First reported case in an Irish flock of MCF- like systemic necrotizing vasculitis in sheep associated with ovine herpesvirus 2.

BACKGROUND: Ovine gammaherpesvirus 2 (OvHV-2) is the causative agent of sheep associated malignant catarrhal fever (MCF). As sheep are the adapted host for OvHV-2, it is generally presumed that infection is not associated with disease in this species. However, a recent case review combined in-situ hybridisation, PCR and histopathology and correlated the viral distribution with systemic necrotizing vasculitis and concluded OvHV-2 was the likely agent responsible for sporadic, MCF-like vascular disease in sheep. CASE PRESENTATION: Using similar methods this case study reports on the findings of the first reported cases in an Irish Flock of MCF- like systemic necrotizing vasculitis in sheep associated with OvHV-2. Sheep A, a 16-month-old Texel-cross hogget displayed signs of ill- thrift, Sheep B, a nine-month-old Belclare-cross lamb, was found dead having displayed no obvious symptoms. Both cases occurred on the same farm, however the animals were not related. Lymphohistiocytic vasculitis of various tissues was the predominant histopathological finding in both animals. CONCLUSION: By combining histopathology, PCR and in-situ hybridisation results, MCF- like systemic necrotizing vasculitis associated with OvHV-2 has been diagnosed for the first time in an Irish flock.

The Influence of Social Determinants on Receiving Outpatient Treatment with Monoclonal Antibodies, Disease Risk, and Effectiveness for COVID-19.

BACKGROUND: Limited research has studied the influence of social determinants of health (SDoH) on the receipt, disease risk, and subsequent effectiveness of neutralizing monoclonal antibodies (nMAbs) for outpatient treatment of COVID-19. OBJECTIVE: To examine the influence of SDoH variables on receiving nMAb treatments and the risk of a poor COVID-19 outcome, as well as nMAb treatment effectiveness across SDoH subgroups. DESIGN: Retrospective observational study utilizing electronic health record data from four health systems. SDoH variables analyzed included race, ethnicity, insurance, marital status, Area Deprivation Index, and population density. PARTICIPANTS: COVID-19 patients who met at least one emergency use authorization criterion for nMAb treatment. MAIN MEASURE: We used binary logistic regression to examine the influence of SDoH variables on receiving nMAb treatments and risk of a poor outcome from COVID-19 and marginal structural models to study treatment effectiveness. RESULTS: The study population included 25,241 (15.1%) nMAb-treated and 141,942 (84.9%) non-treated patients. Black or African American patients were less likely to receive treatment than white non-Hispanic patients (adjusted odds ratio (OR) = 0.86; 95% CI = 0.82-0.91). Patients who were on Medicaid, divorced or widowed, living in rural areas, or living in areas with the highest Area Deprivation Index (most vulnerable) had lower odds of receiving nMAb treatment, but a higher risk of a poor outcome. For example, compared to patients on private insurance, Medicaid patients had 0.89 (95% CI = 0.84-0.93) times the odds of receiving nMAb treatment, but 1.18 (95% CI = 1.13-1.24) times the odds of a poor COVID-19 outcome. Age, comorbidities, and COVID-19 vaccination status had a stronger influence on risk of a poor outcome than SDoH variables. nMAb treatment benefited all SDoH subgroups with lower rates of 14-day hospitalization and 30-day mortality. CONCLUSION: Disparities existed in receiving nMAbs within SDoH subgroups despite the benefit of treatment across subgroups.

Neutralizing Monoclonal Antibody Use and COVID-19 Infection Outcomes.

Importance: Evidence on the effectiveness and safety of COVID-19 therapies across a diverse population with varied risk factors is needed to inform clinical practice. Objective: To assess the safety of neutralizing monoclonal antibodies (nMAbs) for the treatment of COVID-19 and their association with adverse outcomes. Design, Setting, and Participants: This retrospective cohort study included 167 183 patients from a consortium of 4 health care systems based in California, Minnesota, Texas, and Utah. The study included nonhospitalized patients 12 years and older with a positive COVID-19 laboratory test collected between November 9, 2020, and January 31, 2022, who met at least 1 emergency use authorization criterion for risk of a poor outcome. Exposure: Four nMAb products (bamlanivimab, bamlanivimab-etesevimab, casirivimab-imdevimab, and sotrovimab) administered in the outpatient setting. Main Outcomes and Measures: Clinical and SARS-CoV-2 genomic sequence data and propensity-adjusted marginal structural models were used to assess the association between treatment with nMAbs and 4 outcomes: all-cause emergency department (ED) visits, hospitalization, death, and a composite of hospitalization or death within 14 days and 30 days of the index date (defined as the date of the first positive COVID-19 test or the date of referral). Patient index dates were categorized into 4 variant epochs: pre-Delta (November 9, 2020, to June 30, 2021), Delta (July 1 to November 30, 2021), Delta and Omicron BA.1 (December 1 to 31, 2021), and Omicron BA.1 (January 1 to 31, 2022). Results: Among 167 183 patients, the mean (SD) age was 47.0 (18.5) years; 95 669 patients (57.2%) were female at birth, 139 379 (83.4%) were White, and 138 900 (83.1%) were non-Hispanic. A total of 25 241 patients received treatment with nMAbs. Treatment with nMAbs was associated with lower odds of ED visits within 14 days (odds ratio [OR], 0.76; 95% CI, 0.68-0.85), hospitalization within 14 days (OR, 0.52; 95% CI, 0.45-0.59), and death within 30 days (OR, 0.14; 95% CI, 0.10-0.20). The association between nMAbs and reduced risk of hospitalization was stronger in unvaccinated patients (14-day hospitalization: OR, 0.51; 95% CI, 0.44-0.59), and the associations with hospitalization and death were stronger in immunocompromised patients (hospitalization within 14 days: OR, 0.31 [95% CI, 0.24-0.41]; death within 30 days: OR, 0.13 [95% CI, 0.06-0.27]). The strength of associations of nMAbs increased incrementally among patients with a greater probability of poor outcomes; for example, the ORs for hospitalization within 14 days were 0.58 (95% CI, 0.48-0.72) among those in the third (moderate) risk stratum and 0.41 (95% CI, 0.32-0.53) among those in the fifth (highest) risk stratum. The association of nMAb treatment with reduced risk of hospitalizations within 14 days was strongest during the Delta variant epoch (OR, 0.37; 95% CI, 0.31-0.43) but not during the Omicron BA.1 epoch (OR, 1.29; 95% CI, 0.68-2.47). These findings were corroborated in the subset of patients with viral genomic data. Treatment with nMAbs was associated with a significant mortality benefit in all variant epochs (pre-Delta: OR, 0.16 [95% CI, 0.08-0.33]; Delta: OR, 0.14 [95% CI, 0.09-0.22]; Delta and Omicron BA.1: OR, 0.10 [95% CI, 0.03-0.35]; and Omicron BA.1: OR, 0.13 [95% CI, 0.02-0.93]). Potential adverse drug events were identified in 38 treated patients (0.2%). Conclusions and Relevance: In this study, nMAb treatment for COVID-19 was safe and associated with reductions in ED visits, hospitalization, and death, although it was not associated with reduced risk of hospitalization during the Omicron BA.1 epoch. These findings suggest that targeted risk stratification strategies may help optimize future nMAb treatment decisions.

Breed and ruminal fraction effects on bacterial and archaeal community composition in sheep.

While the breed of cattle can impact on the composition and structure of microbial communities in the rumen, breed-specific effects on rumen microbial communities have rarely been examined in sheep. In addition, rumen microbial composition can differ between ruminal fractions, and be associated with ruminant feed efficiency and methane emissions. In this study, 16S rRNA amplicon sequencing was used to investigate the effects of breed and ruminal fraction on bacterial and archaeal communities in sheep. Solid, liquid and epithelial rumen samples were obtained from a total of 36 lambs, across 4 different sheep breeds (Cheviot (n = 10), Connemara (n = 6), Lanark (n = 10) and Perth (n = 10)), undergoing detailed measurements of feed efficiency, who were offered a nut based cereal diet ad-libitum supplemented with grass silage. Our results demonstrate that the feed conversion ratio (FCR) was lowest for the Cheviot (most efficient), and highest for the Connemara breed (least efficient). In the solid fraction, bacterial community richness was lowest in the Cheviot breed, while Sharpea azabuensis was most abundant in the Perth breed. Lanark, Cheviot and Perth breeds exhibited a significantly higher abundance of epithelial associated Succiniclasticum compared to the Connemara breed. When comparing ruminal fractions, Campylobacter, Family XIII, Mogibacterium, and Lachnospiraceae UCG-008 were most abundant in the epithelial fraction. Our findings indicate that breed can impact the abundance of specific bacterial taxa in sheep while having little effect on the overall composition of the microbial community. This finding has implications for genetic selection breeding programs aimed at improving feed conversion efficiency of sheep. Furthermore, the variations in the distribution of bacterial species identified between ruminal fractions, notably between solid and epithelial fractions, reveals a rumen fraction bias, which has implications for sheep rumen sampling techniques.

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting adoption and efficacy of clinical pathway. Machine intelligence can potentially identify clinical variation and may provide useful insights to create and optimize clinical pathways. In this quality improvement project we analyzed the inpatient care of 1786 patients undergoing colorectal surgery from 2015 to 2016 across multiple Ohio hospitals in the Cleveland Clinic System. Data from four information subsystems was loaded in the Clinical Variation Management (CVM) application (Ayasdi, Inc., Menlo Park, CA). The CVM application uses machine intelligence and topological data analysis methods to identify groups of similar patients based on the treatment received. We defined "favorable performance" as groups with lower direct variable cost, lower length of stay, and lower 30-day readmissions. The software auto-generated 9 distinct groups of patients based on similarity analysis. Overall, favorable performance was seen with ketorolac use, lower intra-operative fluid use (< 2000 cc) and surgery for cancer. Multiple sub-groups were easily created and analyzed. Adherence reporting tools were easy to use enabling almost real time monitoring. Machine intelligence provided useful insights to create and monitor care pathways with several advantages over traditional analytic approaches including: (1) analysis across disparate data sets, (2) unsupervised discovery, (3) speed and auto-generation of clinical pathways, (4) ease of use by team members, and (5) adherence reporting.

High-level adoption of electronic health records.

The adoption of electronic health records (EHRs) is now a national priority, but evidence shows that the level of typical adoption is low. We describe the goal of "high-level adoption" of an EHR system defined as a community of clinicians using an EHR system to share patient information, and create a summary of a patient's health, through the timely documentation of care, as well as through effective communication among caregivers, with a focus on patient safety and efficient use of resources. There are 10 key principles to achieving high level-adoption. These are: (1) shared medication lists; (2) shared patient problem lists; (3) established guidelines for communication about patient information among clinicians; (4) use of standard terminology; (5) preventive services; (6) integration of external data; (7) computerized provider order entry; (8) regional health information exchange; (9) adoption of clinical decision support; and (10) reuse of data for public health and research.

Advancing performance measurement in oncology: quality oncology practice initiative participation and quality outcomes.

The American health care system, including the cancer care system, is under pressure to improve patient outcomes and lower the cost of care. Government payers have articulated an interest in partnering with the private sector to create learning communities to measure quality and improve the value of health care. In 2006, the American Society for Clinical Oncology (ASCO) unveiled the Quality Oncology Practice Initiative (QOPI), which has become a key component of the measurement system to promote quality cancer care. QOPI is a physician-led, voluntary, practice-based, quality-improvement program, using performance measurement and benchmarking among oncology practices across the United States. Since its inception, ASCO's QOPI has grown steadily to include 973 practices as of November 2010. One key area that QOPI has addressed is end-of-life care. During the most recent data collection cycle in the Fall of 2010, those practices completing multiple data collection cycles had better performance on care of pain compared with sites participating for the first time (62.61% v 46.89%). Similarly, repeat QOPI participants demonstrated meaningfully better performance than their peers in the rate of documenting discussions of hospice and palliative care (62.42% v 54.65%) and higher rates of hospice enrollment. QOPI demonstrates how a strong performance measurement program can lead to improved quality and value of care for patients.

Advancing performance measurement in oncology.

The American healthcare system, including the cancer care system, is under pressure to improve patient outcomes and lower the cost of care. Government payers have articulated an interest in partnering with the private sector to create learning communities to measure quality and improve the value of healthcare. In 2006, the American Society of Clinical Oncology (ASCO) unveiled the Quality Oncology Practice Initiative (QOPI), which has become a key component of the measurement system to promote quality cancer care. QOPI is a physician-led, voluntary, practice-based, quality-improvement program, using performance measurement and benchmarking among oncology practices across the United States. Since its inception, ASCO's QOPI has grown steadily to include 973 practices as of November 2010. One key area that QOPI has addressed is end-of-life care. During the most recent data collection cycle in the fall of 2010, those practices completing multiple data collection cycles had better performance on care of pain compared with sites participating for the first time (62.61% vs 46.89%). Similarly, repeat QOPI participants demonstrated meaningfully better performance than their peers in the rate of documenting discussions of hospice and palliative care (62.42% vs 54.65%) and higher rates of hospice enrollment. QOPI demonstrates how a strong performance measurement program can lead to improved quality and value of care for patients.

Factors determining the persistence or recurrence of well-differentiated thyroid cancer treated by thyroidectomy and/or radioiodine in the Boston, Massachusetts area: A retrospective chart review.

OBJECTIVE: To assess predictors of well-differentiated thyroid cancer (WDTC) persistence/recurrence. DESIGN: This was a retrospective chart review of thyroid carcinoma patients seen 1979-2007 in a Boston, Massachusetts-area multispecialty group. Of 1,025 patients, 431 met eligibility criteria. Cox proportional hazards models were used to assess predictors (gender, age, ethnicity, tumor size, surgical histology) of WDTC persistence/recurrence (elevated thyroglobulin levels with negative thyroglobulin-antibodies; or positive imaging). Local extension of disease and lymph node involvement could not be assessed. RESULTS: Mean age at initial surgery (n = 431, 74% women, 79% Caucasian) was 45.8 ± 13.5(SD) years. Mean tumor (papillary, 91%; follicular, 5%; Hurthle cell, 2%; ≥1 type, 2%) size was 2.5 ± 1.6(SD) cm. Most tumors were unifocal (57%) and ≥1 cm (89%). Over 2,600 person-years of follow-up, persistence/recurrence occurred in 52 patients (12%) 4.3 years (median; range 0.2-23.2 years) after surgery. Gender, ethnicity, tumor size, multifocality, and histology were not predictive of persistence/recurrence, while older age was predictive in some models. CONCLUSIONS: In WDTC patients treated by total and near total thyroidectomy and radioiodine and analyzed without consideration of local, locoregional, and distant extent of disease, neither size of tumor nor male gender contribute to disease persistence/recurrence. Age at diagnosis seems to have some positive prognostic value even if only patients older than 21 years at diagnosis are considered. Due to the rare occurrence of follicular (also oxyphilic) histotype, this conclusion refers mainly to patients with papillary thyroid cancer.

Reducing the prescribing of heavily marketed medications: a randomized controlled trial.

CONTEXT: Prescription drug costs are a major component of health care expenditures, yet resources to support evidence-based prescribing are not widely available. OBJECTIVE: To evaluate the effectiveness of computerized prescribing alerts, with or without physician-led group educational sessions, to reduce the prescribing of heavily marketed hypnotic medications. DESIGN: Cluster-randomized controlled trial. SETTING: We randomly allocated 14 internal medicine practice sites to receive usual care, computerized prescribing alerts alone, or alerts plus group educational sessions. MEASUREMENTS: Proportion of heavily marketed hypnotics prescribed before and after the implementation of computerized alerts and educational sessions. MAIN RESULTS: The activation of computerized alerts held the prescribing of heavily marketed hypnotic medications at pre-intervention levels in both the alert-only group (adjusted risk ratio [RR] 0.97; 95% CI 0.82-1.14) and the alert-plus-education group (RR 0.98; 95% CI 0.83-1.17) while the usual-care group experienced an increase in prescribing (RR 1.31; 95% CI 1.08-1.60). Compared to the usual-care group, the relative risk of prescribing heavily marketed medications was less in both the alert-group (Ratio of risk ratios [RRR] 0.74; 95% CI 0.57-0.96) and the alert-plus-education group (RRR 0.74; 95% CI 0.58-0.97). The prescribing of heavily marketed medications was similar in the alert-group and alert-plus-education group (RRR 1.02; 95% CI 0.80-1.29). Most clinicians reported that the alerts provided useful prescribing information (88%) and did not interfere with daily workflow (70%). CONCLUSIONS: Computerized decision support is an effective tool to reduce the prescribing of heavily marketed hypnotic medications in ambulatory care settings. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00788346.

Electronic Support for Public Health: validated case finding and reporting for notifiable diseases using electronic medical data.

Health care providers are legally obliged to report cases of specified diseases to public health authorities, but existing manual, provider-initiated reporting systems generally result in incomplete, error-prone, and tardy information flow. Automated laboratory-based reports are more likely accurate and timely, but lack clinical information and treatment details. Here, we describe the Electronic Support for Public Health (ESP) application, a robust, automated, secure, portable public health detection and messaging system for cases of notifiable diseases. The ESP application applies disease specific logic to any complete source of electronic medical data in a fully automated process, and supports an optional case management workflow system for case notification control. All relevant clinical, laboratory and demographic details are securely transferred to the local health authority as an HL7 message. The ESP application has operated continuously in production mode since January 2007, applying rigorously validated case identification logic to ambulatory EMR data from more than 600,000 patients. Source code for this highly interoperable application is freely available under an approved open-source license at http://esphealth.org.

Clinician attitudes towards prescribing and implications for interventions in a multi-specialty group practice.

BACKGROUND: Prescribing decisions are subject to a myriad of external forces, including patient requests for advertised medications. Although numerous factors influence prescribing, resources to support unbiased evidence-based prescribing are not widely available. METHODS: To guide future interventions, we surveyed clinicians about influences on prescribing, awareness of pharmaceutical costs and attitudes towards computerized decision support. A 21-item survey was sent to 604 prescribing clinicians in a large multi-specialty group practice that employs a robust electronic medical record. RESULTS: Surveys were returned from 405 clinicians (67%). Most respondents (87%) felt that direct-to-consumer (DTC) advertising prompts patients to request inappropriate medications, and more than one in five clinicians (22%) reported difficulty declining patients' requests for advertised medications. Providers with more clinical sessions per week reported greater difficulty. Although 93% of clinicians felt they have access to the information needed to guide prescribing, only about half (54%) reported they are aware of how much patients pay for prescription medications. Clinicians' awareness of medication costs varied considerably by specialty, with behavioural health clinicians being the most aware. The majority of providers (79%) stated that computerized prescribing alerts are a clinically useful source of information. CONCLUSIONS: Although the majority of clinicians reported that DTC advertising leads many patients to request medications that are inappropriate for their condition, a sizable proportion of clinicians reported difficulty declining these requests, and many are unaware of medication costs. Interventions to support prescribing decisions should provide the busiest clinicians with up-to-date, specialty-specific evidence and cost information.

Improving quality of care using a diabetes registry and disease management services in an integrated delivery network.

Disease management for chronic conditions is a call for collaboration among all parties of the health care system. The Caritas Christi Health Care System established a unified American Diabetes Association (ADA) recognized outpatient diabetes self-management education program (DSME) in each of its six hospital communities and has established an Internet data portal with managed care organizations to improve preventive care for thousands of patients with diabetes. This article describes the stepwise process of building the successful Caritas Diabetes Care Program and the central role of the Caritas Diabetes Registry over a 5-year period.