A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.

Assessing Platelet Mitochondrial Dysfunction in a Murine Model of Acute Acetaminophen Toxicity.

INTRODUCTION: Acetaminophen (APAP) toxicity remains a significant cause of adult and pediatric liver failure in North America and Europe. Previous research has evaluated the impaired mitochondrial function associated with APAP toxicity. The primary aim of this study was to evaluate the effects of APAP toxicity on platelet mitochondrial function using platelet oxygen consumption in a murine model in vivo. Our secondary objectives were to determine the effect of 4-MP on platelet mitochondrial function and hepatic toxicity in the setting of APAP overdose, and to correlate platelet mitochondrial function with other markers of APAP toxicity. METHODS: Male C57Bl/6 mice were randomized to receive APAP (300 or 500 mg/kg) or vehicle followed 90 minutes later by either 4-MP (50 mg/kg) or vehicle via intraperitoneal injection. Mice were euthanized 0, 12, or 24 hours later and platelets isolated from cardiac blood and counted. Platelet oxygen consumption (POC) was determined using a closed-system respirometer. Liver injury was assessed by measuring alanine transferase (ALT) and histological evaluation. RESULTS: Injection of 500 mg/kg APAP led to increased POC versus pair-matched control (vehicle) (p < 0.001). Administration of 4-MP did not affect POC in control or 300 mg/kg APAP mice. In mice receiving 500 mg/kg APAP and 4-MP, POC decreased significantly compared to mice receiving 500 mg/kg APAP alone (p < 0.01). Serum and histological analysis confirmed APAP-induced hepatic damage in mice receiving 500 mg/kg APAP and these effects blunted by treatment with 4-MP. CONCLUSIONS: Platelet oxygen consumption as a measure of mitochondrial function may be useful as a biomarker of hepatic APAP toxicity in the setting of moderate to severe overdose. Treatment with 4-MP decreases hepatic necrosis and may mitigate the harmful effects of APAP on platelet mitochondrial function detected via POC.

Oral health in patients scheduled for hematopoietic stem cell transplantation in the Orastem study.

Despite advances in transplant medicine, prevalence of complications after hematopoietic stem cell transplantation (HSCT) remains high. The impact of pre-HSCT oral health factors on the incidence and severity of complications post-HSCT is poorly understood. The aim of this prospective, observational study was to analyze oral health in patients planned for HSCT. Patients ≥18 years requiring HSCT were included from five sites between 2011-2018. General health, oral findings and patient-reported symptoms were registered in 272 patients. Oral symptoms around disease onset were reported by 43 patients (15.9%) and 153 patients (58.8%) reported oral complications during previous chemotherapy. One third of patients experienced oral symptoms at the oral examination before conditioning regimen and HSCT. In total, 124 (46.1%) patients had dental caries, 63 (29.0%) had ≥one tooth with deep periodontal pockets, 147 (75.0%) had ≥one tooth with bleeding on probing. Apical periodontitis was observed in almost 1/4 and partially impacted teeth in 17 (6.3%) patients. Oral mucosal lesions were observed in 84 patients (30.9%). A total of 45 (17.4%) of 259 patients had at least one acute issue to be managed prior to HSCT. In conclusion, oral symptoms and manifestations of oral disease were prevalent in patients planned for HSCT. The extent of oral and acute dental diseases calls for general oral screening of patients pre-HSCT.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

Longitudinal tendon healing assessed with multi-modality advanced imaging and tissue analysis.

BACKGROUND: The range of diagnostic modalities available to evaluate superficial digital flexor tendon (SDFT) injury includes magnetic resonance imaging (MRI), computed tomography (CT) and ultrasonography (US). Direct, comprehensive comparison of multi-modality imaging characteristics to end-point data has not previously been performed using a model of tendinopathy but is required to obtain a better understanding of each modality's diagnostic capabilities. OBJECTIVE: To compare CT, MRI and US evaluation to outcome measures for histologic, biochemical and biomechanical parameters using an equine surgical model of tendinopathy. STUDY DESIGN: Controlled experiment. METHODS: Lesions were surgically created in both forelimb SDFTs of eight horses and imaged using MRI, CT and US at seven time points over 12 months. Imaging characteristics were then correlated to end point histologic, biochemical and biomechanical data using lasso regression. Longitudinal lesion size was compared between imaging modalities. RESULTS: Lesion to tendon isoattenuation on CT evaluation correlated with the greatest levels of aggrecan deposition. A significant correlation between cellular density and percentage of tendon involvement on the T2-weighted sequence and signal intensity on the proton density fat saturated (PD FS) sequence was appreciated at the 12-month time point (P = .006, P = .02 respectively). There was no significant correlation between end-point data and US or contrast imaging characteristics. Cross sectional area lesion to tendon measurements were significantly largest on CT evaluation, followed by MRI and then US (P < .001). MAIN LIMITATIONS: Experimentally induced tendon injury with singular end-point data correlation. CONCLUSIONS: Lesion isoattenuation on CT evaluation suggested scar tissue deposition, while T2-weighted hyperintensity indicated hypercellular tendinopathy even in chronic stages of healing. Non contrast-enhanced MRI and CT evaluation correlated most closely to cellular characteristics of surgically damaged tendons assessed over a twelve month study period. Ultrasonographic evaluation underestimates true lesional size and should be interpreted with caution.

A predictive internet-based model for COVID-19 hospitalization census.

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

The pelvic flexure separates distinct microbial communities in the equine hindgut.

As hindgut fermenters, horses are especially dependent on the microbiota residing in their cecum and large intestines. Interactions between these microbial populations and the horse are critical for maintaining gut homeostasis, which supports proper digestion. The current project was motivated to determine if any features of the fecal microbiota are informative of the microbial communities from the cecum, ventral colon, or dorsal colon. Digesta from the cecum, ventral colon, dorsal colon and feces were collected from 6 yearling miniature horses. Microbial DNA was isolated and the microbiota from each sample was characterized by profiling the V4 region of the 16S rRNA. Principal coordinate analysis of the beta diversity results revealed significant (p = 0.0001; F = 5.2393) similarities between the microbial populations from cecal and ventral colon and the dorsal colon and fecal samples, however, there was little overlap between the proximal and distal ends of the hindgut. These distinct population structures observed in our results coincide with the pelvic flexure, which itself separates intestinal compartments with distinct roles in digestive physiology. An indicator species analysis confirmed the population differences, supported by the identification of several microbial families characteristic of the compartments upstream of the pelvic flexure that were not represented following it. Our data suggest that the fecal microbiota is not informative of the proximal hindgut but can provide insight into communities of the distal compartments. Further, our results suggest that the pelvic flexure might be an important anatomical landmark relative to the microbial communities in the equine large intestine.

Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study.

BACKGROUND: Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS: Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. RESULTS: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 - the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS: Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

Use of multiple modes of flight subsidy by a soaring terrestrial bird, the golden eagle Aquila chrysaetos, when on migration.

Large birds regularly use updrafts to subsidize flight. Although most research on soaring bird flight has focused on use of thermal updrafts, there is evidence suggesting that many species are likely to use multiple modes of subsidy. We tested the degree to which a large soaring species uses multiple modes of subsidy to provide insights into the decision-making that underlies flight behaviour. We statistically classified more than 22 000 global positioning satellite-global system for mobile communications telemetry points collected at 30-s intervals to identify the type of subsidized flight used by 32 migrating golden eagles during spring in eastern North America. Eagles used subsidized flight on 87% of their journey. They spent 41.9% ± 1.5 ([Formula: see text], range: 18-56%) of their subsidized northbound migration using thermal soaring, 45.2% ± 2.1 (12-65%) of time gliding between thermals, and 12.9% ± 2.2 (1-55%) of time using orographic updrafts. Golden eagles responded to the variable local-scale meteorological events they encountered by switching flight behaviour to take advantage of multiple modes of subsidy. Orographic soaring occurred more frequently in morning and evening, earlier in the migration season, and when crosswinds and tail winds were greatest. Switching between flight modes allowed migration for relatively longer periods each day and frequent switching behaviour has implications for a better understanding of avian flight behaviour and of the evolution of use of subsidy in flight.

Improved intact soil-core carbon determination applying regression shrinkage and variable selection techniques to complete spectrum laser-induced breakdown spectroscopy (LIBS).

Laser-induced breakdown spectroscopy (LIBS) provides a potential method for rapid, in situ soil C measurement. In previous research on the application of LIBS to intact soil cores, we hypothesized that ultraviolet (UV) spectrum LIBS (200-300 nm) might not provide sufficient elemental information to reliably discriminate between soil organic C (SOC) and inorganic C (IC). In this study, using a custom complete spectrum (245-925 nm) core-scanning LIBS instrument, we analyzed 60 intact soil cores from six wheat fields. Predictive multi-response partial least squares (PLS2) models using full and reduced spectrum LIBS were compared for directly determining soil total C (TC), IC, and SOC. Two regression shrinkage and variable selection approaches, the least absolute shrinkage and selection operator (LASSO) and sparse multivariate regression with covariance estimation (MRCE), were tested for soil C predictions and the identification of wavelengths important for soil C prediction. Using complete spectrum LIBS for PLS2 modeling reduced the calibration standard error of prediction (SEP) 15 and 19% for TC and IC, respectively, compared to UV spectrum LIBS. The LASSO and MRCE approaches provided significantly improved calibration accuracy and reduced SEP 32-55% over UV spectrum PLS2 models. We conclude that (1) complete spectrum LIBS is superior to UV spectrum LIBS for predicting soil C for intact soil cores without pretreatment; (2) LASSO and MRCE approaches provide improved calibration prediction accuracy over PLS2 but require additional testing with increased soil and target analyte diversity; and (3) measurement errors associated with analyzing intact cores (e.g., sample density and surface roughness) require further study and quantification.