Borah, Bijan J.

26 Background: We previously demonstrated that utilization of a Remote Patient Monitoring (RPM) program – characterized by the use of in-home technology for symptom and vital signs assessments with a centralized care team responding to alerts – is associated with a significant reduction in 30-day hospitalization rate among cancer patients with COVID-19. We have subsequently performed a 90-day comparative cost-of-care analysis in this prospectively enrolled, validated cohort of 71 patients who received RPM and 116 patients who received usual care without RPM. Methods: Primary outcomes included 90-day all-cause costs (categorized as hospital and outpatient costs) following the index date (date of COVID-19 diagnosis). Differences in patient characteristics and baseline costs (incurred 90 days prior to index date) were determined using Standardized Differences and controlled for using Inverse Probability Weighting (IPW). IPW balancing was based on baseline covariates known to be associated with poorer COVID-19 outcomes, as previously described. Association of costs with RPM was examined by generalized linear modeling while adjusting for relevant variables. Outcomes are reported as the average treatment effect on the treated (ATET). Results: Differences in patient characteristics and baseline costs were well-balanced following IPW modeling. The index ATET was found to be comparable among patients receiving RPM and usual care on the date of COVID-19 diagnosis -$89.75 (95% CI: -$144.33 to $323.84; p = 0.452). However, patients receiving RPM experienced a 90-day ATET of -$6,994 (95% CI: -$14,635 to $646; p = 0.073) when compared with patients receiving usual care. Conclusions: There was a trend towards decreased 90-day all-cause costs for cancer patients with COVID-19 who utilized the RPM program as compared with usual care. Larger studies are needed to understand the true cost (and cost savings) associated with this innovative model of care delivery which can be leveraged for cancer care beyond COVID-19.

Objectives Pancreas transplant is the only treatment that establishes normal glucose levels for patients diagnosed with diabetes. We analyzed the outcome of pancreas transplant alone (PTA) versus standard of care in the United States from 2008 to 2018. We also developed an economic model to analyze the cost-effectiveness of pancreas transplant versus continuing standard of care. Methods We used the Scientific Registry of Transplant Recipients database and analyzed PTA recipient survival. Using those results, we developed a Markov model that followed a cohort of 40-year-old patients with type 1 diabetes over a 10-year time horizon. The primary outcomes were (i) the survival benefit of a pancreas transplant, (ii) quality-adjusted life-years (QALYs), and (iii) total costs. Results We found no difference in survival advantage of PTA compared with standard of care (hazard ratio, 1.09; 95% confidence interval, 0.56–2.14). However, pancreas transplant ($172,823, 6.87 QALY) was cost-saving compared with standard of care ($232,897, 6.04 QALY) for type 1 diabetes. Pancreas transplantation was cost-effective in 95% of 10,000 simulations in probabilistic sensitivity analysis, using a $100,000/QALY willingness-to-pay threshold. Conclusions Although there is no difference in survival for PTA compared with standard of care, PTA is a cost-saving therapy for type 1 diabetes.

1512 Background: In response to the COVID-19 pandemic, many cancer practices adopted telehealth, including telephone and video appointments. Following a period of initial expansion that began in March 2020, sustained telehealth integration has emerged across the Mayo Clinic Cancer Practice (MCCP) in 2021. The primary objective of this study was to identify factors associated with utilization of telehealth appointments. Methods: A cross-sectional, multi-site, retrospective analysis was conducted across MCCP – a multisite, multiregional cancer practice with tertiary referral campuses in Minnesota, Florida, and Arizona, as well as rural, community-based hospitals and clinics throughout the Upper Midwest. Multivariable models were used to examine the association of patient- and provider-level variables with telehealth utilization. Results: Outpatient appointments conducted in July – August 2019 (n = 32,932) were compared with those from 2020 (n = 33,662) and 2021 (n = 35,486). The rate of telehealth appointment utilization increased from <0.01% in 2019 to 11.0% in 2020 and 14.0% in 2021. The strongest provider-level predictor of telehealth utilization was female physician provider type (OR 1.06, 95% CI 1.01 to 1.11; P = 0.0297), a trend consistently observed across career stages, practice locations and settings in 2020 and 2021. Additionally, while the rate of telehealth utilization was not significantly different at referral and community-based campuses in 2020, providers at referral campuses were significantly more likely to utilize telehealth than community-based campuses in 2021 (OR 1.1, 95% CI 1.01 to 1.12; P = 0.0289). Regarding patient-level factors, rural residence (defined by Rural-Urban Commuting Area codes), which accounted for 44.2% of the patient population, was significantly associated with lower telehealth utilization as compared to patients with urban residences, particularly for video appointments (OR 1.04, 95% CI 1.02 to 1.07; P < 0.0001). Notably, the disparity in telehealth utilization between rural and urban populations was found to be less pronounced in 2021 as compared to 2020. Conclusions: Multivariable analysis across a multi-site, multi-regional cancer practice identified several factors associated with increased telehealth utilization. These included female physician provider type, referral-based campuses, and patients residing in urban settings. A detailed understanding of the factors that influence telehealth utilization – a method of care delivery which represents a “new normal” across many cancer practices – will be essential to enable continued equitable access to high-quality, high-impact, patient-centered cancer care.

AbstractObjectiveTo assess the effect of Enhanced Recovery After Surgery (ERAS) with and without liposomal bupivacaine (LB) on opioid use, hospital length of stay (LOS), costs, and morbidity of women undergoing sacrocolpopexy.MethodsRetrospective cohort of women who underwent abdominal sacrocolpopexy between April 1, 2009 and November 30, 2017. Costs for relevant healthcare services were determined by assigning 2017 charges multiplied by 2017 Medicare Cost Report's cost to charge ratios. Outcomes were compared among periods with multivariable regression models adjusted for age, American Society of Anesthesiologists score, and concurrent hysterectomy and posterior repair.ResultsPatients were subdivided into pre‐ERAS (G1, n = 128), post‐ERAS (G2, n = 83), and post‐ERAS plus LB (G3, n = 91). The proportion of patients needing opioids during postoperative days 0–2 was significantly less for G3 (75.8%) compared with G1 (97.7%) and G2 (92.8%); P < 0.001). The median morphine equivalent units (MEU) with interquartile ranges, mean LOS, and adjusted mean standardized costs were significantly lower in G3 compared with the other two groups (35 [20–75] vs. 67 [31–109], and 60 [30–122] MEUs; 1.8 vs. 2.3 vs. 2.9 days; and $2391, $2975, and $3844, for G3, G2, and G1, respectively; P < 0.001).ConclusionImplementation of an ERAS pathway led to significant decreases in opioid use, LOS, and costs. Supplementation with LB further improved these measures.

AbstractThe legitimacy of findings from cancer health economics research depends on study design and methods. A breakout session, Methods and Study Design for Cancer Health Economics Research, was convened at the Future of Cancer Health Economics Research Conference to discuss 2 commonly used analytic tools for cancer health economics research: observational studies and decision-analytic modeling. Observational studies include analysis of data collected with the primary purpose of supporting economic evaluation or secondary use of data collected for another purpose. Modeling studies develop a parametrized structure, such as a decision tree, to estimate hypothetical impact. Whereas observational studies focus on what has happened and why, modeling studies address what may happen. We summarize the discussion at this breakout session, focusing on 3 key elements of high-quality cancer health economics research: study design, analytical methods, and addressing uncertainty.

Background: Heart failure (HF) with improved ejection fraction (HFiEF) is defined as a new subset in addition to the subsets of HF with reduced EF (≤40%; HFrEF) and HF with preserved EF (>40%). The characteristics and outcomes of patients with ischemic HFiEF after coronary revascularization are unknown. Methods and Results: We identified 1,816 patients with coronary artery diseases and reduced EF (≤40%) in a large hospital in China, who underwent either coronary artery bypass graft surgery (n=1,126) or percutaneous coronary intervention with drug eluting stent (n=687) from January 2005 to December 2014. All patients had EF reassessment during follow-up. The improvement of EF peaked at 3 months and persisted for more than one year after revascularization. Thus, 589 patients who had EF reassessment between 3 to 12 months after revascularization were included in the definitions of HFiEF and HFrEF patient cohort and subsequent survival analysis. 367 (62.3%) patients had EF >40% which constituted HFiEF cohort while 222 (37.7%) patients with EF≤40% constituted HFrEF cohort. Using stepwise logistic regression, we identified preoperative EF (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.02-1.11; P =0.006), left ventricular end-systolic diameter (OR, 0.93; 95% CI, 0.91-0.95; P =0.000) and history of myocardial infarction (OR, 0.61; 95% CI, 0.42-0.88; P =0.008) were associated with being in HFiEF group. Using Cox proportional hazard models, HFiEF patients had lower risk of all-cause mortality (hazard ratios [HR], 0.39; 95% CI, 0.23-0.68; P =0.001), sudden cardiac death (HR, 0.32; 95% CI, 0.14-0.75; P =0.009) and death due to HF (HR, 0.13; 95% CI, 0.03-0.52; P =0.004) compared to HFrEF patients. Nonproportional outcome of composite end points (i.e., combination of all-cause mortality, HF hospitalization and repeat revascularization) between HFiEF and HFrEF groups was detected by using the Schoenfeld residuals. By using flexible parametric model, patients with HFiEF had similar risk of composite end points in the first 5 years (HR, 0.84; 95% CI, 0.53-1.31; P =0.433), but significantly lower risk during years 6 to 11(HR, 0.12; 95% CI, 0.03-0.56; P =0.007). In the HFiEF group, the risk of death were age (HR, 1.07; 95% CI, 1.02-1.12; P =0.008), current smoking (HR, 5.62; 95% CI, 1.93-18.18; P =0.002) and history of myocardial infarction (HR, 2.89; 95% CI, 1.05-7.93; P =0.039). By contrast, in the HFrEF group, the risk factors were moderate or severe mitral regurgitation (HR, 1.81; 95% CI, 1.06-3.11; P =0.031) and estimated glomerular filtration rate (HR, 0.97; 95% CI, 0.95-0.99; P=0.014). Conclusions: Patients with ischemic HFiEF have distinct clinical characteristics and favorable outcomes. Our data support HFiEF as a distinct HF category in ischemic HF patients after revascularization therapy.