A genomic-augmented multivariate prognostic model for the survival of natural-killer/T-cell lymphoma patients from an international cohort.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57-10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell's C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, χ2 test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.

The economic burden of kidney graft failure in the United States.

Despite improvements in outcomes for kidney transplant recipients in the past decade, graft failure continues to impose substantial burden on patients. However, the population-wide economic burden of graft failure has not been quantified. This study aims to fill that gap by comparing outcomes from a simulation model of kidney transplant patients in which patients are at risk for graft failure with an alternative simulation in which the risk of graft failure is assumed to be zero. Transitions through the model were estimated using Scientific Registry of Transplant Recipients data from 1987 to 2017. We estimated lifetime costs, overall survival, and quality-adjusted life-years (QALYs) for both scenarios and calculated the difference between them to obtain the burden of graft failure. We find that for the average patient, graft failure will impose additional medical costs of $78 079 (95% confidence interval [CI] $41 074, $112 409) and a loss of 1.66 QALYs (95% CI 1.15, 2.18). Given 17 644 kidney transplants in 2017, the total incremental lifetime medical costs associated with graft failure is $1.38B (95% CI $725M, $1.98B) and the total QALY loss is 29 289 (95% CI 20 291, 38 464). Efforts to reduce the incidence of graft failure or to mitigate its impact are urgently needed.

Evaluating the financial impact of utilizing recombinant porcine factor VIII or recombinant FVIIa for patients with acquired hemophilia A.

OBJECTIVE: To evaluate the financial impact of utilizing rpFVIII or rFVIIa during a hospital admission for the diagnosis of acquired hemophilia A (AHA) by reviewing the margin between the cost to the hospital for providing care and the amount the hospital is reimbursed by the Centers for Medicare & Medicaid Services (CMS) in the US. METHODS: Data source was the Medicare Limited Data Set, which contains claims for hospitalizations, charges, and amounts reimbursed by CMS. Study patients were hospitalized with AHA and treated with rpFVIII and/or rFVIIa between 1/1/2015 and 12/31/2019. CMS Fiscal Year 2020 Impact Files, with hospital-level cost-to-charge ratios (CCRs), were used to estimate hospital costs. Sensitivity analyses were conducted to estimate margins at different CCRs. RESULTS: Hospital margins were, on average, positive with use of either rpFVIII or rFVIIa (rpFVIII: $51,548.89; rFVIIa: $35,943.80). Sensitivity analysis results suggest that the use of rpFVIII is similiar, compared with rFVIIa for a large majority of hospitals. CONCLUSIONS: While there may be higher reimbursement for rpFVIII hospitalizations, this analysis suggests that the use of rpFVIII, compared to rFVIIa, may have no impact on hospital finances for the majority of hospitals, despite rpFVIII's higher per unit cost.

Simulating the Fiscal Impact of Anti-Obesity Medications as an Obesity Reduction Strategy.

While substantial public health investment in anti-smoking initiatives has had demonstrated benefits on health and fiscal outcomes, similar investment in reducing obesity has not been undertaken, despite the substantial burden obesity places on society. Anti-obesity medications (AOMs) are poorly prescribed despite evidence that weight loss is not sustained using other strategies alone.We used a simulation model to estimate the potential impact of 100% uptake of AOMs on Medicare and Medicaid spending, disability payments, and taxes collected relative to status quo with negligible AOM use. Relative to status quo, AOM use simulation would result in Medicare and Medicaid savings of $231.5 billion and $188.8 billion respectively over 75 years. Government tax revenues would increase by $452.8 billion. Overall, the net benefit would be $746.6 billion. Anti-smoking efforts have had substantial benefits for society. A similar investment in obesity reduction, including broad use of AOMs, should be considered.

Changes in mortality associated with cancer drug approvals in the United States from 2000 to 2016.

AIMS: To estimate the extent to which the approvals of new pharmacological therapies were associated with cancer mortality in the USA between 2000 and 2016. MATERIALS AND METHODS: The analysis quantified cancer drug approvals across the 15 tumor types with the highest incidence. Number of approvals in a given time period for each tumor was translated into a treatment stock measure, defined as a weighted sum of new indication approvals since 1976. The primary outcome was the annual tumor-specific cancer mortality, defined as the number of deaths per 100,000 U.S. population. The analysis used a multivariable ordinary least squares and a fixed effects model, controlling for incidence (new cases per 100,000 U.S. population) and the primary exposure, the treatment stock measure by year. RESULTS: Between 2000 and 2016, deaths per 100,000 population across the 15 most common tumor types declined by 24%. Additionally, 10.2 new indications were approved per year across the 15 most common tumor types. Cancer drug approvals were associated with statistically significant deaths averted in 2016 for colorectal cancer (4,991, p = 0.004), lung cancer (33,825, p < 0.001), breast cancer (11,502, p < 0.001), non-Hodgkin's lymphoma (6,636, p < 0.001), leukemia (4,011, p < 0.001), melanoma (1,714, p < 0.001), gastric cancer (758, p = 0.019), and renal cancer (739, p < 0.001). Between 2000 and 2016, new cancer treatments were correlated with 1,291,769 (p < 0.001) total deaths prevented across the 15 most common tumor types. LIMITATIONS AND CONCLUSIONS: Cancer drug approvals between 2000 and 2016 were associated with significant reduction in deaths from the most common cancers in the USA. Mortality changes were largest in prevalent tumor types with relatively more approvals, i.e. lung cancer, breast cancer, melanoma, lymphoma and leukemia. Future research evaluating the relationship between drug approvals and cancer mortality post 2016 is needed.

Risk factors and costs associated with 30-day readmissions following alcohol-related hospitalizations in the United States from 2010 to 2015.

Patients with alcohol-related diagnoses at initial hospitalization are at high risk of 30-day readmission. Understanding risk factors for 30-day readmission among these patients may help to identify those who would benefit from efforts to reduce risk of readmission. The Nationwide Readmissions Database was used to estimate 30-day all-cause readmissions among United States patients with an alcohol-related index hospitalization and to evaluate risk factors and costs associated with these readmissions. Included patients were 18 years of age or older at initial hospitalization, had an alcohol-related primary diagnosis (based on ICD-9-CM codes), and were discharged between 2010 and 2015. They were followed for 30 days after initial hospitalization within the calendar year to identify all-cause readmissions. A logistic regression analysis assessed the association between risk factors and 30-day readmission. Average costs of initial admissions and readmissions were estimated. Among 113,931,723 adult index hospitalizations, 1,124,228 had alcohol-related diagnoses. Patients had a mean age of 49 years, 73% were male, and 45% had public insurance coverage. The annual rate of 30-day readmissions among patients with index alcohol-related hospitalizations increased from 119 readmissions per 1000 admissions in 2010 to 140 per 1000 in 2015, while the rate of readmissions among patients with all-cause hospitalizations declined from 103 to 98 per 1000. The regression analysis suggested that age, male sex, comorbid conditions, discharge against medical advice, admission to large and teaching hospitals, and Medicaid vs. non-Medicaid payment were all risk factors for 30-day readmission. Mean costs of initial alcohol-related hospitalizations were greater among those with a 30-day readmission than without a 30-day readmission, and the mean cost of 30-day readmission was even greater. Mitigating the upward trend in rates of readmission following alcohol-related initial hospitalizations may be addressed through better identification of high-risk patients who are admitted with an alcohol-related diagnosis and greater use of existing evidence-based psychosocial and pharmacotherapy treatment methods.

Modeling the impact of patient treatment preference on health outcomes in relapsing-remitting multiple sclerosis.

Aims: Model how moving from current disease-modifying drug (DMD) prescribing patterns for relapsing-remitting multiple sclerosis (RRMS) observed in the United Kingdom (UK) to prescribing patterns based on patient preferences would impact health outcomes over time.Materials and methods: A cohort-based Markov model was used to measure the effect of DMDs on long-term health outcomes for individuals with RRMS. Data from a discrete choice experiment were used to estimate the market shares of DMDs based on patient preferences (i.e. preference shares). These preference shares and real-world UK market shares were used to calculate the effect of prescribing behavior on relapses, disability progression, and quality-adjusted life-years (QALYs). The incremental benefit of patient-centered prescribing over current practices for the UK RRMS population was then estimated; scenario and sensitivity analyses were also conducted.Results: Compared to current prescribing practices, when UK patients with RRMS were treated following patient preferences, health outcomes were improved. This population was expected to experience 501,690 relapses and gain 1,003,263 discounted QALYs over 50 years under patient-centered prescribing practices compared to 538,417 relapses and 958,792 discounted QALYs under current practices (-6.8% and +4.6%, respectively). Additionally, less disability progression was observed when prescribed treatment was based on patient preferences. In a scenario analysis where only oral treatments were considered, the results were similar, although the magnitude of benefit was smaller. Number of relapses was most sensitive to how the annualized relapse rate was modeled; disability progression was most sensitive to mortality rate assumptions.Limitations: Treatment efficacy estimates applied to various models in this study were based on data derived from clinical trials, rather than real-world data; the impact of patient-centered prescribing on treatment adherence and/or switching was not modeled.Conclusions: The population of UK RRMS patients may experience overall health gains if patient preferences are better incorporated into prescribing practices.

The potential impact of CAR T-cell treatment delays on society.

OBJECTIVES: To date, breakthrough chimeric antigen receptor (CAR) T-cell therapies, such as tisagenlecleucel, indicated for pediatric acute lymphoblastic leukemia (pALL) and diffuse large B-cell lymphoma (DLBCL), and axicabtagene ciloleucel, indicated for DLBCL, although clinically effective, have been limited by treatment delays. Our study measured the social value of CAR T-cell therapy (CAR T) for relapsed or refractory pALL and DLBCL in the United States and quantified social value lost due to treatment delays. STUDY DESIGN: We used an economic framework for therapy valuation, measuring social value as the sum of consumer surplus and manufacturer profit. Consumer surplus is the difference between the value of health gains from a therapy and its incremental cost, while accounting for indirect costs and benefits to patients. METHODS: For 20 incident cohorts of pALL (n = 20 × 400 = 8000) and DLBCL (n = 20 × 5902 = 118,040), we quantified patient value, calculated as the value of additional quality-adjusted life-years gained with CAR T, minus the incremental cost of CAR T compared with standard of care (SOC). We calculated manufacturer profits using a range of production costs given uncertainties in the production process. Patient value and manufacturer profits were summed to obtain total social value. We measured social value lost from treatment delays, assuming that patients received the SOC while awaiting CAR T-cell treatment. RESULTS: Depending on production costs, as much as $6.5 billion and $34.8 billion in social value was generated for patients with pALL and DLBCL, respectively. However, with 1, 2, or 6 months of treatment delay (assuming $200,000 production costs), the pALL population lost 9.8%, 36.2%, and 67.3% of social value, respectively, whereas the DLBCL population lost 4.2%, 11.5%, and 46.0%, relative to no delay. CONCLUSIONS: The social value of CAR T is significantly limited by treatment delays. Efficient payment mechanisms, adequate capital, and payment policy reform are urgently needed to increase patient access and maximize the value of CAR T.