The present meta-analysis provides evidence that MSD improves outcomes of glycemic control, body weight and cardiovascular risk factors in T2D patients.

BackgroundWe aimed to develop a real-time nosocomial infection surveillance system (RT-NISS) to monitor all nosocomial infections (NIs) and outbreaks in a Chinese comprehensive hospital to better prevent and control NIs.MethodsThe screening algorithm used in RT-NISS included microbiological reports, antibiotic usage, serological and molecular testing, imaging reports, and fever history. The system could, in real-time, identify new NIs, record data, and produce time-series reports to align NI cases.ResultsCompared with a manual survey of NIs (the gold standard), the sensitivity and specificity of RT-NISS was 98.8% (84/85) and 93.0% (827/889), with time-saving efficiencies of about 200 times. RT-NISS obtained the highest hospital-wide monthly NI rate of 2.62%, while physician and medical record reviews reported rates of 1.52% and 2.35% respectively. It took about two hours for one infection control practitioner (ICP) to deal with 70 new suspicious NI cases; there were 3,500 inpatients each day in the study hospital. The system could also provide various updated data (i.e. the daily NI rate, surgical site infection (SSI) rate) for each ward, or the entire hospital. Within 3 years of implementing RT-NISS, the ICPs monitored and successfully controlled about 30 NI clusters and 4 outbreaks at the study hospital.ConclusionsJust like the “ICPs’ eyes”, RT-NISS was an essential and efficient tool for the day-to-day monitoring of all NIs and outbreak within the hospital; a task that would not have been accomplished through manual process.

Background To quantify the five year incidence trend of all healthcare-associated infections (HAI) using a real-time HAI electronic surveillance system in a tertiary hospital in Beijing, China. Methods The real-time surveillance system scans the hospital’s electronic databases related to HAI (e.g. microbiological reports and antibiotics administration) to identify HAI cases. We conducted retrospective secondary analyses of the data exported from the surveillance system for inpatients with all types of HAIs from January 1st 2013 to December 31st 2017. Incidence of HAI is defined as the number of HAIs per 1000 patient-days. We modeled the incidence data using negative binomial regression. Results In total, 23361 HAI cases were identified from 633990 patients, spanning 6242375 patient-days during the 5-year period. Overall, the adjusted five-year HAI incidence rate had a marginal reduction from 2013 (4.10 per 1000 patient days) to 2017 (3.62 per 1000 patient days). The incidence of respiratory tract infection decreased significantly. However, the incidence rate of bloodstream infections and surgical site infection increased significantly. Respiratory tract infection (43.80%) accounted for the most substantial proportion of HAIs, followed by bloodstream infections (15.74%), and urinary tract infection (12.69%). A summer peak in HAIs was detected among adult and elderly patients. Conclusions This study shows how continuous electronic incidence surveillance based on existing hospital electronic databases can provide a practical means of measuring hospital-wide HAI incidence. The estimated incidence trends demonstrate the necessity for improved infection control measures related to bloodstream infections, ventilator-associated pneumonia, non-intensive care patients, and non-device-associated HAIs, especially during summer months. Electronic supplementary material The online version of this article (10.1186/s13756-019-0582-7) contains supplementary material, which is available to authorized users.