Pharmacoeconomics: theory and practice
№1, 2022, Vol.10
AIM. To compare the cost per responder (CPR) between different groups of biologic agents and targeted synthetic disease-modifying antirheumatic drugs in patients with rheumatoid arthritis (RA). Materials and Methods. A total of 320 patients with RA were included in the study. Patients were divided into 4 groups (n=80 in each group): group 1 received B cell-depleting agent (rituximab), group 2 – TNF-alpha (α) inhibitors, group 3 –interleukin-6 inhibitor (tocilizumab), and group 4 – Janus kinase inhibitor (tofacitinib). The effectiveness of therapy was assessed using the generally accepted index of rheumatoid arthritis activity DAS28 and the dynamics of this index after 12 months of therapy. A good response on DAS28 (current DAS28 scores of 3.2 or less with reductions in DAS28 of more than 1.2) was accepted as a response to therapy. For the leading CPR-DAS28 drugs, an ad- ditional CPR study was performed using the SDAI, CDAI, and RAPID-3 indices. Results. A good response on DAS28 was observed more frequently in group 3 (56.3%) and group 4 (6.3%) compared to group 1 (11.5%, p<0.05 in both cases) and group 2 patients (22.5%, p<0.05 in both cases). CPR-DAS28 in group 3 was RUB 1094772.6, in group 4 – RUB 1089201.3; and that was 3.3 times lower than in group 1 (RUB 3587662.2, p<0.05 in both cases) and 2.3 times lower than in group 4 (RUB 2545747.3, p<0.05 in both cases). In group 3 (tocilizumab) CPR-SDAI was RUB 786867.9, CPR-CDAI – RUB 774285.7, and CPR-RAPID-3 – RUB 786867.9. In group 4 (tofacitinib) CPR-SDAI was RUB 1048831.2, CPR-CDAI – RUB 1088379.9, and CPR-RAPID – RUB 915730.8. Conclusion. CPR were lower with interleukin-6 inhibitor and Janus kinase in- hibitor therapy than with B cell-depleting agent and TNF-α inhibitors therapy. Determination of CPR by SDAI, CDAI, and RAPID-3 indices revealed an ad- vantage of interleukin-6 inhibitor therapy over Janus kinase inhibitor therapy.
The use of strategic analysis methods is relevant in optimizing the manage- ment of medical property (MP) resources in military medical organizations (MMO). One of the methods is a SWOT analysis, which allows to conduct a joint study and establish a relationship between the factors of the external and internal environment in determining the strengths and weaknesses of the processes of managing medical resources in a large MMO in modern socio-economic conditions. The purpose of the work was to conduct a SWOT analysis of the management of MP resources in large MMO and to compile a SWOT analysis decision matrix, which is an effective tool for strategic planning and decision making. The SWOT analysis model for MP resource management in a large MMO included the following elements: compilation of a standard SWOT analysis matrix; application of the method of expert assessments (with the determination of the competence and consistency of opinions of experts); drawing up a cross (summary) matrix of SWOT analysis; SWOT analysis of the weighted score; statistical SWOT analysis; construction of the McKinsey matrix model. Based on the results of the assessment of the McKinsey model, it is noted that the implementation of the capabilities of a large MMO in managing medical resources under the influence of external threats and factors of the internal en- vironment has unstable success, which indicates the need for careful attention to attempts to realize one or another existing growth opportunity. As part of the SWOT analysis, 4 strategies were substantiated: a breakthrough strategy (correlation of strengths and opportunities); transition strategy 1 (correlating strengths and threats, and describing what benefits can be used to avoid emerging threats and mitigate risks); transition strategy 2 (correlating weaknesses and opportunities, and presenting the effect of how weaknesses can be moved away from and turned into strengths); survival strategy (correlation of weaknesses and threats, as well as a reflection of how, by eliminating weaknesses, identified threats can be reduced). The described strategies and the matrix of SWOT-analysis decisions based on them, based on data on the most significant and most significant potential for implementation factors of the internal and external environment, can be an effective tool for strategic planning and decision-making when optimizing the management of medical resources in large MMO in modern conditions for the development of military and civilian health care.
RELEVANCE. Traditional criteria for assessing glycemic control (HbA1c, fasting plasma glucose, and postprandial glycemia) have limited information value for assessing the risk of adverse outcomes in diabetes mellitus (DM). It was found that increased glycemic variability (GV) has a clear relationship with the risk of developing severe hypoglycemia. GV is an independent predictor of diabetes vascular complications, general and cardiovascular mortality in type 2 diabetes. Currently, the mechanisms of severe GV negative effect on the occurrence and progression of diabetes complications have been studied. According to the international consensus on the use of continuous glucose monitoring (CGM) in 2017 and 2019, the calculation of the coefficient of variation (КВ) based CGM data is used to assess GV with the optimal recommended duration of monitoring at least 14 days (Tab.1). However, the CGM procedure has its limitations: it is expensive, usually carried out within 5-7 days maximum. PURPOSE. To develop a unified method for GV assessing acceptable for widespread use in routine practice by comparing the CV calculated using CGM and self-measured blood glucose (SMBG) with the calculation of the minimum period and minimum number of measurements without loss of correlation. MATERIALS AND METHODS. The retrospective analysis based on routine research of CGM and SMBG data (583 measurements) in 35 patients with T1D. The patients performed self-control during ~ 17 days (95 CI: 14.88-18.43). The simulation was carried out by the Monte Carlo method, for each patient m = 1500 times were taken from the CGM data during each day. The following parameters were determined by the method of simulation modeling: the time of the first measurement, the number of measurements per day, the duration of the self-control period to maximize the sensitivity and specificity of the algorithm. RESULTS. An algorithm was developed and validated for assessing CV according to SMBG data, which provides for: at least 7 measurements per day. The duration of the self-control period at least 14 days. The maximum error in assessing the variability is observed when the CV value calculated from SMBG data falls within the range of 35% - 40%, which requires CGM. If CV value below 35% and above 40% it can be concluded with an accuracy of at least 95% that the true CV is low or high. CONCLUSIONS. The resulting algorithm for assessing the CV according to SMBG data provides a sensitivity and specificity of at least 96% and 84%, respectively.