A Kolbin

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.

The data retrieval optimization from the perspective of evidence-based medicine

The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions - types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested includingMultinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.

Optimization Approach for Estimating the Required Amount of Pharmaceuticals in the Russian Federation

OBJECTIVES To propose an algorithm that relates the effectiveness of drugs for a wide range of diseases with the financial capabilities of patients. METHODS Estimates of the volume of pharmaceuticals that are consumed in the Russian Federation by all segments of the population regardless of household income were considered. These were calculated using statistically valid probabilities of the appearance of various diseases, official state data on the structure of expenditures of various strata of the population, and the optimal choice of the most effective medicines with income restrictions taken into account. The main idea was to introduce the utility function of the drug and the cost of treatment. For each disease, its own set of drugs was selected. RESULTS On the basis of the real-world data for several diseases, optimal estimates were calculated using the proposed algorithm. In the process of approbation, some weak points of the algorithm were found, such as the methods of packaging pharmaceuticals and associated cost of a packaging unit. These characteristics should be discussed separately, introducing conventional units of drug volumes. A unit of quantity corresponding to the maximum effect of the drug in question is proposed in the work. CONCLUSIONS The proposed algorithm for estimating the amount of medicines can be successfully used by both pharmaceutical (or dealer) companies and government agencies for objective population provision. The usual sources of such estimates are based either on market surveys or on pharmacy network data. Both ways are very expensive and do not allow predicting mass demand in the future, for example, with an unexpected epidemic or the emergence of new medicines. In addition, the proposed algorithm can be successfully applied to the pricing problem: a variation in price may show a change in the volume of use.

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.

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.