Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Comparing outcomes and costs among warfarin-sensitive patients versus warfarin-insensitive patients using The Right Drug, Right Dose, Right Time: Using genomic data to individualize treatment (RIGHT) 10K warfarin cohort.

Oral anticoagulant (OAC) therapy has been the main treatment approach for stroke prevention for decades. Warfarin is the most widely prescribed OAC in the United States, but is difficult to manage due to variability in dose requirements across individuals. Pharmacogenomics may mitigate risk concerns related to warfarin use by fostering the opportunity to facilitate individualized medicine approaches to warfarin treatment (e.g., genome-guided dosing). While various economic evaluations exist examining the cost-effectiveness of pharmacogenomics testing for warfarin, few observational studies exist to support these studies, with even fewer using genotype as the main exposure of interest. We examined a cohort of individuals initiating warfarin therapy between 2004 and 2017 and examined bleeding and cost outcomes for the year following initiation using Mayo Clinic's billing and administrative data, as well the Mayo Clinic Rochester Cost Data Warehouse. Analyses included descriptive summaries, comparison of characteristics across exposure groups, reporting of crude outcomes, and multivariate analyses. We included N = 1,143 patients for analyses. Just over a third of our study population (34.9%) carried a warfarin-sensitive phenotype. Sensitive individuals differed in their baseline characteristics by being of older age and having a higher number of comorbid conditions; myocardial infarction, diabetes, and cancer in particular. The occurrence of bleeding events was not significantly different across exposure groups. No significant differences across exposure groups existed in either the likelihood of incurring all-cause healthcare costs or in the magnitude of those costs. Warfarin-sensitive individuals were no more likely to utilize cardiovascular-related healthcare services; however, they had lower total and inpatient cardiovascular-related costs compared to warfarin-insensitive patients. No significant differences existed in any other categories of costs. We found limited evidence that warfarin-sensitive individuals have different healthcare spending than warfarin-insensitive individuals. Additional real-world studies are needed to support the traditional economic evaluations currently existing in the literature.

Impact of patient factors and procedure on readmission after aortic dissection admission in the states of Florida and New York.

BACKGROUND: Readmissions after aortic dissection (AD) admission are not well described. Using state-based administrative claims data, we sought to define readmission rates after AD and to identify factors associated with them. METHODS: State Inpatient Databases for Florida (2007-2012) and New York (2008-2012) were queried for AD index admissions. Admissions were stratified by initial treatment strategy: type A open surgery repair (TAOR), type B open surgery repair (TBOR), thoracic endovascular aortic repair (TEVAR), or medical management (MM). All-cause readmission rates were calculated at 30 days, 90 days, and 2 years. Logistic regression was used to identify factors associated with readmission at each time point for all type A admissions (TAOR) or type B admissions (TBOR, TEVAR, MM). RESULTS: We identified 4670 patients with an AD index admission. Treatment was with TAOR in 1031 (22%), TBOR in 761 (16%), TEVAR in 412 (9%), and MM in 2466 (53%). Patients were predominantly male (59.4%) and white (61.9%), with a median age of 66 years. Overall mortality during AD index admission was 14.8% (TAOR, 15.8%; TBOR, 17.1%; TEVAR, 9.0%; MM, 14.7%; P = .002 across all groups). All-cause readmission rates were similar across treatment groups at 30 days (9.6%-11%; P = .56), 90 days (15.2%-20%; P = .26), and 2 years (49.2%-54.4%; P = .15). Higher income quartile (vs lowest) was associated with lower odds of early readmission (at 30 days and 90 days) after type B admissions but not after type A admissions. At 2 years, self-pay (vs Medicare) was associated with lower odds of readmission in both type A and type B admissions, whereas higher comorbidity count and black race (vs white) were associated with higher odds of readmission. TEVAR (vs MM) was also associated with higher odds of readmission. Cardiovascular disease was the most common cause for readmission at all time points. Emergency department readmission counts were highest after MM admissions, and ambulatory surgical admissions were highest after TBOR. Both TEVAR and MM initial costs were lower than TAOR and TBOR costs, but at 2 years, costs remained significantly lower only for MM. CONCLUSIONS: In-state 30-day, 90-day, and 2-year readmission rates after AD were not associated with initial treatment type. Two-year readmissions are common. Strategies to target socioeconomic, race, and geographic factors may reduce variations in readmission patterns after AD admission.

Systematic review of the evidence on the cost-effectiveness of pharmacogenomics-guided treatment for cardiovascular diseases.

PURPOSE: To examine the evidence on the cost-effectiveness of implementing pharmacogenomics (PGx) in cardiovascular disease (CVD) care. METHODS: We conducted a systematic review using multiple databases from inception to 2018. The titles and abstracts of cost-effectiveness studies on PGx-guided treatment in CVD care were screened, and full texts were extracted. RESULTS: We screened 909 studies and included 46 to synthesize. Acute coronary syndrome and atrial fibrillation were the predominantly studied conditions (59%). Most studies (78%) examined warfarin-CYP2C9/VKORC1 or clopidogrel-CYP2C19. A payer's perspective was commonly used (39%) for cost calculations, and most studies (46%) were US-based. The majority (67%) of the studies found PGx testing to be cost-effective in CVD care, but cost-effectiveness varied across drugs and conditions. Two studies examined PGx panel testing, of which one examined pre-emptive testing strategies. CONCLUSION: We found mixed evidence on the cost-effectiveness of PGx in CVD care. Supportive evidence exists for clopidogrel-CYP2C19 and warfarin-CYP2C9/VKORC1, but evidence is limited in other drug-gene combinations. Gaps persist, including unclear explanation of perspective and cost inputs, underreporting of study design elements critical to economic evaluations, and limited examination of PGx panel and pre-emptive testing for their cost-effectiveness. This review identifies the need for further research on economic evaluations of PGx implementation.

Understanding Failure to Rescue After Esophagectomy in the United States.

BACKGROUND: Data on failure to rescue (FTR) after esophagectomy are sparse. We sought to better understand the patient factors associated with FTR and to assess whether FTR is associated with hospital volume. METHODS: We identified all patients undergoing esophagectomy between 2010 and 2014 from the Agency for Healthcare Research and Quality Nationwide Readmission Database. We defined FTR as mortality after a major complication. Multiple logistic regression was used to identify patient factors and hospital-volume associations with FTR. RESULTS: Of 26,820 patients undergoing an esophagectomy, 7130 (26.6%) experienced a major complication. Of those, 1321 did not survive the index hospitalization (FTR rate, 18.5%). Risk factors for FTR included increasing age (adjusted odds ratio [aOR], 1.06; P < .001), congestive heart failure (aOR, 2.07; P < .001), bleeding disorders (aOR, 2.9; P < .001), liver disease (aOR, 2.37; P = .001), and renal failure (aOR, 2.37; P = .002). At the hospital level there was wide variation in FTR rates across hospital volume quintiles, with 21.2% of patients suffering a complication not surviving to discharge at low-volume hospitals compared with 13.4% at high-volume hospitals (P < .001). At low-volume hospitals the highest FTR rates were acute renal failure (35.3%), postoperative hemorrhage (31.9%), and pulmonary failure (28.1%). CONCLUSIONS: One in 5 esophagectomy patients suffering a complication at low-volume hospitals do not survive to discharge. Several patient factors are associated with death after a major complication. Strategies to improve the recognition and management of complications in at-risk patients may be essential to improve outcomes at low-volume hospitals.

Improving Value of Care for Older Adults With Advanced Medical Illness and Functional Decline: Cost Analyses of a Home-Based Palliative Care Program.

CONTEXT: Identifying high-value health care delivery for patients with clinically complex and high-cost conditions is important for future reimbursement models. OBJECTIVES: The objective of this study was to assess the Medicare reimbursement savings of an established palliative care homebound program. METHODS: This is a retrospective cohort study involving 50 participants enrolled in a palliative care homebound program and 95 propensity-matched control patients at Mayo Clinic in Rochester, Minnesota, between September 1, 2012, and March 31, 2013. Total Medicare reimbursement was compared in the year before enrollment with the year after enrollment for participants and controls. RESULTS: No significant differences were observed in demographic characteristics or prognostic indices between the two groups. Total Medicare reimbursement per program participant the year before program enrollment was $16,429 compared with $14,427 per control patient, resulting in $2004 higher charges per program patient. In 12 months after program enrollment, mean annual payment was $5783 per patient among participants and $22,031 per patient among the matched controls. In the second year, the intervention group had a decrease of $10,646 per patient; the control group had an increase of $7604 per patient. The difference between the participant group and control group was statistically significant (P < 0.001) and favored the palliative care homebound program enrollees by $18,251 (95% CI, $11,268-$25,234). CONCLUSION: The Mayo Clinic Palliative Care Homebound Program reduced annual Medicare expenditures by $18,251 per program participant compared with matched control patients. This supports the role of home-based palliative medicine in delivering high-value care to high-risk older adults.