Contributing Factors to Operating Room Delays Identified from an Electronic Health Record: A Retrospective Study.

The operating room (OR) is considered a major cost center and revenue generator for hospitals. Multiple factors contribute to OR delays and impact patient safety, patient satisfaction scores, and hospital financial performance. Reducing OR delays allows better utilization of OR resources and staffing and improves patient satisfaction while decreasing operating costs. Accurate scheduling can be the basis to achieve these goals. The objective of this initial study was to identify factors not normally documented in the electronic health record (EHR) that may contribute to or be indicators of OR delays. Materials and Methods. A retrospective data analysis was performed analyzing 67,812 OR cases from 12 surgical specialties at a small university medical center from 2010 through the first quarter of 2017. Data from the hospital's EHR were exported and subjected to statistical analysis using Statistical Analysis System (SAS) software (SAS Institute, Cary, NC). Results. Statistical analysis of the extracted EHR data revealed factors that were associated with OR delays including, surgical specialty, preoperative assessment testing, patient body mass index, American Society of Anesthesiologists (ASA) physical status classification, daily procedure count, and calendar year. Conclusions. Delays hurt OR efficiency on many levels. Identifying those factors may reduce delays and better accommodate the needs of surgeons, staff, and patients thereby leading to improved patient's outcomes and patient satisfaction. Reducing delays can decrease operating costs and improve the financial position of the operating theater as well as that of the hospital. Anesthesiology teams can play a key role in identifying factors that cause delays and implementing mitigating efficiencies.

In-hospital outcomes of COVID-19 infection in patients with underlying cardiovascular disease.

BACKGROUND: The effects of cardiovascular comorbidities on outcomes in COVID-19 hospitalized patients has not been well studied. METHODS: This is a hospital-based study evaluating the effects of CVD on the outcomes in patients admitted with COVID-19. Clinical outcomes were studied in patients with and without CVD. RESULTS: Eighty-seven patients had CVD, and 193 patients had no history of CVD. Ischemic heart disease was the most common CVD (63%). When compared with patients with no CVD, those with CVD had higher mortality (29% vs 9%, p < 0.001), discharge to a skilled nursing facility (SNF) (36% vs 15%, p < 0.001), and change of code status to 'do not resuscitate' (41% vs 14%, p < 0.001). The odds for mortality were high with ischemic heart disease (OR 3.6, 95% CI 1.8-7.3, p < 0.001), and systolic heart failure (OR 3.8,95% CI 1.2-12.3, p = 0.02). Patients in the CVD group were more likely to have incident atrial fibrillation (22% vs 3%, p < 0.001), type 2 Mi (17% vs 6%, p = 0.002), high BNP (57% vs 14%, p < 0.001), acute kidney injury (64% vs 29%, p < 0.001), and any type of circulatory shock (27% vs 12%, p = 0.001). CONCLUSION: CVD is associated with increased mortality, myocardial injury, arrhythmias, and discharges to an SNF.

Coronavirus disease 2019 (COVID-19) and QTc prolongation.

INTRODUCTION: The cause-and-effect relationship of QTc prolongation in Coronavirus disease 2019 (COVID-19) patients has not been studied well. OBJECTIVE: We attempt to better understand the relationship of QTc prolongation in COVID-19 patients in this study. METHODS: This is a retrospective, hospital-based, observational study. All patients with normal baseline QTc interval who were hospitalized with the diagnosis of COVID-19 infection at two hospitals in Ohio, USA were included in this study. RESULTS: Sixty-nine patients had QTc prolongation, and 210 patients continued to have normal QTc during hospitalization. The baseline QTc intervals were comparable in the two groups. Patients with QTc prolongation were older (mean age 67 vs. 60, P 0.003), more likely to have underlying cardiovascular disease (48% versus 26%, P 0.001), ischemic heart disease (29% versus 17%, P 0.026), congestive heart failure with preserved ejection fraction (16% versus 8%, P 0.042), chronic kidney disease (23% versus 10%, P 0.005), and end-stage renal disease (12% versus 1%, P < 0.001). Patients with QTc prolongation were more likely to have received hydroxychloroquine (75% versus 59%, P 0.018), azithromycin (18% vs. 14%, P 0.034), a combination of hydroxychloroquine and azithromycin (29% vs 7%, P < 0.001), more than 1 QT prolonging agents (59% vs. 32%, P < 0.001). Patients who were on angiotensin-converting enzyme inhibitors (ACEi) were less likely to develop QTc prolongation (11% versus 26%, P 0.014). QTc prolongation was not associated with increased ventricular arrhythmias or mortality. CONCLUSION: Older age, ESRD, underlying cardiovascular disease, potential virus mediated cardiac injury, and drugs like hydroxychloroquine/azithromycin, contribute to QTc prolongation in COVID-19 patients. The role of ACEi in preventing QTc prolongation in COVID-19 patients needs to be studied further.

NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks.

Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.

Myocardial injury in hospitalized COVID-19 patients: a retrospective study, systematic review, and meta-analysis.

INTRODUCTION: The majority of studies evaluating the effect of myocardial injury on the survival of COVID-19 patients have been performed outside of the United States (U.S.). These studies have often utilized definitions of myocardial injury that are not guideline-based and thus, not applicable to the U.S. METHODS: The current study is a two-part investigation of the effect of myocardial injury on the clinical outcome of patients hospitalized with COVID-19. The first part is a retrospective analysis of 268 patients admitted to our healthcare system in Toledo, Ohio, U.S.; the second part is a systematic review and meta-analysis of all similar studies performed within the U.S. RESULTS: In our retrospective analysis, patients with myocardial injury were older (mean age 73 vs. 59 years, P 0.001), more likely to have hypertension (86% vs. 67%, P 0.005), underlying cardiovascular disease (57% vs. 24%, P 0.001), and chronic kidney disease (26% vs. 10%, P 0.004). Myocardial injury was also associated with a lower likelihood of discharge to home (35% vs. 69%, P 0.001), and a higher likelihood of death (33% vs. 10%, P 0.001), acute kidney injury (74% vs. 30%, P 0.001), and circulatory shock (33% vs. 12%, P 0.001). Our meta-analysis included 12,577 patients from 8 U.S. states and 55 hospitals who were hospitalized with COVID-19, with the finding that myocardial injury was significantly associated with increased mortality (HR 2.43, CI 2.28-3.6, P 0.0005). The prevalence of myocardial injury ranged from 9.2 to 51%, with a mean prevalence of 27.2%. CONCLUSION: Hospitalized COVID-19 patients in the U.S. have a high prevalence of myocardial injury, which was associated with poorer survival and outcomes.

NetRed, an algorithm to reduce genome-scale metabolic networks and facilitate the analysis of flux predictions.

Flux balance analysis (FBA) of large, genome-scale stoichiometric models (GSMs) is a powerful and popular method to predict cell-wide metabolic activity. FBA typically generates a flux vector containing O(1,000) fluxes. The interpretation of such a flux vector is difficult, even for expert users, because of the large size and complex topology of the underlying metabolic network. This interpretation could be simplified by condensing the network to a reduced, yet fully representative version. Toward this goal we report NetRed, an algorithm that systematically reduces a stoichiometric matrix and a corresponding flux vector to a more easily interpretable form. The reduction offered by NetRed is transparent because it relies purely on matrix algebra and not on optimization. Uniquely, it involves zero information loss; therefore, the original unreduced network can be easily recovered from the reduced network. The inputs to NetRed are (i) a stoichiometric matrix, (ii) a flux vector with numerical flux values, and (iii) a list of "protected" metabolites recommended by the user to remain in the reduced network. NetRed outputs a reduced metabolic network containing a reduced number of metabolites, of which the protected metabolites are a subset. The algorithm also generates a corresponding reduced flux vector. Due to its simplified presentation and easier interpretability, the reduced network allows the user to quickly find fluxes through metabolites and reaction modes or pathways of interest. In this manuscript, we first demonstrate NetRed on a simple network consisting of glycolysis and the pentose phosphate pathway (PPP), wherein NetRed reduced the PPP to a single net reaction. We followed this with applications of NetRed to E. coli and yeast GSMs. NetRed reduced the size of an E. coli GSM by 20- to 30-fold and enabled a comprehensive comparison of aerobic and anaerobic metabolism. The application of NetRed to a yeast GSM allowed for easy mechanistic interpretation of a double-gene knockout that rerouted flux toward dihydroartemisinic acid. When applied to an E. coli strain engineered for enhanced valine production, NetRed allowed for a holistic interpretation of the metabolic rerouting resulting from multiple genetic interventions.

Achieving a Predictive Understanding of Antimicrobial Stress Physiology through Systems Biology.

The dramatic spread and diversity of antibiotic-resistant pathogens has significantly reduced the efficacy of essentially all antibiotic classes, bringing us ever closer to a postantibiotic era. Exacerbating this issue, our understanding of the multiscale physiological impact of antimicrobial challenge on bacterial pathogens remains incomplete. Concerns over resistance and the need for new antibiotics have motivated the collection of omics measurements to provide systems-level insights into antimicrobial stress responses for nearly 20 years. Although technological advances have markedly improved the types and resolution of such measurements, continued development of mathematical frameworks aimed at providing a predictive understanding of complex antimicrobial-associated phenotypes is critical to maximize the utility of multiscale data. Here we highlight recent efforts utilizing systems biology to enhance our knowledge of antimicrobial stress physiology. We provide a brief historical perspective of antibiotic-focused omics measurements, highlight new measurement discoveries and trends, discuss examples and opportunities for integrating measurements with mathematical models, and describe future challenges for the field.

Systems biology investigation of cAMP modulation to increase SMN levels for the treatment of spinal muscular atrophy.

Spinal muscular atrophy (SMA), a leading genetic cause of infant death worldwide, is an autosomal recessive disorder caused by the loss of SMN1 (survival motor neuron 1), which encodes the protein SMN. The loss of SMN1 causes a deficiency in SMN protein levels leading to motor neuron cell death in the anterior horn of the spinal cord. SMN2, however, can also produce some functional SMN to partially compensate for loss of SMN1 in SMA suggesting increasing transcription of SMN2 as a potential therapy to treat patients with SMA. A cAMP response element was identified on the SMN2 promoter, implicating cAMP activation as a step in the transcription of SMN2. Therefore, we investigated the effects of modulating the cAMP signaling cascade on SMN production in vitro and in silico. SMA patient fibroblasts were treated with the cAMP signaling modulators rolipram, salbutamol, dbcAMP, epinephrine and forskolin. All of the modulators tested were able to increase gem formation, a marker for SMN protein in the nucleus, in a dose-dependent manner. We then derived two possible mathematical models simulating the regulation of SMN2 expression by cAMP signaling. Both models fit well with our experimental data. In silico treatment of SMA fibroblasts simultaneously with two different cAMP modulators resulted in an additive increase in gem formation. This study shows how a systems biology approach can be used to develop potential therapeutic targets for treating SMA.