A Kurylev

What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

A mathematical model for predicting the development of bacterial resistance based on the relationship between the level of antimicrobial resistance and the volume of antibiotic consumption

OBJECTIVES Infections that are inadequately treated owing to acquired bacterial resistance are a leading cause of mortality. Rates of multidrug-resistant bacteria are rising, resulting in increased antibiotic failures and worsening patient outcomes. Mathematical modelling makes it possible to predict the future spread of bacterial antimicrobial resistance. The aim of this study was to construct a mathematical model that can describe the dependency between the level of antimicrobial resistance and the amount of antibiotic usage. METHODS After reviewing existing mathematical models, a cross-sectional, retrospective study was carried out to collect clinical and microbiological data across 3000 patients for the construction of the mathematical model. Based on these data, a model was developed and tested to determine the dependency between antibiotic usage and resistance. RESULTS Consumption of inhibitor/cephalosporins and fluoroquinolones increases inhibitor/penicillin resistance. Consumption of inhibitor/penicillins increases cephalosporin resistance. Consumption of inhibitor/penicillins increases inhibitor/cephalosporin resistance. CONCLUSIONS It was demonstrated that in some antibiotic-micro-organism pairs, the level of antibiotic usage significantly influences the level of resistance. The model makes it possible to predict the change in resistance and also shows the quantitative effect of antibiotic consumption on the level of bacterial resistance.

Retrospective surveillance of antibiotic use in maternity wards and neonatal intensive care units in Saint Petersburg, Russia

Antibiotic overuse in infants is associated with an increased risk of serious adverse events. Development of antibiotic stewardship programs aimed at reducing overall antibiotic consumption requires epidemiological surveillance. Retrospective surveillance and evaluation of all antibiotics provided to every infant admitted to maternal wards or neonatal intensive care units (NICUs) from 01 January 2014 to 31 December 2014 were performed in five medical centers of Saint Petersburg, Russia. Types of antibiotics and dates of administration were recorded. Antibiotic use was quantified by length of therapy (length of therapy, LOT, per 1000 patient-days, PD) and days of therapy (DOT/1000 PD). An additional parameter named “instant DOT/1000 PD” was introduced by authors for assessment of longitudinal patterns of administrations. Antibiotic load was 825.6 DOT/1000 PD in maternity wards and 1425.8 DOT/1000 PD in the NICUs. These levels are two to four times higher than DOTs reported in the USA for a level III NICU (348 DOT/1000PD). Antibiotic load was associated with the length of hospital stay (LOS) and birth weight. These associations were distorted when assessed using the conventional parameters, LOT and DOT, because they do not reflect the longitudinal component of treatment and underestimate antibiotic load when a patient stays in hospital without treatment. The proposed additional parameter successfully overcame these flaws and uncovered hidden associations. Severe overuse of antibiotics may be taking place in Russia and antibiotic stewardship development should be urged. Instant DOT/1000 PD is a more powerful tool in assessing treatment patterns than DOT/1000 PD.

MATHEMATICAL MODEL OF REIMBURSEMENT DECISION MAKING IN RUSSIA. RESULTS OF VIM LIST FOR 2016

Background: In 2014 we firstly analyzed the formalized system (points and expert opinions) of drug inclusion and exclusion into the reimbursement lists in Russian Federation. The liner mathematical model of decision making was developed and adopted. Aim. Update the existing model using the results of reimbursement procedures acting from 2106. Material and methods. The linear models developed and adopted in 2014 were used. In 2015 we included data on 141 drug dossiers. We analyzed the decision of the expert body, chief Ministry of Health expert and the final committee decision. Results. 43 new drugs were included into the reimbursement lists acting from 2016. The model of expert body decision had an error 7,09% (12,4% in 2014). The model of chief Ministry of Health expert decision had an error – 7% (10% in 2014). The above mentioned experts became more experienced in the formalized procedure of decision making. The model of final decision had an error about 42% (35% in 2014). Conclusion. Linear models are working tools for modelling reimbursement system decisions. At the mean time the existing system of decision making needs more formalization.

AN ANALYSIS OF COMPILATION OF THE RESTRICTIVE INVENTORIES IN THE RUSSIAN FEDERATION AS EXEMPLIFIED WITH «VITALLY ESSENTIAL AND INDISPENSABLE DRUGS» (VEID): THE ROLE OF PHARMACOECONOMICS

Background. In 2014, the first experiment of drug inclusion into or exclusion from restrictive inventories based on a score system and independent expert assessments has been carried out. Based on data obtained in 2014, the present authors have developed and tested linear mathematical models for making decisions concerning drug inclusion into restrictive inventories. The objective of the present work was to develop a model for the year 2016 with account of novel and supplementary data derived from the analysis of such inventories. Materials and methods. The linear models that have been developed and adopted earlier were used. The analysis included 141 records of medicinal drugs. Analyzed were verdicts provided by expert institutions and by chief non-stuff experts and the final decisions made by Interdisciplinary Panel. Results. In 2016, the Restrictive Inventory of VEID included 43 drugs. It was shown that the model for making decisions by expert organizations was associated with a 7,09% error (12,4% in 2014). The model for chief on-stuff experts was associated with a 7% error (10% in 2014). The above positive changes suggest that persons involved are able to become trained in using the formal approaches. The model for making the final decision by interdisciplinary panel was associated with a 42% error vs. 35% found earlier. Conclusion. Linear models are effective instruments for making prognoses concerning the inclusion of medicinal drugs into restrictive inventories. However, the currently adopted system should be formalized further.

Pharmacoeconomic Analysis of the use of Everolimus Compared to Axitinib in Second Line Therapy of Patients with Metastatic Renal Cell Carcinoma.

diversity of the health professionals and the basic scenario. The costliest scenarios were the one implementing HPV DNA testing which did not provide further participation despite a high cost and the one based on P4P incentives towards GP, although it allows high participation rates. ConClusions: Using a comprehensive BIM, we show that full coverage of OS might be the most cost-effective way to implement it, although practical and financial issues might favour other scenarios that may be more balanced regarding the distribution of costs between stakeholders or may be more easily implemented and accepted by health professionals.

Budget Impact Analysis of the Treatment of Chronic Myeloid Leukemia with Tyrosine Kinase Inhibitors – Nilotinib in the First and Second Lines of Therapy

chemotherapy after the index date to the earliest of mean time to HSCT, death, loss to follow-up, or last chemotherapy dose plus 30 days. The primary endpoint was the percent of time in the hospital during the salvage chemotherapy period. Key secondary endpoints were number of hospitalisations and length of hospital stay. Hospitalisations associated with HSCT were excluded. Results are presented as mean (SD) unless indicated. Results: Twenty-two patients were included, with a mean age of 44 (18) years. After the index date, 19 patients died and 8 patients received a HSCT. During the chemotherapy salvage period, patients spent a mean of 56% (95% CI: 46%-69%) of their time in the hospital. There were a mean of 2.2 (1.5) inpatient hospitalisations, 3.2 (6.2) day stays, and 1.6 (3.0) outpatient visits per patient, and the mean length of inpatient hospitalisation was 20.0 (20.0) days. From the index date to death, there were a mean of 2.8 (1.4) inpatient hospitalisations, 4.7 (7.3) day stays, and 4.6 (7.4) outpatient visits per patient and the mean length of inpatient hospitalisation was 19.0 (19.0) days. ConClusions: Adult patients receiving salvage chemotherapy for R/R ALL in Italy spend more than half their time in the hospital. Costs of hospitalisations will be presented.