Цель. Определить распространенность устойчивости к антибиотикам и продукции основных приобретенных механизмов резистентности (бета-лактамаз расширенного спектра [БЛРС] и карбапенемаз) у нозокомиальных штаммов Enterobacterales, а также генотипы и принадлежность к «международным клонам высокого риска» карбапенемазопродуцирующих штаммов Klebsiella pneumoniae, выделенных в различных регионах России в рамках многоцентрового эпидемиологического исследования «МАРАФОН 2015-2016». Материалы и методы. Всего исследовано 2786 неповторяющихся изолятов энтеробактерий, включая 1316 изолятов Klebsiella pneumoniae и 837 изолятов Escherichia coli, выделенных в 49 стационарах 25 городов России в 2015-2016 гг. Видовую идентификацию изолятов проводили методом MALDI-TOF масс-спектрометрии. Определение чувствительности выполняли референтным методом микро

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Background and Aims Bone and mineral disorders (BMD) is a common complication of CKD in patients on chronic dialysis. Timely and adequate correction of BMD is the most important aspect of patient's treatment. This work presents a system for forecasting of phosphate-binding agents (PBA) dosage and vitamin D receptor activators (VDRA) dosage. The system consists of sequentially triggering artificial neural network forecasting models (separate model for each drug type). Method As an input dataset, system uses patient’s results of laboratory studies (blood calcium, phosphate and PTH) for the period of 6 months, information on previous drug therapy and data on adequacy of patient’s dialysis therapy. The output of the system are dosages of PBA an VDRA that have to be administered in order to bring the patient’s parameters as close as possible to target range of values (2.1-2.5 mmol/l for calcium, 0.87-1.5 mmol/l for phosphate and 150-300 pg/ml for PTH). The system consists of two sequentially triggering forecasting models (for PBA and for VDRA), where each model is an artificial neural network, that has been trained on a data, collected in more than 20 “Nefrosovet” hemodialysis clinics for the period of 3 years. The effect of system usage was examined for the group of 356 hemodialysis patients with median follow-up time of 3 month. The primary end-points were a number of patients in target range of values With respect to calcium (2.1-2.5 mmol/l), phosphate (0.87-1.5 mmol/l) and PTH (150-300 pg/ml). Results During the study we determined that as a result of using the dose forecasting system, number of patients in target range of values significantly increased with respect to calcium (from 178 to 209, p=.0196), phosphate (from 99 to 152, p=.0000), and PTH (from 83 to 109, p=.0281). Conclusion Employment of automated drug dosage forecasting system based on artificial neural network models, has a positive effect on BMD correction quality, which, in turn, reduces the risk of possible complications.

Background and Aims Quality of life of hemodialysis patients and adequacy of hemodialysis therapy in general, is defined by the number and duration of incidents during hemodialysis procedures. In this study we examined the effect of telemedical system for control and monitoring of hemodialysis procedures on patients’ condition and their quality of life. Method The system described in this work included: doctor/patient video call functionality initiated from both ends; functionality of hemodialysis procedure parameters and patient’s condition parameters monitoring and registration; functionality of alerting medical staff about registered incidents, functionality of visual control of hemodialysis procedure. The effect of control and monitoring system usage was studied on population of 2300 hemodialysis patients (at the start of the study) with median follow-up of 2 years. The primary end-point was doctor’s reaction time on patient’s complaint, medical staff reaction time on intradialysis hypertension incidents. Secondary end-points were: number of patients who left the clinic due to reasons besides lethality, patients’ satisfaction by hemodialysis therapy (according to survey), number of incidents of intradialysis and interdialysis hypertension. Results During the study we observed that as a result of system deployment average doctor’s reaction time on patient’s complaint (defined as the time from emergence of the complaint to start of patient/doctor communication) reduced from 8 to 1.5 minutes, average staff reaction time on intradialysis hypertension incidents (defined as time from registration of hypertension incident to start of blood pressure normalization actions) reduced from 5 to 2 minutes. Number of patients who left the clinic due to reasons besides lethality reduced from 2.5 per 100 patients before system deployment to 1.7 per 100 patients at the end of the study. Average value of patient’s satisfaction by dialysis therapy increased from 7.2 to 9.1 points on 10-point scale (according to survey conducted at the beginning and at the end of the study). By the end of the study, average number (across population) of hypertension incidents per month reduced from 8.3 to 6.2 and from 20.7 to 16.5 episodes for intradialysis and interdialysis hypertension correspondingly. Conclusion The use of telemedical tools of hemodialysis procedures control and monitoring has positive impact on patients’ satisfaction level by the dialysis procedure and on duration/frequency of incidents registered by these tools, which, in return may improve the quality of patient’s life.

Background and Aims Anemia is a most common complication of CKD in patients on chronic dialysis. Adequacy of anemia correction directly affects both patient’s life quality and patient’s long-term survival. The most important aspect of anemia correction is drug therapy. In this work, we present a system for forecasting of iron supplements and ESA (Epoetin alfa) dosage, that is based on logical rules and artificial intelligence (AI) models. Method As an input dataset, system uses patient’s anthropomorphic parameters, results of laboratory studies, and information on previous drug therapy. The output of the system are dosages of ESA an iron supplements that have to be administered in order to bring the patient’s hemoglobin as close as possible to target range of values (100-120 g/l). The system consists of two sequentially triggering forecasting models (for ESA and for iron supplements), where each model is a combination of logical rules and artificial neural network, that has been trained on a data, collected in more than 20 “Nefrosovet” hemodialysis clinics for the period of 3 years. The effect of system usage was examined for the group of 356 hemodialysis patients with median follow-up time of 4 month. The primary end-point was a number of patients in target range of hemoglobin values (100-120 g/l). Results During the study we determined that as a result of using the dose forecasting system, number of patients in target range of hemoglobin values significantly increased from 239 patients at the beginning of system employment to 266 patients at the end of the study (p=.0318). Furthermore, we observed that there was a concomitant effect of system usage – significant reduction of average monthly ESA dosage from 14300 IU at the beginning of system employment to 13900 IU at the end of the study (p=.0331). Conclusion Employment of automated drug dosage forecasting system based on logical rules and AI models, allows to improve the efficiency of anemia correction in hemodialysis patients and reduce the dosage of administered ESA, which, in turn, reduces the risk of possible complications and treatment cost.