Objectives: To assess the cost-effectiveness of Abiraterone Acetate plus Prednisone (A-P) compared with Cabazitaxel plus Prednisone (C-P) in Dominican Republic, in patients with Metastatic Castration-Resistant Prostate Cancer (mCRPC) that have failed to chemotherapy with Docetaxel. MethOds: A three-health state cohort simulation Markov Model (progression-free, post-progression and death) was developed based on overall and progression free survival data. The time frame was 10 years. The perspective was that of the Public System of Health of Dominican Republic. The health outcomes of interest were Quality Adjusted Life Years (QALYs) and Life Years (LYs). Efficacy data was taken from clinical trials (COU-AA-301 for A-P and TROPIC for C-P). Utilities for health states and negative utilities for adverse events were estimated based on quality of life endpoints of the COU-AA-301 trial. The base year was 2012. All costs are presented in Dominican currency (Dominican Pesos -RD$). Costs and outcomes were discounted at 5%. Probabilistic sensitivity (PSA) analysis was performed to evaluate uncertainty surrounding the parameters. Results: A-P resulted in 0.79 QALYs and 1.35 LYs, per patient, respectively. C-P resulted in 0.71 QALYs and 1.28 LYs, per patient, respectively. Mean total costs per patient were: RD$ 2.204.289 for A-P and RD$ 2.732.365 for C-P. The results of the probabilistic sensitivity analysis showed that, when compared with C-Z, A-P was found dominant (associated with reduced costs and increased QALYs) in the majority of the iterations. A-P had a 75% probability of being cost effective, independent of the willingness to pay, when compared to C-P. cOnclusiOns: A-P can be considered cost-saving (dominant), when compared with C-P, in patients with Metastatic Castration-Resistant Prostate Cancer that have failed to chemotherapy with Docetaxel, from the perspective of the Public System of Health of Dominican Republic.

Objectives: To estimate cost of a sickle cell (SC) crisis, describe setting of care, and type of crisis; and compare costs of sequential crises among adult SC patients. Methods: We used a large US health plan claims database 2 Truven Commercial & Medicare Supplemental. Patients selected had $2 SC claims, presence of SC crisis between 2009-2016 (i.e., SC crisis diagnosis in emergency department [ED] or hospitalization), age $18y at index (i.e., first crisis), 1-year pre-and post-index continuous enrollment, and no crisis during 1-year pre-index period. Healthcare setting, type of crisis, and medical and pharmacy costs during the crisis encounter were measured. Costs of sequential crises were restricted to patients with fee-for-service insurance, adjusted for 2017 inflation, controlled for age, gender and comorbidities, and were compared using gamma models. Results: There were 1,583 patients (1,234 fee-for-service), mean (SD) age 38y ( 14) and 58% female. Mean number of crises during 1-year post-index was 1.9. Average time between crises was 4.2 months. Number of patients with 1+, 2+, 3+ and 4+ crises within 1 year were 1,583 (100%), 679 (43%), 306 (19%) and 160 (10%), respectively. Among these, 52%, 55%, 59% and 61% were hospitalized for the 1st-4th crises (average length of stay: w7 days), and the rest had ED visits. After one crisis, patients tended to have the same setting of care for their next crisis. Across all crises, majority were pain crises (w63%), followed by acute chest syndrome (w11%). Mean (SE) adjusted costs of 1st-4th crises were $12,685

Objective: Budget impact analysis of teriflunomide inclusion as first-line disease modifying drugs (DMD) therapy in the List to ensure patients with relapsing-remitting multiple sclerosis (RRMS) within the programme '7 high-cost nosologies' on the budgets of Russian Federation (RF) federal and regional public authorities in the field of health care. Methods 132,89 RUB in 2017 and 16 893 811 827,74 RUB in 2018. Inclusion of teriflunomide is connected with total costs for patient management equal to 14 908 830 663,19 RUB (2017) and 16 406 368 100,05 RUB (2018). Conclusion:Inclusion of teriflunomide into the programme '7 high-cost nosologies' will cause total cost saving for budget of federal authorities of the RF in the field of healthcare of 268 666 174,08 RUB and 517 023 249,99 RUB in 2017 and 2018, respectively, and total costs of federal and regional authorities of the RF in the field of healthcare for provision of patients with multiple sclerosis will be reduced by 239 237 469,70 RUB in 2017 and by 487 443 727,70 RUB in 2018.

The goal of this article is to provide an in-depth review of rare disease policies and the reimbursement of ODs in 3 European countries, two EU members (Poland, the Netherlands) and a non-EU one (Russia). A review of publicly available information on rare disorder policies and HTA processes was performed. Experts were consulted in case of unclear or scarce information. Russia has a five times higher frequency threshold for its rare disease definition than Poland and the Netherlands (both using the EU definition). The Netherlands has vastly increased its disease registries by instituting 300 expert centers via its National Plan, in Poland there are only 6 registries while in Russia one central registry exists. All 3 countries have an HTA process in place, however, the Russian one is relatively undeveloped. Access to ODs in the Netherlands is the broadest with 80 out of 83 EMA approved ODs reimbursed in 2015; Poland reimbursed 49, whereas Russia reimbursed 4 on the federal level and 43 in Moscow region. In all countries, new rare disease policies are under development. The availability of healthcare system solutions and the reimbursement of ODs differs greatly between all 3 countries. Even though both states are EU member with common regulations and access to EMA approved drugs, marked differences exist between Poland and the Netherlands in the range of policies, access to treatments and screening programs.

Objectives: Budget impact analysis of teriflunomide inclusion as first-line disease modifying therapy (DMT) for patients with relapsing-remitting multiple sclerosis in Russia. MethOds: Analysis was conducted with the time horizon of 5 years. Scenarios, that were considered: existing, in which teriflunomide isn't financed and patients receive interferon beta-1a (i.m. or s.c. injection), interferon beta-1b, glatiramer acetate and natalizumab, and new scenario, in which teriflunomide is financed by government. Total costs include: costs of DMT and administration, costs of diagnosis and monitoring of MS, costs of relapse treatment. Changes in the use of different DMTs, due to switching from one drug to another were taken into account in the analysis. In the models, patients are switching to the new treatment gradually. Results: We have created budget impact model assuming that, in new scenario, teriflunomide will be received by the patients with EDSS, that has increased within the scope from 0,5 to 1,49 (11,01%). For natalizumab 2 groups of patients were chosen: patients in whom EDSS rate has increased by 1,5 and more and there was minimum one relapse per year (1,50%) and patients in whom EDSS rate has increased within the range from 0,5 to 1,49 and there was minimum one relapse per year (7,39%). In new scenario, natalizumab is prescribed for the first group of patients, in existing scenario, natalizumab -for the both groups. It was calculated, that if teriflunomide is not included into financing, total costs of patient management are (costs calculated according on Russian National Bank currency exchange rate on 25.05.2016) 201,880,300€ (2017) and 225,146,056€ (2018). Inclusion of teriflunomide causes total costs of patient management -198,691,951€ (2017) and 218,649,833€ (2018). cOnclusiOns: Inclusion of teriflunomide into governmental financing causes total cost saving for federal budget of 3,580,550€ (2017) and 6,890,437€ (2018).

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. To exploit the potential of data-driven technologies, further integration of artificial intelligence (AI) into healthcare systems is warranted. A systematic literature review (SLR) of published SLRs evaluated evidence of ML applications in healthcare settings published in PubMed, IEEE Xplore, Scopus, Web of Science, EBSCO, and the Cochrane Library up to March 2023. Studies were classified based on the disease area and the type of ML algorithm used. In total, 220 SLRs covering 10,462 ML algorithms were identified, the majority of which aimed at solutions towards clinical prediction, categorisation, and disease prognosis in oncology and neurology primarily using imaging data. Accuracy, specificity, and sensitivity were 56%, 28%, and 25%, respectively. Internal validation was reported in 53% of the ML algorithms and external validation in below 1%. The most common modelling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). The review indicated that there is potential for greater adoption of AI in healthcare, with 10,462 ML algorithms identified compared to 523 approved by the Food and Drug Administration (FDA). However, the considerable reporting gaps call for more effort towards internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms.

Objectives: According to experts from the Moscow City Health Department, prostate cancer (PC), breast cancer (BC), colon cancer (CC), melanoma (MEL) and renal cell carcinoma (RCC) are the most high-cost oncological diseases. The aim of our study was to calculate the costs for each of these nosologies from the point of view of Moscow's budget and compare them with each other. MethOds: To assess the annual costs of drug therapy in Moscow in patients with PC, BC, CC, MEL and RCC we have developed an analytical model, taking into account the data of Cancer Register for 2015 and 2016, as well as literature sources. Results: We have estimated that if the costs of drug therapy for all five of assessed types of cancer are taken as 100%, then the most costly is BC (41% of costs), then MEL (20%), RCC (15%), CC (13%) and PC (12%). We have also calculated, that if the number of patients with all five types of assessed cancer undergoing drug therapy, we would consider as 100%, the highest percentage of them is in BC (50% of all patients), then -PC (36%), CC (9%), MEL (3%) and RCC (1%). cOnclusiOns: The structure of drug therapy costs in patients with PC, BC, MEL, CC and RCC in Moscow shows that the most expensive is the treatment of patients with melanoma (for 3% of patients Moscow City Health Department spends 20% of costs) and RCC (1% of patients cost 15% of costs).

(TreeAge Pro ®) was performed. Systematic reviews and meta-analysis were analyzed to establish the sensibility and specificity of CRP, procalcitonin and presepsin, a consensus of experts determined the length of hospitalization, costs of hospitalization and tests evaluated were determined from the average direct costs (USD). The cutoffs used were < 0.5 mg/dl for procalcitonin, > 40 mg/L for CRP and 625 pg/ml for Presepsin. The pre-test probability ranged from 10% to 90%, considering low, intermediate and high probability of SBI. Results: The different strategies had similar cost-effectiveness for a correctly diagnosed patient with SBI. However, presepsin was the most C/E strategy for the pretest probability scenarios between 30%-90%, ranging from $911 to $2685 per diagnosed patient. In the lowest pre-test probability, 10%, the CRP performed better. ConClusions: In the clinical practice, a large amount of children with fever without source have a wide range of pre-test probabilities of SBI. Our results found that presepsin can be a good diagnostic tool in patients with a 30% or higher probability of presenting SBI in children in Colombia. Additional research of new diagnostic tools is necessary to improve care evidence in children with SBI.