Does Epidural Corticosteroid Application During Spinal Surgery Reduce Postoperative Pain?: An Adjunct to Multimodal Analgesia

STUDY DESIGN

A prospective, randomized, placebo-controlled, double-blinded study.

OBJECTIVE

To examine the effect of intraoperative epidural administration of Depo-Medrol on postoperative back pain and radiculitis symptoms in patients undergoing Transforaminal Lumbar Interbody Fusion (TLIF).

SUMMARY OF BACKGROUND DATA

Postoperative pain is commonly experienced by patients undergoing spinal fusion surgery. Adequate management of intense pain is necessary to encourage early ambulation, increase patient satisfaction, and limit opioid consumption. Intraoperative steroid application has been shown to improve postoperative pain in patients undergoing lumbar decompression surgeries. There have been no studies examining the effect of epidural steroids on both back pain and radicular pain in patients undergoing TLIF.

METHOD

In all, 151 patients underwent TLIF surgery using rh-BMP2 with 3 surgeons at a single institution. Of those, 116 remained in the study and were included in the final analysis. Based on a 1:1 randomization, a collagen sponge saturated with either Saline (1 cc) or Depo-Medrol (40 mg/1 cc) was placed at the annulotomy site on the TLIF level. Follow-up occurred on postoperative days 1, 2, 3, 7, and postoperative months 1, 2, and 3. Lumbar radiculopathy was measured by a modified symptom- and laterality-specific Visual Analog Scale (VAS) regarding the severity of back pain and common radiculopathy symptoms.

RESULTS

The patients who received Depo-Medrol, compared with those who received saline, experienced significantly less back pain on postoperative days 1, 2, 3, and 7 (P<0.05). There was no significant difference in back pain beyond day 7. Radiculopathy-related symptoms such as leg pain, numbness, tingling, stiffness, and weakness tended to be reduced in the steroid group at most time points.

CONCLUSION

This study provides Level 1 evidence that intraoperative application of Depo-Medrol during a TLIF surgery with rh-BMP2 significantly reduces back pain for the first week after TLIF surgery. The use of epidural Depo-Medrol may be a useful adjunct to multimodal analgesia for pain relief in the postoperative period.

Pulmonary Co-Infections Detected Premortem Underestimate Postmortem Findings in a COVID-19 Autopsy Case Series

Bacterial and fungal co-infections are reported complications of coronavirus disease 2019 (COVID-19) in critically ill patients but may go unrecognized premortem due to diagnostic limitations. We compared the premortem with the postmortem detection of pulmonary co-infections in 55 fatal COVID-19 cases from March 2020 to March 2021. The concordance in the premortem versus the postmortem diagnoses and the pathogen identification were evaluated. Premortem pulmonary co-infections were extracted from medical charts while applying standard diagnostic definitions. Postmortem co-infection was defined by compatible lung histopathology with or without the detection of an organism in tissue by bacterial or fungal staining, or polymerase chain reaction (PCR) with broad-range bacterial and fungal primers. Pulmonary co-infection was detected premortem in significantly fewer cases (15/55, 27%) than were detected postmortem (36/55, 65%; p < 0.0001). Among cases in which co-infection was detected postmortem by histopathology, an organism was identified in 27/36 (75%) of cases. Pseudomonas, Enterobacterales, and Staphylococcus aureus were the most frequently identified bacteria both premortem and postmortem. Invasive pulmonary fungal infection was detected in five cases postmortem, but in no cases premortem. According to the univariate analyses, the patients with undiagnosed pulmonary co-infection had significantly shorter hospital (p = 0.0012) and intensive care unit (p = 0.0006) stays and significantly fewer extra-pulmonary infections (p = 0.0021). Bacterial and fungal pulmonary co-infection are under-recognized complications in critically ill patients with COVID-19.

1044. Identification and Characterization of an Unconventional NK Subset in COVID-19

Background

Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is associated with dysregulation in the innate immune response including NK cells. NK cells are integral in the innate immune response against viral infections. Canonical NK cells are classified as CD56dim CD16+ and CD56bright CD16-. An unconventional subset of CD56dim CD16- NK cells has previously been identified in COVID-19 that is not present in other viral infections. Here we characterize phenotypic changes in the NK cells of patients with severe COVID-19 as work towards determining the functional status of this unconventional subset.

Methods

Peripheral blood mononuclear cells (PBMCs) and plasma were isolated from healthy donors (n=5) and patients with severe COVID-19 on Extra Corporeal Membrane Oxygenation (ECMO) (n=15). Primary NK cells were stimulated in vitro with plasma from patients with severe COVID-19 or healthy donors. Flow cytometry was used to phenotype the NK cells. A separate cohort of PBMC samples (n=7) from patients requiring hospitalization for COVID-19 underwent Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) analysis.

Results

The CD56bright CD16- NK subset was expanded in PBMCs from patients with severe COVID-19 as compared to healthy controls. CITE-Seq demonstrated that NK cells without surface CD16 clustered separately based on transcriptional profiling and did express FCGR3A at the translational level. Stimulation with COVID-19 plasma recapitulated the loss of CD16 from primary human NK cells and led to increased activity of Caspase 3/7. Figure 1.NK cells shift from the CD56dim CD16+ subset to the CD56dim CD16-subset in patients with severe COVID-19.

a) Representative gating of NK cell subsets by Flow Cytometry in healthy and COVID-19 patient peripheral blood mononuclear cells (PBMCs). b) Percentage of total NK cells belonging to a particular cell subset compared between healthy donor samples (n=4) and COVID-19 patient samples (n=8). Data points represent an individual patient sample. Error bars represent the standard deviation of the mean. Differences between groups was analyzed using a two tailed t-test. *: p&lt; 0.05, ns: not significant Figure 2.NK cells shift from the CD56dim CD16+ subset to the CD56dim CD16-subset after stimulation with COVID-19 plasma in vitro

a) Representative gating of NK cell subsets by Flow Cytometry analysis in healthy donor NK cells stimulated by healthy plasma and COVID-19 patient plasma. b)Relative change in percentage of total NK cells belonging to a particular cell subset compared between healthy donor plasma (n=6)and COVID-19 patient plasma (n=15) stimulation conditions. Error bars represent the standard deviation of the mean and the difference between groups was analyzed using a two-tailed T-test. **: p&lt; 0.01, ***: p&lt; 0.001, ns: not significant.

Conclusion

We demonstrate and characterize a nonclassical population of CD56dim CD16- NK cells that are present in patients with severe COVID-19 and replicate this phenotype in vitro. Reproduction of this in vivo phenotype in an in vitro system will allow for additional studies on the functional state of NK cell subsets in COVID-19. The presence of this NK cell population may reflect a dysregulated innate immune response and immunopathogenesis of COVID-19.

Disclosures

All Authors: No reported disclosures.

Advantages and disadvantages of aggregating fluxes into synthetic and degradative fluxes when modelling metabolic pathways

It is now widely accepted that mathematical models are needed to predict the behaviour of complex metabolic networks in the cell, in order to have a rational basis for planning metabolic engineering with biotechnological or therapeutical purposes. The great complexity of metabolic networks makes it crucial to simplify them for analysis, but without violating key principles of stoichiometry or thermodynamics. We show here, however, that models for branched complex systems are sometimes obtained that violate the stoichiometry of fluxes at branch points and as a result give unrealistic metabolite concentrations at the steady state. This problem is especially important when models are constructed with the S-system form of biochemical systems theory. However, the same violation of stoichiometry can occur in metabolic control analysis if control coefficients are assumed to be constant when trying to predict the effects of large changes. We derive the appropriate matrix equations to analyse this type of problem systematically and to assess its extent in any given model.

Mathematical models of purine metabolism in man

Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.

Analysis of abnormalities in purine metabolism leading to gout and to neurological dysfunctions in man

A modelling approach is used to analyse diseases associated with purine metabolism in man. The specific focus is on deficiencies in two enzymes, hypoxanthine:guanine phosphoribosyltransferase and adenylosuccinate lyase. These deficiencies can lead to a number of symptoms, including neurological dysfunctions and mental retardation. Although the biochemical mechanisms of dysfunctions associated with adenylosuccinate lyase deficiency are not completely understood, there is at least general agreement in the literature about possible causes. Simulations with our model confirm that accumulation of the two substrates of the enzyme can lead to significant biochemical imbalance. In hypoxanthine:guanine phosphoribosyltransferase deficiency the biochemical mechanisms associated with neurological dysfunctions are less clear. Model analyses support some old hypotheses but also suggest new indicators for possible causes of neurological dysfunctions associated with this deficiency. Hypoxanthine:guanine phosphoribosyltransferase deficiency is known to cause hyperuricaemia and gout. We compare the relative importance of this deficiency with other known causes of gout in humans. The analysis suggests that defects in the excretion of uric acid are more consequential than defects in uric acid synthesis such as hypoxanthine:guanine phosphoribosyltransferase deficiency.

Validation and steady-state analysis of a power-law model of purine metabolism in man

The paper introduces a model of human purine metabolism in situ. Chosen from among several alternative system descriptions, the model is formulated as a Generalized Mass Action system within Biochemical Systems Theory and validated with analyses of steady-state and dynamic characteristics. Eigenvalue and sensitivity analyses indicate that the model has a stable and robust steady-state. The model quite accurately reproduces numerous biochemical and clinical observations in healthy subjects as well as in patients with disorders of purine metabolism. These results suggest that the model can be used to assess biochemical and clinical aspects of human purine metabolism. It provides a means of exploring effects of enzyme deficiencies and is a potential tool for identifying steps of the pathway that could be the target of therapeutical intervention. Numerous quantitative comparisons with data are given. The model can be used for biomathematical exploration of relationships between enzymic deficiencies and clinically manifested diseases.

Comparative characterization of the fermentation pathway of Saccharomyces cerevisiae using biochemical systems theory and metabolic control analysis: model definition and nomenclature

Mathematical tools that involve the determination of systemic responses to small changes in metabolites or enzymes have demonstrated their utility for analyzing metabolic pathways. The different methodologies based on these ideas allow for modeling and analyzing biochemical pathways focusing on the coordinate behavior of the whole system. However, one must become familiar with the difference in nomenclature and methodology to relate the models and results obtained by applying these techniques and to appreciate their potential for answering fundamental questions about biochemical systems. In the following three papers we show how this can be facilitated by comparing the nomenclature, methodology, and results of the two leading techniques in this area, metabolic control analysis and biochemical systems theory, using a model of the fermentation pathway in Saccharomyces cerevisiae as a reference system. In the present paper we review the nomenclature, technical concepts, and related experimental measurements while creating a practical dictionary for the reference system that makes the relatedness of the two approaches more apparent. In the second paper, subtitled Steady-State Analysis, we show that both approaches give the same picture for many systemic responses of the reference system. In the third paper of this series, subtitled Model Validation and Dynamic Behavior, we show that the quality of the model can be assessed by studying the sensitivity to changes in the system parameters. We hope to illustrate the usefulness of these tools in providing an interpretation of the experimental measurements in a specific metabolic pathway.

Comparative characterization of the fermentation pathway of Saccharomyces cerevisiae using biochemical systems theory and metabolic control analysis: steady-state analysis

In the preceding paper in this issue, we have shown that metabolic control analysis and biochemical systems theory use the same experimental information to describe a metabolic system. In this paper, we analyze the steady-state properties of this pathway by applying both methods. Our results show the correspondence of the steady-state characterizations and illustrate the relationships between the different nomenclatures used. With both approaches, we identify metabolite pools that are strongly influenced by changes in enzyme concentration when cells are immobilized at pH 5.5. In the final paper of this series, which follows, we discuss the need to assess the quality of a model and the potential difficulties that may arise if the steady-state characterization is accepted without testing its quality. We then validate the different models using parameter sensitivity concepts.

Comparative characterization of the fermentation pathway of Saccharomyces cerevisiae using biochemical systems theory and metabolic control analysis: model validation and dynamic behavior

In the first two papers of this series (immediately preceding, this issue), we characterized the steady-state properties of a model of a fermentation pathway in Saccharomyces cerevisiae in four experimental conditions. In each of these conditions, the pictures obtained by metabolic control analysis and biochemical systems theory were coincident, which illustrates the relatedness of the two approaches. In this paper we analyze the quality of this description by means of the tools available within biochemical systems theory, and we show that in some of the experimental conditions studied the system is poorly characterized. The most critical condition corresponds to the immobilization of the cells at pH 5.5, in which the kinetic characterization appears to be inaccurate. Furthermore, sensitivity analysis and the study of the local steady-state stability identify the most critical parameters. The results of these analyses are confirmed by the predictions of the dynamic response of the model using its S-system representation. This illustrates the utility of these tools and warns against using the steady-state characterization without testing its validity.