Molecular Mechanisms Associated with the Development of the Metritis Complex in Dairy Cattle.

The metritis complex (MC), a group of post-partum uterine diseases, is associated with increased treatment costs and reduced milk yield and fertility. The goal of this study was to identify genetic variants, genes, or genomic regions that modulate MC disease. A genome-wide association study was performed using a single-locus mixed linear model of 1967 genotypes (624,460 SNPs) and metritis complex records. Then, in-silico functional analyses were performed to detect biological mechanisms and pathways associated with the development of MC. The ATP8A2, COX16, AMN, and TRAF3 genes, located on chromosomes 12, 10, and 21, were associated with MC at p ≤ 0.0001. These genes are involved in the regulation of cholesterol metabolism in the stromal tissue of the uterus, which can be directly associated with the mode of transmission for pathogens causing the metritis complex. The modulation of cholesterol abundance alters the efficiency of virulence factors and may affect the susceptibility of the host to infection. The SIPA1L1, DEPDC5, and RNF122 genes were also significantly associated with MC at p ≤ 0.0001 and are involved in the PI3k-Akt pathway, responsible for activating the autophagic processes. Thus, the dysregulation of these genes allows for unhindered bacterial invasion, replication, and survival within the endometrium.

Evaluating preferences for colorectal cancer screening in individuals under age 50 using the Analytic Hierarchy Process.

BACKGROUND: In 2021, the United States Preventive Services Task Force updated their recommendation, stating that individuals ages 45-49 should initiate screening for colorectal cancer. Since several screening strategies are recommended, making a shared decision involves including an individual's preferences. Few studies have included individuals under age 50. In this study, we use a multicriteria decision analysis technique called the Analytic Hierarchy Process to explore preferences for screening strategies and evaluate whether preferences vary by age. METHODS: Participants evaluated a hierarchy with 3 decision alternatives (colonoscopy, fecal immunochemical test, and computed tomography colonography), 3 criteria (test effectiveness, the screening plan, and features of the test) and 7 sub-criteria. We used the linear fit method to calculate consistency ratios and the eigenvector method for group preferences. We conducted sensitivity analysis to assess whether results are robust to change and tested differences in preferences by participant variables using chi-square and analysis of variance. RESULTS: Of the 579 individuals surveyed, 556 (96%) provided complete responses to the AHP portion of the survey. Of these, 247 participants gave responses consistent enough (CR < 0.18) to be included in the final analysis. Participants that were either white or have lower health literacy were more likely to be excluded due to inconsistency. Colonoscopy was the preferred strategy in those < 50 and fecal immunochemical test was preferred by those over age 50 (p = 0.002). These results were consistent when we restricted analysis to individuals ages 45-55 (p = 0.011). Participants rated test effectiveness as the most important criteria for making their decision (weight = 0.555). Sensitivity analysis showed our results were robust to shifts in criteria and sub-criteria weights. CONCLUSIONS: We reveal potential differences in preferences for screening strategies by age that could influence the adoption of screening programs to include individuals under age 50. Researchers and practitioners should consider at-home interventions using the Analytic Hierarchy Process to assist with the formulation of preferences that are key to shared decision-making. The costs associated with different preferences for screening strategies should be explored further if limited resources must be allocated to screen individuals ages 45-49.

Voice Interface Technology Adoption by Patients With Heart Failure: Pilot Comparison Study.

BACKGROUND: Heart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients' self-care while providing frequent feedback to their health care providers about the patient's compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients. OBJECTIVE: The aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use. METHODS: We used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon's Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients' demographic and clinical characteristics and past technology use. RESULTS: In both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (-5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (-15.96; P=.08). CONCLUSIONS: Older patients' higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population.

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could impact genetic evaluations for CE. The objective of the current study was to evaluate the use of deep neural networks (DNNs) for classifying contemporary groups (CGs) based on the method used to generate BW phenotypes. CGs (n = 120,000,000) ranging between 10 and 250 animals were simulated assuming 12 data collection and CG formation scenarios that could impact CG phenotypic variance, including weights recorded with a digital scale (REAL), hoof tape (TAPE), erroneous data collection (DIRTY), and those that were fabricated (FAB). The performance of eight activation functions (AFs; ReLu, Sigmoid, Exponential, ReLu6, Softmax, Softplus, Leaky ReLu, and Tanh) was evaluated. Four hidden layers were used with seven different scenarios relative to the number of neurons. Simulations were replicated 10 times. In general, accuracy (proportion of correct predictions) across AF and numbers of neurons were similar, with mean correlations ranging between 0.91 and 0.99. The AF ReLu, Sigmoid, Exponential, and ReLu6 had the greatest consistency (mean pair-wise correlation among replicates) with an average correlation of greater than 0.85. Independent of the number of neurons used, the sigmoid function produced the highest accuracy (0.99) and consistency (0.93). The model with the greatest accuracy and consistency was then applied to real BW data supplied by the American Hereford Association. In the real data, the lowest phenotypic variance was for FAB CG (2.65 kg2), REAL CG had the largest (15.84 kg2), and TAPE CG was intermediate (6.84 kg2). To investigate the potential impact of FAB data on routine genetic evaluations, CGs classified as FAB in 90% or more of the replicates were removed from the evaluation for CE, and the rank of resulting genetic predictions were compared with the case where records were not removed. The removal of FAB CG had a moderate impact on the prediction of CE expected progeny differences, primarily for animals with intermediate to high accuracy. The results suggest that a well-trained DNN can be effectively used to classify data based on quality metrics prior to the inclusion in routine genetic evaluation.

A continuous-time Markov model for estimating readmission risk for hospital inpatients.

Research concerning hospital readmissions has mostly focused on statistical and machine learning models that attempt to predict this unfortunate outcome for individual patients. These models are useful in certain settings, but their performance in many cases is insufficient for implementation in practice, and the dynamics of how readmission risk changes over time is often ignored. Our objective is to develop a model for aggregated readmission risk over time - using a continuous-time Markov chain - beginning at the point of discharge. We derive point and interval estimators for readmission risk, and find the asymptotic distributions for these probabilities. Finally, we validate our derived estimators using simulation, and apply our methods to estimate readmission risk over time using discharge and readmission data for surgical patients.

Impact of Global Budget Revenue Policy on Emergency Department Efficiency in the State of Maryland.

INTRODUCTION: On January 1, 2014, the State of Maryland implemented the Global Budget Revenue (GBR) program. We investigate the impact of GBR on length of stay (LOS) for inpatients in emergency departments (ED) in Maryland. METHODS: We used the Hospital Compare data reports from the Centers for Medicare and Medicaid Services (CMS) and CMS Cost Reports Hospital Form 2552-10 from January 1, 2012-March 31, 2016, with GBR hospitals from Maryland and hospitals from West Virginia (WV), Delaware (DE), and Rhode Island (RI). We implemented difference-in-differences analysis and investigated the impact of GBR implementation on the LOS or ED1b scores of Maryland hospitals using a mixed-effects model with a state-level fixed effect, a hospital-level random effect, and state-level heterogeneity. RESULTS: The GBR impact estimator was 9.47 (95% confidence interval [CI], 7.06 to 11.87, p-value<0.001) for Maryland GBR hospitals, which implies, on average, that GBR implementation added 9.47 minutes per year to the time that hospital inpatients spent in the ED in the first two years after GBR implementation. The effect of the total number of hospital beds was 0.21 (95% CI, 0.089 to 0.330, p-value = 0 .001), which suggests that the bigger the hospital, the longer the ED1b score. The state-level fixed effects for WV were -106.96 (95% CI, -175.06 to -38.86, p-value = 0.002), for DE it was 6.51 (95% CI, -8.80 to 21.82, p-value=0.405), and for RI it was -54.48 (95% CI, -82.85 to -26.10, p-value<0.001). CONCLUSION: Our results indicate that GBR implementation has had a statistically significant negative impact on the efficiency measure ED1b of Maryland hospital EDs from January 2014 to April 2016. We also found that the significant state-level fixed effect implies that the same inpatient might experience different ED processing times in each of the four states that we studied.

Genetic correlations among weight and cumulative productivity of crossbred beef cows.

Mature weight of beef cows in the United States has been increasing as a correlated response to selection for calf growth. Unfavorable genetic correlations between cow weight and various measures of female fertility, stayability, and lifetime production suggest declining cow productivity might also be expected as a correlated response to growth selection. National cattle evaluations, however, show increasing trends for stayability and sustained fertility. Random regression (RR) models were employed to further examine genetic relationships among cow weight and productivity, and to assess cumulative productivity traits observed throughout cows' productive lives. Records were from 13,707 females born in the Germplasm Evaluation (GPE) project and mated to calve first as 2-yr olds. Weights observed at pregnancy testing (n = 65,086) and calf production from each exposure to breeding (n = 71,583) were included in uni- and bivariate RR analyses. Production following each breeding season was added to previous production to obtain cumulative production records for each season that the female was exposed to breeding. Zero was added if the cow failed to produce after a breeding season. The number of pregnancies, calves born and calves weaned, as well as age and weight of weaned calves, were accumulated. Projected age-specific heritability (h2) estimates for cumulative production were low (<0.1) at age 2 but increased with age (0.12 to 0.26 at age 6; 0.32 to 0.48 at age 10). Estimated h2 for cow weight were high, fluctuating between 0.6 and 0.7 from ages 2 through 10. Genetic correlations (rg) were positive among all ages within each trait. Between ages 3 and 9, estimated rg were negative between cumulative weaning productivity and cow weight. The correlations were usually weak enough (<-0.2) that small correlated declines from following yearling weight trends might be overcome by culling females after their first reproductive failure. More noticeable increases might be realized by selection among sires with EBV based on productivity of several daughters. The RR EBV for cow weight and cumulative weight weaned represent major sources of variation in cow costs and income, and can be incorporated into economic selection indexes to project differences in cow profitability and value at any age. The RR approach utilizes all available records, enabling later productivity to be projected from observations on young cows.

Impact of Health Policy Changes on Emergency Medicine in Maryland Stratified by Socioeconomic Status.

INTRODUCTION: On January 1, 2014, the financing and delivery of healthcare in the state of Maryland (MD) profoundly changed. The insurance provisions of the Patient Protection and Affordable Care Act (ACA) began implementation and a major revision of MD's Medicare waiver ushered in a Global Budget Revenue (GBR) structure for hospital reimbursement. Our objective was to analyze the impact of these policy changes on emergency department (ED) utilization, hospitalization practices, insurance profiles, and professional revenue. We stratified our analysis by the socioeconomic status (SES) of the ED patient population. METHODS: We collected monthly mean data including patient volume, hospitalization percentages, payer mix, and professional revenue from January 2013 through December 2015 from a convenience sample of 11 EDs in Maryland. Using regression models, we compared each of the variables 18 months after the policy changes and a six-month washout period to the year prior to ACA/GBR implementation. We included the median income of each ED's patient population as an explanatory variable and stratified our results by SES. RESULTS: Our 11 EDs saw an annualized volume of 399,310 patient visits during the study period. This ranged from a mean of 41 daily visits in the lowest volume rural ED to 171 in the highest volume suburban ED. After ACA/GBR, ED volumes were unchanged (95% confidence interval [CI] [-1.58-1.24], p=.817). Hospitalization percentages decreased significantly by 1.9% from 17.2% to 15.3% (95% CI [-2.47%-1.38%], p<.001). The percentage of uninsured patients decreased from 20.4% to 11.9%. This 8.5% change was significant (95% CI [-9.20%-7.80%], p<.001). The professional revenue per relative value unit increased significantly by $3.97 (95% CI [3.20-4.74], p<.001). When stratified by the median patient income of each ED, changes in each outcome were significantly more pronounced in EDs of lower SES. CONCLUSION: Health policy changes at the federal and state levels have resulted in significant changes to emergency medicine practice and finances in MD. Admission and observation percentages have been reduced, fewer patients are uninsured, and professional revenue has increased. All changes are significantly more pronounced in EDs with patients of lower SES.

Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle.

BACKGROUND: Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals. METHODS: Carcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation. RESULTS: Methods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms. CONCLUSIONS: Single-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.

Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals.

BACKGROUND: Two types of models have been used for single-step genomic prediction and genome-wide association studies that include phenotypes from both genotyped animals and their non-genotyped relatives. The two types are breeding value models (BVM) that fit breeding values explicitly and marker effects models (MEM) that express the breeding values in terms of the effects of observed or imputed genotypes. MEM can accommodate a wider class of analyses, including variable selection or mixture model analyses. The order of the equations that need to be solved and the inverses required in their construction vary widely, and thus the computational effort required depends upon the size of the pedigree, the number of genotyped animals and the number of loci. THEORY: We present computational strategies to avoid storing large, dense blocks of the MME that involve imputed genotypes. Furthermore, we present a hybrid model that fits a MEM for animals with observed genotypes and a BVM for those without genotypes. The hybrid model is computationally attractive for pedigree files containing millions of animals with a large proportion of those being genotyped. APPLICATION: We demonstrate the practicality on both the original MEM and the hybrid model using real data with 6,179,960 animals in the pedigree with 4,934,101 phenotypes and 31,453 animals genotyped at 40,214 informative loci. To complete a single-trait analysis on a desk-top computer with four graphics cards required about 3 h using the hybrid model to obtain both preconditioned conjugate gradient solutions and 42,000 Markov chain Monte-Carlo (MCMC) samples of breeding values, which allowed making inferences from posterior means, variances and covariances. The MCMC sampling required one quarter of the effort when the hybrid model was used compared to the published MEM. CONCLUSIONS: We present a hybrid model that fits a MEM for animals with genotypes and a BVM for those without genotypes. Its practicality and considerable reduction in computing effort was demonstrated. This model can readily be extended to accommodate multiple traits, multiple breeds, maternal effects, and additional random effects such as polygenic residual effects.

Drivers of ED efficiency: a statistical and cluster analysis of volume, staffing, and operations.

STUDY OBJECTIVE: The percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates. METHODS: The Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP. RESULTS: LOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC. CONCLUSION: Volume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.

The impact of electronic health record implementation on emergency physician efficiency and patient throughput.

BACKGROUND: In emergency departments (EDs), the implementation of electronic health records (EHRs) has the potential to impact the rapid assessment and management of life threatening conditions. In order to quantify this impact, we studied the implementation of EHRs in the EDs of a two hospital system. METHODS: using a prospective pre-post study design, patient processing metrics were collected for each ED physician at two hospitals for 7 months prior and 10 months post-EHR implementation. Metrics included median patient workup time, median length of stay, and the composite outcome indicator "processing time." RESULTS: median processing time increased immediately post-implementation and then returned to, and surpassed, the baseline level over 10 months. Overall, we see significant decreases in processing time as the number of patients treated increases. CONCLUSIONS: implementation of new EHRs into the ED setting can be expected to cause an initial decrease in efficiency. With adaptation, efficiency should return to baseline levels and may eventually surpass them. IMPLICATIONS: while EDs can expect long term gains from the implementation of EHRs, they should be prepared for initial decreases in efficiency and take preparatory measures to avert adverse effects on the quality of patient care.

The impact of hospital utilization on patient readmission rate.

We investigate the issue of patient readmission at a large academic hospital in the U.S. Specifically, we look for evidence that patients discharged when post-operative unit utilization is high are more likely to be readmitted. After examining data from 7,800 surgeries performed in 2007, we conclude that patients who are discharged from a highly utilized post-operative unit are more likely to be readmitted within 72 h. Each additional bed utilized at time of discharge increases the odds of readmission on average by 0.35% (Odds Ratio = 1.008, 95% CI [1.003, 1.012]). We propose that this effect is due to an increased discharge rate when the unit is highly utilized.

Contribution of interfacility patient movement to overall methicillin-resistant Staphylococcus aureus prevalence levels.

OBJECTIVES: The effect of patient movement between hospitals and long-term care facilities (LTCFs) on methicillin-resistant Staphylococcus aureus (MRSA) prevalence levels is unknown. We investigated these effects to identify scenarios that may lead to increased prevalence in either facility type. METHODS: We used a hybrid simulation model to simulate MRSA transmission among hospitals and LTCFs. Transmission within each facility was determined by mathematical model equations. The model predicted the long-term prevalence of each facility and was used to assess the effects of facility size, patient turnover, and decolonization. RESULTS: Analyses of various healthcare networks suggest that the effect of patients moving from a LTCF to a hospital is negligible unless the patients are consistently admitted to the same unit. In such cases, MRSA prevalence can increase significantly regardless of the endemic level. Hospitals can cause sustained increases in prevalence when transferring patients to LTCFs, where the population size is smaller and patient turnover is less frequent. For 1 particular scenario, the steady-state prevalence of a LTCF increased from 6.9% to 9.4% to 13.8% when the transmission rate of the hospital increased from a low to a high transmission rate. CONCLUSIONS: These results suggest that the relative facility size and the patient discharge rate are 2 key factors that can lead to sustained increases in MRSA prevalence. Consequently, small facilities or those with low turnover rates are especially susceptible to sustaining increased prevalence levels, and they become more so when receiving patients from larger, high-prevalence facilities. Decolonization is an infection-control strategy that can mitigate these effects.

Examining the discharge practices of surgeons at a large medical center.

We investigate the discharge practices at a large medical center. Specifically, we look for indications that patients are being discharged sooner because of hospital bed-capacity constraints. Using survival analysis techniques, we find statistically significant evidence to indicate that surgeons adjust their discharge practices to accommodate the surgical schedule and number of available recovery beds. We find higher discharge rates on days when utilization is high. We also find an increased discharge rate on days when more surgeries are scheduled. Our findings suggest that discharge decisions are made with bed-capacity constraints in mind. We discuss possible explanations for this, as well as the medical and managerial implications of our findings.