Use and effectiveness of dapagliflozin in routine clinical practice. An Italian multicenter retrospective study

In randomized controlled trials (RCTs), sodium-glucose co-transporter-2 (SGLT2) inhibitors have been shown to confer glycaemic and extra-glycaemic benefits. The DARWIN-T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes) study was a multicentre retrospective study designed to evaluate the baseline characteristics of patients receiving dapagliflozin vs those receiving selected comparators (dipeptidyl peptidase-4 inhibitors, gliclazide, or glucagon-like peptide-1 receptor agonists), and drug effectiveness in routine clinical practice. From a population of 281 217, the analysis included 17 285 patients initiating dapagliflozin or comparator glucose-lowering medications (GLMs), 6751 of whom had a follow-up examination. At baseline, participants starting dapagliflozin were younger, had a longer disease duration, higher glycated haemoglobin (HbA1c) concentration, and a more complex history of previous GLM use, but the clinical profile of patients receiving dapagliflozin changed during the study period. Dapagliflozin reduced HbA1c by 0.7%, body weight by 2.7 kg, and systolic blood pressure by 3.0 mm Hg. Effects of comparator GLMs were also within the expected range, based on RCTs. This real-world study shows an initial channelling of dapagliflozin to difficult-to-treat patients. Nonetheless, dapagliflozin provided significant benefits with regard to glucose control, body weight and blood pressure that were in line with findings from RCTs

Machine learning in clinical and epidemiological research: Isn't it time for biostatisticians to work on it?

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques

A cross-layer architecture to improve mobile host rate performance and to solve unfairness problem in WLANs

The evolution of the Internet has been mainly promoted in recent years by the emergence and pro- liferation of wireless access networks towards a global ambient and pervasive network accessed from mobile devices. These new access networks have introduced new MAC layers independently of the legacy "wire- oriented" protocols that are still at the heart of the pro- tocol stacks of the end systems. This principle of isola- tion and independence between layers advocated by the OSI model has its drawbacks of maladjustment between new access methods and higher-level protocols built on the assumption of a wired Internet. In this paper, we introduce and deliver solutions for several pathologi- cal communication behaviors resulting from the malad- justment between WLAN MAC and higher layer stan- dard protocols such as TCP/IP and UDP/IP. Specially, based on an efficient analytical model for WLANs band- width estimation, we address in this paper the two fol- lowing issues: 1) Performance degradation due to the lack of flow control between the MAC and upper layer resulting in potential MAC buffer overflow; 2) Unfair bandwidth share issues between various type of flows. We show how these syndromes can be efficiently solved from neutral "cross layer" interactions which entail no changes in the considered protocols and standards

Social skills and psychopathology are associated with autonomic function in children: a cross-sectional observational study

none5noIn recent years, the increase of psychopathological disorders in the population has become a health emergency, leading to a great effort to understand psychological vulnerability mechanisms. In this scenario, the role of the autonomic nervous system (ANS) has become increasingly important. This study investigated the association between ANS, social skills, and psychopathological functioning in children. As an ANS status proxy, we measured heart rate variability (HRV). Infants admitted to the neonatal intensive care unit of the University Hospital of Padova because of preterm birth or neonatal hypoxic-ischemic encephalopathy were sequentially recruited from January 2011 to June 2013 and followed long-term up to school age in this cross-sectional observational study. We recorded 5 minutes of HRV immediately before measuring performance in social abilities tasks (affect recognition and theory of mind, NEPSY-II) in 50 children (mean age 7.4 ± 1.4 years) with and without risk factors for developing neuropsychiatric disorders due to pre-/perinatal insults without major sequelae. Children also completed extensive cognitive, neuropsychological, and psychosocial assessment. Parents were assessed with psychopathological interviews and a questionnaire (CBCL 6-18). Analysis in a robust Bayesian framework was used to unearth dependencies between HRV, social skills, and psychopathological functioning. Social task scores were associated with HRV components, with high frequency the most consistent. HRV bands were also associated with the psychopathological questionnaire. Only normalized HRV high frequency was able to distinguish impaired children in the affect recognition task. Our data suggest that ANS may be implicated in social cognition both in typical and atypical developmental conditions and that HRV has cross-disease sensitivity. We suggest that HRV parameters may reflect a neurobiological vulnerability to psychopathology. The study was approved by the Ethics Committee of the University Hospital of Padova (Comitato Etico per la Sperimentazione, Azienda Opedaliera di Padova, approval No. 1693P).openCainelli E.; Vedovelli L.; Bottigliengo D.; Boschiero D.; Suppiej A.Cainelli, E.; Vedovelli, L.; Bottigliengo, D.; Boschiero, D.; Suppiej, A

A machine learning approach for investigating delirium as a multifactorial syndrome

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance