Owner experience and veterinary involvement with unlicensed GS-441524 treatment of feline infectious peritonitis: a prospective cohort study

IntroductionFeline Infectious Peritonitis (FIP) has historically been a fatal coronavirus disease in cats. In recent years, the therapeutic agent GS-441524, developed by Gilead Sciences, was found to be a successful treatment for FIP in most patients in clinical trials. However, this particular drug has remained stalled in the therapeutic pipeline, leaving patients and cat owners without a licensed medication. In the meantime, online social media platforms began to emerge, connecting cat owners with a community of citizen non-veterinary professionals sourcing unlicensed GS-441524.MethodsThis study prospectively followed participants (N = 141) that successfully completed 12 weeks of treatment, capturing their treatment experiences with self-administered GS-441524-like medication. A one-time survey was administered to enrolled participants with mixed format of questions (open-ended and multiple-choice) asking about treatment administration techniques, observed side effects of GS-441524, accrued cost, veterinarian involvement, impact on the cat-human bond, and social media usage.ResultsOur results show cat owners experienced a shift in treatment modality from injectable GS-441524 to pill formulation across the treatment period. The average total cost of medication has decreased since 2021 to approximately USD 3100, and participants reported the human–animal bond being affected negatively. Additionally, there was an increased trend in veterinarian awareness of GS-441524-like therapeutics and monitoring of clients undergoing treatment. Social media usage was reported as being important at the beginning of treatment to establish treatment administration but lessened by the end of treatment.DiscussionThis study is the first detailed, prospective account of owner experiences with unlicensed GS-441524, raising an important discussion surrounding citizen veterinary medicine

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Evaluating comorbidities in total hip and knee arthroplasty: available instruments

Each year millions of patients are treated for joint pain with total joint arthroplasty, and the numbers are expected to rise. Comorbid disease is known to influence the outcome of total joint arthroplasty, and its documentation is therefore of utmost importance in clinical evaluation of the individual patient as well as in research. In this paper, we examine the various methods for obtaining and assessing comorbidity information for patients undergoing joint replacement. Multiple instruments are reliable and validated for this purpose, such as the Charlson Index, Index of Coexistent Disease, and the Functional Comorbidity Index. In orthopedic studies, the Charnley classification and the American Society of Anesthesiologists physical function score (ASA) are widely used. We recommend that a well-documented comorbidity index that incorporates some aspect of mental health is used along with other appropriate instruments to objectively assess the preoperative status of the patient

Medicaid Payer Status Is Associated with In-Hospital Morbidity and Resource Utilization Following Primary Total Joint Arthroplasty

Background: Previous reports suggest that there are major disparities in outcomes following total joint arthroplasty among patients with different payer statuses. The explanation for these differences is largely unknown and may result from confounding variables. The Affordable Care Act expansion of Medicaid coverage in 2014 makes the examination of these disparities particularly relevant. Methods: The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database was used to identify patients who had undergone primary hip or knee arthroplasty from 2002 through 2011. Complications, costs, and length of hospital stay for patients with Medicaid were compared with those for non-Medicaid patients. Each Medicaid patient was matched to a non-Medicaid patient according to age, sex, race, type of total joint arthroplasty, procedure year, hospital characteristics, smoking status, and all twenty-nine comorbidities defined in the NIS-modified Elixhauser comorbidity measure. Results: It was determined that 191,911 patients who underwent total joint arthroplasty had Medicaid payer status (2.8% of the entire total joint arthroplasty population), and 107,335 (56%) of these Medicaid patients were matched one to one to a non-Medicaid patient for all variables for the adjusted analysis. After matching, Medicaid patients were found to have a higher prevalence of postoperative in-hospital infection (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.3 to 2.1), wound dehiscence (OR, 2.2; 95% CI, 1.4 to 3.4), and hematoma or seroma (OR, 1.3; 95% CI, 1.2 to 1.4) but a lower risk of cardiac complications (OR, 0.7; CI, 0.6 to 0.9). The length of the hospital stay was longer, total cost was higher, and discharge to an inpatient facility was more frequent for patients with Medicaid status (p < 0.01). Conclusions: Compared with non-Medicaid patients, Medicaid patients have a significantly higher risk for certain postoperative in-hospital complications and consume more resources following total joint arthroplasty even when the two groups have been matched for patient-related factors and comorbid conditions commonly associated with low socioeconomic status. Additional work is needed to understand the complex interplay between socioeconomic status and outcomes, to ensure appropriate resources are allocated to maintain access for this patient population, and to develop appropriate risk stratification