Are We the Problem? A Call to Action for Addressing Institutional Challenges to Engaging Community Partners in Research.

Community-engaged research (CEnR) is a potent tool for addressing health inequities and fostering equitable relationships among communities, researchers, and institutions. CEnR involves collaboration throughout the research process, demonstrating improvements in study recruitment and retention, intervention efficacy, program sustainability, capacity building among partners, and enhanced cultural relevance. Despite the increasing demand for CEnR, institutional policies, particularly human participation protection training (HPP), lag behind, creating institutional barriers to community partnerships. Here, we highlight challenges encountered in our ongoing study, Fostering Opportunities in Research through Messaging and Education (FOR ME), focused on promoting shared decision-making around clinical trial participation among Black women diagnosed with breast cancer. Grounded in CEnR methods, FOR ME has a partnership with a community-based organization (CBO) that addresses the needs of Black women with breast cancer. Our CBO partner attempted to obtain HPP training, which was administratively burdensome and time-consuming. As CEnR becomes more prevalent, academic and research institutions, along with researchers, are faced with a call to action to become more responsive to community partner needs. Accordingly, we present a guide to HPP training for community partners, addressing institutional barriers to community partner participation in research. This guide outlines multiple HPP training pathways for community partners, aiming to minimize institutional barriers and enhance their engagement in research with academic partners.

Risk of bacterial bloodstream infection does not vary by central-line type during neutropenic periods in pediatric acute myeloid leukemia.

BACKGROUND: Bloodstream infections (BSIs) are a frequent cause of morbidity in patients with acute myeloid leukemia (AML), due in part to the presence of central venous access devices (CVADs) required to deliver therapy. OBJECTIVE: To determine the differential risk of bacterial BSI during neutropenia by CVAD type in pediatric patients with AML. METHODS: We performed a secondary analysis in a cohort of 560 pediatric patients (1,828 chemotherapy courses) receiving frontline AML chemotherapy at 17 US centers. The exposure was CVAD type at course start: tunneled externalized catheter (TEC), peripherally inserted central catheter (PICC), or totally implanted catheter (TIC). The primary outcome was course-specific incident bacterial BSI; secondary outcomes included mucosal barrier injury (MBI)-BSI and non-MBI BSI. Poisson regression was used to compute adjusted rate ratios comparing BSI occurrence during neutropenia by line type, controlling for demographic, clinical, and hospital-level characteristics. RESULTS: The rate of BSI did not differ by CVAD type: 11 BSIs per 1,000 neutropenic days for TECs, 13.7 for PICCs, and 10.7 for TICs. After adjustment, there was no statistically significant association between CVAD type and BSI: PICC incident rate ratio [IRR] = 1.00 (95% confidence interval [CI], 0.75-1.32) and TIC IRR = 0.83 (95% CI, 0.49-1.41) compared to TEC. When MBI and non-MBI were examined separately, results were similar. CONCLUSIONS: In this large, multicenter cohort of pediatric AML patients, we found no difference in the rate of BSI during neutropenia by CVAD type. This may be due to a risk-profile for BSI that is unique to AML patients.

Cross-reactivity of SARS-CoV-2- and influenza A-specific T cells in individuals exposed to SARS-CoV-2.

Cross-reactive immunity between SARS-CoV-2 and other related coronaviruses has been well-documented, and it may play a role in preventing severe COVID-19. Epidemiological studies early in the pandemic showed a geographical association between high influenza vaccination rates and lower incidence of SARS-CoV-2 infection. We, therefore, analyzed whether exposure to influenza A virus (IAV) antigens could influence the T cell repertoire in response to SARS-CoV-2, indicating a heterologous immune response between these 2 unrelated viruses. Using artificial antigen-presenting cells (aAPCs) combined with real-time reverse-transcription PCR (RT-qPCR), we developed a sensitive assay to quickly screen for antigen-specific T cell responses and detected a significant correlation between responses to SARS-CoV-2 epitopes and IAV dominant epitope (M158-66). Further analysis showed that some COVID-19 convalescent donors exhibited both T cell receptor (TCR) specificity and functional cytokine responses to multiple SARS-CoV-2 epitopes and M158-66. Utilizing an aAPC-based stimulation/expansion assay, we detected cross-reactive T cells with specificity to SARS-CoV-2 and IAV. In addition, TCR sequencing of the cross-reactive and IAV-specific T cells revealed similarities between the TCR repertoires of the two populations. These results indicate that heterologous immunity shaped by our exposure to other unrelated endemic viruses may affect our immune response to novel viruses such as SARS-CoV-2.

Center-Based Experiences Implementing Strategies to Reduce Risk of Horizontal Transmission of SARS-Cov-2: Potential for Compromise of Neonatal Microbiome Assemblage.

Perinatal transmission of COVID-19 is poorly understood and many neonatal intensive care units' (NICU) policies minimize mother-infant contact to prevent transmission. We present our unit's approach and ways it may impact neonatal microbiome acquisition. We attended COVID-19 positive mothers' deliveries from March-August 2020. Delayed cord clamping and skin-to-skin were avoided and infants were admitted to the NICU. No parents' visits were allowed and discharge was arranged with COVID-19 negative family members. Maternal breast milk was restricted in the NICU. All twenty-one infants tested negative at 24 and 48 hours and had average hospital stays of nine days. 40% of mothers expressed breastmilk and 60% of infants were discharged with COVID-19 negative caregivers. Extended hospital stays, no skin-to-skin contact, limited maternal milk use, and discharge to caregivers outside primary residences, potentially affect the neonatal microbiome. Future studies are warranted to explore how ours and other centers' similar policies influence this outcome.

The Role of Feedback Control Design in Developing Anemia Management Protocols.

The optimal use of erythropoiesis stimulating agents to treat anemia of end-stage renal disease remains difficult due to reported associations with adverse events. A patient's hemoglobin response to these agents cannot be accurately described using population-level models due to many individual factors including chronic inflammation, red blood cell lifespan, and acute blood loss. As a consequence, it is generally understood that current one-size-fits-all anemia management protocols result in suboptimal outcomes. In this paper, we report on our collaboration with the medical community in designing anemia management protocols. In clinical implementation, these new dosing protocols have led to improved outcomes due to their use of control-relevant modelling, model parameter identification, and principles of feedback control. This is an example of medical professionals and control engineers working together to positively affect the performance of anemia management protocols in end-stage renal disease.

Individualization of Ultrafiltration in Hemodialysis.

OBJECTIVES: There are approximately 660 000 end-stage renal disease patients in the USA, with hemodialysis (HD) the primary form of treatment. High ultrafiltration rates (UFRs) are associated with intradialytic hypotension, a complication associated with adverse clinical outcomes including mortality. Individualized UFR profiles could reduce the incidence of intradialytic hypotension. METHODS: The patient's fluid dynamics during HD is described by a nonlinear model comprising intravascular and interstitial pools, whose parameters are given by the patient's estimated nominal parameter values with uncertainty ranges; the output measurement is hematocrit. We design UFR profiles that minimize the maximal UFR needed to remove a prescribed volume of fluid within a set time, with hematocrit not exceeding a specified time-varying critical profile. RESULTS: We present a novel approach to design individualized UFR profiles, and give theoretical results guaranteeing that the system remains within a predefined physiologically plausible region and does not exceed a specified time-invariant critical hematocrit level for all parameters in the uncertainty ranges. We test the performance of our design using a real patient data example. The designed UFR maintains the system below a time-varying critical hematocrit profile in the example. CONCLUSION: Theoretical results and simulations show that our designed UFR profiles can remove the target amount of fluid in a given time period while keeping the hematocrit below a specified critical profile. SIGNIFICANCE: Individualization of UFR profiles is now feasible using current HD technology and may reduce the incidence of intradialytic hypotension.

Variable-Volume Kinetic Model to Estimate Absolute Blood Volume in Patients on Dialysis Using Dialysate Dilution.

Long- and short-term adverse outcomes in hemodialysis (HD) have been associated with intradialytic hypotension, a common HD complication and significant cause of morbidity. It has been suggested that knowledge of absolute blood volume (ABV) could be used to significantly improve treatment outcomes. Different dilution-based protocols have been proposed for estimating ABV, all relying on the classic mono-exponential back-extrapolation algorithm (BEXP). In this paper, we introduce a dialysate dilution protocol and an estimation algorithm based on a variable-volume, two-compartment, intravascular blood water content kinetic model (VVKM). We compare ABV estimates derived using the two algorithms in a dialysate dilution study including three arterio-venous (AV) and three central-venous (CV) access patients, and multiple bolus injection tests (3-5) within each of several (2-6) HD treatments. The distribution of differences between ABV estimated from the two methods showed negligible systematic difference between the mean values of ABVs estimated from the BEXP and VVKM algorithms, however, the VVKM estimates were 53% and 42% more precise for the CV and AV patients, respectively. Good agreement was observed between measured and VVKM-estimated blood water concentration with the root-mean-square error (RMSE) less than 0.02 kg/kg (2%) and 0.03 kg/kg (3%) for AV and CV patients, respectively. The dilution protocol and the new VVKM-based estimation algorithm offer a noninvasive, inexpensive, safe, and practical approach for ABV estimation in routine HD settings.

Creation of a pediatric mature B-cell non-Hodgkin lymphoma cohort within the Pediatric Health Information System Database.

Mature B-cell non-Hodgkin lymphoma (B-NHL) constitutes a collection of relatively rare pediatric malignancies. In order to utilize administrative data to perform large-scale epidemiologic studies within this population, a two-step process was used to assemble a 12-year cohort of B-NHL patients treated between 2004 and 2015 within the Pediatric Health Information System database. Patients were identified by ICD-9 codes, and their chemotherapy data were then manually reviewed against standard B-NHL treatment regimens. A total of 1,409 patients were eligible for cohort inclusion. This process was validated at a single center, utilizing both an institutional tumor registry and medical record review as the gold standards. The validation demonstrated appropriate sensitivity (91.5%) and positive predictive value (95.1%) to allow for the future use of this cohort for epidemiologic and comparative effectiveness research.

Models for the red blood cell lifespan.

The lifespan of red blood cells (RBCs) plays an important role in the study and interpretation of various clinical conditions. Yet, confusion about the meanings of fundamental terms related to cell survival and their quantification still exists in the literature. To address these issues, we started from a compartmental model of RBC populations based on an arbitrary full lifespan distribution, carefully defined the residual lifespan, current age, and excess lifespan of the RBC population, and then derived the distributions of these parameters. For a set of residual survival data from biotin-labeled RBCs, we fit models based on Weibull, gamma, and lognormal distributions, using nonlinear mixed effects modeling and parametric bootstrapping. From the estimated Weibull, gamma, and lognormal parameters we computed the respective population mean full lifespans (95 % confidence interval): 115.60 (109.17-121.66), 116.71 (110.81-122.51), and 116.79 (111.23-122.75) days together with the standard deviations of the full lifespans: 24.77 (20.82-28.81), 24.30 (20.53-28.33), and 24.19 (20.43-27.73). We then estimated the 95th percentiles of the lifespan distributions (a surrogate for the maximum lifespan): 153.95 (150.02-158.36), 159.51 (155.09-164.00), and 160.40 (156.00-165.58) days, the mean current ages (or the mean residual lifespans): 60.45 (58.18-62.85), 60.82 (58.77-63.33), and 57.26 (54.33-60.61) days, and the residual half-lives: 57.97 (54.96-60.90), 58.36 (55.45-61.26), and 58.40 (55.62-61.37) days, for the Weibull, gamma, and lognormal models respectively. Corresponding estimates were obtained for the individual subjects. The three models provide equally excellent goodness-of-fit, reliable estimation, and physiologically plausible values of the directly interpretable RBC survival parameters.

The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure.

Introduction The optimal use of erythropoiesis stimulating agents (ESAs) to treat anemia in end stage renal disease remains controversial due to reported associations with adverse events. In analyzing these associations, studies often utilize ESA resistance indices (ERIs), to characterize a patient's response to ESA. In this study, we examine whether ERI is an adequate measure of ESA resistance. Methods We used retrospective data from a nonconcurrent cohort study of incident hemodialysis patients in the United States (n = 9386). ERI is defined as average weekly erythropoietin (EPO) dose per kg body weight (wt) per average hemoglobin (Hgb), over a 3-month period (ERI = (EPO/wt)/Hgb). Linear regression was used to demonstrate the relationship between ERI and weight-adjusted EPO. The coefficient of variation was used to compare the variability of Hgb with that of weight-adjusted EPO to explain this relationship. This analysis was done for each quarter during the first year of dialysis. Findings ERI is strongly linearly related with weight-adjusted EPO dose in each of the four quarters by the equation ERI = 0.0899*(EPO/wt) (range of R(2) = 0.97-0.98) and weakly linearly related to 1/Hgb (range of R(2) = 0.06-0.16). These correlations hold independent of age, sex, hgb level, ERI level, and epo-naïve stratifications. Discussion ERI is strongly linearly related to weight-adjusted (and nonweight-adjusted) EPO dose by a "universal," not patient-specific formula, and thus is a surrogate of EPO dose. Therefore, associations between ERI and clinical outcomes are associations between a confounded EPO dose and those outcomes.

Control-relevant erythropoiesis modeling in end-stage renal disease.

Anemia is prevalent in end-stage renal disease (ESRD). The discovery of recombinant human erythropoietin (rHuEPO) over 30 years ago has shifted the treatment of anemia for patients on dialysis from blood transfusions to rHuEPO therapy. Many anemia management protocols (AMPs) used by clinicians comprise a set of experience-based rules for weekly-to-monthly titration of rHuEPO doses based on hemoglobin (Hb) measurements. In order to facilitate the design of an AMP using model-based feedback control theory, we present a physiologically relevant erythropoiesis model and demonstrate its applicability using clinical data.

Expanded Fermi solution for estimating the survival of ingested pathogenic and probiotic microbial cells and spores.

The expanded Fermi solution was originally developed for estimating the number of food-poisoning victims when information concerning the circumstances of exposure is scarce. The method has been modified for estimating the initial number of pathogenic or probiotic cells or spores so that enough of them will survive the food preparation and digestive tract's obstacles to reach or colonize the gut in sufficient numbers to have an effect. The method is based on identifying the relevant obstacles and assigning each a survival probability range. The assumed number of needed survivors is also specified as a range. The initial number is then estimated to be the ratio of the number of survivors to the product of the survival probabilities. Assuming that the values of the number of survivors and the survival probabilities are uniformly distributed over their respective ranges, the sought initial number is construed as a random variable with a probability distribution whose parameters are explicitly determined by the individual factors' ranges. The distribution of the initial number is often approximately lognormal, and its mode is taken to be the best estimate of the initial number. The distribution also provides a credible interval for this estimated initial number. The best estimate and credible interval are shown to be robust against small perturbations of the ranges and therefore can help assessors achieve consensus where hard knowledge is scant. The calculation procedure has been automated and made freely downloadable as a Wolfram Demonstration.

Probabilistic model of microbial cell growth, division, and mortality.

After a short time interval of length deltat during microbial growth, an individual cell can be found to be divided with probability Pd(t)deltat, dead with probability Pm(t)deltat, or alive but undivided with the probability 1-[Pd(t)+Pm(t)]deltat, where t is time, Pd(t) expresses the probability of division for an individual cell per unit of time, and Pm(t) expresses the probability of mortality per unit of time. These probabilities may change with the state of the population and the habitat's properties and are therefore functions of time. This scenario translates into a model that is presented in stochastic and deterministic versions. The first, a stochastic process model, monitors the fates of individual cells and determines cell numbers. It is particularly suitable for small populations such as those that may exist in the case of casual contamination of a food by a pathogen. The second, which can be regarded as a large-population limit of the stochastic model, is a continuous mathematical expression that describes the population's size as a function of time. It is suitable for large microbial populations such as those present in unprocessed foods. Exponential or logistic growth with or without lag, inactivation with or without a "shoulder," and transitions between growth and inactivation are all manifestations of the underlying probability structure of the model. With temperature-dependent parameters, the model can be used to simulate nonisothermal growth and inactivation patterns. The same concept applies to other factors that promote or inhibit microorganisms, such as pH and the presence of antimicrobials, etc. With Pd(t) and Pm(t) in the form of logistic functions, the model can simulate all commonly observed growth/mortality patterns. Estimates of the changing probability parameters can be obtained with both the stochastic and deterministic versions of the model, as demonstrated with simulated data.

An expanded Fermi solution for microbial risk assessment.

'Fermi solution' refers to an estimate of a quantity of interest derived from a sequence of guesses about factors of which detailed knowledge is unavailable. When one makes such guesses, it is unlikely that the large majority of them will be either too high or too low. Most probably, some of the overestimates will be offset by some of the underestimates, and the final result will be often close to the correct value. The method has been popularized as recreational physics but it has also been applied in risk assessment, where the factors involved, but not their exact magnitudes, are known. The concept has potential application in certain types of food poisoning risk assessments, and in estimating the number victims of a bioterrorist attack on the food or water supply, where some guessing is inevitable because of the absence of accurate relevant data. We consider a version of the method in which ranges instead of single values are entered as the factors' estimates. For simplicity, the risk to be assessed is taken to be the product of the factors, and their true values are regarded as being uniformly distributed over their respective ranges. The risk itself is therefore construed as a random variable with a probability distribution whose parameters are explicitly determined by the individual factors' ranges and which can often be approximated by a lognormal distribution. The mode of this lognormal distribution is taken to be the "best guess" of the risk, and a credible interval is constructed with a specified level of "confidence". The best guess and credible interval are shown to be robust against small perturbations of the ranges. Thus, even if the ranges are misspecified to some degree, assessments based on the best guess or credible interval will not be substantially altered. This can help to achieve consensus among assessors in situations where very little hard knowledge exists. The calculation procedure has been automated in software that has been made freely available over the Internet. The concept is demonstrated with two hypothetical problems: predicting the number of persons who would come down with acute food poisoning after consuming a contaminated dish, and estimating the number of daily salmonellosis cases in a large metropolitan area.