated with a statistically or clinically significant change in emergency department visits or hospital admissions within 30 postoperative days.

Meeting Report: First Cardiovascular Outcomes Research in Perioperative Medicine Conference.

The first Cardiovascular Outcomes Research in Perioperative Medicine (COR-PM) conference took place on May 13, 2022, in Palm Springs, CA, and online. Here, we: (1) summarize the background, objective, and aims of the COR-PM meeting; (2) describe the conduct of the meeting; and (3) outline future directions for scientific meetings aimed at fostering high-quality clinical research in the broader perioperative medicine community.

Sleep Disorders and Coronary Artery Disease.

A sufficient amount and quality sleep is vital components for maintaining optimal physical wellbeing, cognition, and cardiovascular health. Normal sleep provides us with a low physiological stress that promotes overall cardiovascular health. Sleep disorders are common, and often goes undiagnosed. Patients with sleep disorders, commonly suffer from poor quality of life due to the poor quality of their sleep. They are associated with adverse effects on cardiovascular health, such as coronary artery disease. Sleep disorders discussed in this review article include sleep-disordered breathing (obstructive sleep apnea and central sleep apnea), insomnia and restless leg syndrome. Sleep disorders primarily have four acute adverse cardiovascular consequences. First, intermittent hypoxemia-reoxygenation in arterial blood along with PCO 2 fluctuation; second is excessive arousals; third, decreased parasympathetic and increased sympathetic tone; fourth being the large pressure swings in the intrathoracic cavity. This review focuses on classifications, and prevalence of the three major classes of sleep disorders, and the evidence to date on the association between these sleep disorders and coronary artery disease.

Association Between State Limits on Opioid Prescribing and the Incidence of Persistent Postoperative Opioid Use Among Surgical Patients.

OBJECTIVE: To examine whether laws limiting opioid prescribing have been associated with reductions in the incidence of persistent postoperative opioid use. SUMMARY OF BACKGROUND DATA: In an effort to address the opioid epidemic, 26 states (as of 2018) have passed laws limiting opioid prescribing for acute pain. However, it is unknown whether these laws have achieved their reduced the risk of persistent postoperative opioid use. METHODS: We identified 957,639 privately insured patients undergoing one of 10 procedures between January 1, 2004 and September 30, 2018. We then estimated the association between persistent postoperative opioid use, defined as having filled ≥10 prescriptions or ≥120 days supply of opioids during postoperative days 91-365, and whether opioid prescribing limits were in effect on the day of surgery. States were classified as having: no limits, a limit of ≤7 days supply, or a limit of >7 days supply. The regression models adjusted for observable confounders such as patient comorbidities and also utilized a difference-in-differences approach, which relied on variation in state laws over time, to further minimize confounding. RESULTS: The adjusted incidence of persistent postoperative opioid use was 3.5% (95%CI 3.3%-3.7%) for patients facing a limit of ≤7 days supply, compared with 3.3% (95%CI 3.3%-3.3%) for patients facing no prescribing limits ( P = 0.13 for difference compared to no prescribing limits) and 3.4%, (95%CI 3.2%-3.6%) for patients facing a limit of >7 days supply ( P = 0.43 for difference compared to no prescribing limits). CONCLUSIONS: Laws limiting opioid prescriptions were not associated with subsequent reductions in persistent postoperative opioid use.